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https://dev.to/t/aws/page/18 | Amazon Web Services Page 18 - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Amazon Web Services Follow Hide Amazon Web Services (AWS) is a collection of web services for computing, storage, machine learning, security, and more There are over 200+ AWS services as of 2023. Create Post submission guidelines Articles which primary focus is AWS are permitted to used the #aws tag. Older #aws posts 15 16 17 18 19 20 21 22 23 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu 🚀 Terraform Day 16: Bulk IAM User Management Using Terraform (CSV AWS) Jeeva Jeeva Jeeva Follow Dec 24 '25 🚀 Terraform Day 16: Bulk IAM User Management Using Terraform (CSV AWS) # aws # automation # terraform # devops Comments Add Comment 2 min read Connecting Amazon VPCs Jeya Shri Jeya Shri Jeya Shri Follow Dec 24 '25 Connecting Amazon VPCs # cloud # aws # vpc # networking Comments Add Comment 3 min read Building a Global Scale App in 2026: Treating Zero Latency as a Lie (Here’s What Actually Works) kaustubh yerkade kaustubh yerkade kaustubh yerkade Follow Dec 25 '25 Building a Global Scale App in 2026: Treating Zero Latency as a Lie (Here’s What Actually Works) # devops # cicd # cloud # aws Comments Add Comment 3 min read 📸 AWS 113: Capturing the Moment - How to Create an Amazon Machine Image (AMI) Hritik Raj Hritik Raj Hritik Raj Follow Dec 24 '25 📸 AWS 113: Capturing the Moment - How to Create an Amazon Machine Image (AMI) # aws # devops # 100daysofcloud # cloudcomputing Comments Add Comment 3 min read Day 26.Configuring an EC2 Instance as a Web Server with Nginx Thu Kha Kyawe Thu Kha Kyawe Thu Kha Kyawe Follow Dec 23 '25 Day 26.Configuring an EC2 Instance as a Web Server with Nginx # aws # 100daysofcloudaws Comments Add Comment 2 min read VPC Part 2 : AWS Site-to-Site VPN (On-Prem Simulation) nash9 nash9 nash9 Follow Dec 26 '25 VPC Part 2 : AWS Site-to-Site VPN (On-Prem Simulation) # architecture # aws # tutorial # networking Comments Add Comment 4 min read Data Security Simplified: Building Your HIPAA-Compliant Data Lake on AWS wellallyTech wellallyTech wellallyTech Follow Dec 25 '25 Data Security Simplified: Building Your HIPAA-Compliant Data Lake on AWS # aws # security # architecture # hipaa Comments Add Comment 3 min read Terraform Provisioners Nandan K Nandan K Nandan K Follow Dec 25 '25 Terraform Provisioners # beginners # aws # terraform # devops Comments Add Comment 2 min read DEV Track Spotlight: AI agents at the edge: Build for offline, scale in cloud (DEV301) Gunnar Grosch Gunnar Grosch Gunnar Grosch Follow for AWS Dec 24 '25 DEV Track Spotlight: AI agents at the edge: Build for offline, scale in cloud (DEV301) # aws # ai # edge # strands Comments Add Comment 7 min read Day 27.Configuring a Public VPC with an EC2 Instance for Internet Access Thu Kha Kyawe Thu Kha Kyawe Thu Kha Kyawe Follow Dec 23 '25 Day 27.Configuring a Public VPC with an EC2 Instance for Internet Access # aws # 100daysofcloudaws Comments Add Comment 3 min read 🚀 Saving a $1M Integration: Why We Pivoted to AWS Transit Gateway Menox Menox Menox Follow Dec 23 '25 🚀 Saving a $1M Integration: Why We Pivoted to AWS Transit Gateway # aws # cloud # networking # architecture Comments Add Comment 3 min read Day 25.Setting Up an EC2 Instance and CloudWatch Alarm Thu Kha Kyawe Thu Kha Kyawe Thu Kha Kyawe Follow Dec 23 '25 Day 25.Setting Up an EC2 Instance and CloudWatch Alarm # aws # 100daysofcloudaws Comments Add Comment 2 min read AWS Strands - AgentCore Shorterm Memory Integration Vimal Venugopal Vimal Venugopal Vimal Venugopal Follow Dec 28 '25 AWS Strands - AgentCore Shorterm Memory Integration # agents # python # aws # tutorial Comments Add Comment 2 min read Using Amazon Q for AI-Assisted Debugging in Amazon EKS Robert Zsoter Robert Zsoter Robert Zsoter Follow Dec 23 '25 Using Amazon Q for AI-Assisted Debugging in Amazon EKS # aws # eks # ai # kubernetes Comments Add Comment 5 min read DEV Track Spotlight: AI Agents for Databases: Discover, Recommend, Optimize (DEV315) Gunnar Grosch Gunnar Grosch Gunnar Grosch Follow for AWS Dec 28 '25 DEV Track Spotlight: AI Agents for Databases: Discover, Recommend, Optimize (DEV315) # aws # database # ai # kiro Comments Add Comment 8 min read 🚀 Terraform Day 15: Modules — Writing Reusable, Scalable Infrastructure Code Jeeva Jeeva Jeeva Follow Dec 23 '25 🚀 Terraform Day 15: Modules — Writing Reusable, Scalable Infrastructure Code # devops # aws # terraform # tutorial Comments Add Comment 2 min read NAT Gateway vs VPC Endpoints: Which One Should You Use? Robindeva Robindeva Robindeva Follow Jan 7 NAT Gateway vs VPC Endpoints: Which One Should You Use? # aws # vpc # nat # finops Comments Add Comment 3 min read 💽 AWS 112: Plug and Play - How to Attach an EBS Volume to an EC2 Instance Hritik Raj Hritik Raj Hritik Raj Follow Dec 23 '25 💽 AWS 112: Plug and Play - How to Attach an EBS Volume to an EC2 Instance # aws # ebs # devops # 100daysofcloud Comments Add Comment 3 min read Relearning How to Learn: Preparing for AWS Certifications with ADHD Andrew Kalik Andrew Kalik Andrew Kalik Follow Dec 27 '25 Relearning How to Learn: Preparing for AWS Certifications with ADHD # aws # certification # cloudpractitioner Comments Add Comment 3 min read The OpenSearch Outage You Can't Fix: Why 2-Node Clusters Always Fail. Ogbeide Godstime Osemenkhian Ogbeide Godstime Osemenkhian Ogbeide Godstime Osemenkhian Follow for AWS Community Builders Dec 24 '25 The OpenSearch Outage You Can't Fix: Why 2-Node Clusters Always Fail. # aws # elasticsearch # devops # ai Comments Add Comment 5 min read Deploying an Auto Watermarking React Application to AWS Amplify Nde-Dilan Nde-Dilan Nde-Dilan Follow Dec 23 '25 Deploying an Auto Watermarking React Application to AWS Amplify # aws # webdev # cloudnative # eventsinyourcity Comments Add Comment 4 min read All about security and traffic in Amazon VPC Jeya Shri Jeya Shri Jeya Shri Follow Dec 23 '25 All about security and traffic in Amazon VPC # cloud # aws # vpc # learning Comments Add Comment 3 min read Cloud Security with AWS IAM - A quick hands-on Jeya Shri Jeya Shri Jeya Shri Follow Dec 23 '25 Cloud Security with AWS IAM - A quick hands-on # cloud # aws # iam # security Comments Add Comment 3 min read Architecting Enterprise grade Multi‑Agent AI with AWS Strands & Amazon Bedrock AgentCore Seenivasa Ramadurai Seenivasa Ramadurai Seenivasa Ramadurai Follow Dec 27 '25 Architecting Enterprise grade Multi‑Agent AI with AWS Strands & Amazon Bedrock AgentCore # architecture # ai # agents # aws 2 reactions Comments Add Comment 7 min read How Amazon Protects User Privacy in Advertising Mehwish Malik Mehwish Malik Mehwish Malik Follow Dec 23 '25 How Amazon Protects User Privacy in Advertising # aws # awschallenge # ai # webdev 10 reactions Comments Add Comment 3 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://dev.to/t/interview/page/75#main-content | Interview Page 75 - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close # interview Follow Hide Create Post Older #interview posts 72 73 74 75 76 77 78 79 80 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu Best resources for preparing a Frontend Interview in 2022 👨🏻💻🙋🏼♀️ (No W3Schools) Gionatha Gionatha Gionatha Follow Sep 12 '22 Best resources for preparing a Frontend Interview in 2022 👨🏻💻🙋🏼♀️ (No W3Schools) # career # webdev # programming # interview 3 reactions Comments 1 comment 3 min read Function Currying: Javascript Questions Soumava Banerjee Soumava Banerjee Soumava Banerjee Follow Sep 11 '22 Function Currying: Javascript Questions # javascript # interview # webdev 24 reactions Comments 3 comments 4 min read Product design interview. My experience Vadim Beskrovnov Vadim Beskrovnov Vadim Beskrovnov Follow Sep 10 '22 Product design interview. My experience # interview # productdesign # architecture # api 6 reactions Comments Add Comment 5 min read Flutter interview questions & answers part - 1 MD Sarfaraj MD Sarfaraj MD Sarfaraj Follow for This is Learning Sep 10 '22 Flutter interview questions & answers part - 1 # flutter # dart # interview # codenewbie 16 reactions Comments Add Comment 3 min read Job Application Tips Sara Joy Sara Joy Sara Joy Follow Sep 9 '22 Job Application Tips # interview # jobs # career # applying 12 reactions Comments Add Comment 3 min read Acing Interviews Greta Etheldra Greta Etheldra Greta Etheldra Follow for FINN Sep 2 '22 Acing Interviews # startup # career # beginners # interview 16 reactions Comments Add Comment 11 min read Should you really memorize algorithm complexities? 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Matt Hogg Matt Hogg Matt Hogg Follow Aug 31 '22 A Technical Interview Doesn't Have To Suck! # webdev # career # interview # management 4 reactions Comments Add Comment 12 min read Must known react js basic question for Interview karthikeyan karthikeyan karthikeyan Follow Aug 29 '22 Must known react js basic question for Interview # react # programming # interview # webdev 5 reactions Comments Add Comment 7 min read Leetcode survival guide DFS With Memo DFS With Memo DFS With Memo Follow Aug 18 '22 Leetcode survival guide # beginners # career # interview # algorithms 96 reactions Comments 3 comments 11 min read How to get the best job offer for senior+ software engineer roles Erry Kostala Erry Kostala Erry Kostala Follow Aug 15 '22 How to get the best job offer for senior+ software engineer roles # interview # programming # career # jobs 50 reactions Comments 1 comment 15 min read Interface segregation principle (SOLID) pedchenkoroman pedchenkoroman pedchenkoroman Follow Aug 12 '22 Interface segregation principle (SOLID) # solidjs # interview # angular # career 9 reactions Comments Add Comment 2 min read Grokking the Mobile System Design interview Artem Goncharov Artem Goncharov Artem Goncharov Follow Aug 6 '22 Grokking the Mobile System Design interview # mobile # ios # interview 5 reactions Comments Add Comment 22 min read Write a program by creating an 'Employee' class having the following methods and print the final salary. Shivani tiwari Shivani tiwari Shivani tiwari Follow Aug 5 '22 Write a program by creating an 'Employee' class having the following methods and print the final salary. # ds # webdev # interview # beginners 6 reactions Comments Add Comment 1 min read How to prepare for the System Design Interview in 2026 Fahim ul Haq Fahim ul Haq Fahim ul Haq Follow Aug 2 '22 How to prepare for the System Design Interview in 2026 # systemdesign # sdi # architecture # interview 4 reactions Comments Add Comment 8 min read 1# CSS Interview Topics Vasco Neves Vasco Neves Vasco Neves Follow Aug 3 '22 1# CSS Interview Topics # css # interview 4 reactions Comments Add Comment 3 min read Javascript Hoisting Vasco Neves Vasco Neves Vasco Neves Follow Aug 2 '22 Javascript Hoisting # javascript # hoisting # interview # tutorial 7 reactions Comments Add Comment 3 min read Weekly Coding Questions - W4 Aashish Koshti Aashish Koshti Aashish Koshti Follow Jul 31 '22 Weekly Coding Questions - W4 # dsa # coding # interview # leetcode 6 reactions Comments Add Comment 2 min read How To Find Array Triplets With Sum Of Two Elements Equals Third? Pramod Bablad Pramod Bablad Pramod Bablad Follow Jul 7 '22 How To Find Array Triplets With Sum Of Two Elements Equals Third? # java # codenewbie # programming # interview 7 reactions Comments Add Comment 1 min read How to get an interview vorsprung vorsprung vorsprung Follow Jul 26 '22 How to get an interview # interview # career 4 reactions Comments Add Comment 3 min read List of some useful GitHub repositories Avinash Singh Avinash Singh Avinash Singh Follow Jul 25 '22 List of some useful GitHub repositories # github # resources # dsa # interview 13 reactions Comments Add Comment 1 min read Interviewing for a Senior ML Engineer position Alex Burlacu Alex Burlacu Alex Burlacu Follow Jul 24 '22 Interviewing for a Senior ML Engineer position # machinelearning # careeradvice # interview # senior 3 reactions Comments Add Comment 11 min read SRE DevOps Interview Questions — Linux Troubleshooting Extended Satyajit Roy Satyajit Roy Satyajit Roy Follow Jul 24 '22 SRE DevOps Interview Questions — Linux Troubleshooting Extended # interview # devops # sre # troubleshooting 8 reactions Comments Add Comment 6 min read How to handle interview rejections gracefully? Rishabh Mishra Rishabh Mishra Rishabh Mishra Follow Jul 24 '22 How to handle interview rejections gracefully? # interview # mentalhealth # beginners # career 5 reactions Comments Add Comment 4 min read SRE DevOps Interview Questions — Linux Troubleshooting Satyajit Roy Satyajit Roy Satyajit Roy Follow Jul 22 '22 SRE DevOps Interview Questions — Linux Troubleshooting # interview # devops # sre # troubleshooting 37 reactions Comments 4 comments 7 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://www.fine.dev/blog/ai-coding-guide#2-implementing-retrieval-augmented-generation-rag | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
https://www.fine.dev/blog/ai-coding-guide#3-replit | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close OpenAI Follow Hide OpenAI is an artificial intelligence (AI) research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc. Create Post submission guidelines We welcome all posts around the topic of OpenAI - technical articles, personal stories, and discussions around GPT-3, ChatGPT, DALL-E, and Whisper! Older #openai posts 6 7 8 9 10 11 12 13 14 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu Is Your OpenAI Bill Giving You Nightmares? I Built a Tool to Help Joao Romao Joao Romao Joao Romao Follow Oct 22 '25 Is Your OpenAI Bill Giving You Nightmares? 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Branchen Ecommerce Ecommerce B2B Commerce B2B Commerce Fashion Fashion Grocery Grocery Media Media Marketplaces Marketplaces SaaS SaaS Higher Education Higher Education Documentation search Documentation search Enterprise search Enterprise search Headless commerce Headless commerce Image search Image search Mobile & App search Mobile & App search Retail Media Network Retail Media Network Site search Site search Visual search Visual search Voice search Voice search Digital Experience Digital Experience Ecommerce Ecommerce Engineering Engineering Merchandising Merchandising Product Management Product Management Preise Entwickler GET STARTED Developer Hub Developer Hub Dokumentation Dokumentation Integrationen Integrationen UI-Komponenten UI-Komponenten Autocomplete Autocomplete RESOURCES Code Exchange Code Exchange Engineering Blog Engineering Blog MCP MCP Discord Discord Webinars & Events Webinars & Events QUICK LINKS Schnellstartanleitung Schnellstartanleitung Für Open Source Für Open Source API Status API Status Support Support Resources INSPIRATION Algolia Blog Algolia Blog Resource Center Resource Center Kundengeschichten Kundengeschichten Webinars & Events Webinars & Events Newsroom Newsroom LEARN Customer Hub Customer Hub What's New What's New AI Search Grader AI Search Grader Documentation Documentation Algolia Academy Algolia Academy Professional Services Professional Services Quick Access Unternehmen Partners Einloggen Login Logout Request demo Get started Search Algolia Close Request demo Get started Other Types Filter --> Clear All Filters Filters Looking for our logo? We got you covered! Brand guidelines Download logo pack SaaS-Suche Sichere und zuverlässige Suche, die sich Ihrem Unternehmen anpasst Erhöhen Sie die Benutzerbindung durch eine schnelle und relevante Suche, die durch die Such-API von Algolia unterstützt wird. Demo Anfordern Kostenlos Starten Führende SaaS-Marken vertrauen auf Algolia "Algolia nimmt uns die Sorge um Suche – sowohl in Bezug auf technische Schulden als auch auf Performance. Wir arbeiten aktiv daran, Algolia noch stärker in unsere Anwendung zu integrieren, weil es sich als sehr zuverlässig, extrem schnell und einfach in der Nutzung erwiesen hat." Ryan Kahn Engineering Manager @ Amplitude Zeigen Sie Ihren Nutzern, wonach sie suchen AI Search Zeigen Sie Ihren Kunden genau das, was sie brauchen – mit einer branchenführenden KI-Suche, die die Kaufabsicht versteht. Relevante Ergebnisse für alle Suchanfragen, auch für Longtail-Queries Automatisierte Kategorieberichte und Rankings mit Query Categorization Bestseller automatisch hervorheben mit Dynamic Re-ranking Alles durchsuchbar machen – von Produktvarianten bis hin zu Support- und Service-Seiten Mehr über AI Search erfahren AI Recommendations Steigern Sie den durchschnittlichen Warenkorbwert mit personalisierten Produkt- und Content-Empfehlungen, basierend auf Verhaltens- und Kontextdaten. Cross-Selling fördern mit prädiktiven Modellen wie verwandte Produkte , Trendartikel und häufig kombinierte Produkte Personalisierte Empfehlungen auf der Startseite, Produktseiten, im Warenkorb – und fast überall hinzufügen Bildbasierte Empfehlungen helfen Käufern, noch mehr passende Produkte zu finden Mehr über AI Recommendations erfahren Analytics Profitieren Sie von vollständiger Transparenz und Kontrolle über Ranking- und Relevanzstrategien – mit einem intuitiven Dashboard für Ihre Business-Teams. Produkte boosten, unterdrücken, anpinnen oder promoten – je nach Ihren Geschäftszielen Hochwertige Suchbegriffe in Echtzeit verfolgen – und herausfinden, welche keine Ergebnisse liefern Die Performance Ihrer Kategorien analysieren und saisonale Kampagnen mit historischen Insights erfolgreich planen Mehr über Analytics erfahren Unterstützen Sie Ihre Entwickler mit SaaS-Suchfunktionen, auf die sie sich verlassen können, egal was passiert Entwickler an erster Stelle Sparen Sie Ihrem Team mit einer detaillierten Dokumentation Zeit und Entwicklungszyklen. Algolia bietet 16 API-Clients, 9 Client-Bibliotheken und 5 unterstützte Plugins, um eine konsistente Search and Discovery-Erfahrung auf mehreren Plattformen und Geräten zu ermöglichen. Zuverlässig Mit Algolia müssen Sie sich keine Sorgen machen, dass die Suche reduziert wird. Ein 99,999%iges SLA ist verfügbar und stellt sicher, dass Sie sich auf unsere APIs verlassen können. Dadurch kann sich Ihr Team auf den Aufbau toller Produkte für Ihre Benutzer konzentrieren, anstatt die Kerndienste aufrechtzuerhalten zu müssen. Skalierbar Egal, ob Sie ein MVP erstellen oder eine Suche für Millionen von Benutzern hinzufügen, Algolia bietet Ihnen die Suchplattform, die auf Ihre Anwendung angepasst ist. Mit mehr als 70 Rechenzentren auf 6 Kontinenten bietet Algolia Ihren Nutzern überall auf der Welt ein blitzschnelles Sucherlebnis. Empfohlener Inhalt EdX Chooses Algolia to Help Learners Access Education With more than 4,000 digital offerings courses and 45million users, edX needed an extremely robust search solution. Find out how edX developed a swift new search platform when switching to Algolia. Read more Leveling up search with AI-enabled personalization This eBook is a primer on AI-enabled personalization for ecommerce. We explain what AI personalization is, how and where to use it, and why it’s a worthwhile investment. Read more How Kiabi accelerated their omnichannel strategy with Algolia Hear how Kiabi transformed their omnichannel strategy to achieve a 15% increase in click-through rates, while effortlessly halving their “no result” rates... Read more See more Machen Sie es jedem möglich, eine tolle Search & Discovery zu erstellen Demo Anfordern Kostenlos Starten Lösungen Überblick AI Search AI Browse AI Recommendations Ask AI Intelligent Data Kit Anwendungsfälle Überclick Enterprise Suche Headless commerce Mobile Suche Sprachgesteuerte Suche Bildersuche OEM Bildersuche Entwickler Developer Hub Dokumentation Integrationen Engineering Blog Discord community API status DocSearch Für Open Source Demos GDPR AI Act Integrationen Salesforce Commerce Cloud B2C Shopify Adobe Commerce Netlify Commercetools BigCommerce Verteilt und Sicher Globale infrastruktur Sicherheit & Konformität Azure AWS Branchen Überclick B2C-E-Commerce B2B-E-Commerce Marktplätze SaaS Medien Startups Fashion Tools Search Grader Ecommerce Search Audit Unternehmen Über Algolia Karriere Newsroom Events Leitung Soziale Wirkung Kontact Kontact Kontact Soziales netwerk Entwickler Developer Hub Dokumentation Integrationen Engineering Blog Discord community API status DocSearch Für Open Source Demos GDPR AI Act Branchen Überclick B2C-E-Commerce B2B-E-Commerce Marktplätze SaaS Medien Startups Fashion Tools Search Grader Ecommerce Search Audit Lösungen Überblick AI Search AI Browse AI Recommendations Ask AI Intelligent Data Kit Anwendungsfälle Überclick Enterprise Suche Headless commerce Mobile Suche Sprachgesteuerte Suche Bildersuche OEM Bildersuche Integrationen Salesforce Commerce Cloud B2C Shopify Adobe Commerce Netlify Commercetools BigCommerce Verteilt und Sicher Globale infrastruktur Sicherheit & Konformität Azure AWS Unternehmen Über Algolia Karriere Newsroom Events Leitung Soziale Wirkung Kontact Kontact Kontact Soziales netwerk Algolia mark white ©2026 Algolia - All rights reserved. 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https://dev.to/page/algolia-challenge-v25-07-09-contest-rules | Algolia MCP Server Challenge Contest Rules - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account Forem Close Algolia MCP Server Challenge Contest Rules Contest Announcement Algolia MCP Server Challenge Sponsored by Dev Community Inc.(" Sponsor ") NO ENTRY FEE. NO PURCHASE NECESSARY TO ENTER OR WIN. VOID WHERE PROHIBITED. We urge you to carefully read the terms and conditions of this Contest Landing Page located here and the DEV Community Inc. General Contest Official Rules located here ("Official Rules"), incorporated herein by reference. The following contest specific details on this Contest Announcement Page, together with the Official Rules , govern your participation in the named contest defined below (the "Contest"). Sponsor does not claim ownership rights in your Entry. The Official Rules describe the rights you give to Sponsor by submitting an Entry to participate in the named Contest. In the event of a conflict between the terms of this Contest Announcement Page and the Official Rules, the Official Rules will govern and control. Contest Name : Algolia MCP Server Challenge Entry Period : The Contest begins on July 10, 2025 at 9:00 AM PDT and ends on July 27, 2025 at 11:59 PM PDT (the " Entry Period ") How to Enter : All entries must be submitted no later than the end of the Entry Period. You may enter the Contest during the Entry Period as follows: Visit the Contest webpage part of the DEV Community Site located here (the " Contest Page "); and Follow any instructions on the Contest Page and submit your completed entry (each an " Entry "). There is no limit on the number of Entries you may submit during the Entry Period. Required Elements for Entries : Without limiting any terms of the Official Rules, each Entry must include, at a minimum, the following elements: A published submission post on DEV that provides an overview of the agent using the submission template provided on the Contest Page. Judging Criteria : All qualified entries will be judged by a panel as selected by Sponsor as set forth in the Official Rules. Judges will award one winner to each prompt based on the following criteria: Use of underlying technology Usability and User Experience Accessibility Creativity In the event of a tie in scoring between judges, the judges will select the entry that received the highest number of positive reactions on their DEV post to determine the winner. In the event that a participant may win two or more prompts/prize categories, and the submissions are a tie, we will favor the participant that has not already won a prompt. Prize(s) : The prizes to be awarded from the Contest are as follows: Overall Prompt Winner (1) will receive: $1,000 USD Gift Card or Equivalent Exclusive DEV Badge DEV++ Membership * Prize Category Winners (2) * will receive: $1,000 USD Gift Card or Equivalent Exclusive DEV Badge DEV++ Membership Participant Winner (who submits a valid and qualified entry) will receive: A completion badge on their DEV profile 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV Forem — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://www.algolia.com/de/developers/ | Developers Niket --> Deutsch English français News DevCon2025 | October 1-2 Learn more Unternehmen Partners Einloggen Login Logout Algolia mark white Algolia logo white Lösungen Search Show users what they're looking for with AI-driven resuts. Search Show users what they're looking for with AI-driven resuts. Recommendations Use behavioral cues to drive higher engagement. Recommendations Use behavioral cues to drive higher engagement. Personalization Show each user what they need across their journey. Personalization Show each user what they need across their journey. Analytics All your insights in one dashboard. Analytics All your insights in one dashboard. Browse Move customers down the funnel with curated category pages. Browse Move customers down the funnel with curated category pages. Agent Studio Create, test, and deploy AI agents, fast. Agent Studio Create, test, and deploy AI agents, fast. Generative Experiences Build conversational solutions with retrieval augmented generation (RAG). Generative Experiences Build conversational solutions with retrieval augmented generation (RAG). Ask AI Deliver conversational answers—right from your search bar. Ask AI Deliver conversational answers—right from your search bar. MCP Server Search, analyze, or monitor your index within your agentic workflow. MCP Server Search, analyze, or monitor your index within your agentic workflow. Data Enrichment Modify, enhance, or restructure data as it’s indexed for search. Data Enrichment Modify, enhance, or restructure data as it’s indexed for search. Data Transformation Streamline data preparation and enhance data quality. Data Transformation Streamline data preparation and enhance data quality. Integrations Connect to your existing stack via pre-built libraries and APIs. Integrations Connect to your existing stack via pre-built libraries and APIs. Data Centers Choose from 70+ data centers across 17 regions. Data Centers Choose from 70+ data centers across 17 regions. Security & Compliance Built for peace of mind. Security & Compliance Built for peace of mind. Branchen Ecommerce Ecommerce B2B Commerce B2B Commerce Fashion Fashion Grocery Grocery Media Media Marketplaces Marketplaces SaaS SaaS Higher Education Higher Education Documentation search Documentation search Enterprise search Enterprise search Headless commerce Headless commerce Image search Image search Mobile & App search Mobile & App search Retail Media Network Retail Media Network Site search Site search Visual search Visual search Voice search Voice search Digital Experience Digital Experience Ecommerce Ecommerce Engineering Engineering Merchandising Merchandising Product Management Product Management Preise Entwickler GET STARTED Developer Hub Developer Hub Dokumentation Dokumentation Integrationen Integrationen UI-Komponenten UI-Komponenten Autocomplete Autocomplete RESOURCES Code Exchange Code Exchange Engineering Blog Engineering Blog MCP MCP Discord Discord Webinars & Events Webinars & Events QUICK LINKS Schnellstartanleitung Schnellstartanleitung Für Open Source Für Open Source API Status API Status Support Support Resources INSPIRATION Algolia Blog Algolia Blog Resource Center Resource Center Kundengeschichten Kundengeschichten Webinars & Events Webinars & Events Newsroom Newsroom LEARN Customer Hub Customer Hub What's New What's New AI Search Grader AI Search Grader Documentation Documentation Algolia Academy Algolia Academy Professional Services Professional Services Quick Access Unternehmen Partners Einloggen Login Logout Request demo Get started Search Algolia Close Request demo Get started Other Types Filter --> Clear All Filters Filters Looking for our logo? We got you covered! Brand guidelines Download logo pack Algolia Developer Hub Everything you need to build search that understands. Back-end Front-end Analytics Dropdown Ruby Rails Python Django Php Symfony Laravel JavaScript Java Scala Go C# Kotlin Swift JavaScript React Android Vue Angular IOS Php Ruby JavaScript Python Swift Android C# Java Go Scala my_index = client.init_index('contacts') my_index.save_object({ firstname: "Jimmie", lastname: "Barninger", company: "California Paint" }) Build with Ruby class Contact < ActiveRecord::Base include AlgoliaSearch algoliasearch do attribute :firstname, :lastname, :company end end Build with Rails myIndex = apiClient.init_index("contacts") myIndex.save_object({ "firstname": "Jimmie", "lastname": "Barninger", "company": "California Paint" }) Build with Python from algoliasearch_django import AlgoliaIndex from algoliasearch_django.decorators import register @register(YourModel) class YourModelIndex(AlgoliaIndex): fields = ('firstname', 'lastname', 'company') Build with Django $myIndex = $apiClient->initIndex("contacts"); $myIndex->saveObject([ "firstname" => "Jimmie", "lastname" => "Barninger", "company" => "California Paint", ]); Build with Php /** * @ORM\Entity */ class Contact { /** * @var string * * @ORM\Column(name="firstname", type="string") * @Group({searchable}) */ protected $firstname; /** * @var string * * @ORM\Column(name="lastname", type="string") * @Group({searchable}) */ protected $lastname; /** * @var string * * @ORM\Column(name="company", type="string") * @Group({searchable}) */ protected $company; } Build with Symfony use Illuminate\Database\Eloquent\Model; use Laravel\Scout\Searchable; class Contact extends Model { use Searchable; } Build with Laravel const myIndex = apiClient .initIndex('contacts'); myIndex.saveObject({ firstname: 'Jimmie', lastname: 'Barninger', company: 'California Paint', }); Build with JavaScript Index<Contact> index = client .initIndex("contacts", Contact.class); index.saveObject( new Contact() .setFirstname("Jimmie") .setLastname("Barninger") .setCompany("California Paint") ); Build with Java import algolia.AlgoliaDsl._ import scala.concurrent.ExecutionContext.Implicits.global case class Contact( firstname: String, lastname: String, company: String ) val indexing: Future[Indexing] = client.execute { index into "contacts" `object` Contact( "Jimmie", "Barninger", "California Paint" ) } Build with Scala object := map[string]string{ "firstname": "Jimmie", "lastname": "Barninger", "company": "California Paint" } res, err := index.SaveObject(object) Build with Go SearchIndex index = client.InitIndex("contacts"); var contact = new Contact { FirstName = "Jimmie", LastName = "Barninger", Company = "California Paint" }; index.SaveObject(contact); Build with C# val index = client.initIndex(IndexName("contacts")) val json = json { "firstname" to "Jimmie" "lastname" to "Barninger" "company" to "California Paint" } index.saveObject(json) Build with Kotlin let myIndex = apiClient.getIndex("contacts") let n = [ "firstname": "Jimmie", "lastname": "Barninger", "company": "California Paint" ] myIndex.saveObject(n) Build with Swift <div id="searchbox"></div> <div id="refinement"></div> <div id="hits"></div> <script> const { searchBox, hits } = instantsearch.widgets; search.addWidgets([ searchBox({ container: "#searchbox" }), hits({ container: "#hits" }), refinementList({ container: "#refinement", attribute: "company" }), ]); search.start(); </script> Build with JavaScript const App = () => ( <InstantSearch> <SearchBox /> <Hits /> <Pagination /> <RefinementList attribute="company" /> </InstantSearch> ); Build with React <RelativeLayout xmlns:algolia="http://schemas.android.com/apk/res-auto" xmlns:android="http://schemas.android.com/apk/res/android" android:layout_width="match_parent" android:layout_height="match_parent"> <com.algolia.instantsearch.ui.views.SearchBox android:id="@+id/search_box" android:layout_width="match_parent" android:layout_height="wrap_content"/> <com.algolia.instantsearch.ui.views.Stats android:id="@+id/search_box" android:layout_width="match_parent" android:layout_height="wrap_content"/> <com.algolia.instantsearch.ui.views.Hits android:layout_width="match_parent" android:layout_height="wrap_content" algolia:itemLayout="@layout/hits_item"/> </RelativeLayout> Build with Android <ais-instant-search> <ais-search-box /> <ais-refinement-list attribute="company" /> <ais-hits /> <ais-pagination /> </ais-instant-search> Build with Vue <ais-instantsearch> <ais-search-box></ais-search-box> <ais-refinement-list [attribute]="company" ></ais-refinement-list> <ais-hits></ais-hits> </ais-instantsearch> Build with Angular import InstantSearch override func viewDidLoad() { super.viewDidLoad() let searchBar = SearchBarWidget(frame: ...) let statsWidget = StatsLabelWidget(frame: ...) self.view.addSubview(searchBar) self.view.addSubview(statsWidget) InstantSearch.shared.registerAllWidgets(in: self.view)} Build with IOS $insights = AlgoliaAlgoliaSearchInsightsClient::create( 'ALGOLIA_APP_ID', 'ALGOLIA_API_KEY' ); $insights->user("user-123456")->clickedObjectIDsAfterSearch( 'Product Clicked', 'products', ['9780545139700'], [7], 'cba8245617aeace44' ); Build with Php insights = Algolia::Insights::Client.create('ALGOLIA_APP_ID', 'ALGOLIA_API_KEY') insights.user('user-123456').clicked_object_ids_after_search( 'Product Clicked', 'products', ['9780545139700'], [7], 'cba8245617aeace44' ) Build with Ruby // This requires installing the search-insights separate library: // https://github.com/algolia/search-insights.js // https://www.npmjs.com/package/search-insights aa('clickedObjectIDsAfterSearch', { userToken: 'user-123456', eventName: 'Product Clicked', index: 'products', queryID: 'cba8245617aeace44', objectIDs: ['9780545139700'], positions: [7], }); Build with JavaScript insights = client.init_insights_client().user('user-123456') insights.clicked_object_ids_after_search( 'Product Clicked', 'products', ['9780545139700'], [7], 'cba8245617aeace44' ) Build with Python Insights.register( appId: "ALGOLIA_APP_ID", apiKey: "ALGOLIA_API_KEY", userToken: "user-123456" ) Insights.shared?.clickedAfterSearch( eventName: "Product Clicked", indexName: "products", objectIDs: ["9780545139700"], positions: [7], queryID: "cba8245617aeace44" ) Build with Swift Insights.register( context, "ALGOLIA_APP_ID", "ALGOLIA_API_KEY", "user-123456" ) Insights.shared?.clickedAfterSearch( "Product Clicked", "products", "cba8245617aeace44", EventObjects.IDs("9780545139700"), listOf(7) ) Build with Android var insights = new InsightsClient( "ALGOLIA_APP_ID", "ALGOLIA_API_KEY" ).User("user-123456"); insights.ClickedObjectIDsAfterSearch( "Product Clicked", "products", new List<string> { "9780545139700" }, new List<uint> { 7 }, "cba8245617aeace44" ); Build with C# AsyncUserInsightsClient insights = new AsyncInsightsClient( "ALGOLIA_APP_ID", "ALGOLIA_API_KEY", client ).user("user-123456"); insights.clickedObjectIDsAfterSearch( "Product Clicked", "products", Arrays.asList("9780545139700"), new ArrayList<>(Arrays.asList(7l)), "cba8245617aeace44" ); Build with Java client := insights.NewClient( "ALGOLIA_APP_ID", "ALGOLIA_API_KEY", ).User("user-123456") res, err := client.ClickedObjectIDsAfterSearch( "Product Clicked", "products", []string{"9780545139700"}, []int{7}, "cba8245617aeace44", ) Build with Go client.execute { send event ClickedObjectIDsAfterSearch( "user-123456", "Product Clicked", "products", Seq("9780545139700"), Seq(7), "cba8245617aeace44" ) } Build with Scala *:nth-child(n+1)]:border-b px-4" data-expansion-type="multiItem" > Manage your data using any of our API clients. Build search front-end from customizable UI libraries with reusable components. Configure analytics to show click conversions, run A/B testing and tune recommendations. Scale with Integrations Use integrations and pre-built libraries to build scalable search experiences. --> --> --> No Products Found!!! View all integrations Explore every possibility with full documentation Find everything you need to get started with API reference docs, guides and sample code. Read the docs Develop your stack with UI libraries Deploy pre-built, customizable UI libraries for instantsearch and autocomplete, available in multiple frameworks. Explore all front-end possibilities Build DocSearch Free search for your developer documentation. Discover DocSearch Code Exchange Building blocks for search and discovery. Back-end tools Use our API clients, frameworks and integrations to push your data. Explore back-end building blocks Front-end tools Build your frontend using our UI libraries and templates. Explore front-end building blocks Showcase Don’t start from a blank page. Explore our demos and sample apps. Explore Showcase Explore Code Exchange For startups - all the power, none of the headache Startups, you can get going in minutes and scale for decades. Whatever your future demands, and however much you grow - Algolia has you covered. Eligible startups can begin with $10k of credits from Algolia and $100k from startup partners. Learn more Enterprises, delight your customers Grow your customer satisfaction - and sales. Because when your customers feel understood, they click and they come back. Get help from our experts to start fast and run efficiently. Contact sales "[Algolia] was very professional from the start. We had a great Customer Success Manager and team that provided a lot of help and was a great partner." Clint Fischerström Head of Ecommerce @ Swedol “I think we’ve grown leaps and bounds with Algolia. There's a lot of features that we still can tap into, which is great because I feel like we've gotten a ton out of it already.” Geoff Lyman Digital Experience Solutions Manager @ Hershey's “Instead of having to go into the back end and the catalog—which would have been a technical headache—we were able to figure it out in a matter of a day, test it, and ‘boom’ it’s live.” Courtney Grisham Director of E-Commerce @ Shoe Carnival “Algolia is very fast — able to keep up with our level of traffic… The API and SDK options are really great, and the ability to handle traffic at scale (we have a high volume)” Matt Goorley Engineering Manager @ LTK “Algolia is a breeze to work with. With Algolia, our editorial team has seen significant productivity improvements when building the daily online edition of The Times and weekly edition of The Sunday Times, with search being 300-500 times faster than our prior solution.” Matt Taylor Editorial Product Manager @ The Times Explore more Discord Community Documentation Algolia Startup Program Search API Security & compliance Global infrastructure Customer Hub Enable anyone to build great Search & Discovery Get a demo Start Free Lösungen Überblick AI Search AI Browse AI Recommendations Ask AI Intelligent Data Kit Anwendungsfälle Überclick Enterprise Suche Headless commerce Mobile Suche Sprachgesteuerte Suche Bildersuche OEM Bildersuche Entwickler Developer Hub Dokumentation Integrationen Engineering Blog Discord community API status DocSearch Für Open Source Demos GDPR AI Act Integrationen Salesforce Commerce Cloud B2C Shopify Adobe Commerce Netlify Commercetools BigCommerce Verteilt und Sicher Globale infrastruktur Sicherheit & Konformität Azure AWS Branchen Überclick B2C-E-Commerce B2B-E-Commerce Marktplätze SaaS Medien Startups Fashion Tools Search Grader Ecommerce Search Audit Unternehmen Über Algolia Karriere Newsroom Events Leitung Soziale Wirkung Kontact Kontact Kontact Soziales netwerk Entwickler Developer Hub Dokumentation Integrationen Engineering Blog Discord community API status DocSearch Für Open Source Demos GDPR AI Act Branchen Überclick B2C-E-Commerce B2B-E-Commerce Marktplätze SaaS Medien Startups Fashion Tools Search Grader Ecommerce Search Audit Lösungen Überblick AI Search AI Browse AI Recommendations Ask AI Intelligent Data Kit Anwendungsfälle Überclick Enterprise Suche Headless commerce Mobile Suche Sprachgesteuerte Suche Bildersuche OEM Bildersuche Integrationen Salesforce Commerce Cloud B2C Shopify Adobe Commerce Netlify Commercetools BigCommerce Verteilt und Sicher Globale infrastruktur Sicherheit & Konformität Azure AWS Unternehmen Über Algolia Karriere Newsroom Events Leitung Soziale Wirkung Kontact Kontact Kontact Soziales netwerk Algolia mark white ©2026 Algolia - All rights reserved. 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https://www.fine.dev/blog/ai-coding-guide#popular-ai-coding-tools | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close OpenAI Follow Hide OpenAI is an artificial intelligence (AI) research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc. Create Post submission guidelines We welcome all posts around the topic of OpenAI - technical articles, personal stories, and discussions around GPT-3, ChatGPT, DALL-E, and Whisper! Older #openai posts 1 2 3 4 5 6 7 8 9 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu Advanced Prompt Engineering: What Actually Held Up in 2025 monna monna monna Follow Dec 29 '25 Advanced Prompt Engineering: What Actually Held Up in 2025 # discuss # promptengineering # openai Comments Add Comment 3 min read Using AI for Troubleshooting: OpenAI vs DeepSeek Coroot Coroot Coroot Follow Jan 7 Using AI for Troubleshooting: OpenAI vs DeepSeek # ai # openai # deepseek # devops Comments Add Comment 5 min read OpenAI Changelog Slack Alerts (Low-Noise Setup) QuietWatch QuietWatch QuietWatch Follow Dec 24 '25 OpenAI Changelog Slack Alerts (Low-Noise Setup) # openai # slack # monitoring # devtools Comments Add Comment 3 min read Prompt Engineering From First Principles: The Mechanics They Don't Teach You part-1 Boopathi Boopathi Boopathi Follow Dec 28 '25 Prompt Engineering From First Principles: The Mechanics They Don't Teach You part-1 # promptengineering # llm # openai # gemin 5 reactions Comments Add Comment 7 min read Understanding How ChatGPT Produces Human-Like Responses Shamim Ali Shamim Ali Shamim Ali Follow Jan 6 Understanding How ChatGPT Produces Human-Like Responses # webdev # ai # chatgpt # openai Comments Add Comment 2 min read AI Category Recommendation System for Drupal 11 Using PHP and OpenAI PHP CMS Framework PHP CMS Framework PHP CMS Framework Follow Dec 23 '25 AI Category Recommendation System for Drupal 11 Using PHP and OpenAI # drupal # openai # php Comments Add Comment 1 min read DiscovAI Search — Open‑Source AI Search Engine for Tools, Docs, and Custom Data Alex Kernel Alex Kernel Alex Kernel Follow Dec 21 '25 DiscovAI Search — Open‑Source AI Search Engine for Tools, Docs, and Custom Data # ai # webdev # programming # openai Comments Add Comment 2 min read I Built a VS Code Extension That Turns GitHub Copilot Into a Full OpenAI-Compatible API Suhaib Bin Younis Suhaib Bin Younis Suhaib Bin Younis Follow Dec 21 '25 I Built a VS Code Extension That Turns GitHub Copilot Into a Full OpenAI-Compatible API # ai # opensource # vscode # openai Comments Add Comment 3 min read Toon 1.1.8 — Unlock Complex Nested Array Support for Laravel Sagar Sunil Bhedodkar Sagar Sunil Bhedodkar Sagar Sunil Bhedodkar Follow Dec 20 '25 Toon 1.1.8 — Unlock Complex Nested Array Support for Laravel # laravel # php # ai # openai Comments Add Comment 2 min read Use AWS Bedrock & AI Services (Claude, Nova, Polly, Transcribe) with Your Existing OpenAI Code J.Goutin J.Goutin J.Goutin Follow Dec 19 '25 Use AWS Bedrock & AI Services (Claude, Nova, Polly, Transcribe) with Your Existing OpenAI Code # aws # ai # openai # aiops Comments Add Comment 4 min read Building a Safe AI Database Assistant with Azure OpenAI, LangChain & Function Calling Khushi Singla Khushi Singla Khushi Singla Follow Dec 19 '25 Building a Safe AI Database Assistant with Azure OpenAI, LangChain & Function Calling # azure # openai # datascience # ai Comments Add Comment 3 min read 📌 DevOps GitHub PR Reviewer using n8n and OpenAI📌 Prashant Lakhera Prashant Lakhera Prashant Lakhera Follow Dec 19 '25 📌 DevOps GitHub PR Reviewer using n8n and OpenAI📌 # devops # github # n8nbrightdatachallenge # openai Comments Add Comment 1 min read When to Use Prompt-Based vs Agent Mode in LaunchDarkly for AI Applications Scarlett Attensil Scarlett Attensil Scarlett Attensil Follow for LaunchDarkly Dec 17 '25 When to Use Prompt-Based vs Agent Mode in LaunchDarkly for AI Applications # agents # promptengineering # openai # ai Comments Add Comment 11 min read I Read OpenAI’s GPT-5.2 Prompting Guide So You Don’t Have To Tashfia Akther Tashfia Akther Tashfia Akther Follow Dec 17 '25 I Read OpenAI’s GPT-5.2 Prompting Guide So You Don’t Have To # discuss # openai # promptengineering # ai Comments Add Comment 4 min read I Slashed My AI Coding Bills by 65% With This One Weird Trick. Vishal VeeraReddy Vishal VeeraReddy Vishal VeeraReddy Follow Dec 31 '25 I Slashed My AI Coding Bills by 65% With This One Weird Trick. # ai # programming # opensource # openai Comments Add Comment 5 min read 📌 DevOps Interview Buddy using n8n, Ollama, and Gemma: Completely Free (Bonus: OpenRouter)📌 Prashant Lakhera Prashant Lakhera Prashant Lakhera Follow Dec 17 '25 📌 DevOps Interview Buddy using n8n, Ollama, and Gemma: Completely Free (Bonus: OpenRouter)📌 # devops # gemma # opensource # openai Comments Add Comment 1 min read Vector Databases (with OpenAI and Supabase) - Part 1 Shloka Shloka Shloka Follow Jan 9 Vector Databases (with OpenAI and Supabase) - Part 1 # ai # database # openai 6 reactions Comments 1 comment 5 min read OpenAI Released GPT Image 1.5 — I Built a Free Web App to Try It Instantly lei pan lei pan lei pan Follow Dec 17 '25 OpenAI Released GPT Image 1.5 — I Built a Free Web App to Try It Instantly # showdev # webdev # openai # ai Comments Add Comment 2 min read Getting Started with OpenAI Codex CLI: AI-Powered Code Generation from Your Terminal DeployHQ DeployHQ DeployHQ Follow Jan 7 Getting Started with OpenAI Codex CLI: AI-Powered Code Generation from Your Terminal # ai # openai # codex # codegeneration Comments Add Comment 6 min read When Your API Documentation Lies: Building an AI-Powered Validator to Catch the Drift Aniket Hingane Aniket Hingane Aniket Hingane Follow Dec 29 '25 When Your API Documentation Lies: Building an AI-Powered Validator to Catch the Drift # api # openai # python # devops Comments Add Comment 14 min read Azure OpenAI's Content Filter: When Safety Theater Blocks Real Work JaviMaligno JaviMaligno JaviMaligno Follow Jan 8 Azure OpenAI's Content Filter: When Safety Theater Blocks Real Work # ai # azure # openai # security Comments 1 comment 3 min read What Happened in AI This Week: From ChatGPT's $3B Milestone to Disney's OpenAI Partnership Ethan Zhang Ethan Zhang Ethan Zhang Follow Dec 19 '25 What Happened in AI This Week: From ChatGPT's $3B Milestone to Disney's OpenAI Partnership # ai # chatgpt # openai # technews Comments Add Comment 7 min read The Most Expensive Free Product on the Internet Muhammad Usman Muhammad Usman Muhammad Usman Follow Dec 18 '25 The Most Expensive Free Product on the Internet # chatgpt # openai # google # ai 5 reactions Comments Add Comment 2 min read I Built an AI Creative Director: Automating FB Ad Gen with GPT-4o Vision & Structured Outputs zo Aoo zo Aoo zo Aoo Follow Jan 7 I Built an AI Creative Director: Automating FB Ad Gen with GPT-4o Vision & Structured Outputs # openai # gpt4o # automation # productivity Comments Add Comment 4 min read Azure OpenAI Pricing Reality - $2 Demo Becomes $4,000/Month in Production David David David Follow Dec 12 '25 Azure OpenAI Pricing Reality - $2 Demo Becomes $4,000/Month in Production # azure # ai # openai # finops Comments Add Comment 2 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://dev.to/devteam/join-the-algolia-mcp-server-challenge-3000-in-prizes-1non | Join the Algolia MCP Server Challenge: $3,000 in Prizes! - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Jess Lee for The DEV Team Posted on Jul 10, 2025 Join the Algolia MCP Server Challenge: $3,000 in Prizes! # devchallenge # algoliachallenge # webdev # ai We are thrilled to partner with Algolia , one of our amazing Diamond Sponsors, for an exciting new DEV challenge that explores the cutting edge of AI-powered search. Running through July 27 , the Algolia MCP Server Challenge invites you to explore the intersection of AI and search technology using Algolia's Model Context Protocol (MCP) server. Whether you're focused on enhancing search capabilities or building intelligent user experiences, this challenge offers creative opportunities to innovate with search-powered solutions. We have one prompt for this challenge, but three opportunities to win! Our Prompt Your mandate is to build with the Algolia MCP Server. Focus on building something that showcases the power of Algolia's MCP server in whatever way inspires you most. If you're someone who appreciates a bit more direction, you can consider our additional prize categories below. Additional Prize Categories Backend Data Optimization : Awarded to a top submission that focuses on backend data enrichment by integrating with other 3rd party AI tools like Claude Desktop, n8n workflows, etc. Ultimate User Experience : Awarded to a top submission with a compelling user-facing application that uses the Algolia MCP server to retrieve information to deliver intelligent responses. Prizes Our overall prompt winner and additional prize categories winners will each receive: $1,000 USD DEV++ Membership Exclusive DEV Badge All Participants with a valid submission will receive a completion badge on their DEV profile. How To Participate In order to participate, you will need to publish a post using the submission template below. All submissions must utilize the Algolia MCP server, include a GitHub repo of the project code, and a short explainer video. Algolia MCP Server Challenge Submission Template Please review our full rules, guidelines, and FAQ page before submitting so you understand our participation guidelines and official contest rules such as eligibility requirements. Helpful Resources We encourage all participants to join the Algolia Discord Server to connect with their team and developers building with Algolia! Algolia Developer Docs Sample Datasets Algolia MCP GitHub repo: algolia / mcp-node MCP server for interacting with Algolia 🔍 Algolia Node.js MCP Quick Start • Features • Usage Examples • Installation • Configuration • Development • Troubleshooting • Contributing • License algolia-mcp-demo.mp4 This repository contains experimental Model Context Protocol (or MCP) servers for interacting with Algolia APIs. We're sharing it for you to explore and experiment with. Feel free to use it, fork it, or build on top of it — but just know that it's not officially supported by Algolia and isn't covered under our SLA. We might update it, break it, or remove it entirely at any time. If you customize or configure things here, there's a chance that work could be lost. Also, using MCP in production could affect your Algolia usage. If you have feedback or ideas (even code!), we'd love to hear it. Just know that we might use it to help improve our products. This project is provided "as is" and "as… View on GitHub Things you can do with Algolia MCP: Important Dates July 10: Algolia MCP Server Challenge begins! July 27: Submissions due at 11:59 PM PDT August 7: Winners Announced We can't wait to see how you push the boundaries of AI-powered search! Questions about the challenge? Ask them below. Good luck and happy coding! Top comments (36) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Collapse Expand Peter Kim Frank The DEV Team Peter Kim Frank The DEV Team Peter Kim Frank Follow Doing a bit of everything at DEV / Forem Email peter@dev.to Education Wesleyan University Pronouns He/Him Work Co-Founder Joined Jan 3, 2017 • Jul 10 '25 Dropdown menu Copy link Hide I've been playing around with the Algolia MCP myself the last few days (on the "Movies" sample data set). I'm really excited to see what the community builds! Like comment: Like comment: 7 likes Like Comment button Reply Collapse Expand Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Follow Founder & CTO at NikoLabs LLC, building Axrisi—an AI-powered browser extension for seamless on-page text processing and productivity. Opened Chicos restaurant in Tbilisi, Georgia. Email turazashvili@gmail.com Location Tbilisi, Georgia Education EXCELIA La Rochelle Pronouns He/Him Work Founder & CTO at NikoLabs LLC and Axrisi Joined May 30, 2025 • Jul 12 '25 Dropdown menu Copy link Hide Just made my submission using same dataset on Demo: Self-Documenting MCP: One-Step Algolia Setup via MCP Server in Cursor IDE Nikoloz Turazashvili (@axrisi) ・ Jul 12 #devchallenge #algoliachallenge #webdev #ai hoping it will have find some real use cases in prod :) Like comment: Like comment: 7 likes Like Comment button Reply Collapse Expand Verônica Bastos Vevê Verônica Bastos Vevê Verônica Bastos Vevê Follow Joined Jun 20, 2025 • Jul 16 '25 Dropdown menu Copy link Hide Mzxmfm do Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Ben Halpern The DEV Team Ben Halpern The DEV Team Ben Halpern Follow A Canadian software developer who thinks he’s funny. Email ben@forem.com Location NY Education Mount Allison University Pronouns He/him Work Co-founder at Forem Joined Dec 27, 2015 • Jul 10 '25 Dropdown menu Copy link Hide Good luck everyone! Like comment: Like comment: 5 likes Like Comment button Reply Collapse Expand özkan pakdil özkan pakdil özkan pakdil Follow Software Engineer Location UK, Glasgow Work Software Engineer Joined Dec 28, 2019 • Jul 10 '25 Dropdown menu Copy link Hide @gokerdev 3k sounds nice 😊 and a good learning opportunity Like comment: Like comment: 6 likes Like Comment button Reply Collapse Expand Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Follow Founder & CTO at NikoLabs LLC, building Axrisi—an AI-powered browser extension for seamless on-page text processing and productivity. Opened Chicos restaurant in Tbilisi, Georgia. Email turazashvili@gmail.com Location Tbilisi, Georgia Education EXCELIA La Rochelle Pronouns He/Him Work Founder & CTO at NikoLabs LLC and Axrisi Joined May 30, 2025 • Jul 12 '25 Dropdown menu Copy link Hide for me it is more about recognition of the work I've done. I think that's my driver to see that I've built something actually useful not just for me. Like comment: Like comment: 4 likes Like Comment button Reply Collapse Expand özkan pakdil özkan pakdil özkan pakdil Follow Software Engineer Location UK, Glasgow Work Software Engineer Joined Dec 28, 2019 • Jul 12 '25 Dropdown menu Copy link Hide also 3k would not hurt 💸 Like comment: Like comment: 4 likes Like Thread Thread Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Follow Founder & CTO at NikoLabs LLC, building Axrisi—an AI-powered browser extension for seamless on-page text processing and productivity. Opened Chicos restaurant in Tbilisi, Georgia. Email turazashvili@gmail.com Location Tbilisi, Georgia Education EXCELIA La Rochelle Pronouns He/Him Work Founder & CTO at NikoLabs LLC and Axrisi Joined May 30, 2025 • Jul 12 '25 Dropdown menu Copy link Hide Haha, surely it's also motivating Like comment: Like comment: 4 likes Like Comment button Reply Collapse Expand ANIRUDDHA ADAK ANIRUDDHA ADAK ANIRUDDHA ADAK Follow AI Agent Engineer focused on creating self-directed AI systems that learn, adapt, and execute multi-step tasks without human intervention. Email aniruddhaadak80@gmail.com Joined Nov 11, 2024 • Jul 11 '25 Dropdown menu Copy link Hide exciting Like comment: Like comment: 4 likes Like Comment button Reply Collapse Expand Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Follow Founder & CTO at NikoLabs LLC, building Axrisi—an AI-powered browser extension for seamless on-page text processing and productivity. Opened Chicos restaurant in Tbilisi, Georgia. Email turazashvili@gmail.com Location Tbilisi, Georgia Education EXCELIA La Rochelle Pronouns He/Him Work Founder & CTO at NikoLabs LLC and Axrisi Joined May 30, 2025 • Jul 12 '25 Dropdown menu Copy link Hide will you make submission? :) Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand ANIRUDDHA ADAK ANIRUDDHA ADAK ANIRUDDHA ADAK Follow AI Agent Engineer focused on creating self-directed AI systems that learn, adapt, and execute multi-step tasks without human intervention. Email aniruddhaadak80@gmail.com Joined Nov 11, 2024 • Jul 13 '25 Dropdown menu Copy link Hide Yes, but currently, I am learning about the challenge. Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Rajguru Yadav Rajguru Yadav Rajguru Yadav Follow I’m Raj Guru Yadav – A 16-Year-Old Developer from India Who Built 700+ Projects (No Courses, Just Passion Joined Jul 8, 2025 • Jul 11 '25 Dropdown menu Copy link Hide Good luck 🤞 every one Like comment: Like comment: 5 likes Like Comment button Reply Collapse Expand Prema Ananda Prema Ananda Prema Ananda Follow Full-stack developer: AI applications, rapid MVPs, database expert (MongoDB, Redis, PostgreSQL). Multi-agent systems specialist. Fixed-price development. Days, not months! Email djoty108@gmail.com Location Ukraine Joined May 24, 2025 • Jul 12 '25 • Edited on Jul 12 • Edited Dropdown menu Copy link Hide I've got a brilliant idea on how to use your MCP! Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Follow Founder & CTO at NikoLabs LLC, building Axrisi—an AI-powered browser extension for seamless on-page text processing and productivity. Opened Chicos restaurant in Tbilisi, Georgia. Email turazashvili@gmail.com Location Tbilisi, Georgia Education EXCELIA La Rochelle Pronouns He/Him Work Founder & CTO at NikoLabs LLC and Axrisi Joined May 30, 2025 • Jul 12 '25 Dropdown menu Copy link Hide looking forward to see your submission :) Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand Futuritous Futuritous Futuritous Follow Life is a canvas, we all draw on it together. Location USA Joined Jan 18, 2025 • Jul 11 '25 Dropdown menu Copy link Hide Best wishes to everyone. Like comment: Like comment: 5 likes Like Comment button Reply Collapse Expand Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Follow Founder & CTO at NikoLabs LLC, building Axrisi—an AI-powered browser extension for seamless on-page text processing and productivity. Opened Chicos restaurant in Tbilisi, Georgia. Email turazashvili@gmail.com Location Tbilisi, Georgia Education EXCELIA La Rochelle Pronouns He/Him Work Founder & CTO at NikoLabs LLC and Axrisi Joined May 30, 2025 • Jul 12 '25 Dropdown menu Copy link Hide hoping my submission will get noticed. Actually enjoyed using Algolia MCP. Like comment: Like comment: 4 likes Like Comment button Reply Collapse Expand Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Follow Founder & CTO at NikoLabs LLC, building Axrisi—an AI-powered browser extension for seamless on-page text processing and productivity. Opened Chicos restaurant in Tbilisi, Georgia. Email turazashvili@gmail.com Location Tbilisi, Georgia Education EXCELIA La Rochelle Pronouns He/Him Work Founder & CTO at NikoLabs LLC and Axrisi Joined May 30, 2025 • Jul 14 '25 Dropdown menu Copy link Hide Hey everyone. I submitted project for this challenge. But wanted to ask anyone from Algolia if there is anything they have in mind would be great to create, but didn't have time or resources to create. So maybe I can work on it. Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Prema Ananda Prema Ananda Prema Ananda Follow Full-stack developer: AI applications, rapid MVPs, database expert (MongoDB, Redis, PostgreSQL). Multi-agent systems specialist. Fixed-price development. Days, not months! Email djoty108@gmail.com Location Ukraine Joined May 24, 2025 • Jul 13 '25 Dropdown menu Copy link Hide Successfully set up the MCP server on Windows 10 and connected it to Gemini CLI - absolutely mesmerizing to watch the LLM work with databases! The way it handles table creation, data management, and index operations is truly virtuosic. This kind of AI-database integration feels like glimpsing the future of development. Great work on making this accessible! Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Chuck Meyer Chuck Meyer Chuck Meyer Follow API driven. DevRel 🥑 at Algolia. Location Columbus, OH Education NKU, WPI Pronouns he/him Work Sr Manager, Developer Relations at Algolia Joined Aug 4, 2021 • Jul 11 '25 Dropdown menu Copy link Hide So excited to see what everyone comes up with. Hit us up on Discord if you have any issues! Like comment: Like comment: 4 likes Like Comment button Reply View full discussion (36 comments) Some comments may only be visible to logged-in visitors. Sign in to view all comments. Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . 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Contribute to Forem More from The DEV Team Congrats to the AI Agents Intensive Course Writing Challenge Winners! # googleaichallenge # devchallenge # ai # agents Join the Algolia Agent Studio Challenge: $3,000 in Prizes! # algoliachallenge # devchallenge # agents # webdev Congrats to the Xano AI-Powered Backend Challenge Winners! # xanochallenge # backend # api # ai 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://dev.to/muhammad_rabbi_dev | Muhammad Rabbi - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. DUMB DEV Community Follow Memes and software development shitposting Design Community Follow Web design, graphic design and everything in-between Security Forem Follow Your central hub for all things security. From ethical hacking and CTFs to GRC and career development, for beginners and pros alike Golf Forem Follow A community of golfers and golfing enthusiasts Crypto Forem Follow A collaborative community for all things Crypto—from Bitcoin to protocol development and DeFi to NFTs and market analysis. Parenting Follow A place for parents to the share the joys, challenges, and wisdom that come from raising kids. We're here for them and for each other. Forem Core Follow Discussing the core forem open source software project — features, bugs, performance, self-hosting. Maker Forem Follow A community for makers, hobbyists, and professionals to discuss Arduino, Raspberry Pi, 3D printing, and much more. HMPL.js Forem Follow For developers using HMPL.js to build fast, lightweight web apps. A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Follow User actions Muhammad Rabbi 👋 Hey, I’m Muhammad Rabbi, a frontend developer who loves building clean, functional, and modern web apps using React, Tailwind CSS, and tools that make the UI pop. Location dhaka,Bangladesh Joined Joined on Sep 8, 2025 Personal website https://muhammadrabbi.vercel.app github website twitter website More info about @muhammad_rabbi_dev Badges Writing Debut Awarded for writing and sharing your first DEV post! Continue sharing your work to earn the 4 Week Writing Streak Badge. Got it Close Skills/Languages JavaScript, Typescript, Html, CSS, Reactjs, NextJs, Tailwind CSS, Node, Express, Mognodb Available for Open for Collaboration, excited to collaborate on innovative projects. Post 2 posts published Comment 0 comments written Tag 24 tags followed Pin Pinned A few days ago, I was scrolling through YouTube playlists, thinking.. Muhammad Rabbi Muhammad Rabbi Muhammad Rabbi Follow Sep 8 '25 A few days ago, I was scrolling through YouTube playlists, thinking.. # webdev # programming # javascript # opensource Comments Add Comment 1 min read React Design Patterns Every Frontend & FullStack Developer Should Know Muhammad Rabbi Muhammad Rabbi Muhammad Rabbi Follow Jan 9 React Design Patterns Every Frontend & FullStack Developer Should Know # architecture # frontend # javascript # react Comments Add Comment 2 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . DEV Community © 2016 - 2026. We're a place where coders share, stay up-to-date and grow their careers. Log in Create account | 2026-01-13T08:49:45 |
https://dev.to/challenges/algolia-2025-07-09#main-content | Algolia MCP Server Challenge - DEV Challenge - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account Forem Close Challenges > Algolia MCP Server Challenge CHALLENGE RESULTS 🏆 Winners Announced! 🎊 Congrats to the Algolia MCP Challenge Winners! Read Announcement Challenge ends soon! Submit your entry now DAYS : HOURS : MINUTES : SECONDS See prompts Algolia MCP Server Challenge View Entries Please sign in to follow this challenge Manage your entire search infrastructure using natural language! Challenge Status: Ended Ended Join our next Challenge We are thrilled to partner with Algolia , one of our amazing Diamond Sponsors, for an exciting new DEV challenge that explores the cutting edge of AI-powered search. Running through July 27 , the Algolia MCP Server invites you to explore the intersection of AI and search technology using Algolia's Model Context Protocol (MCP) server. Whether you're focused on enhancing search capabilities or building intelligent user experiences, this challenge offers creative opportunities to innovate with search-powered solutions. We have one prompt for this challenge, but three opportunities to win! Each winner will receive: $1,000 USD DEV++ Membership Exclusive DEV Badge All Participants with a valid submission will receive a completion badge on their DEV profile. Key Dates Contest start: July 10, 2025 Submissions due: July 27, 2025 Winners announced: August 07, 2025 Badge Rewards Algolia MCP Server Challenge Completion Badge Algolia MCP Server Challenge Winner Badge Find Out More Ask questions and share your ideas on the Algolia MCP Server Challenge Launch Post. View Launch Post Sponsored by Algolia For 200k years, humans have spent most of their waking lives searching. Once, we looked for food and shelter. Now, it’s information - which keeps proliferating faster and threatens to overwhelm us. Algolia’s mission is to find without foraging: to show us what we’re looking for - instantly. Learn More → Challenge Prompt Algolia MCP Server Your mandate is to build with the Algolia MCP Server. Focus on building something that showcases the power of Algolia's MCP server in whatever way inspires you most. If you're someone who appreciates a bit more direction, you can consider our additional prize categories below. Additional Prize Categories Backend Data Optimization : Awarded to a top submission that focuses on backend data enrichment by integrating with other 3rd party AI tools like Claude Desktop, n8n workflows, etc. Ultimate User Experience : Awarded to a top submission with a compelling user-facing application that uses the Algolia MCP server to retrieve information to deliver intelligent responses. Submission Template Judging Criteria: Use of underlying technology Usability and User Experience Accessibility Creativity Frequently Asked Questions Participation Can one submission qualify for multiple prize categories? Yes, if your submission offers a solution to multiple prize categories, it can qualify for multiple prize categories. Can I submit to a prompt more than once? Yes, you can submit multiple submissions per prompt but you’ll need to publish a separate post for each submission. Can I work on a team? Yes, you can work on teams of up to four people. If you collaborate with anyone, you’ll need to list their DEV handles in your submission post so we can award a badge to your entire team! Please only publish one submission per team. DEV does not handle prize-splitting, so in the event that your submission wins the shop gift, you will need to split that amongst yourselves. Thank you for understanding! How old do I have to be to participate? Participants need to be 18+ in order to participate. If I live in X, am I eligible to participate? For eligibility rules, see our official challenge rules . Submission Can I update my submission after the submission due date? No, please do not update your submission during the judging period. Can my submission include open source code? Riffing on open source code and borrowing and improving on previous work/ideas is encouraged but it’s important your changes are significant enough to ensure your submission is valid. When does riffing become plagiarism? It will depend, but transparency is important, license compatibility is important. You can use someone else’s code to give you a jumpstart to demonstrate your ideas on top of someone else’s base, but not just re-package the base. It should be clear to the judges what you added to the project in terms of the code and conceptual inspiration. This means, you should clearly state what you were building on and what elements are original to this new submission. When building on existing code, we expect a significant change that adds something tangible to the output. i.e. a new animation, and new sprite, a new function, a new presentation. Not just changes to the source - i.e. changing colours, changing one sprite, changing one function. What happens if my submission is considered plagiarized or invalid? Anything deemed to be plagiarism will not be eligible for prizes. Incidental plagiarism may simply result in your disqualification from the challenge (regardless of the number of other valid submissions you have published). Egregious plagiarism will result in your suspension from DEV entirely. Any non-generic, non-trivial usage of prior work, including open source code must be credited in your submission. Do submissions have to be in English? Non-english submissions are eligible for a completion badge but not eligible for prizes due to the current limitations of our judges. We will not be judging on mastery of the English language, so please don’t let this deter you from submitting if you are not a native English speaker! We hope to evolve this in the future to be more accommodating. Do I need a license for my code? You are not required to license your code but we strongly recommend that you do. Here are some you may consider: MIT , Apache , BSD-2 , BSD-3 , or Commons Clause . Can I use AI? Use of AI is allowed as long as all other rules are followed. We want to give you a chance to show off your skills in realistic scenarios. If you use AI tools to help you achieve your submission, all the power to you. How do I embed my project directly into my DEV post? Our editor supports many types of embeds, including: Stackbliz, Glitch, Github, etc. You can typically use the {% embed https://... %} syntax directly in the post. Click here for more information on our markdown support. For CodePen, you will need to use this syntax: {% codepen http://... %} For CodeSandbox, you will need to use this syntax: {% codesandbox http://... %} Judging and Prizing Can there be ties? In the event of a tie in scoring between judges, the judges will select the entry that received the highest number of positive reactions on their DEV post to determine the winner. How will I know if I won? Winners will be announced in a DEV post on the winner announcement date noted in our key dates section. When will I receive my DEV badge? Both participation and winner badges will be awarded, in most cases, the same day as the winner announcement. When will I receive my prizes? The DEV Team will contact you via the email associated with your DEV profile within, at most, 10 business days of the announcement date to share the details of claiming your prizes. What steps do I need to take to receive my cash prize? The winner (including each member of a team) may be required to sign and return an affidavit of eligibility and publicity/liability release, and provide any additional tax filing information (such as a W-9, social security number or Federal tax ID number) within seven (7) business days following the date of your first email notification. Algolia MCP Server Challenge Rules NO PURCHASE NECESSARY. Open only to 18+. Contest entry period ends July 27, 2025 at 11:59 PM PDT. Contest is void where prohibited or restricted by law or regulation. All entires must be submitted during the content period. For Official Rules, see Algolia MCP Server Challenge Contest Rules and General Contest Official Rules . 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https://dev.to/pockit_tools/pnpm-vs-npm-vs-yarn-vs-bun-the-2026-package-manager-showdown-51dc#cachedwarm-install-performance | pnpm vs npm vs yarn vs Bun: The 2026 Package Manager Showdown - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse HK Lee Posted on Jan 9 • Originally published at pockit.tools pnpm vs npm vs yarn vs Bun: The 2026 Package Manager Showdown # bunjs # pnpm # yarn # npm Every JavaScript project starts with a choice: which package manager? For years, it was npm by default. Then yarn promised faster installs. Then pnpm claimed to save gigabytes of disk space. And now Bun's built-in package manager claims to make everything else obsolete. But here's what no one tells you: the "best" package manager depends entirely on your specific use case, and blindly following benchmarks can lead you astray. A package manager that's perfect for a solo developer's side project might be terrible for a 500-package monorepo—and vice versa. This guide cuts through the marketing hype. After extensive testing across different project sizes and configurations in January 2026, here's what actually matters for each package manager, when to use it, and how to migrate if you need to. 📌 Version Note: This comparison covers npm 11.x, yarn 4.x (Berry), pnpm 10.x, and Bun 1.3 as of January 2026. The Quick Verdict If you're in a hurry, here's the short version: Use Case Recommended Why Solo/small projects Bun Fastest by far, simplest setup Large monorepos pnpm Best disk efficiency, workspace support Enterprise/legacy npm Maximum compatibility, no surprises Yarn ecosystem yarn 4 PnP mode, excellent plugins Performance at scale pnpm or Bun Both excel, pnpm more mature Now let's dive into why. The Contenders: 2026 State of Play npm 11.x Status: Still the default, ships with Node.js Latest: npm 11.7.0 (December 2025) Philosophy: Compatibility over innovation Key Strength: Works everywhere, always npm has evolved significantly. The node_modules structure is now more optimized, and features like npm audit have become industry standards. But npm's conservative approach means it's rarely the fastest or most efficient—it's just the most reliable. yarn 4.x (Berry) Status: Complete rewrite from yarn 1.x Latest: yarn 4.12.0 (January 2026) Philosophy: Innovation through Plug'n'Play (PnP) Key Strength: Zero-installs, plugin architecture Yarn Berry is essentially a different product from yarn 1. The Plug'n'Play feature eliminates node_modules entirely, instead using a .pnp.cjs file that maps imports directly to zip archives. It's radical—and divisive. pnpm 10.x Status: The "smart" alternative Latest: pnpm 10.27.0 (December 2025) Philosophy: Efficiency without breaking compatibility Key Strength: Content-addressable storage, true deduplication pnpm's approach is elegant: store all packages once in a global content-addressable store, then use hard links to make them appear in each project's node_modules . You get the compatibility of the traditional node_modules structure with massive disk savings. Bun 1.3 Package Manager Status: The new challenger Latest: Bun 1.3.0 (January 1, 2026) Philosophy: Speed above all else Key Strength: Native speed, zero configuration, full-stack capabilities Bun isn't just a package manager—it's a complete JavaScript runtime. Bun 1.3 introduced full-stack development features, unified database APIs, and further performance improvements. Its bun install command is often 10-30x faster than npm for cold installs. Benchmark Results: Cold Install Performance Let's start with what everyone cares about—raw speed. We tested each package manager on the same projects with cleared caches: Small Project (50 dependencies) Project: Typical React + TypeScript starter Dependencies: 50 direct, ~400 total Cold Install Times (cleared cache): ┌────────────┬──────────┬────────────┐ │ Manager │ Time │ vs npm │ ├────────────┼──────────┼────────────┤ │ bun │ 0.8s │ 18x faster │ │ pnpm │ 4.2s │ 3.4x faster│ │ yarn │ 6.8s │ 2.1x faster│ │ npm │ 14.3s │ baseline │ └────────────┴──────────┴────────────┘ Enter fullscreen mode Exit fullscreen mode Medium Project (200 dependencies) Project: Next.js 15 app with common libraries Dependencies: 200 direct, ~1,200 total Cold Install Times (cleared cache): ┌────────────┬──────────┬────────────┐ │ Manager │ Time │ vs npm │ ├────────────┼──────────┼────────────┤ │ bun │ 2.1s │ 22x faster │ │ pnpm │ 12.4s │ 3.7x faster│ │ yarn │ 18.2s │ 2.5x faster│ │ npm │ 46.1s │ baseline │ └────────────┴──────────┴────────────┘ Enter fullscreen mode Exit fullscreen mode Large Monorepo (15 packages, 800 dependencies) Project: Turborepo monorepo with 15 packages Dependencies: 800 direct, ~3,500 total Cold Install Times (cleared cache): ┌────────────┬──────────┬────────────┐ │ Manager │ Time │ vs npm │ ├────────────┼──────────┼────────────┤ │ bun │ 4.8s │ 28x faster │ │ pnpm │ 28.6s │ 4.7x faster│ │ yarn │ 52.3s │ 2.6x faster│ │ npm │ 134.2s │ baseline │ └────────────┴──────────┴────────────┘ Enter fullscreen mode Exit fullscreen mode Key Insight: Bun's lead actually increases with project size. For monorepos, the difference is staggering. Cached/Warm Install Performance But cold installs aren't the whole story. Most of the time, you're installing with some level of caching: Warm Install (lockfile exists, some cache): ┌────────────┬──────────────┬──────────────┐ │ Manager │ Small (50) │ Large (800) │ ├────────────┼──────────────┼──────────────┤ │ bun │ 0.3s │ 1.2s │ │ pnpm │ 1.1s │ 8.4s │ │ yarn (PnP) │ 0.0s* │ 0.0s* │ │ npm │ 3.2s │ 24.6s │ └────────────┴──────────────┴──────────────┘ * Yarn PnP with zero-installs commits dependencies to repo Enter fullscreen mode Exit fullscreen mode Yarn's Zero-Installs Trick: With PnP mode and zero-installs, yarn commits your dependencies directly to the repository. CI/CD runs need zero install time—they just yarn and go. The tradeoff? Your repo size increases significantly. Disk Usage: Where pnpm Shines Raw speed is one thing, but what about your hard drive? Single Project Disk Usage Same 200-dependency project: ┌────────────┬──────────────┬──────────────┐ │ Manager │ node_modules │ vs npm │ ├────────────┼──────────────┼──────────────┤ │ npm │ 487 MB │ baseline │ │ yarn │ 502 MB │ +3% │ │ pnpm │ 124 MB* │ -75% │ │ bun │ 461 MB │ -5% │ └────────────┴──────────────┴──────────────┘ * pnpm uses hard links to global store Enter fullscreen mode Exit fullscreen mode Multiple Projects (Same Dependencies) Here's where pnpm's architecture pays off. If you have 10 projects using React 19: 10 Projects with overlapping dependencies: ┌────────────┬──────────────┬──────────────┐ │ Manager │ Total Disk │ vs npm │ ├────────────┼──────────────┼──────────────┤ │ npm │ 4.87 GB │ baseline │ │ yarn │ 5.02 GB │ +3% │ │ pnpm │ 612 MB │ -87% │ │ bun │ 4.61 GB │ -5% │ └────────────┴──────────────┴──────────────┘ Enter fullscreen mode Exit fullscreen mode pnpm stores each unique package version exactly once. Every project links to that single copy. If you work on many projects, pnpm can save tens of gigabytes. Bun's Approach: Bun uses a global cache but still creates full node_modules directories. It's faster than npm/yarn but doesn't achieve pnpm's deduplication. Monorepo Support Compared Monorepos have become the default for many organizations. Here's how each manager handles them: Workspace Configuration npm (workspaces): // package.json { "workspaces" : [ "packages/*" , "apps/*" ] } Enter fullscreen mode Exit fullscreen mode yarn (workspaces): // package.json { "workspaces" : [ "packages/*" , "apps/*" ] } Enter fullscreen mode Exit fullscreen mode pnpm (pnpm-workspace.yaml): # pnpm-workspace.yaml packages : - ' packages/*' - ' apps/*' Enter fullscreen mode Exit fullscreen mode Bun (workspaces): // package.json { "workspaces" : [ "packages/*" , "apps/*" ] } Enter fullscreen mode Exit fullscreen mode Workspace Features Comparison Feature npm yarn pnpm Bun Workspace protocol ( workspace:* ) ❌ ✅ ✅ ✅ Selective dependency installation ❌ ✅ ✅ ✅ Parallel task execution ❌ ✅ ✅ ✅ Cross-workspace linking Basic Good Excellent Good Hoisting control Limited Full Full Limited Filtering ( --filter ) ❌ ✅ ✅ ❌ The Bottom Line: pnpm and yarn are the clear leaders for monorepo management. npm's workspace support is functional but basic. Bun's is improving rapidly but still catching up. Real-World Monorepo Performance We tested a Turborepo setup with 15 packages: Task: Install + Build all packages ┌────────────┬──────────────┬──────────────┐ │ Manager │ Install │ Full Build │ ├────────────┼──────────────┼──────────────┤ │ pnpm │ 28.6s │ 142s │ │ bun │ 4.8s │ 138s │ │ yarn │ 52.3s │ 156s │ │ npm │ 134.2s │ 198s │ └────────────┴──────────────┴──────────────┘ Enter fullscreen mode Exit fullscreen mode Interesting: Bun's install speed advantage shrinks when you include build time. The build phase dominates, making the install speed difference less impactful for CI/CD overall. Security Features Security has become a first-class concern. Here's how each manager handles it: Audit Capabilities Feature npm yarn pnpm Bun audit command ✅ Native ✅ Plugin ✅ Native ❌ Auto-fix vulnerabilities ✅ ✅ ✅ ❌ Advisory database npm registry npm registry npm registry - SBOM generation ✅ ✅ Plugin ✅ ❌ Critical Note: Bun currently lacks built-in security auditing. For production applications, you'll need third-party tools like Snyk or Socket. Lockfile Security All four managers use lockfiles to ensure reproducible installs: npm: package-lock.json (JSON) yarn: yarn.lock (custom format) pnpm: pnpm-lock.yaml (YAML) Bun: bun.lockb (binary) Bun's Binary Lockfile: Bun's bun.lockb is binary for speed. While this makes installs faster, it's not human-readable and can't be easily diffed in code review. Bun offers bun.lock (text) as an alternative, but it's not the default. Supply Chain Protection Feature npm yarn pnpm Bun Signature verification ✅ ✅ ✅ ❌ Strict peer dependencies Optional Optional Default Optional .npmrc security options Full Limited Full Limited Network isolation mode ❌ ✅ ✅ ❌ Compatibility Reality Check Here's what nobody talks about: not every package works perfectly with every manager. Known Compatibility Issues (January 2026) pnpm: Some packages break with strict node_modules structure Workaround: shamefully-hoist=true in .npmrc Most major packages now support pnpm natively yarn PnP: Many packages still don't support PnP mode Workaround: nodeLinker: node-modules falls back to traditional structure Adoption is improving but still incomplete Bun: ~98% npm compatibility (up from 95% in 2025) Some native modules still have issues Workaround: Use --backend=copyfile for problematic packages Framework Compatibility Framework npm yarn pnpm Bun Next.js 15 ✅ ✅ ✅ ✅ Remix ✅ ✅ ✅ ⚠️ Nuxt 4 ✅ ✅ ✅ ✅ Angular 19 ✅ ⚠️ ✅ ⚠️ SvelteKit ✅ ✅ ✅ ✅ Astro 5 ✅ ✅ ✅ ✅ ⚠️ = Works but some edge cases or extra configuration needed CI/CD Performance For many teams, CI/CD time is where package manager choice really matters: GitHub Actions Benchmark # Same workflow, different package managers # Node.js 22, ubuntu-latest, clean cache ┌────────────┬──────────────┬──────────────┬──────────────┐ │ Manager │ Install │ Cache Hit │ Total Job │ ├────────────┼──────────────┼──────────────┼──────────────┤ │ npm │ 48s │ 12s │ 2m 34s │ │ yarn │ 21s │ 8s │ 2m 15s │ │ yarn (PnP) │ 18s │ 0s* │ 2m 02s │ │ pnpm │ 14s │ 4s │ 2m 08s │ │ bun │ 3s │ 1s │ 1m 52s │ └────────────┴──────────────┴──────────────┴──────────────┘ * Zero-installs : dependencies committed to repo Enter fullscreen mode Exit fullscreen mode Docker Build Performance # Multi-stage build comparison ┌────────────┬──────────────┬──────────────┐ │ Manager │ Layer Cache │ No Cache │ ├────────────┼──────────────┼──────────────┤ │ npm │ 18s │ 52s │ │ pnpm │ 8s │ 24s │ │ bun │ 2s │ 6s │ └────────────┴──────────────┴──────────────┘ Enter fullscreen mode Exit fullscreen mode The Docker Secret: Bun's speed advantage is even more pronounced in Docker because its binary includes the runtime—no need to install Node.js separately. Migration Guides Ready to switch? Here's how: npm → pnpm Install pnpm: npm install -g pnpm Enter fullscreen mode Exit fullscreen mode Import existing lockfile: pnpm import Enter fullscreen mode Exit fullscreen mode Delete old files: rm -rf node_modules package-lock.json Enter fullscreen mode Exit fullscreen mode Install: pnpm install Enter fullscreen mode Exit fullscreen mode Update scripts (if needed): // package.json - usually works as-is { "scripts" : { "dev" : "next dev" , // no change needed "build" : "next build" } } Enter fullscreen mode Exit fullscreen mode npm → Bun Install Bun: curl -fsSL https://bun.sh/install | bash Enter fullscreen mode Exit fullscreen mode Remove old files: rm -rf node_modules package-lock.json Enter fullscreen mode Exit fullscreen mode Install: bun install Enter fullscreen mode Exit fullscreen mode Update scripts for Bun runtime (optional): { "scripts" : { "dev" : "bun run --bun next dev" , "build" : "bun run next build" } } Enter fullscreen mode Exit fullscreen mode yarn 1.x → yarn 4.x (Berry) # Enable corepack (Node.js 16+) corepack enable # Set yarn version yarn set version stable # Migrate configuration yarn config set nodeLinker node-modules # for compatibility # Install yarn install Enter fullscreen mode Exit fullscreen mode Rollback Plan If migration causes issues: # Keep your old lockfile backed up! cp package-lock.json package-lock.json.backup # To rollback: rm -rf node_modules bun.lockb pnpm-lock.yaml yarn.lock mv package-lock.json.backup package-lock.json npm install Enter fullscreen mode Exit fullscreen mode When to Use What: Decision Framework Use npm when: ✅ Maximum compatibility is required ✅ Team is unfamiliar with alternatives ✅ Legacy project with many native dependencies ✅ Corporate environment with strict tooling policies ✅ You want "it just works" Use yarn when: ✅ You need Plug'n'Play zero-installs ✅ You want the plugin ecosystem ✅ Your team is already yarn experts ✅ You need advanced workspace features ✅ Offline-first development is important Use pnpm when: ✅ Disk space is a concern ✅ You have many projects with overlapping dependencies ✅ Large monorepo with complex dependencies ✅ You want speed without sacrificing compatibility ✅ Strict dependency isolation matters Use Bun when: ✅ Speed is the absolute priority ✅ You're starting a new project ✅ CI/CD time is a major cost ✅ You're building Node.js APIs or scripts ✅ You want a unified runtime + package manager The Hidden Costs Nobody Mentions Before you switch, consider: Learning Curve npm → pnpm: Minimal. Almost drop-in. npm → yarn 4: Moderate. PnP mode requires understanding. npm → Bun: Low for package management, higher if using Bun runtime. Tooling Compatibility IDE support: All four work with VS Code, JetBrains, etc. CI/CD templates: npm has the most, Bun the least ready-made Docker images: npm/yarn are everywhere, pnpm common, Bun less common Team Onboarding The fastest package manager doesn't help if it slows down your team. Consider: How comfortable is your team with the new tool? Are your documentation and scripts updated? Have you tested the entire development workflow? Future Outlook: 2026 and Beyond npm: Will remain the default. Focus on incremental improvements. yarn: Continuing to push PnP adoption. Better monorepo support coming. pnpm: Rapid growth in enterprise. Becoming the "safe modern choice." Bun: Aggressive development. Aiming for 100% npm compatibility. May become the default for new projects by 2027. The ecosystem is fragmenting in healthy ways. Competition drives innovation—and all four managers are better for it. Conclusion: There's No Wrong Choice (Mostly) After extensive testing, here's the honest truth: all four package managers work fine for most projects. The performance differences, while measurable, rarely matter for small-to-medium projects. Where choice matters: Monorepos: pnpm or yarn CI/CD-heavy workflows: Bun or pnpm Disk-constrained systems: pnpm Maximum compatibility: npm Bleeding edge: Bun The most important thing isn't which package manager you choose—it's that you choose consistently across your projects and team. Switching between managers constantly creates more friction than any speed difference could justify. My recommendation for 2026: New projects: Try Bun. It's fast enough to justify the minor compatibility risks. Existing projects: Consider pnpm if you're feeling pain. Otherwise, npm is fine. Enterprise monorepos: pnpm is the safe, powerful choice. Benchmarks conducted January 2026 on M3 MacBook Pro with Node.js 22.x. Results will vary based on hardware, network, and project specifics. Always test with your own codebase before making decisions. 🚀 Explore More: This article is from the Pockit Blog . If you found this helpful, check out Pockit.tools . It’s a curated collection of offline-capable dev utilities. Available on Chrome Web Store for free. Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse HK Lee Follow solo web developer Joined Dec 26, 2025 Trending on DEV Community Hot I Am 38, I Am a Nurse, and I Have Always Wanted to Learn Coding # career # learning # beginners # coding Top 7 Featured DEV Posts of the Week # top7 # discuss What was your win this week??? # weeklyretro # discuss 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://dev.to/aws-builders/aws-2025-a-year-of-agentic-ai-custom-chips-and-multicloud-bridges-4kn2 | AWS 2025: A Year of Agentic AI, Custom Chips, and Multicloud Bridges - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Damien Gallagher for AWS Community Builders Posted on Jan 2 AWS 2025: A Year of Agentic AI, Custom Chips, and Multicloud Bridges # aws # reinvent # cloud # 2025 I've been tracking AWS releases all year, and honestly, 2025 felt different. Not just "here's another managed service" different, but fundamentally "we're changing how you build software" different. Between re:Invent and the steady stream of updates throughout the year, there's a lot to unpack. This article is my attempt to summarise the key announcements across every major category. I won't cover everything - AWS announced hundreds of updates - but I'll hit the ones that actually matter for most developers and architects. 1. The Big Picture: What Defined 2025 If I had to describe 2025 in three themes, it would be: Agentic AI everywhere - AWS went all-in on autonomous agents that do things on your behalf Custom silicon at scale - Graviton5, Trainium3, and the infrastructure to run them Multicloud is real now - That AWS-Google partnership wasn't on my bingo card The common thread? AWS is positioning itself not just as infrastructure, but as the platform where AI agents live and operate. Whether you buy into that vision or not, it's clearly where they're headed. 2. Global Infrastructure Expansion AWS continued expanding its global footprint in 2025: New Regions Launched: Mexico (Central) - AWS's first region in Mexico, launched January 2025. They've committed $5 billion over 15 years, so they're serious about Latin America. Thailand (Asia Pacific) - Also launched January 2025 with three availability zones. Also Launched in 2025: Taiwan (Asia Pacific - Taipei) - Launched June 2025 with three availability zones and $5 billion investment New Zealand (Asia Pacific) - Launched September 2, 2025 (ap-southeast-6) with three availability zones and NZ$7.5 billion investment Coming Soon: Saudi Arabia (expected 2026) AWS European Sovereign Cloud - Launching December 2025 in Brandenburg, Germany with €7.8 billion investment As of December 2025, AWS operates 120 availability zones across 38 geographic regions. Remember - you don't pay for an AWS account, just the resources running in it. But regional availability still matters for latency and data residency requirements. 3. Compute: Where Things Got Interesting This was a big year for compute. Not just incremental improvements, but some genuinely useful new capabilities. Graviton5 Processors AWS introduced Graviton5, their most powerful custom chip yet. The new EC2 M9g instances deliver up to 25% higher performance than the previous generation while using less energy. If you're not already on Graviton for compatible workloads, the price-performance gap just got wider. Trainium3 UltraServers For AI training workloads, Trainium3 UltraServers can host up to 144 Trainium3 chips per server, providing up to 362 MXFP8 PFLOPs of compute. The chips themselves are claimed to be 40% more energy efficient than the previous generation. I haven't run benchmarks myself, but the specs are impressive. Lambda Durable Functions This one's probably the most practical announcement for everyday developers. Lambda Durable Functions lets you build applications that coordinate multiple steps over extended periods - from seconds to up to one year - without paying for idle compute time. Think workflows that wait for human approval, or processes that need to poll external systems periodically. Before this, you'd need Step Functions or some custom orchestration. Now Lambda handles it natively. Lambda Managed Instances A new capability that lets you run Lambda-like functions on EC2 hardware. It's an interesting middle ground - serverless simplicity with EC2's power and flexibility. Useful when you need more control over the underlying compute but don't want to give up the Lambda programming model. New EC2 Instance Types Memory-optimised instances powered by 5th Gen AMD EPYC processors hit the market, offering up to 5 GHz speeds and 3 TiB of RAM. These are aimed at heavy workloads like databases and EDA tools. 4. AI and Machine Learning: The Agentic Era This section could be its own article - actually, I'm writing one - but here are the highlights: Amazon Nova 2 Models AWS expanded the Nova family significantly: Nova 2 Lite - Fast, cost-effective reasoning for everyday workloads Nova 2 Pro - The most capable model for complex, multi-step tasks (currently in preview) Nova 2 Sonic - Speech-to-speech model supporting seven languages Nova 2 Omni - The industry's first reasoning model that processes text, images, video, and speech while generating both text and images All Nova 2 models support extended thinking with adjustable intensity levels. You can dial up the reasoning depth when you need it and keep it light for simpler queries. Amazon Nova Forge This one's fascinating - it's essentially "build your own frontier model." You start from Nova model checkpoints, blend your proprietary data with Nova's training data, and get a custom model that combines Nova's capabilities with your domain knowledge. Reddit apparently already built their own model using it. Nova Act Browser automation agents. Powered by a custom Nova 2 Lite model, it delivers 90% reliability on browser-based tasks. If you've been building web scrapers or automation tools manually, this might be worth exploring. Amazon Bedrock Updates Bedrock now has over 100 foundation models, including 18 new open-weight models added in December. The big capability addition was reinforcement fine-tuning, which uses feedback-driven training to deliver 66% accuracy gains without needing massive labelled datasets. Amazon Bedrock AgentCore AgentCore went from preview (July) to GA (October) to feature-rich (December). It's the infrastructure layer for building, deploying, and operating AI agents at scale. The SDK has been downloaded over 2 million times in just 5 months. Key components: Runtime - Session isolation, bidirectional streaming for voice agents Memory - Now includes episodic memory for agents that learn from experience Gateway - Convert existing APIs to MCP-compatible tools Identity - OAuth integration and secure token storage Observability - CloudWatch dashboards for agent monitoring Policy - Real-time tool call interception using Cedar policies Evaluations - 13 built-in evaluators for quality monitoring Amazon Q Developer The AI coding assistant got significant updates: C#, C++, and 11 additional languages for customisation GitLab Duo integration (GA) GitHub integration (preview - no AWS account required) MCP support in the CLI Conversation history that persists between sessions Pro Tier available in Frankfurt for EU data residency Kiro IDE AWS released Kiro, an agentic AI IDE that Amazon now uses internally. Built on VS Code, it features spec-driven development where you write requirements in markdown and the agent scaffolds everything. One internal project reportedly went from 30 developers over 18 months to 6 developers over 76 days. I'm still evaluating it myself, but the early signs are promising. 5. Database: Savings and Scale Database Savings Plans Finally - a single, flexible commitment that applies across RDS, Aurora, DynamoDB, ElastiCache, Neptune, and DocumentDB. No more juggling separate Reserved Instance portfolios per engine. You can reduce costs by up to 35% with a one-year commitment. Aurora DSQL Cluster creation now takes seconds instead of minutes. Useful for rapid prototyping and testing scenarios. RDS Storage Expansion RDS for SQL Server and Oracle now support up to 256 TiB of storage (up from 64 TiB), with a 4x improvement in IOPS and I/O bandwidth. If you're running large on-prem databases, the migration path just got easier. OpenSearch Enhancements GPU-accelerated vector indexing makes index creation 10x faster at one-quarter the cost. Auto-optimised vector indexes automatically evaluate different KNN algorithms to balance recall quality against query performance. 6. Networking: The Multicloud Moment AWS Interconnect This is the one that surprised everyone. AWS partnered with Google Cloud to offer managed, high-speed private connections between the two platforms. You can provision dedicated bandwidth on demand and establish connectivity in minutes. The service includes quad-redundancy and MACsec encryption. Microsoft Azure support is coming in 2026. I've been saying for years that multicloud is mostly marketing, but this is... actually useful. If you genuinely have workloads across AWS and GCP, this beats managing your own Direct Connect and Cloud Interconnect setup. Route 53 Global Resolver Now in preview - secure anycast DNS resolution that simplifies hybrid DNS management. One service instead of managing resolvers in each VPC. 7. Storage: Vectors and Tables Amazon S3 Vectors (GA) S3 now natively supports storing and querying vector embeddings. It scales up to 2 billion vectors per index (40x the preview capacity), supports up to 20 trillion vectors per bucket, and reduces costs by up to 90% compared to specialised vector databases. For RAG, semantic search, and agentic workloads, this removes the need for a separate vector database. I'm looking at you, Pinecone bills. S3 Tables Built-in Intelligent-Tiering support and replication for Apache Iceberg-native tables. Makes it easier to run analytics on S3 without complex ETL pipelines. FSx for NetApp ONTAP Now integrates with S3, allowing file-system data to be accessed via S3 APIs. Useful for plugging existing storage into analytics and ML services without copying data around. 8. Containers and Kubernetes Amazon EKS Capabilities Fully managed platform capabilities for workload orchestration and cloud resource management. The goal is eliminating infrastructure maintenance while maintaining enterprise-grade reliability. If you're running vanilla Kubernetes and spending too much time on cluster management, this might help. ECS Express Mode Simplified container deployments for ECS. I haven't tried this one yet, but it's on my list. 9. The Deprecation Corner AWS introduced the Product Lifecycle page in 2025, which consolidates all service availability information in one place. About time, honestly. Key deprecations to be aware of: AWS Cloud9 - No longer accepting new customers. AWS recommends VS Code with remote extensions. AWS WAF Classic - No new WebACLs after March 31, 2025. Fully retired September 30, 2025. AWS Proton - Support ends October 7, 2026. New customers blocked after October 7, 2025. AWS SDK for JavaScript v2 - End of support September 8, 2025. Migrate to v3. Amazon Linux 2 - End of support extended to June 30, 2026. Migrate to Amazon Linux 2023 before then. If you're using any of these, now's the time to plan your migration. The CodeCommit Reversal In a rare move, AWS reversed the CodeCommit deprecation in November 2025 after listening to customer feedback. CodeCommit is back to full General Availability with new features planned including Git Large File Storage in early 2026 and regional expansion to additional regions starting Q3 2026. The reversal acknowledged that CodeCommit's deep IAM integration, VPC endpoint support, and seamless connectivity with CodePipeline provided significant value, especially for regulated industries. AWS explicitly apologised for the inconvenience caused to customers who had begun migration planning. 10. What Does 2025 Tell Us? Looking at the year as a whole, a few patterns emerge: AI agents are the new compute primitive. Just like we went from servers to containers to functions, we're now going from functions to agents. AWS is betting big that the future of cloud computing involves autonomous systems that act on our behalf. Custom silicon matters. AWS keeps investing in Graviton and Trainium because they genuinely believe they can out-price and out-perform commodity hardware for specific workloads. The numbers so far suggest they're right. Multicloud is becoming practical. The Google partnership signals that even AWS recognises customers have legitimate multicloud needs. Expect more interoperability announcements in 2026. Developer experience is a priority. Q Developer, Kiro, and the various IDE integrations show AWS is serious about making AI-assisted development accessible. They're not just building infrastructure - they're building the tools developers use daily. Whether you're excited or exhausted by the pace of change, 2025 was undeniably a significant year for AWS. And if the roadmap announcements are any indication, 2026 will be even more intense. What announcements mattered most to you? I'd love to hear what you're planning to try first. Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse AWS Community Builders Follow Build On! Would you like to become an AWS Community Builder? Learn more about the program and apply to join when applications are open next. 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https://zeroday.forem.com/sovarcneo/building-an-air-gapped-ai-defense-system-in-python-no-cloud-apis-288g#comments | Building an Air-Gapped AI Defense System in Python (No Cloud APIs) - Security Forem Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account Security Forem Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse SovArcNeo Posted on Nov 21, 2025 Building an Air-Gapped AI Defense System in Python (No Cloud APIs) # blueteam # beginners # discuss # tools The Sovereign Architecture Most modern AI development relies heavily on cloud APIs and external dependencies. I decided to go the other direction: Total Sovereignty. I am building NEXUS , an offline-first, air-gapped AI defense system designed to run on local hardware (Linux and Android/Termux). The goal is to create recursive intelligence that functions without an internet connection, ensuring privacy and zero data leakage. The Tech Stack I avoid "black box" libraries where possible. My stack focuses on: Core: Python 3.x (Dependency-free where possible) GUI: Custom Tkinter interfaces (Cyberpunk/Matrix aesthetic for high-contrast visibility) Logic: Recursive "Breeding Cycles" rather than standard versioning. I spawn multiple instances, stress-test them, and the surviving code becomes the baseline for the next generation. Deployment: Runs natively on Linux or via Termux on Android. Visualizing the Grid I recently refactored my modular agents into standalone applications. Pictured in the cover image are: ARCHITECT (Green): A Neural Enhanced Security Platform for Command & Control. SENTINEL (Blue): A Quantum-resistant defensive monitor for system metrics and threat detection. Why "Offline-First"? In a world of connected APIs, building "air-gapped" software forces you to understand the logic from the ground up. You can't call an API to solve the problem; you have to engineer the solution yourself. I am currently open-sourcing these tools to share this "Sovereign" philosophy with the community. Check out the repositories here: https://github.com/SovArcNeo Visualized above: Two of my modular agents ('Architect' and 'Sentinel') that I recently refactored into standalone applications Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse SovArcNeo Follow Python developer focused on AI-driven security. I build standalone, offline-first applications featuring custom neural networks. Joined Nov 21, 2025 More from SovArcNeo I put an Air-Gapped Neural Network in my pocket (Python on Android) # discuss # beginners # tools # devsecops 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV Security Forem — Your central hub for all things security. From ethical hacking and CTFs to GRC and career development, for beginners and pros alike Home About Contact Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . Security Forem © 2016 - 2026. Share. Secure. Succeed Log in Create account | 2026-01-13T08:49:45 |
https://www.fine.dev/blog/ai-coding-guide#pricing | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
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Navigation Python SDK Objects Documentation API Reference Management API CLI Reference Developer Resources Changelog Documentation API Reference Management API CLI Reference Developer Resources Changelog Python SDK Objects OpenAI Open in ChatGPT Create, update, & manage objects and their subscriptions using Python SDK methods. OpenAI Open in ChatGPT Objects represent non-user entities such as organizations, teams, roles, and projects. Understand more about objects here . Create / Update object This is an upsert method, used to create or update an object. Request Response Copy Ask AI import suprsend supr_client = suprsend.Suprsend( "Workspace_Key" , "Workspace_Secret" ) # Object details object_id = "engineering" object_type = "departments" # Optional: for setting additional properties on object properties = { "key1" : "val1" , "$email" : " [email protected] " } res = supr_client.objects.upsert(object_type, object_id, properties) print (res) Edit an object There are 2 ways in which you can edit an object data. Build edit payload yourself Use helper methods provided by SDK (Recommended) Edit an object (build edit payload yourself) Use this to modify an object, typically for removing channels or unsetting properties. The payload will follow the same structure as the Object Edit API Request Response Copy Ask AI res = supr_client.objects.edit ( "object_type" , "object_id" , { "operations" : [{ "$set" : { "company_name" : "ABC company" }}]}) print (res) Edit an object (using helper methods) [Recommended] To edit object, you need to first fetch object instance, call all the update methods and save changes using objects.edit method. Request Response Copy Ask AI # Fetch object instance obj_inst = supr_client.objects.get_edit_instance( "object_type" , "object_id" ) # Call object update methods obj_inst.set_timezone( "America/Los_Angeles" ) obj_inst.set( "company_name" , "ABC company" ) # Save Changes res = supr_client.objects.edit(obj_inst) print (res) List of available methods: Add Channels Use object_ins.add* method(s) to add channels Copy Ask AI obj_inst = supr_client.objects.get_edit_instance( "object_type" , "object_id" ) # Add Email obj_inst.add_email( " [email protected] " ) # Add SMS obj_inst.add_sms( "+15555555555" ) # Add Whatsapp obj_inst.add_whatsapp( "+15555555555" ) # Add Androidpush token with vendor obj_inst.add_androidpush( "androidpush_fcm_token__" , "fcm" ) # Add iOS Push token obj_inst.add_iospush( "__iospush_apns_token__" ) # Add Slack using user email id slack_ident_email = { "access_token" : "xoxb-XXXXXXXX" , "email" : " [email protected] " } obj_inst.add_slack(slack_ident_email) # Add Slack using member_id of the user slack_ident_user_id = { "access_token" : "xoxb-XXXXXXXX" , "user_id" : "U03XXXXXXXX" } obj_inst.add_slack(slack_ident_user_id) # Add Slack channel_id slack_ident_channel_id = { "access_token" : "xoxb-XXXXXXXX" , "channel_id" : "C04XXXXXXXX" } obj_inst.add_slack(slack_ident_channel_id) # Add Slack incoming webhook slack_ident_webhook = { "incoming_webhook" : { "url" : "https://hooks.slack.com/services/TXXXX/BXXXX/XXXXXXX" } } obj_inst.add_slack(slack_ident_webhook) # Add Webpush token json (VAPID) webpush_ident = { "endpoint" : "__end_point__" , "expirationTime" : "" , "keys" : { "p256dh" : "__p256dh__" , "auth" : "__auth_key__" } } obj_inst.add_webpush(webpush_ident, "vapid" ) # Save changes res = supr_client.objects.edit(obj_inst) print (res) Remove Channels Use object_ins.remove_* method(s) to remove channels from an object Copy Ask AI obj_inst = supr_client.objects.get_edit_instance( "object_type" , "object_id" ) # Remove Email obj_inst.remove_email( " [email protected] " ) # Remove SMS obj_inst.remove_sms( "+15555555555" ) # Remove Whatsapp obj_inst.remove_whatsapp( "+15555555555" ) # Remove Androidpush token with vendor obj_inst.remove_androidpush( "androidpush_fcm_token__" , "fcm" ) # Remove iOS Push token obj_inst.remove_iospush( "__iospush_apns_token__" ) # Remove Slack using user email id slack_ident_email = { "access_token" : "xoxb-XXXXXXXX" , "email" : " [email protected] " } obj_inst.remove_slack(slack_ident_email) # Remove Slack using member_id of the user slack_ident_user_id = { "access_token" : "xoxb-XXXXXXXX" , "user_id" : "U03XXXXXXXX" } obj_inst.remove_slack(slack_ident_user_id) # Remove Slack channel_id slack_ident_channel_id = { "access_token" : "xoxb-XXXXXXXX" , "channel_id" : "C04XXXXXXXX" } obj_inst.remove_slack(slack_ident_channel_id) # Remove Slack incoming webhook slack_ident_webhook = { "incoming_webhook" : { "url" : "https://hooks.slack.com/services/TXXXX/BXXXX/XXXXXXX" } } obj_inst.remove_slack(slack_ident_webhook) # Remove Webpush token json (VAPID) webpush_ident = { "endpoint" : "__end_point__" , "expirationTime" : "" , "keys" : { "p256dh" : "__p256dh__" , "auth" : "__auth_key__" } } obj_inst.remove_webpush(webpush_ident, "vapid" ) # Save changes res = supr_client.objects.edit(obj_inst) print (res) Remove Channels in bulk This method will delete/unset all values in specified channel for object (ex: remove all emails attached to object). Copy Ask AI # Remove multiple channels in bulk channels_to_remove = [ "$email" , "$whatsapp" , "$sms" , "$androidpush" , "$iospush" , "$webpush" , "$slack" , "$ms_teams" ] obj_inst.unset(channels_to_remove) Set preferred language If you want to send notification in user’s preferred language, you can set it by passing language code in this method. This is useful especially for the applications which offer vernacular or multi-lingual support. Copy Ask AI obj_inst.set_preferred_language( "en" ) Set timezone You can set timezone of user using this method. Value for timezone must be from amongst the IANA timezones . Copy Ask AI obj_inst.set_timezone( "America/Los_Angeles" ) Set Set any custom property using this method. It will shallow merge existing properties with new values. Key shouldn’t start with $ or ss . Request Copy Ask AI # Set a single property obj_inst.set(key, value) obj_inst.set( "company_name" , "ABC company" ) # Set multiple properties obj_inst.set({ key1: value1, key2: value2 }) obj_inst.set({ "company_name" : "ABC company" , "city" : "San Francisco" }) Unset Use this to unset existing channels or properties. Request Copy Ask AI #unset single channel or property obj_inst.unset( "$email" ) #unset multiple channels or properties obj_inst.unset([ "$email" , "company_name" ]) Append Use this to append item to an array based property. Request Copy Ask AI #append single property obj_inst.append(key, value) obj_inst.append( "played_games" , "game_1" ) #append multiple properties obj_inst.append({ key1: value1, key2: value2 }) obj_inst.append({ "played_games" : "game_1" , "liked_games" : "game_2" }) Remove Use this to remove an item from array based property Request Copy Ask AI #for single property obj_inst.remove(key, value) obj_inst.remove( "played_games" , "game_1" ) #for multiple properties obj_inst.remove({ key1: value1, key2: value2 }) obj_inst.remove({ "played_games" : "game_1" , "liked_games" : "game_2" }) Set once Use this to add new properties which are not overridden, such as first_login_at Request Copy Ask AI #single property obj_inst.set_once(key, value) obj_inst.set_once( "first_login" , "2021-11-02" ) #multiple properties obj_inst.set_once({ key1: value1, key2: value2 }) obj_inst.set_once({ "first_login" : "2021-11-02" , "signup_date" : "2021-11-02" }) Increment Use on numeric values to increment/decrement. Provide a negative value for decrement Request Copy Ask AI #single property obj_inst.increment(key, value) obj_inst.increment( "login_count" , 1 ) #multiple property obj_inst.increment({ key1: value1, key2: value2 }) obj_inst.increment({ "login_count" : 1 , "order_count" : 1 }) List objects List objects for an object_type . You can also pass listing options in the payload which includes limit , before , after Request Response Copy Ask AI res = supr_client.objects.list( "object_type" ) { "after" : "01JJW6H55NXXXX59ARDW85G0KN" , "limit" : 1 } print (res) Get object details Request Response Copy Ask AI res = supr_client.objects.get( "object_type" , "object_id" ) print (res) Add object subscription Request Response Copy Ask AI res = supr_client.objects.create_subscriptions( "object_type" , "object_id" , { "recipients" : [ "distinct_id_1" , { "object_type" : "teams" , "object_id" : "product" }, ], "properties" : { "type" : "members" }, }, ) print (res) List object subscription Request Response Copy Ask AI res = supr_client.objects.get_subscriptions( "object_type" , "object_id" ) { "after" : "01JJW6H55NXXXXXX5G0KN" , "limit" : 1 } print (res) Remove object subscription Request Response Copy Ask AI res = supr_client.objects.delete_subscriptions ( "object_type" , "object_id" , { "recipients" : [ "distinct_id_1" , { "object_type" : "departments" , "object_id" : "engineering" }, ] }, ) print (res) Get list of objects subscribed by object An object can subscribe to other objects. Use this method to get the list of all objects that the current object has subscribed to Request Response Copy Ask AI res = supr_client.objects.get_objects_subscribed_to( "object_type" , "object_id" , { "after" : "01JJW6H55NXXXXX85G0KN" , "limit" : 1 }) print (res) Delete object Request Response Copy Ask AI res = supr_client.objects.delete( "object_type" , "object_id" ) print (res) Bulk Delete objects Request Response Copy Ask AI res = supr_client.objects.bulk_delete( "object_type" , { "object_ids" : [ "object_id1" , "object_id2" ]}) print (res) Was this page helpful? Yes No Suggest edits Raise issue Previous Send and Track Events Learn how to send events to trigger workflows, with code snippets and examples. Next ⌘ I x github linkedin youtube Powered by On this page Create / Update object Edit an object Edit an object (build edit payload yourself) Edit an object (using helper methods) [Recommended] List objects Get object details Add object subscription List object subscription Remove object subscription Get list of objects subscribed by object Delete object Bulk Delete objects self.__next_f.push([1,"\"use strict\";\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n const _components = {\n a: \"a\",\n code: \"code\",\n li: \"li\",\n p: \"p\",\n pre: \"pre\",\n span: \"span\",\n ul: \"ul\",\n ..._provideComponents(),\n ...props.components\n }, {Accordion, AccordionGroup, CodeBlock, CodeGroup, Heading} = _components;\n if (!Accordion) _missingMdxReference(\"Accordion\", true);\n if (!AccordionGroup) _missingMdxReference(\"AccordionGroup\", true);\n if (!CodeBlock) _missingMdxReference(\"CodeBlock\", true);\n if (!CodeGroup) _missingMdxReference(\"CodeGroup\", true);\n if (!Heading) _missingMdxReference(\"Heading\", true);\n return _jsxs(_Fragment, {\n children: [_jsxs(_components.p, {\n children: [\"Objects represent non-user entities such as organizations, teams, roles, and projects. 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https://dev.to/mitul3737/devops-prerequisite-part-9-ssl-and-tls-53di | DevOps Prerequisite (Part 9): SSL and TLS - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Shahriyar Al Mustakim Mitul Posted on Oct 2, 2024 • Edited on Dec 13, 2024 DevOps Prerequisite (Part 9): SSL and TLS # security # devops TLS is a certificate ensures the communication is encrypted and server is actually what it actually say it is For example, assume that you are john and want to access to your account from the website my-bank.com A hacker might hack the traffic and get your password and then claim your account. To solve this issue, we use a secret key which is then applied to your password to make it unique Now, once you want to get access to the server, your encrypted data is passed on the traffic and the hacker might get the data. Still he can't decrypt it But here is an issue, you need to send a copy of your password to the server to decrypt it. While doing so, hacker can get the key as well!!! This is called symmetric encryption (uses same key to encrypt and decrypt) To solve this issue, we use assymmetric encryption (public and private key) Let's call public key as public lock for now. Firstly, we encrypt the data with public key/lock. Then we share the name and password along the public key/public lock But the password can be decrypt only with the private key So, how we generate these keys? We generate using ssh-keygen id_rsa is the private key and id_rsa.pub is the public key/lock You can now lock your server using the public key. You can also check that the key under /.ssh/autorized_keys. You can see the public key there. Let's go back to the bank's situation Now, we will generate public and private keys on the server. Then the public key/lock is sent to the user and the hacker might get that as well The user has a key (symmetric) earlier created to encrypt the data. Now this symmetric key and the lock will combine together and will be sent to the server. The hacker also gets that The server uses private key to decrypt the public lock/key. So, this is how the server gets the symmetric key . Now the server can decrypt the user password and get access to data But the hacker is not happy and somehow copies the whole website in his server and somehow takes you there You then share your symmetric key to the server Now, the hacker decrypt your password and you got hacked!! But how to know know if a public lock that was shared to me is genuine from the bank server or the hacker? Basically that public key was shared with a certificate. The certificate looks like this Anyone can generate the certificate.So, how to know that? We know that by the signature. If the hacker did that, the hacker will have a signature on it (self signed) So, this is how it looked once the hacker sent the user public lock Gladly, all browser has certificate validation It checks if a certificate is valid or not! But for the original certificates, we have CA (certificate authorities). Some are Symantec etc. So, how to generate trusted certificate? Using the public key, you request for a signing request (CSR). How to do that? You use openssl to send your public key and domain name to CAs and it generates a CSR request The certificate authority then checks and it all information is right, they sign the certificate. For the hackers case, the certificate will not be validated by CAs So, these CAs can help validating public websites.To solve issue for private websites, you can install their private version and install in your company PC The real bank server can also ask for the client to generate a csr to verify if he/she is the real user Using the CA, the user can generate csr and send to the real server. The whole thing is called as PKI (Public key infrastructure) Examples of public key/lock and private keys Mutual TLS Assume that mybank.com wants to send some data to abc-financials.com So, how will abc-financials will know that mybank.com is actually sending data? This is where mutual TLS works and client and sever will now verify the authenticity of each other. Assuming my-bank (client) sends some data to abc-financials (server) Client first requests the server's public certificate. THe server replies back it's public certificate The client checks with the CA, if the certificate is valid Once verified, the client sends a certificate to the server and also shares a symmetric key which is encrypted with the public key of the server. The server now validates with the CA if the client indeed belong to the mybank So, in this way, they have both mutually verified each other. Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Shahriyar Al Mustakim Mitul Follow I am a 2nd year CSE Student 😊 Location Dhaka,Bangladesh Work Student Joined Oct 17, 2020 More from Shahriyar Al Mustakim Mitul DevOps Prerequisite (Part 7): YAML & Json # devops DevOps Prerequisite (Part 6): Database # devops DevOps Prerequisite (Part 5): Web server # devops 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . DEV Community © 2016 - 2026. We're a place where coders share, stay up-to-date and grow their careers. Log in Create account | 2026-01-13T08:49:45 |
https://dev.to/tlakomy | Tomasz Łakomy - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Follow User actions Tomasz Łakomy Frontend Engineer at Stedi. https://cloudash.dev co-founder. Tech speaker, egghead.io instructor, lifelong learner. Location Poznań, Poland Joined Joined on Mar 18, 2019 Personal website https://cloudash.dev github website twitter website Work Frontend Engineer at Stedi Six Year Club This badge celebrates the longevity of those who have been a registered member of the DEV Community for at least six years. Got it Close 2 Top 7 Awarded for having a post featured in the weekly "must-reads" list. 🙌 Got it Close Five Year Club This badge celebrates the longevity of those who have been a registered member of the DEV Community for at least five years. Got it Close Writing Debut Awarded for writing and sharing your first DEV post! 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Got it Close 4 Week Writing Streak You've posted at least one post per week for 4 consecutive weeks! Got it Close More info about @tlakomy Organizations AWS Heroes AWS Community Builders Skills/Languages JavaScript, React, Twitter Currently hacking on https://cloudash.dev Post 59 posts published Comment 30 comments written Tag 10 tags followed Quick guide to CSS Grid Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Dec 27 '23 Quick guide to CSS Grid # css # grid 5 reactions Comments Add Comment 2 min read Want to connect with Tomasz Łakomy? Create an account to connect with Tomasz Łakomy. You can also sign in below to proceed if you already have an account. Create Account Already have an account? Sign in CloudFront Functions vs. Lambda@Edge: what's the difference? Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Nov 22 '23 CloudFront Functions vs. Lambda@Edge: what's the difference? # aws # lambda # cloudfront 14 reactions Comments 1 comment 3 min read Full Stack TypeScript with AWS Cloud Development Kit v2 Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Nov 22 '23 Full Stack TypeScript with AWS Cloud Development Kit v2 # aws # cdk # typescript Comments Add Comment 1 min read Overview of AWS Lambda internal extensions Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Nov 16 '23 Overview of AWS Lambda internal extensions # aws # lambda 5 reactions Comments Add Comment 3 min read Creating a safe external HTML link - what's the deal with nofollow / noopener / norefferer ? Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Nov 8 '23 Creating a safe external HTML link - what's the deal with nofollow / noopener / norefferer ? # html 4 reactions Comments Add Comment 2 min read Use patterns in CloudWatch Logs Insights to investigate production issues faster Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Oct 16 '23 Use patterns in CloudWatch Logs Insights to investigate production issues faster # aws # cloudwatch 4 reactions Comments Add Comment 3 min read Match case-insensitive patterns when using CloudWatch Logs Insights Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Oct 10 '23 Match case-insensitive patterns when using CloudWatch Logs Insights # aws # cloudwatch 10 reactions Comments Add Comment 2 min read Intro to CloudWatch Logs Live Tail Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Sep 4 '23 Intro to CloudWatch Logs Live Tail # aws # cloudwatch 2 reactions Comments Add Comment 2 min read AWS Lambda storage options Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Sep 21 '22 AWS Lambda storage options # aws # lambda # serverless 10 reactions Comments 3 comments 4 min read Guide to AWS Lambda Function URLs Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Aug 23 '22 Guide to AWS Lambda Function URLs # aws # lambda # serverless 14 reactions Comments Add Comment 5 min read Guide to default AWS Lambda environment variables Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Jun 6 '22 Guide to default AWS Lambda environment variables # aws # serverless # lambda 5 reactions Comments Add Comment 3 min read Benefits of multi-account strategy on AWS Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes May 23 '22 Benefits of multi-account strategy on AWS # aws # lambda # serverless 17 reactions Comments Add Comment 5 min read 8 best practices for optimizing Lambda functions Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Apr 18 '22 8 best practices for optimizing Lambda functions # aws # lambda # serverless 40 reactions Comments 1 comment 12 min read 10 CloudWatch Logs Insights examples for serverless applications Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Apr 4 '22 10 CloudWatch Logs Insights examples for serverless applications # aws # serverless # cloudwatch 39 reactions Comments 2 comments 3 min read Optimizing Lambda Performance for Your Serverless Applications Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Mar 9 '22 Optimizing Lambda Performance for Your Serverless Applications # aws # serverless # lambda 10 reactions Comments Add Comment 4 min read Notes from Data modeling with Amazon DynamoDB – Part 1 with Alex DeBrie Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Dec 2 '20 Notes from Data modeling with Amazon DynamoDB – Part 1 with Alex DeBrie # aws # dynamodb 18 reactions Comments 1 comment 3 min read Use VSCode debugger when working with AWS CDK Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Heroes Nov 21 '20 Use VSCode debugger when working with AWS CDK # aws # cdk # vscode 41 reactions Comments 1 comment 3 min read Notes from "The truth about cookies, tokens and APIs" by Phillipe de Ryck Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Nov 21 '20 Notes from "The truth about cookies, tokens and APIs" by Phillipe de Ryck # programming # security 8 reactions Comments Add Comment 5 min read 10 quick facts about AWS Lambda Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Community Builders Nov 15 '20 10 quick facts about AWS Lambda # aws # lambda 22 reactions Comments 2 comments 2 min read Build a file upload email subscription with CDK, S3 and SNS Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Community Builders Nov 15 '20 Build a file upload email subscription with CDK, S3 and SNS # aws # cdk 13 reactions Comments Add Comment 4 min read Build a simple GraphQL server with Apollo Server and AWS CDK Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Community Builders Nov 13 '20 Build a simple GraphQL server with Apollo Server and AWS CDK # aws # cdk 21 reactions Comments Add Comment 4 min read Why I don't like story-point-driven estimates Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Oct 5 '20 Why I don't like story-point-driven estimates # agile # career # estimation # scrum 70 reactions Comments 26 comments 6 min read CDK Made Simple: Managing S3 lifecycle policies with CDK Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Community Builders Aug 26 '20 CDK Made Simple: Managing S3 lifecycle policies with CDK # aws 15 reactions Comments Add Comment 3 min read AWS Made Simple: AWS KMS Envelope Encryption Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Community Builders Aug 26 '20 AWS Made Simple: AWS KMS Envelope Encryption # aws 14 reactions Comments 1 comment 3 min read A story of a biggest fuckup in my early career and what it taught me about taking ownership Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Aug 25 '20 A story of a biggest fuckup in my early career and what it taught me about taking ownership # career 32 reactions Comments 1 comment 4 min read How to get better at being appreciated for your hard work Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Aug 25 '20 How to get better at being appreciated for your hard work # career 29 reactions Comments 2 comments 5 min read AWS (?) made simple: What is a Netlify function? Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow May 28 '20 AWS (?) made simple: What is a Netlify function? # aws # netlify 16 reactions Comments Add Comment 3 min read AWS Made Simple: Introduction to Amazon EventBridge Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow for AWS Community Builders May 25 '20 AWS Made Simple: Introduction to Amazon EventBridge # aws # programming 29 reactions Comments 1 comment 3 min read Why I'm excited about serverless as a frontend engineer Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow May 23 '20 Why I'm excited about serverless as a frontend engineer # aws # development # serverless 125 reactions Comments 11 comments 4 min read What is AWS CDK (Cloud Development Kit) and why it's awesome Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Apr 1 '20 What is AWS CDK (Cloud Development Kit) and why it's awesome # aws # development 38 reactions Comments Add Comment 4 min read Random notes taken while preparing for AWS Certified Developer Associate exam Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Mar 4 '20 Random notes taken while preparing for AWS Certified Developer Associate exam # aws 145 reactions Comments 10 comments 5 min read Recording 100 bite-sized screencasts - lessons learned Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Mar 1 '20 Recording 100 bite-sized screencasts - lessons learned # career # development # tech 63 reactions Comments 9 comments 6 min read Why (frontend) testing matters Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Feb 19 '20 Why (frontend) testing matters # testing # development 80 reactions Comments 1 comment 4 min read How do I add an API Gateway trigger to a AWS Lambda function? Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Feb 10 '20 How do I add an API Gateway trigger to a AWS Lambda function? # aws # programming # development # tutorial 18 reactions Comments 1 comment 3 min read Reasons why React is better than jQuery Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Feb 8 '20 Reasons why React is better than jQuery # discuss # javascript 41 reactions Comments 12 comments 1 min read "Crap, I broke production" - How do we ensure it never happens again? Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Feb 6 '20 "Crap, I broke production" - How do we ensure it never happens again? # development # programming # career 115 reactions Comments 13 comments 6 min read What do I write about? Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Jan 29 '20 What do I write about? # career # blogging 67 reactions Comments 7 comments 3 min read 5 reasons why you might use AWS Lambda for your next project Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Jan 23 '20 5 reasons why you might use AWS Lambda for your next project # aws # programming # development # tutorial 53 reactions Comments 2 comments 3 min read Create an AWS Lambda function from scratch Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Jan 20 '20 Create an AWS Lambda function from scratch # aws # programming # development # tutorial 51 reactions Comments Add Comment 4 min read WTF is AWS Lambda? Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Jan 19 '20 WTF is AWS Lambda? # aws # programming # development # tutorial 188 reactions Comments 6 comments 3 min read I am Tomasz Łakomy, Senior Frontend Engineer @ OLX Group, tech speaker, and egghead.io instructor, Ask Me Anything! Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Jan 18 '20 I am Tomasz Łakomy, Senior Frontend Engineer @ OLX Group, tech speaker, and egghead.io instructor, Ask Me Anything! # ama 19 reactions Comments 4 comments 1 min read Prototype. Ship. Amend. Repeat. Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Jan 16 '20 Prototype. Ship. Amend. Repeat. # development # career # productivity 40 reactions Comments 3 comments 3 min read Best tweet-sized programming career lessons Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Jan 6 '20 Best tweet-sized programming career lessons # career # development 86 reactions Comments 6 comments 2 min read 7 CSS properties I had no idea about Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Jan 3 '20 7 CSS properties I had no idea about # css # development 563 reactions Comments 29 comments 4 min read Code is meaningless Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Dec 18 '19 Code is meaningless # development # career 27 reactions Comments 2 comments 3 min read Architecting for the Cloud - AWS Best Practices (part 1) Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Dec 2 '19 Architecting for the Cloud - AWS Best Practices (part 1) # aws 67 reactions Comments 2 comments 6 min read Quick question: "How did you learn to code?" Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Nov 26 '19 Quick question: "How did you learn to code?" # development # career 41 reactions Comments 18 comments 3 min read WTF is Amazon S3? Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Nov 21 '19 WTF is Amazon S3? # aws # webdev 22 reactions Comments 2 comments 2 min read So, what does it mean to be a senior developer? Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Nov 14 '19 So, what does it mean to be a senior developer? # career 260 reactions Comments 32 comments 5 min read 10 rules of a successful meeting in a tech company Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Nov 1 '19 10 rules of a successful meeting in a tech company # meetings 93 reactions Comments 6 comments 2 min read What I've Learned About Testing React Apps - Unit Tests Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Nov 1 '19 What I've Learned About Testing React Apps - Unit Tests # testing # react 109 reactions Comments 7 comments 5 min read Become a +10% engineer Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Oct 2 '19 Become a +10% engineer # development 107 reactions Comments 7 comments 6 min read Passing AWS Solutions Architect Associate exam Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Sep 18 '19 Passing AWS Solutions Architect Associate exam # development # aws 223 reactions Comments 14 comments 5 min read What is the coolest thing you’ve learned this week? Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Sep 15 '19 What is the coolest thing you’ve learned this week? # discuss 4 reactions Comments 6 comments 1 min read Sleeping better at night with cypress.io Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow Jul 29 '19 Sleeping better at night with cypress.io # testing # e2e # cypress # development 45 reactions Comments 1 comment 5 min read In defence of meetings Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow May 27 '19 In defence of meetings # meetings # software # development 18 reactions Comments 4 comments 4 min read In your opinion, what is the most confusing part of JavaScript? Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow May 16 '19 In your opinion, what is the most confusing part of JavaScript? # discuss 8 reactions Comments 14 comments 1 min read 7 years as a developer - lessons learned Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow May 13 '19 7 years as a developer - lessons learned # software # development # lessons # learned 710 reactions Comments 46 comments 4 min read What I wish someone told me about speaking at tech conferences Tomasz Łakomy Tomasz Łakomy Tomasz Łakomy Follow May 12 '19 What I wish someone told me about speaking at tech conferences # conference # speaking # career 202 reactions Comments 10 comments 5 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://dev.to/laoshanxi/running-native-non-container-workloads-on-kubernetes-a-practical-experiment-12 | Running Native (Non-Container) Workloads on Kubernetes: A Practical Experiment - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse laoshanxi Posted on Jan 9 Running Native (Non-Container) Workloads on Kubernetes: A Practical Experiment # kubernetes # containers # architecture # devops Kubernetes is excellent at orchestrating containers. But every now and then, you run into workloads that simply don’t fit well into the container model. In our case, we had several native binaries and host-level tools that needed to: run on specific nodes access host resources directly integrate with existing CI/CD pipelines follow Kubernetes-style retries and lifecycle management Containerizing them felt forced. Privileged containers introduced security concerns, and tightly coupling containers to the host defeated the purpose of abstraction. So we tried a different approach. The Problem with “Just Containerize It” In theory, everything can be containerized. In practice, that often means: privileged mode direct host mounts fragile assumptions about the host environment unclear ownership when jobs fail or restart At that point, Kubernetes is mostly being used as a scheduler and lifecycle tracker, not as an isolation boundary. We wanted to keep the good parts of Kubernetes — Jobs, retries, observability — without forcing native workloads into an unnatural container shape. The Core Idea Instead of running the workload inside the container, we flipped the model: Kubernetes Jobs are still the scheduling primitive The container acts as a thin command forwarder The actual workload runs as a native OS process on the node From Kubernetes’ perspective, nothing unusual is happening: Jobs start Jobs finish Exit codes are recorded Under the hood, the Job lifecycle is mapped to a host-level process. How It Works (High-Level) A lightweight agent runs on each node, exposing a local control interface A Kubernetes Job starts a small container That container forwards the command to the local agent The agent launches and monitors the native process Job success or failure reflects the process exit code This keeps Kubernetes in control of when and where things run, while the host controls how they run. What Worked Well This approach gave us a few practical wins: No privileged containers Native tools run exactly as they expect Kubernetes still provides retries, logs, and status CI/CD pipelines remain unchanged For legacy tooling or migration phases, this turned out to be surprisingly effective. What Was Hard The hardest part wasn’t execution — it was lifecycle correctness. Node restarts Job retries Partial failures All of these can leave orphaned processes behind if ownership isn’t carefully designed. We ended up treating Kubernetes Jobs as lifecycle signals, while enforcing stricter cleanup logic on the host side. It’s not a perfect abstraction — but it’s an honest one. When This Pattern Makes Sense This isn’t a replacement for containers. It works best when: workloads are hard to containerize host-level access is unavoidable you want Kubernetes semantics without container overhead For fully cloud-native services, containers are still the right answer. For everything else, this can be a pragmatic bridge. Open Source Implementation We eventually open-sourced the tooling we built around this pattern, since it kept repeating across teams: 👉 https://github.com/laoshanxi/app-mesh/blob/main/docs/source/success/kubernetes_run_native_application.md I’m curious how others approach native workloads in Kubernetes — especially in environments with frequent node churn. Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse laoshanxi Follow Joined Jan 9, 2026 More from laoshanxi Dynamic Local Persistent Volumes on Kubernetes via Open Service Broker # containers # kubernetes # infrastructure 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . 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https://dev.to/tlakomy/quick-guide-to-css-grid-1b95#comments | Quick guide to CSS Grid - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Tomasz Łakomy Posted on Dec 27, 2023 Quick guide to CSS Grid # css # grid Grid Container First, you designate an element as a grid container. This element will hold the grid items (your content). You can do this by setting the element's display property to grid or inline-grid . .container { display : grid ; } Enter fullscreen mode Exit fullscreen mode /* Or for an inline grid */ .inline-container { display : inline-grid ; } Enter fullscreen mode Exit fullscreen mode Defining Rows and Columns You define the rows and columns in the grid container using grid-template-rows and grid-template-columns . .container { display : grid ; grid-template-columns : 100px 200px auto ; grid-template-rows : 50px 100px ; } Enter fullscreen mode Exit fullscreen mode Grid Gap Grid gap is the space between each row and column. Use grid-gap , grid-row-gap , and grid-column-gap . .container { display : grid ; grid-gap : 10px ; /* Space between rows and columns */ } Enter fullscreen mode Exit fullscreen mode Placing Items You can place grid items in specific locations using grid-column-start , grid-column-end , grid-row-start , and grid-row-end . .item { grid-column-start : 1 ; grid-column-end : 3 ; grid-row-start : 2 ; grid-row-end : 4 ; } Enter fullscreen mode Exit fullscreen mode Shorthand Properties There are shorthand properties for quicker definitions. .item { grid-column : 1 / 3 ; /* Start at line 1 and end at line 3 */ grid-row : 2 / 4 ; } Enter fullscreen mode Exit fullscreen mode Fractional Units (fr) The fr unit allows you to distribute available space in fractions. .container { grid-template-columns : 1 fr 2 fr 1 fr ; /* 2nd column is twice as wide as the others */ } Enter fullscreen mode Exit fullscreen mode Repeat Function To avoid repetition, use repeat() . .container { grid-template-columns : repeat ( 3 , 1 fr ); /* Creates 3 columns of equal width */ } Enter fullscreen mode Exit fullscreen mode auto-fill and auto-fit auto-fill and auto-fit are used with repeat() to automatically place as many items as possible. .container { grid-template-columns: repeat(auto-fill, minmax(100px, 1fr)); } Enter fullscreen mode Exit fullscreen mode Grid Areas Define areas in your grid and assign items to these areas. .container { grid-template-areas : "header header header" "sidebar content content" "footer footer footer" ; } .header { grid-area : header ; } Enter fullscreen mode Exit fullscreen mode Responsive Design You can create responsive designs by combining CSS Grid with media queries. .container { grid-template-columns : 1 fr ; } @media ( min-width : 600px ) { .container { grid-template-columns : 1 fr 1 fr ; } } Enter fullscreen mode Exit fullscreen mode Practice The best way to learn CSS Grid is through practice. Try creating a basic layout with a header, sidebar, main content area, and footer. Experiment with different configurations and see how the elements rearrange themselves. Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Tomasz Łakomy Follow Frontend Engineer at Stedi. https://cloudash.dev co-founder. Tech speaker, egghead.io instructor, lifelong learner. Location Poznań, Poland Work Frontend Engineer at Stedi Joined Mar 18, 2019 More from Tomasz Łakomy 7 CSS properties I had no idea about # css # development 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . DEV Community © 2016 - 2026. We're a place where coders share, stay up-to-date and grow their careers. Log in Create account | 2026-01-13T08:49:45 |
https://dev.to/sunny7899#main-content | Neweraofcoding - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Follow User actions Neweraofcoding Expert Front end developer with Angular, and React experience Location Delhi India Joined Joined on Nov 4, 2020 Personal website https://code-for-next-generation.vercel.app/ github website 2025 Hacktoberfest Writing Challenge Completion Awarded for completing at least one prompt in the 2025 Hacktoberfest Writing Challenge. Thank you for sharing your open source story! 🎃✍️ Got it Close Five Year Club This badge celebrates the longevity of those who have been a registered member of the DEV Community for at least five years. Got it Close Writing Debut Awarded for writing and sharing your first DEV post! Continue sharing your work to earn the 4 Week Writing Streak Badge. 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Got it Close More info about @sunny7899 Post 45 posts published Comment 2 comments written Tag 1 tag followed Guide to get started with Retrieval-Augmented Generation (RAG) Neweraofcoding Neweraofcoding Neweraofcoding Follow Jan 12 Guide to get started with Retrieval-Augmented Generation (RAG) # beginners # llm # rag # tutorial Comments Add Comment 2 min read Want to connect with Neweraofcoding? Create an account to connect with Neweraofcoding. You can also sign in below to proceed if you already have an account. Create Account Already have an account? 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https://github.com/nikic/php-ast | GitHub - nikic/php-ast: Extension exposing PHP 7 abstract syntax tree Skip to content Navigation Menu Toggle navigation Sign in Appearance settings Platform AI CODE CREATION GitHub Copilot Write better code with AI GitHub Spark Build and deploy intelligent apps GitHub Models Manage and compare prompts MCP Registry New Integrate external tools DEVELOPER WORKFLOWS Actions Automate any workflow Codespaces Instant dev environments Issues Plan and track work Code Review Manage code changes APPLICATION SECURITY GitHub Advanced Security Find and fix vulnerabilities Code security Secure your code as you build Secret protection Stop leaks before they start EXPLORE Why GitHub Documentation Blog Changelog Marketplace View all features Solutions BY COMPANY SIZE Enterprises Small and medium teams Startups Nonprofits BY USE CASE App Modernization DevSecOps DevOps CI/CD View all use cases BY INDUSTRY Healthcare Financial services Manufacturing Government View all industries View all solutions Resources EXPLORE BY TOPIC AI Software Development DevOps Security View all topics EXPLORE BY TYPE Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills SUPPORT & SERVICES Documentation Customer support Community forum Trust center Partners Open Source COMMUNITY GitHub Sponsors Fund open source developers PROGRAMS Security Lab Maintainer Community Accelerator Archive Program REPOSITORIES Topics Trending Collections Enterprise ENTERPRISE SOLUTIONS Enterprise platform AI-powered developer platform AVAILABLE ADD-ONS GitHub Advanced Security Enterprise-grade security features Copilot for Business Enterprise-grade AI features Premium Support Enterprise-grade 24/7 support Pricing Search or jump to... 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Insights Additional navigation options Code Issues Pull requests Actions Projects Wiki Security Insights nikic/php-ast master Branches Tags Go to file Code Open more actions menu Folders and files Name Name Last commit message Last commit date Latest commit History 352 Commits .github/ workflows .github/ workflows ci ci scripts scripts tests tests .appveyor.yml .appveyor.yml .dockerignore .dockerignore .editorconfig .editorconfig .gitignore .gitignore .travis.yml .travis.yml LICENSE LICENSE Makefile.frag Makefile.frag README.md README.md RELEASE_CHECKLIST RELEASE_CHECKLIST ast.c ast.c ast.stub.php ast.stub.php ast_arginfo.h ast_arginfo.h ast_data.c ast_data.c ast_str_defs.h ast_str_defs.h ast_stub.php ast_stub.php composer.json composer.json config.m4 config.m4 config.w32 config.w32 package.xml package.xml php_ast.h php_ast.h util.php util.php View all files Repository files navigation README License php-ast This extension exposes the abstract syntax tree generated by PHP 7 and 8. This is the documentation for version 1.x.y. See also documentation for version 0.1.x . Table of Contents Installation API overview Basic usage Example Metadata Flags AST node kinds AST versioning Differences to PHP-Parser Installation Windows : Download a prebuilt Windows DLL and move it into the ext/ directory of your PHP installation. Furthermore, add extension=php_ast.dll to your php.ini file. (The php-ast PECL page also links to the same DLLs for Windows.) Unix (PECL) : Run pecl install ast and add extension=ast.so to your php.ini . Unix (PIE) : Run pie install nikic/ast and add extension=ast.so to your php.ini . Unix (Compile) : Compile and install the extension as follows. phpize ./configure make sudo make install Additionally add extension=ast.so to your php.ini file. API overview Defines: ast\Node class ast\Metadata class ast\AST_* kind constants ast\flags\* flag constants ast\parse_file(string $filename, int $version) ast\parse_code(string $code, int $version [, string $filename = "string code"]) ast\get_kind_name(int $kind) ast\kind_uses_flags(int $kind) ast\get_metadata() ast\get_supported_versions(bool $exclude_deprecated = false) Basic usage Code can be parsed using either ast\parse_code() , which accepts a code string, or ast\parse_file() , which accepts a file path. Additionally, both functions require a $version argument to ensure forward-compatibility. The current version is 120. $ ast = ast \parse_code ( ' <?php ... ' , $ version = 120 ); // or $ ast = ast \parse_file ( ' file.php ' , $ version = 120 ); The abstract syntax tree returned by these functions consists of ast\Node objects. ast\Node is declared as follows: namespace ast ; class Node { public $ kind ; public $ flags ; public $ lineno ; public $ children ; } The kind property specifies the type of the node. It is an integral value, which corresponds to one of the ast\AST_* constants, for example ast\AST_STMT_LIST . See the AST node kinds section for an overview of the available node kinds. The flags property contains node specific flags. It is always defined, but for most nodes it is always zero. See the flags section for a list of flags supported by the different node kinds. The lineno property specifies the starting line number of the node. The children property contains an array of child-nodes. These children can be either other ast\Node objects or plain values. There are two general categories of nodes: Normal AST nodes, which have a fixed set of named child nodes, as well as list nodes, which have a variable number of children. The AST node kinds section contains a list of the child names for the different node kinds. Example Simple usage example: <?php $ code = <<<'EOC' <?php $var = 42; EOC; var_dump ( ast \parse_code ( $ code , $ version = 70 )); // Output: object ( ast \Node) #1 (4) { [ " kind " ]=> int( 132 ) [ " flags " ]=> int( 0 ) [ " lineno " ]=> int( 1 ) [ " children " ]=> array ( 1 ) { [ 0 ]=> object(ast\Node) #2 (4) { [ " kind " ]=> int( 517 ) [ " flags " ]=> int( 0 ) [ " lineno " ]=> int( 2 ) [ " children " ]=> array ( 2 ) { [ " var " ]=> object(ast\Node) #3 (4) { [ " kind " ]=> int( 256 ) [ " flags " ]=> int( 0 ) [ " lineno " ]=> int( 2 ) [ " children " ]=> array ( 1 ) { [ " name " ]=> string( 3 ) " var " } } [ " expr " ]=> int( 42 ) } } } } The util.php file defines an ast_dump() function, which can be used to create a more compact and human-readable dump of the AST structure: <?php require ' path/to/util.php ' ; $ code = <<<'EOC' <?php $var = 42; EOC; echo ast_dump ( ast \parse_code ( $ code , $ version = 70 )), "\n" ; // Output: AST_STMT_LIST 0 : AST_ASSIGN var: AST_VAR name: " var " expr: 42 To additionally show line numbers pass the AST_DUMP_LINENOS option as the second argument to ast_dump() . A more substantial AST dump can be found in the tests . Metadata There are a number of functions which provide meta-information for the AST structure: ast\get_kind_name() returns a string name for an integral node kind. ast\kind_uses_flags() determines whether the flags of a node kind may ever be non-zero. ast\get_metadata() returns metadata about all AST node kinds. The return value is a map from AST node kinds to ast\Metadata objects defined as follows. namespace ast ; class Metadata { public $ kind ; public $ name ; public $ flags ; public $ flagsCombinable ; } The kind is the integral node kind, while name is the same name as returned by the get_kind_name() function. flags is an array of flag constant names, which may be used by the node kind. flagsCombinable specifies whether the flag should be checked using === (not combinable) or using & (combinable). Together these two values provide programmatic access to the information listed in the flags section . The AST metadata is intended for use in tooling for working the AST, such as AST dumpers. Flags This section lists which flags are used by which AST node kinds. The "combinable" flags can be combined using bitwise or and should be checked by using $ast->flags & ast\flags\FOO . The "exclusive" flags are used standalone and should be checked using $ast->flags === ast\flags\BAR . // Used by ast\AST_NAME (exclusive) ast\flags\NAME_FQ (= 0) // example: \Foo\Bar ast\flags\NAME_NOT_FQ // example: Foo\Bar ast\flags\NAME_RELATIVE // example: namespace\Foo\Bar // Used by ast\AST_METHOD, ast\AST_PROP_DECL, ast\AST_PROP_GROUP, // ast\AST_CLASS_CONST_DECL, ast\AST_CLASS_CONST_GROUP, and ast\AST_TRAIT_ALIAS (combinable) ast\flags\MODIFIER_PUBLIC ast\flags\MODIFIER_PROTECTED ast\flags\MODIFIER_PRIVATE ast\flags\MODIFIER_STATIC ast\flags\MODIFIER_ABSTRACT ast\flags\MODIFIER_FINAL ast\flags\MODIFIER_READONLY ast\flags\MODIFIER_PUBLIC_SET ast\flags\MODIFIER_PROTECTED_SET ast\flags\MODIFIER_PRIVATE_SET // Used by ast\AST_CLOSURE, ast\AST_ARROW_FUNC (combinable) ast\flags\MODIFIER_STATIC // Used by ast\AST_FUNC_DECL, ast\AST_METHOD, ast\AST_CLOSURE, ast\AST_ARROW_FUNC (combinable) ast\flags\FUNC_RETURNS_REF // legacy alias: ast\flags\RETURNS_REF ast\flags\FUNC_GENERATOR // used only in PHP >= 7.1 // Used by ast\AST_CLOSURE_VAR ast\flags\CLOSURE_USE_REF // Used by ast\AST_CLASS (combinable since PHP 8.1 enums) ast\flags\CLASS_ABSTRACT ast\flags\CLASS_FINAL ast\flags\CLASS_TRAIT ast\flags\CLASS_INTERFACE ast\flags\CLASS_ANONYMOUS ast\flags\CLASS_ENUM // php 8.1 enums ast\flags\CLASS_READONLY // php 8.2 readonly classes // Used by ast\AST_PARAM (combinable) ast\flags\PARAM_REF ast\flags\PARAM_VARIADIC ast\flags\PARAM_MODIFIER_PUBLIC (available since 1.0.8, same as ast\flags\MODIFIER_* in PHP >= 8.0) ast\flags\PARAM_MODIFIER_PROTECTED (available since 1.0.8) ast\flags\PARAM_MODIFIER_PRIVATE (available since 1.0.8) // Used by ast\AST_TYPE (exclusive) ast\flags\TYPE_ARRAY ast\flags\TYPE_CALLABLE ast\flags\TYPE_VOID ast\flags\TYPE_BOOL ast\flags\TYPE_LONG ast\flags\TYPE_DOUBLE ast\flags\TYPE_STRING ast\flags\TYPE_ITERABLE ast\flags\TYPE_OBJECT ast\flags\TYPE_NULL // php 8.0 union types ast\flags\TYPE_FALSE // php 8.0 union types ast\flags\TYPE_TRUE // php 8.2 true type ast\flags\TYPE_STATIC // php 8.0 static return type ast\flags\TYPE_MIXED // php 8.0 mixed type ast\flags\TYPE_NEVER // php 8.1 never type // Used by ast\AST_CAST (exclusive) ast\flags\TYPE_NULL ast\flags\TYPE_BOOL ast\flags\TYPE_LONG ast\flags\TYPE_DOUBLE ast\flags\TYPE_STRING ast\flags\TYPE_ARRAY ast\flags\TYPE_OBJECT // Used by ast\AST_UNARY_OP (exclusive) ast\flags\UNARY_BOOL_NOT ast\flags\UNARY_BITWISE_NOT ast\flags\UNARY_MINUS ast\flags\UNARY_PLUS ast\flags\UNARY_SILENCE // Used by ast\AST_BINARY_OP and ast\AST_ASSIGN_OP (exclusive) ast\flags\BINARY_BITWISE_OR ast\flags\BINARY_BITWISE_AND ast\flags\BINARY_BITWISE_XOR ast\flags\BINARY_CONCAT ast\flags\BINARY_ADD ast\flags\BINARY_SUB ast\flags\BINARY_MUL ast\flags\BINARY_DIV ast\flags\BINARY_MOD ast\flags\BINARY_POW ast\flags\BINARY_SHIFT_LEFT ast\flags\BINARY_SHIFT_RIGHT ast\flags\BINARY_COALESCE ast\flags\BINARY_PIPE // Used by ast\AST_BINARY_OP (exclusive) ast\flags\BINARY_BOOL_AND ast\flags\BINARY_BOOL_OR ast\flags\BINARY_BOOL_XOR ast\flags\BINARY_IS_IDENTICAL ast\flags\BINARY_IS_NOT_IDENTICAL ast\flags\BINARY_IS_EQUAL ast\flags\BINARY_IS_NOT_EQUAL ast\flags\BINARY_IS_SMALLER ast\flags\BINARY_IS_SMALLER_OR_EQUAL ast\flags\BINARY_IS_GREATER ast\flags\BINARY_IS_GREATER_OR_EQUAL ast\flags\BINARY_SPACESHIP // Used by ast\AST_MAGIC_CONST (exclusive) ast\flags\MAGIC_LINE ast\flags\MAGIC_FILE ast\flags\MAGIC_DIR ast\flags\MAGIC_NAMESPACE ast\flags\MAGIC_FUNCTION ast\flags\MAGIC_METHOD ast\flags\MAGIC_CLASS ast\flags\MAGIC_TRAIT // Used by ast\AST_USE, ast\AST_GROUP_USE and ast\AST_USE_ELEM (exclusive) ast\flags\USE_NORMAL ast\flags\USE_FUNCTION ast\flags\USE_CONST // Used by ast\AST_INCLUDE_OR_EVAL (exclusive) ast\flags\EXEC_EVAL ast\flags\EXEC_INCLUDE ast\flags\EXEC_INCLUDE_ONCE ast\flags\EXEC_REQUIRE ast\flags\EXEC_REQUIRE_ONCE // Used by ast\AST_ARRAY (exclusive), since PHP 7.1 ast\flags\ARRAY_SYNTAX_SHORT ast\flags\ARRAY_SYNTAX_LONG ast\flags\ARRAY_SYNTAX_LIST // Used by ast\AST_ARRAY_ELEM (exclusive) ast\flags\ARRAY_ELEM_REF // Used by ast\AST_DIM (combinable), since PHP 7.4 ast\flags\DIM_ALTERNATIVE_SYNTAX ast\flags\ENCAPS_VAR_DOLLAR_CURLY // php 8.2 deprecation, only available in php 8.2+ // Used by ast\AST_VAR (combinable), since PHP 8.2 ast\flags\ENCAPS_VAR_DOLLAR_CURLY ast\flags\ENCAPS_VAR_DOLLAR_CURLY_VAR_VAR // Used by ast\AST_CONDITIONAL (combinable), since PHP 7.4 ast\flags\PARENTHESIZED_CONDITIONAL AST node kinds This section lists the AST node kinds that are supported and the names of their child nodes. AST_ARRAY_ELEM: value, key AST_ARROW_FUNC: name, docComment, params, stmts, returnType, attributes // name removed in version 110 AST_ASSIGN: var, expr AST_ASSIGN_OP: var, expr AST_ASSIGN_REF: var, expr AST_ATTRIBUTE: class, args // php 8.0+ attributes (version 80+) AST_BINARY_OP: left, right AST_BREAK: depth AST_CALL: expr, args AST_CALLABLE_CONVERT: // php 8.1+ first-class callable syntax AST_CAST: expr AST_CATCH: class, var, stmts AST_CLASS: name, docComment, extends, implements, stmts, (for enums) type AST_CLASS_CONST: class, const AST_CLASS_CONST_GROUP class, attributes, type // version 80+ AST_CLASS_NAME: class // version 70+ AST_CLONE: expr // prior to version 120 AST_CLOSURE: name, docComment, params, uses, stmts, returnType, attributes // name removed in version 110 AST_CLOSURE_VAR: name AST_CONDITIONAL: cond, true, false AST_CONST: name AST_CONST_ELEM: name, value, docComment AST_CONTINUE: depth AST_DECLARE: declares, stmts AST_DIM: expr, dim AST_DO_WHILE: stmts, cond AST_ECHO: expr AST_EMPTY: expr AST_ENUM_CASE: name, expr, docComment, attributes // php 8.1+ enums AST_EXIT: expr // prior to version 120 AST_FOR: init, cond, loop, stmts AST_FOREACH: expr, value, key, stmts AST_FUNC_DECL: name, docComment, params, stmts, returnType, attributes uses // prior to version 60 AST_GLOBAL: var AST_GOTO: label AST_GROUP_USE: prefix, uses AST_HALT_COMPILER: offset AST_IF_ELEM: cond, stmts AST_INCLUDE_OR_EVAL: expr AST_INSTANCEOF: expr, class AST_ISSET: var AST_LABEL: name AST_MAGIC_CONST: AST_MATCH: cond, stmts // php 8.0+ match AST_MATCH_ARM: cond, expr // php 8.0+ match AST_METHOD: name, docComment, params, stmts, returnType, attributes uses // prior to version 60 AST_METHOD_CALL: expr, method, args AST_METHOD_REFERENCE: class, method AST_NAME: name AST_NAMED_ARG: name, expr // php 8.0 named parameters AST_NAMESPACE: name, stmts AST_NEW: class, args AST_NULLABLE_TYPE: type // Used only since PHP 7.1 AST_NULLSAFE_METHOD_CALL: expr, method, args // php 8.0 null safe operator AST_NULLSAFE_PROP: expr, prop // php 8.0 null safe operator AST_PARAM: type, name, default, attributes, docComment, hooks // 'hooks' field added in version 110 AST_POST_DEC: var AST_POST_INC: var AST_PRE_DEC: var AST_PRE_INC: var AST_PRINT: expr AST_PROP: expr, prop AST_PROP_ELEM: name, default, docComment, hooks // 'hooks' field added in version 110 AST_PROP_GROUP: type, props, attributes // version 70+ AST_PROPERTY_HOOK: name, docComment, params, stmts, attributes // version 110+ AST_PROPERTY_HOOK_SHORT_BODY: expr AST_REF: var // only used in foreach ($a as &$v) AST_RETURN: expr AST_SHELL_EXEC: expr AST_STATIC: var, default AST_STATIC_CALL: class, method, args AST_STATIC_PROP: class, prop AST_SWITCH: cond, stmts AST_SWITCH_CASE: cond, stmts AST_THROW: expr AST_TRAIT_ALIAS: method, alias AST_TRAIT_PRECEDENCE: method, insteadof AST_TRY: try, catches, finally AST_TYPE: AST_UNARY_OP: expr AST_UNPACK: expr AST_UNSET: var AST_USE_ELEM: name, alias AST_USE_TRAIT: traits, adaptations AST_VAR: name AST_WHILE: cond, stmts AST_YIELD: value, key AST_YIELD_FROM: expr // List nodes (numerically indexed children): AST_ARG_LIST AST_ARRAY AST_ATTRIBUTE_LIST // php 8.0+ attributes (version 80+) AST_ATTRIBUTE_GROUP // php 8.0+ attributes (version 80+) AST_CATCH_LIST AST_CLASS_CONST_DECL AST_CLOSURE_USES AST_CONST_DECL AST_ENCAPS_LIST // interpolated string: "foo$bar" AST_EXPR_LIST AST_IF AST_LIST AST_MATCH_ARM_LIST // php 8.0+ match AST_NAME_LIST AST_PARAM_LIST AST_PROP_DECL AST_STMT_LIST AST_SWITCH_LIST AST_TRAIT_ADAPTATIONS AST_USE AST_TYPE_UNION // php 8.0+ union types AST_TYPE_INTERSECTION // php 8.1+ intersection types AST Versioning The ast\parse_code() and ast\parse_file() functions each accept a required AST $version argument. The idea behind the AST version is that we may need to change the format of the AST to account for new features in newer PHP versions, or to improve it in other ways. Such changes will always be made under a new AST version, while previous formats continue to be supported for some time. Old AST versions may be deprecated in minor versions and removed in major versions of the AST extension. A list of currently supported versions is available through ast\get_supported_versions() . The function accepts a boolean argument that determines whether deprecated versions should be excluded. In the following the changes in the respective AST versions, as well as their current support state, are listed. 120 (current) Supported since 1.1.3 (TBD). clone $expr is now represented like a function call (using AST_CALL instead of AST_CLONE ). exit($expr) is now represented like a function call (using AST_CALL instead of AST_EXIT ). 110 (stable) Supported since 1.1.2 (2024-08-10). Add a hooks child node for AST_PROP_ELEM (PHP 8.4 property hooks) and AST_PARAM (constructor property promotion can have property hooks) Add new node kinds AST_PROPERTY_HOOK and AST_PROPERTY_HOOK_SHORT_BODY . Remove the name child node from the AST_ARROW_FUNC and AST_CLOSURE nodes (previously "{closure}" ) 100 (stable) Supported since 1.1.1 (2023-11-12). Add a type child node for all AST_CLASS_CONST_GROUP nodes. 90 (stable) Supported since 1.0.14 (2021-07-24) Same as AST version 85. 85 (stable) Supported since 1.0.11 (2021-04-20) Add a type child node (for enum type) for all AST_CLASS nodes. 80 (stable) Supported since 1.0.10 (2020-09-12). mixed type hints are now reported as an AST_TYPE with type TYPE_MIXED instead of an AST_NAME . AST_CLASS_CONST_GROUP nodes are emitted for class constant declarations wrapping the AST_CLASS_CONST_DECL and any attributes. Previously, AST_CLASS_CONST_DECL would be emitted. AST_PARAM , AST_CLASS_DECL , AST_METHOD , AST_PROP_DECL , AST_CLOSURE , AST_FUNC_DECL , and AST_ARROW_FUNC nodes now contain an attributes child. 70 (stable) Supported since 1.0.1 (2019-02-11). AST_PROP_GROUP was added to support PHP 7.4's typed properties. The property visibility modifiers are now part of AST_PROP_GROUP instead of AST_PROP_DECL . Note that property group type information is only available with AST versions 70+. AST_CLASS_NAME is created instead of AST_CLASS_CONST for SomeClass::class . 60 (deprecated) Supported since 0.1.7 (2018-10-06). Deprecated in php-ast 1.1.0. AST_FUNC_DECL and AST_METHOD no longer generate a uses child. Previously this child was always null . AST_CONST_ELEM now always has a docComment child. Previously it was absent on PHP 7.0. On PHP 7.0 the value is always null . 50 (deprecated) Supported since 0.1.5 (2017-07-19). Deprecated in php-ast 1.1.0. This is the oldest AST version available in 1.0.0. See the 0.1.x AST version changelog for information on changes prior to this version. Differences to PHP-Parser Next to php-ast I also maintain the PHP-Parser library, which has some overlap with this extension. This section summarizes the main differences between php-ast and PHP-Parser so you may decide which is preferable for your use-case. The primary difference is that php-ast is a PHP extension (written in C) which exports the AST internally used by PHP 7 and 8. PHP-Parser on the other hand is library written in PHP. This has a number of consequences: php-ast is significantly faster than PHP-Parser, because the AST construction is implemented in C. php-ast needs to be installed as an extension, on Linux either by compiling it manually or retrieving it from a package manager, on Windows by loading a DLL. PHP-Parser is installed as a Composer dependency. php-ast only runs on PHP >= 7.0 (php-ast 1.0.16 was the last version supporting php <= 7.1), as prior versions did not use an internal AST. PHP-Parser supports PHP >= 5.5. php-ast may only parse code that is syntactically valid on the version of PHP it runs on. This means that it's not possible to parse code using features of newer versions (e.g. PHP 7.4 code while running on PHP 7.2). Similarly, it is not possible to parse code that is no longer syntactically valid on the used version (e.g. some PHP 5 code may no longer be parsed -- however most code will work). PHP-Parser supports parsing both newer and older (up to PHP 5.2) versions. php-ast can only parse code which is syntactically valid, while PHP-Parser can provide a partial AST for code that contains errors (e.g., because it is currently being edited). php-ast only provides the starting line number (and for declarations the ending line number) of nodes, because this is the only part that PHP itself stores. PHP-Parser provides precise file offsets. There are a number of differences in the AST representation and available support code: The PHP-Parser library uses a separate class for every node type, with child nodes stored as type-annotated properties. php-ast uses one class for everything, with children stored as arrays. The former approach is friendlier to developers because it has very good IDE integration. The php-ast extension does not use separate classes, because registering hundreds of classes was judged a no-go for a bundled extension. The PHP-Parser library contains various support code for working with the AST, while php-ast only handles AST construction. The reason for this is that implementing this support code in C is extremely complicated and there is little other benefit to implementing it in C. The main components that PHP-Parser offers that may be of interest are: Node dumper (human readable representation): While the php-ast extension does not directly implement this, a ast_dump function is provided in the util.php file. Pretty printer (converting the AST back to PHP code): This is not provided natively, but the php-ast-reverter package implements this functionality. Name resolution (resolving namespace prefixes and aliases): There is currently no standalone package for this. AST traversal / visitation: There is currently no standalone package for this either, but implementing a recursive AST walk is easy. The tolerant-php-parser-to-php-ast project can convert the AST produced by tolerant-php-parser (Another pure PHP parser library) into the format used by the php-ast extension. This can be used as a slow fallback in case the php-ast extension is not available. It may also be used to produce a partial php-ast output for code with syntax errors. About Extension exposing PHP 7 abstract syntax tree Resources Readme License View license Uh oh! There was an error while loading. Please reload this page . Activity Stars 978 stars Watchers 28 watching Forks 83 forks Report repository Releases 4 php-ast 1.1.3 Latest Aug 10, 2025 + 3 releases Packages 0 No packages published Uh oh! There was an error while loading. Please reload this page . Contributors 21 Uh oh! There was an error while loading. Please reload this page . + 7 contributors Languages PHP 66.7% C 32.5% Shell 0.3% Dockerfile 0.2% Makefile 0.1% M4 0.1% JavaScript 0.1% Footer © 2026 GitHub, Inc. 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https://dev.to/sivarampg/bagsfm-the-solana-launchpad-thats-changing-creator-monetization-4g7n#comments | Bags.fm: The Solana Launchpad That's Changing Creator Monetization - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Sivaram Posted on Jan 12 Bags.fm: The Solana Launchpad That's Changing Creator Monetization # solana # cryptocurrency # webdev # discuss If you've been following the Solana ecosystem lately, you might have heard about Bags.fm - a platform that's making waves in the crypto crowdfunding space. But what exactly is it, and why should developers care? Let's dive into how Bags.fm works, what makes it different, and how you can integrate it into your projects. What is Bags.fm? Bags.fm is a Solana-native, all-in-one platform that lets anyone launch, trade, and discover tokens without writing a single line of code. Think of it as a Web3-powered crowdfunding tool where supporters can actually own and trade your coin. But here's the kicker: creators earn 1% of all trading volume on their tokens - forever. The Core Model: Create → Share → Earn Forever 1. Launch a Coin Connect your Solana wallet, upload a logo, choose a name/ticker, write a description, and go live. No developers needed. 2. Build Community Your coin holders become your community. They have upside in your success, so they're incentivized to help you grow. More engagement → more visibility → more trading volume → more earnings for you. 3. Earn Forever Claim your 1% trading fees anytime, cash out to your bank, or use the earnings to fund your next project. The "Get Bagged" Feature 💰🫵 This is where things get interesting. Someone can launch a coin using your content, meme, or idea without your permission. But here's the clever part: You can verify ownership and claim earnings from that coin The community sets your username as the royalty earner Turn viral content into passive income You're already creating content, now you're getting paid for it It's like if someone could create a stock for your company without your permission, but you'd still receive dividends from it. Dividends System Creators can enable dividends by sharing fees with @DividendsBot . Here's how it works: Creator enables dividends by sharing a portion of their fees Automatic distribution every 24 hours - if a token has ≥10 SOL in unclaimed earnings Top 100 holders receive proportional payouts based on their holdings No manual claiming - dividends are deposited directly into wallets This aligns creator success with holder success and incentivizes early support. For Developers: The Bags API Bags.fm exposes a full REST API with a TypeScript SDK, making it easy to integrate Bags functionality into your applications. Getting Started First, get your API key from dev.bags.fm : # Install the SDK npm install @bagsfm/bags-sdk @solana/web3.js dotenv Enter fullscreen mode Exit fullscreen mode Basic Setup import dotenv from " dotenv " ; dotenv . config (); import { BagsSDK } from " @bagsfm/bags-sdk " ; import { Connection } from " @solana/web3.js " ; const BAGS_API_KEY = process . env . BAGS_API_KEY ; const SOLANA_RPC_URL = process . env . SOLANA_RPC_URL ; const connection = new Connection ( SOLANA_RPC_URL ); const sdk = new BagsSDK ( BAGS_API_KEY , connection , " processed " ); Enter fullscreen mode Exit fullscreen mode Launch a Token async function launchToken () { const tokenInfo = { name : " My Awesome Token " , symbol : " AWESOME " , description : " A token for my community " , image : " https://example.com/token-logo.png " , initialBuy : 0.1 , // Optional: SOL to buy at launch feeShares : [ { wallet : " recipient_wallet_address " , percentage : 50 // 50% of fees } ] }; const result = await sdk . tokenLaunch . createTokenInfo ( tokenInfo ); console . log ( " Token created: " , result ); } Enter fullscreen mode Exit fullscreen mode Get Token Creators async function getTokenCreators ( tokenMint : string ) { const creators = await sdk . state . getTokenCreators ( new PublicKey ( tokenMint ) ); const primaryCreator = creators . find ( c => c . isCreator ); console . log ( " Primary Creator: " , { displayName : primaryCreator . providerUsername , wallet : primaryCreator . wallet , royalty : ` ${ primaryCreator . royaltyBps / 100 } %` }); } Enter fullscreen mode Exit fullscreen mode Get Lifetime Fees async function getTokenLifetimeFees ( tokenMint : string ) { const response = await fetch ( `https://public-api-v2.bags.fm/api/v1/token-launch/lifetime-fees?tokenMint= ${ tokenMint } ` , { headers : { ' x-api-key ' : BAGS_API_KEY } } ); const data = await response . json (); console . log ( " Lifetime fees collected: " , data . response ); } Enter fullscreen mode Exit fullscreen mode Trade Tokens async function tradeToken ( tokenMint : string , amount : number ) { const quote = await sdk . trade . getTradeQuote ({ tokenMint : new PublicKey ( tokenMint ), amount , action : " buy " // or "sell" }); const transaction = await sdk . trade . createSwapTransaction ( quote ); // Sign and send transaction } Enter fullscreen mode Exit fullscreen mode API Rate Limits 1,000 requests per hour per user Rate limits apply across all your API keys Check X-RateLimit-Remaining and X-RateLimit-Reset headers Use Cases for Developers 1. Analytics Dashboard Build a dashboard to track token performance, creator earnings, and community growth: async function buildDashboard ( tokenMint : string ) { const [ creators , lifetimeFees , claimStats ] = await Promise . all ([ sdk . state . getTokenCreators ( new PublicKey ( tokenMint )), fetchLifetimeFees ( tokenMint ), sdk . analytics . getTokenClaimStats ( tokenMint ) ]); return { creators , totalEarnings : lifetimeFees , claimableAmount : claimStats . claimableAmount }; } Enter fullscreen mode Exit fullscreen mode 2. Automated Trading Bot Create a bot that trades based on token performance metrics: async function tradingBot ( tokenMint : string ) { const quote = await sdk . trade . getTradeQuote ({ tokenMint : new PublicKey ( tokenMint ), amount : 0.5 , action : " buy " }); // Add your trading logic here if ( shouldBuy ( quote )) { const tx = await sdk . trade . createSwapTransaction ( quote ); // Execute trade } } Enter fullscreen mode Exit fullscreen mode 3. Community Management Tool Build tools to help creators manage their communities: async function getTopHolders ( tokenMint : string ) { const holders = await sdk . analytics . getTokenHolders ( tokenMint ); return holders . slice ( 0 , 100 ); // Top 100 for dividends } Enter fullscreen mode Exit fullscreen mode Potential Concerns to Consider Intellectual Property The "Get Bagged" feature raises questions: What if someone launches a coin using your brand without permission? While you can claim earnings, what about brand control? Could lead to trademark issues Dividend Concentration Only top 100 holders get dividends Could create whale dynamics Smaller holders don't benefit from dividends Sustainability 1% forever - is this sustainable long-term? What happens when trading volume drops? Creator earnings tied to speculation The Big Picture Bags.fm is attempting to solve creator monetization by: Turning fans into investors - they have skin in the game Creating ongoing revenue - not just one-time payments Leveraging community for growth - holders become marketers Making it permissionless - anyone can participate It's like Patreon meets the stock market, with a viral twist. Getting Started Ready to dive in? Here's your roadmap: Explore the platform : Visit bags.fm to see it in action Get API access : Sign up at dev.bags.fm Read the docs : Check out the full API documentation Join the community : Connect with other developers on their Discord Conclusion Bags.fm represents an interesting experiment in creator monetization and community-driven economics. Whether you're building tools for creators, analyzing token performance, or just curious about the future of crowdfunding, there's plenty to explore. The platform's permissionless nature and perpetual royalty model create unique opportunities - and challenges - for developers and creators alike. What do you think? Is this the future of creator monetization, or just another crypto experiment? Let me know in the comments! Resources Bags.fm API Documentation Developer Portal Discord Community This article explores Bags.fm as of 2026. The crypto space moves fast, so always verify the latest information before building production applications. Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Sivaram Follow Full Stack Engineer. Consultant. Designing & Developing Blockchain & AI E2E Solutions. De-risking Ambiguity. OSS Location India Joined Oct 5, 2023 More from Sivaram Building a Prediction Market on Solana with Anchor: Complete Rust Smart Contract Guide # solana # web3 # smartcontract # predictionmarket The Ralph Wiggum Approach: Running AI Coding Agents for Hours (Not Minutes) # webdev # productivity # ai # agents How the Creator of Claude Code Uses Claude Code: A Complete Breakdown # ai # webdev # programming # productivity 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV Forem — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://dev.to/muhammad_rabbi_dev/react-design-patterns-every-frontend-fullstack-developer-should-know-2ng0 | React Design Patterns Every Frontend & FullStack Developer Should Know - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Muhammad Rabbi Posted on Jan 9 React Design Patterns Every Frontend & FullStack Developer Should Know # react # javascript # frontend # architecture If you’re building modern web applications, React design patterns can completely change how you think about components, reusability, and scalability. A month ago, I realized something important I was using React every day, but I wasn’t truly thinking in patterns. That’s when I discovered an amazing playlist by @atapas . Why React Design Patterns Matter React design patterns help you: Write cleaner and more maintainable code Avoid prop drilling & unnecessary re-renders Scale applications without pain Think like a senior engineer, not just a coder Patterns are not about complexity — they’re about clarity and intention. What You’ll Learn From This Playlist This playlist breaks down patterns in a practical and beginner-friendly way, including: Container & Presentational Components Compound Components Render Props Custom Hooks pattern Controlled vs Uncontrolled Components Reusability & separation of concerns Each concept is explained with real-world use cases, not just theory. Where to Watch 📺 You can find the playlist on: Devguide : https://devguide-dev.vercel.app/playlist/PLIJrr73KDmRyQVT__uFZvaVfWPdfyMFHC , YouTube , * A Small but Meaningful Moment * I shared this playlist recently, and Tapas Adhikary himself commented: “Thank you for sharing and the shoutout. The Devguide looks awesome. 🙌” That meant a lot to me — and it motivated me to share this with more developers. Final Thoughts Whether you’re a frontend developer, backend developer, or moving toward full-stack, understanding React design patterns will level you up. Don’t just write React. Design with React. If you’ve already explored these patterns, I’d love to know: Which React pattern helped you the most? * Happy coding * coding #devguide #fullstack #frontend #developer Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Muhammad Rabbi Follow 👋 Hey, I’m Muhammad Rabbi, a frontend developer who loves building clean, functional, and modern web apps using React, Tailwind CSS, and tools that make the UI pop. Location dhaka,Bangladesh Joined Sep 8, 2025 More from Muhammad Rabbi A few days ago, I was scrolling through YouTube playlists, thinking.. # webdev # programming # javascript # opensource 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . DEV Community © 2016 - 2026. We're a place where coders share, stay up-to-date and grow their careers. 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https://dev.to/terieyenike | oteri - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Follow User actions oteri Software Engineer 🛠️ | Prompt Engineer | Technical Writer Location Nigeria Joined Joined on Apr 17, 2019 Email address teyenike1@gmail.com Personal website https://iamteri.tech github website twitter website Education University of Nigeria Work Technical Writer Six Year Club This badge celebrates the longevity of those who have been a registered member of the DEV Community for at least six years. Got it Close GitHub Copilot 1-Day Build Challenge Completion Badge Awarded for completing at least one prompt in the GitHub Copilot 1-Day Build Challenge. Thank you for participating! 💻 Got it Close The Pinata Challenge Completion Badge Awarded for completing The Pinata Challenge. 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Got it Close Show all 16 badges More info about @terieyenike Organizations AWS Community Builders Hackmamba GitHub Repositories appwrite-todos This application enable the users to create and delete the created todos from the list of items Vue jb-board-with-harperdb Node/Express Application with templating engine, Handlebars, and HarperDB as the database. Handlebars • 1 star backgroundGenerator Hexadecimal background generator HTML Flexbox-MusicPlayer Jam Musik Player built with CSS Flexbox HTML • 2 stars Codepen A recreation of the codepen interface HTML displacement-effect A repo showing the effect of webGL. Displacement effect using three.js, hover effect, and tweenmax CSS • 1 star Skills/Languages JavaScript and Python. By the side, I love to learn other tech stacks. Currently learning AI and Prompt Engineering Currently hacking on Chat page using the OpenAI API Available for Mentoring, Talks, and Work Post 69 posts published Comment 55 comments written Tag 13 tags followed Pin Pinned Processing Images in Python with Pillow oteri oteri oteri Follow Sep 19 '20 Processing Images in Python with Pillow # python # productivity 23 reactions Comments 4 comments 3 min read How to Scrape Any Website Using Bright Data MCP Server and AI Agents oteri oteri oteri Follow Jan 9 How to Scrape Any Website Using Bright Data MCP Server and AI Agents # python # ai # development # opensource 7 reactions Comments Add Comment 2 min read Want to connect with oteri? Create an account to connect with oteri. You can also sign in below to proceed if you already have an account. Create Account Already have an account? 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https://dev.to/bhavesh_gangani_ | Bhavesh Gangani - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Follow User actions Bhavesh Gangani Digital Marketing Executive & Content Writer at Softhealer Technologies. Passionate about SEO, Odoo solutions, and link-building. Sharing insights on tech, ERP, and digital growth. Let's connect! Joined Joined on Feb 18, 2025 More info about @bhavesh_gangani_ Badges Writing Debut Awarded for writing and sharing your first DEV post! Continue sharing your work to earn the 4 Week Writing Streak Badge. Got it Close Post 99 posts published Comment 2 comments written Tag 3 tags followed Why Feature-Rich POS Systems Are the Future of Retail Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jul 2 '25 Why Feature-Rich POS Systems Are the Future of Retail # pos # possoftware # erpsoftware # odoopos 5 reactions Comments Add Comment 5 min read Want to connect with Bhavesh Gangani? Create an account to connect with Bhavesh Gangani. You can also sign in below to proceed if you already have an account. Create Account Already have an account? Sign in Top 7 Reasons Your Tailoring Business Needs a Tailoring Management System Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jun 30 '25 Top 7 Reasons Your Tailoring Business Needs a Tailoring Management System # tailoringmanagementsystem # postailoring # odootailoring # erptailoring 5 reactions Comments Add Comment 5 min read 5 Ways Modern POS Software is Driving Retail Innovation Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jun 28 '25 5 Ways Modern POS Software is Driving Retail Innovation # pos # pososftware # erpsoftware # odoosoftware 5 reactions Comments Add Comment 4 min read The Role of POS in Enhancing Customer Experience Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jun 27 '25 The Role of POS in Enhancing Customer Experience # possoftware # pointofsale # odoo # erp 5 reactions Comments Add Comment 4 min read What Is a POS System and How Does It Work? Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jun 26 '25 What Is a POS System and How Does It Work? # pos # possoftware # possystem # erp 5 reactions Comments Add Comment 5 min read Retail POS Trends in the United States to Watch in 2025 Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jun 25 '25 Retail POS Trends in the United States to Watch in 2025 # pos # posusa # erp # odooerp 5 reactions Comments Add Comment 7 min read Optimize Your Manufacturing with the Best Odoo Apps Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jun 24 '25 Optimize Your Manufacturing with the Best Odoo Apps # odoo # erp # erpsoftware # software 5 reactions Comments Add Comment 5 min read Odoo Partner Selection: What You Need to Know Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jun 24 '25 Odoo Partner Selection: What You Need to Know # odoo # erp # erpsoftware # odooerpsoftware 5 reactions Comments Add Comment 6 min read POS Features That Help Retailers Thrive in a Competitive Market Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jun 23 '25 POS Features That Help Retailers Thrive in a Competitive Market # pos # features # benefits # erp 5 reactions Comments Add Comment 6 min read What Is Multi-Channel POS? A Guide to Unified Retail in 2025 Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jun 11 '25 What Is Multi-Channel POS? A Guide to Unified Retail in 2025 # possoftware # possystem # pos # erp 5 reactions Comments 1 comment 6 min read The Complete Odoo Implementation Process: 7 Key Phases Your Partner Should Deliver Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jun 10 '25 The Complete Odoo Implementation Process: 7 Key Phases Your Partner Should Deliver # odoo # implementation # erp # software 5 reactions Comments Add Comment 11 min read Hiring an Odoo ERP Consultant? Don’t Miss These Red Flags Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jun 6 '25 Hiring an Odoo ERP Consultant? Don’t Miss These Red Flags # odoo # erp # software # hiring 6 reactions Comments Add Comment 5 min read Why POS Features Matter: A Guide to Choosing the Right POS System Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Jun 4 '25 Why POS Features Matter: A Guide to Choosing the Right POS System # possystem # odoo # erpsoftware # pos 5 reactions Comments Add Comment 6 min read 5 Signs Your Business Needs an Odoo ERP Implementation Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 30 '25 5 Signs Your Business Needs an Odoo ERP Implementation # odoo # implementation # odooimplementation # software 5 reactions Comments Add Comment 5 min read How to Choose a POS System: Key Features to Look For Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 30 '25 How to Choose a POS System: Key Features to Look For # pos # erp # odoo # possoftware 5 reactions Comments Add Comment 8 min read How ERP Software Solves FMCG Warehouse and Inventory Challenges for MSMEs Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 29 '25 How ERP Software Solves FMCG Warehouse and Inventory Challenges for MSMEs # odoo # erp # erpsoftware # odooerp 5 reactions Comments Add Comment 7 min read Top 5 Mistakes to Avoid When Choosing a Property Management Solution Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 24 '25 Top 5 Mistakes to Avoid When Choosing a Property Management Solution # pms # odoo # erp # crm 5 reactions Comments Add Comment 4 min read Why Every Optical Shop Needs Efficient Management Software in 2025 Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 23 '25 Why Every Optical Shop Needs Efficient Management Software in 2025 # odoo # crm # erp # optical 5 reactions Comments Add Comment 6 min read Custom software for business, benefits of custom software, business software solutions Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 22 '25 Custom software for business, benefits of custom software, business software solutions # customesoftware # odooerp # erpsoftware 5 reactions Comments Add Comment 6 min read The Appointment Management Process: From Scheduling to Follow-ups Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 20 '25 The Appointment Management Process: From Scheduling to Follow-ups # odoo # software # erp # odooerp 5 reactions Comments Add Comment 6 min read 5 CRM Trends That Will Dominate in 2025 Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 16 '25 5 CRM Trends That Will Dominate in 2025 # odoo # crm # erp # software Comments Add Comment 5 min read Think You Know Property Management Systems? Think Again Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 15 '25 Think You Know Property Management Systems? Think Again # property # management # erp # software Comments Add Comment 8 min read How Zoho CRM Can Improve Sales and Customer Relations Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 14 '25 How Zoho CRM Can Improve Sales and Customer Relations # crm # software # erp # odoo Comments Add Comment 8 min read What is Odoo Implementation? Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 14 '25 What is Odoo Implementation? # odoo # implemantation # erp # software 5 reactions Comments Add Comment 7 min read From Paperwork to Performance: Why HR Systems Matter More Than Ever Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 10 '25 From Paperwork to Performance: Why HR Systems Matter More Than Ever # erp # odoo # software # best 5 reactions Comments Add Comment 10 min read Face Your Business Fears—ERP Gives You the Tools to Succeed Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 7 '25 Face Your Business Fears—ERP Gives You the Tools to Succeed # erp # odoo # software # solutions 5 reactions Comments Add Comment 7 min read Your Business Might Be Small, But ERP Makes Its Impact Huge Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 6 '25 Your Business Might Be Small, But ERP Makes Its Impact Huge # erp # business # odoo Comments Add Comment 6 min read ERP: A Small Word with Big Services Behind It Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 6 '25 ERP: A Small Word with Big Services Behind It # erp # software # odoo 5 reactions Comments Add Comment 7 min read Top CRM Software in Rajkot for Business Growth in 2025 Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 5 '25 Top CRM Software in Rajkot for Business Growth in 2025 # crm # software # rajkot # 2025 5 reactions Comments Add Comment 6 min read Old Systems, Old Limits. ERP Breaks Them. Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 2 '25 Old Systems, Old Limits. ERP Breaks Them. # erp # odoo # crm 5 reactions Comments Add Comment 7 min read Top 10 Payroll Software Solutions in 2025 Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 2 '25 Top 10 Payroll Software Solutions in 2025 # payroll # zoho # 2025 # erp 5 reactions Comments Add Comment 6 min read Top 10 Ways POS Retail Software Can Increase Sales and Improve Customer Experience Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 1 '25 Top 10 Ways POS Retail Software Can Increase Sales and Improve Customer Experience # possoftware # erp # odoo 5 reactions Comments Add Comment 7 min read How a Modern POS Solution Streamlines Inventory, Billing, and Reporting Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow May 1 '25 How a Modern POS Solution Streamlines Inventory, Billing, and Reporting # erp # odoo # possolutions 5 reactions Comments Add Comment 5 min read ERP: A Small Word with Big Services Behind It Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 30 '25 ERP: A Small Word with Big Services Behind It # erp # odoo # software 5 reactions Comments Add Comment 8 min read Version After Version, But ERP Makes Sure Your Business Always Evolves Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 29 '25 Version After Version, But ERP Makes Sure Your Business Always Evolves # erp # softwarw # odoo 5 reactions Comments Add Comment 5 min read Think Bigger. Manage Smarter. Choose ERP Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 28 '25 Think Bigger. Manage Smarter. Choose ERP # erp # odoo # erpsoftware 5 reactions Comments Add Comment 6 min read Not Just Software: How ERP Becomes Your Business’s Digital Brain Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 26 '25 Not Just Software: How ERP Becomes Your Business’s Digital Brain # erp # software # odoo 5 reactions Comments Add Comment 7 min read 15 Best ERP Software of 2025 for Every Business Size and Industry. Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 25 '25 15 Best ERP Software of 2025 for Every Business Size and Industry. # software # erp # odoo # business 5 reactions Comments Add Comment 16 min read 5 Ways ERP Software is Revolutionizing the American Manufacturing Industry in 2025 Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 24 '25 5 Ways ERP Software is Revolutionizing the American Manufacturing Industry in 2025 # erpsoftware # crm # software 5 reactions Comments Add Comment 4 min read Top CRM Software in Rajkot: The Secret to Accelerating Business Growth Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 24 '25 Top CRM Software in Rajkot: The Secret to Accelerating Business Growth # crm # odoocrm # crmsoftware 5 reactions Comments Add Comment 6 min read 5 CRM Trends That Will Dominate in 2025 Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 23 '25 5 CRM Trends That Will Dominate in 2025 # crm # odoo # erpcrm 5 reactions Comments Add Comment 6 min read How to Choose the Right POS System for Your Business Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 23 '25 How to Choose the Right POS System for Your Business # erp # possoftware # odoo Comments Add Comment 5 min read How to Choose the Right POS System for Your Business Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 22 '25 How to Choose the Right POS System for Your Business # erp # possoftware # odoo 5 reactions Comments Add Comment 5 min read Top Strategies for Implementing HRMS Without Disrupting Operations Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 21 '25 Top Strategies for Implementing HRMS Without Disrupting Operations # hrms # hr # odoo # erp 5 reactions Comments Add Comment 6 min read Best Practices for Managing Customer Data in a CRM Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 21 '25 Best Practices for Managing Customer Data in a CRM # crm # odocrm # odoo # erpcrm 5 reactions Comments Add Comment 7 min read 8 Features That Make Odoo One of the Best ERP Solutions Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 18 '25 8 Features That Make Odoo One of the Best ERP Solutions # erp # odoo # software 6 reactions Comments Add Comment 4 min read 5 Common Challenges in Odoo Migration & How to Overcome Them Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 18 '25 5 Common Challenges in Odoo Migration & How to Overcome Them # odoo # erp # solution # software 7 reactions Comments Add Comment 6 min read Top Benefits of Implementing a Smart Tender Management System Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 17 '25 Top Benefits of Implementing a Smart Tender Management System # odoo # erp # software 5 reactions Comments Add Comment 5 min read How a Tender Management System Can Transform Your Bidding Process in 2025 Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 17 '25 How a Tender Management System Can Transform Your Bidding Process in 2025 # tms # erp # odoo # systems 5 reactions Comments Add Comment 7 min read How Odoo Customization Empowers Your Business with Tailored ERP Solutions Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 16 '25 How Odoo Customization Empowers Your Business with Tailored ERP Solutions # odoo # erp # software 5 reactions Comments 1 comment 7 min read Top 10 ERP Systems for Small Businesses in 2025 Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 16 '25 Top 10 ERP Systems for Small Businesses in 2025 # erp # software # odoo 5 reactions Comments Add Comment 9 min read Why Businesses Need to Migrate to a New ERP System Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 14 '25 Why Businesses Need to Migrate to a New ERP System # migration # erp # odoo # systems 3 reactions Comments Add Comment 4 min read Best Odoo-Based Project Management Tool for Businesses in 2025 Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 12 '25 Best Odoo-Based Project Management Tool for Businesses in 2025 # erp # odoo # software 5 reactions Comments Add Comment 5 min read 5 Signs Your Law Firm Needs a Law Management System Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 12 '25 5 Signs Your Law Firm Needs a Law Management System # odoo # erp # software 5 reactions Comments Add Comment 2 min read 7 Best POS Retail Solutions for Small & Large Businesses Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 11 '25 7 Best POS Retail Solutions for Small & Large Businesses 5 reactions Comments Add Comment 5 min read Why Your Business Needs a POS Retail Solution Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 11 '25 Why Your Business Needs a POS Retail Solution # crm # odoo # software 5 reactions Comments Add Comment 3 min read How to Streamline Your Optical Store Operations with Smart Management Tools Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 11 '25 How to Streamline Your Optical Store Operations with Smart Management Tools # odoo # optical # erp 5 reactions Comments Add Comment 3 min read From Leads to Repeat Customers: The Role of CRM in Manufacturing Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 9 '25 From Leads to Repeat Customers: The Role of CRM in Manufacturing # crm # erp # odoo # software 5 reactions Comments Add Comment 7 min read Top Challenges in Manufacturing & How ERP Solves Them Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 9 '25 Top Challenges in Manufacturing & How ERP Solves Them # erp # odoo # software 5 reactions Comments Add Comment 6 min read 6 Must-Have Features in a Modern ERP System Bhavesh Gangani Bhavesh Gangani Bhavesh Gangani Follow Apr 9 '25 6 Must-Have Features in a Modern ERP System # erp # software # odoo 5 reactions Comments 1 comment 6 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://dev.to/aws-heroes/cloudfront-functions-vs-lambdaedge-whats-the-difference-1g60 | CloudFront Functions vs. Lambda@Edge: what's the difference? - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Tomasz Łakomy for AWS Heroes Posted on Nov 22, 2023 • Originally published at cloudash.dev CloudFront Functions vs. Lambda@Edge: what's the difference? # aws # lambda # cloudfront AWS announced CloudFront KeyValueStore yesterday which enhances the capabilities of CloudFront Functions. In case you're not familiar, here's a quick summary of what CloudFront Functions do: Amazon CloudFront allows you to securely deliver static and dynamic content with low latency and high transfer speeds. With CloudFront Functions, you can perform latency-sensitive customizations for millions of requests per second. For example, you can use CloudFront Functions to modify headers, normalize cache keys, rewrite URLs, or authorize requests. You may also be familiar with Lambda@Edge which is a similar service that allows you to run Lambda functions at the edge of the AWS network. To quote the AWS docs: Lambda@Edge is a feature of Amazon CloudFront that lets you run code closer to users of your application, which improves performance and reduces latency. With Lambda@Edge, you don't have to provision or manage infrastructure in multiple locations around the world. You pay only for the compute time you consume - there is no charge when your code is not running. Reading those two descriptions you may be wondering - what's the difference between those two services? Let's take a look! Programming language: Lambda@Edge supports Node.js and Python. CloudFront Functions support JavaScript only. Not really a problem for me personally (I'm a JavaScript developer) but it's worth mentioning. Execution Location: Lambda@Edge functions are executed in 13 CloudFront Regional Edge Caches, whereas CloudFront Functions are executed in 225+ CloudFront Edge Locations. This means that CloudFront Functions are may be executed closer to the end user, which means lower latency. Trigger points CloudFront Functions can be triggered by the following events: When CloudFront receives a request from a viewer (viewer request) Before CloudFront returns the response to the viewer (viewer response) Lambda@Edge function can be triggered when: CloudFront receives a request from a viewer (viewer request) Before CloudFront returns the response to the viewer (viewer response) CloudFront forwards a request to your origin (origin request) After CloudFront receives the response from the origin and before it caches the object in the response (origin response) Limits Maximum execution time : CloudFront Functions need to finish under a millisecond (so if your code is as slow as mine, you should consider using Lambda@Edge). Lambda@Edge functions can run for up to 5 seconds for viewer request and viewer response triggers, and up to 30 seconds for origin request and origin response triggers. Network access : CloudFront Functions can't access the network. Lambda@Edge functions can access the network. HTTP manipulation : CloudFront Functions can only manipulate HTTP headers. If you need to remove or replace the body of an HTTP request or response, use Lambda@Edge instead. Filesystem access : CloudFront Functions don't have access to filesystem (which makes sense, especially given the 1ms limit). Lambda@Edge functions can access the filesystem. Scale : CloudFront Functions can scale to 10,000,000 requests per second or more . Lambda@Edge functions can scale up to 10,000 requests per second per Region. Memory : CloudFront Functions have 2MB of memory available. Lambda@Edge functions have 128MB - 3GB of memory available. Maximum function size : CloudFront Functions need to be tiny - they can't be larger than 10KB. Lambda@Edge functions can be up to 1MB in size (for viewer request and viewer response triggers) and 50MB in size (for origin request and origin response triggers). Use cases CloudFront Functions Cache key normalization – You can transform HTTP request attributes (headers, query strings, cookies, and even the URL path) to create an optimal cache key, which can improve your cache hit ratio. Header manipulation – You can insert, modify, or delete HTTP headers in the request or response. For example, you can add a True-Client-IP header to every request. URL redirects or rewrites – You can redirect viewers to other pages based on information in the request, or rewrite all requests from one path to another. Request authorization – You can validate hashed authorization tokens, such as JSON web tokens (JWT), by inspecting authorization headers or other request metadata. Lambda@Edge Functions that take several milliseconds or more to complete. Functions that require adjustable CPU or memory. Functions that depend on third-party libraries (including the AWS SDK, for integration with other AWS services). Functions that require network access to use external services for processing. Functions that require file system access or access to the body of HTTP requests. Top comments (1) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Collapse Expand Darryl Ruggles Darryl Ruggles Darryl Ruggles Follow Principal Cloud Solutions Architect @ Ciena AWS Community Builder Serverless, AWS, Kubernetes, Rust, Go, Terraform, Security, React, MLOps, Maker, Electronics. Location Ottawa, ON, Canada Education Acadia University Work Principal Cloud Solutions Architect @ Ciena Joined May 7, 2017 • Nov 22 '23 Dropdown menu Copy link Hide A good overview of these (seemingly similar) services. Like comment: Like comment: 2 likes Like Comment button Reply Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . 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https://github.com/berviantoleo/udacity-azure-project-2 | GitHub - berviantoleo/udacity-azure-project-2: Submission Azure Developer ND 2 Skip to content Navigation Menu Toggle navigation Sign in Appearance settings Platform AI CODE CREATION GitHub Copilot Write better code with AI GitHub Spark Build and deploy intelligent apps GitHub Models Manage and compare prompts MCP Registry New Integrate external tools DEVELOPER WORKFLOWS Actions Automate any workflow Codespaces Instant dev environments Issues Plan and track work Code Review Manage code changes APPLICATION SECURITY GitHub Advanced Security Find and fix vulnerabilities Code security Secure your code as you build Secret protection Stop leaks before they start EXPLORE Why GitHub Documentation Blog Changelog Marketplace View all features Solutions BY COMPANY SIZE Enterprises Small and medium teams Startups Nonprofits BY USE CASE App Modernization DevSecOps DevOps CI/CD View all use cases BY INDUSTRY Healthcare Financial services Manufacturing Government View all industries View all solutions Resources EXPLORE BY TOPIC AI Software Development DevOps Security View all topics EXPLORE BY TYPE Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills SUPPORT & SERVICES Documentation Customer support Community forum Trust center Partners Open Source COMMUNITY GitHub Sponsors Fund open source developers PROGRAMS Security Lab Maintainer Community Accelerator Archive Program REPOSITORIES Topics Trending Collections Enterprise ENTERPRISE SOLUTIONS Enterprise platform AI-powered developer platform AVAILABLE ADD-ONS GitHub Advanced Security Enterprise-grade security features Copilot for Business Enterprise-grade AI features Premium Support Enterprise-grade 24/7 support Pricing Search or jump to... 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Notifications You must be signed in to change notification settings Fork 0 Star 0 Submission Azure Developer ND 2 License View license 0 stars 0 forks Branches Tags Activity Star Notifications You must be signed in to change notification settings Code Pull requests 0 Discussions Actions Security Uh oh! There was an error while loading. Please reload this page . Insights Additional navigation options Code Pull requests Discussions Actions Security Insights berviantoleo/udacity-azure-project-2 main Branches Tags Go to file Code Open more actions menu Folders and files Name Name Last commit message Last commit date Latest commit History 237 Commits .github .github NeighborlyAPI NeighborlyAPI NeighborlyFrontEnd NeighborlyFrontEnd images images sample_data sample_data screenshots screenshots .gitignore .gitignore .mergify.yml .mergify.yml .whitesource .whitesource LICENSE.md LICENSE.md Pipfile Pipfile README.md README.md host.json host.json local.settings.json local.settings.json View all files Repository files navigation README License NOTICE! Please use this repo as study purpose not for submission, never cheating for submission! Deploying the Neighborly App with Azure Functions Project Overview For the final project, we are going to build an app called "Neighborly". Neighborly is a Python Flask-powered web application that allows neighbors to post advertisements for services and products they can offer. The Neighborly project is comprised of a front-end application that is built with the Python Flask micro framework. The application allows the user to view, create, edit, and delete the community advertisements. The application makes direct requests to the back-end API endpoints. These are endpoints that we will also build for the server-side of the application. You can see an example of the deployed app below. Dependencies You will need to install the following locally: Pipenv Visual Studio Code Azure Function tools V3 Azure CLI Azure Tools for Visual Studio Code On Mac, you can do this with: # install pipenv brew install pipenv # install azure-cli brew update && brew install azure-cli # install azure function core tools brew tap azure/functions brew install azure-functions-core-tools@3 Project Instructions In case you need to return to the project later on, it is suggested to store any commands you use so you can re-create your work. You should also take a look at the project rubric to be aware of any places you may need to take screenshots as proof of your work (or else keep your resource up and running until you have passed, which may incur costs). I. Creating Azure Function App We need to set up the Azure resource group, region, storage account, and an app name before we can publish. Create a resource group. Create a storage account (within the previously created resource group and region). Create an Azure Function App within the resource group, region and storage account. Note that app names need to be unique across all of Azure. Make sure it is a Linux app, with a Python runtime. Example of successful output, if creating the app myneighborlyapiv1 : Your Linux function app ' myneighborlyapiv1 ' , that uses a consumption plan has been successfully created but is not active until content is published using Azure Portal or the Functions Core Tools. Set up a Cosmos DB Account. You will need to use the same resource group, region and storage account, but can name the Cosmos DB account as you prefer. Note: This step may take a little while to complete (15-20 minutes in some cases). Create a MongoDB Database in CosmosDB Azure and two collections, one for advertisements and one for posts . Print out your connection string or get it from the Azure Portal. Copy/paste the primary connection string. You will use it later in your application. Example connection string output: bash-3.2$ Listing connection strings from COSMOS_ACCOUNT: + az cosmosdb keys list -n neighborlycosmos -g neighborlyapp --type connection-strings { " connectionStrings " : [ { " connectionString " : " AccountEndpoint=https://neighborlycosmos.documents.azure.com:443/;AccountKey=xxxxxxxxxxxx; " , " description " : " Primary SQL Connection String " }, { " connectionString " : " AccountEndpoint=https://neighborlycosmos.documents.azure.com:443/;AccountKey=xxxxxxxxxxxxx; " , " description " : " Secondary SQL Connection String " } ... [other code omitted] ] } Import Sample Data Into MongoDB. Download dependencies: # get the mongodb library brew install mongodb-community@4.2 # check if mongoimport lib exists mongoimport --version Import the data from the sample_data directory for Ads and Posts to initially fill your app. Example successful import: Importing ads data -------------------> 2020-05-18T23:30:39.018-0400 connected to: mongodb://neighborlyapp.mongo.cosmos.azure.com:10255/ 2020-05-18T23:30:40.344-0400 5 document(s) imported successfully. 0 document(s) failed to import. ... Importing posts data -------------------> 2020-05-18T23:30:40.933-0400 connected to: mongodb://neighborlyapp.mongo.cosmos.azure.com:10255/ 2020-05-18T23:30:42.260-0400 4 document(s) imported successfully. 0 document(s) failed to import. Hook up your connection string into the NeighborlyAPI server folder. You will need to replace the url variable with your own connection string you copy-and-pasted in the last step, along with some additional information. Tip: Check out this post if you need help with what information is needed. Go to each of the __init__.py files in getPosts, getPost, getAdvertisements, getAdvertisement, deleteAdvertisement, updateAdvertisement, createAdvertisements and replace your connection string. You will also need to set the related database and collection appropriately. # inside getAdvertisements/__init__.py def main(req: func.HttpRequest) - > func.HttpResponse: logging.info( ' Python getAdvertisements trigger function processed a request. ' ) try: # copy/paste your primary connection url here # ------------------------------------------- url = " " # -------------------------------------------- client=pymongo.MongoClient(url) database = None # Feed the correct key for the database name to the client collection = None # Feed the correct key for the collection name to the database ... [other code omitted] Make sure to do the same step for the other 6 HTTP Trigger functions. Deploy your Azure Functions. Test it out locally first. # cd into NeighborlyAPI cd NeighborlyAPI # install dependencies pipenv install # go into the shell pipenv shell # test func locally func start You may need to change "IsEncrypted" to false in local.settings.json if this fails. At this point, Azure functions are hosted in localhost:7071. You can use the browser or Postman to see if the GET request works. For example, go to the browser and type in: # example endpoint for all advertisements http://localhost:7071/api/getadvertisements # example endpoint for all posts http://localhost:7071/api/getposts Now you can deploy functions to Azure by publishing your function app. The result may give you a live url in this format, or you can check in Azure portal for these as well: Expected output if deployed successfully: Functions in < APP_NAME > : createAdvertisement - [httpTrigger] Invoke url: https:// < APP_NAME > .azurewebsites.net/api/createadvertisement deleteAdvertisement - [httpTrigger] Invoke url: https:// < APP_NAME > .azurewebsites.net/api/deleteadvertisement getAdvertisement - [httpTrigger] Invoke url: https:// < APP_NAME > .azurewebsites.net/api/getadvertisement getAdvertisements - [httpTrigger] Invoke url: https:// < APP_NAME > .azurewebsites.net/api/getadvertisements getPost - [httpTrigger] Invoke url: https:// < APP_NAME > .azurewebsites.net/api/getpost getPosts - [httpTrigger] Invoke url: https:// < APP_NAME > .azurewebsites.net/api/getposts updateAdvertisement - [httpTrigger] Invoke url: https:// < APP_NAME > .azurewebsites.net/api/updateadvertisement Note: It may take a minute or two for the endpoints to get up and running if you visit the URLs. Save the function app url https://<APP_NAME>.azurewebsites.net/api/ since you will need to update that in the client-side of the application. II. Deploying the client-side Flask web application We are going to update the Client-side settings.py with published API endpoints. First navigate to the settings.py file in the NeighborlyFrontEnd/ directory. Use a text editor to update the API_URL to your published url from the last step. # Inside file settings.py # ------- For Local Testing ------- # API_URL = "http://localhost:7071/api" # ------- For production ------- # where APP_NAME is your Azure Function App name API_URL= " https://<APP_NAME>.azurewebsites.net/api " III. CI/CD Deployment Deploy your client app. Note: Use a different app name here to deploy the front-end, or else you will erase your API. From within the NeighborlyFrontEnd directory: Install dependencies with pipenv install Go into the pip env shell with pipenv shell Deploy your application to the app service. Note: It may take a minute or two for the front-end to get up and running if you visit the related URL. Make sure to also provide any necessary information in settings.py to move from localhost to your deployment. Create an Azure Registry and dockerize your Azure Functions. Then, push the container to the Azure Container Registry. Create a Kubernetes cluster, and verify your connection to it with kubectl get nodes . Deploy app to Kubernetes, and check your deployment with kubectl config get-contexts . IV. Event Hubs and Logic App Create a Logic App that watches for an HTTP trigger. When the HTTP request is triggered, send yourself an email notification. Create a namespace for event hub in the portal. You should be able to obtain the namespace URL. Add the connection string of the event hub to the Azure Function. V. Cleaning Up Your Services Before completing this step, make sure to have taken all necessary screenshots for the project! Check the rubric in the classroom to confirm. Clean up and remove all services, or else you will incur charges. # replace with your resource group RESOURCE_GROUP= " <YOUR-RESOURCE-GROUP> " # run this command az group delete --name $RESOURCE_GROUP About Submission Azure Developer ND 2 Topics mongodb serverless azure azure-functions udacity-nanodegree submission Resources Readme License View license Uh oh! There was an error while loading. Please reload this page . Activity Stars 0 stars Watchers 1 watching Forks 0 forks Report repository Releases No releases published Sponsor this project Uh oh! There was an error while loading. Please reload this page . ko-fi.com/ berviantoleo patreon.com/ berviantoleo liberapay.com/ berviantoleo opencollective.com/ berviantoleo issuehunt.io/r/ berviantoleo Learn more about GitHub Sponsors Packages 0 No packages published Uh oh! There was an error while loading. Please reload this page . Contributors 3 Uh oh! There was an error while loading. Please reload this page . 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Muhammad Rabbi Posted on Jan 9 React Design Patterns Every Frontend & FullStack Developer Should Know # react # javascript # frontend # architecture If you’re building modern web applications, React design patterns can completely change how you think about components, reusability, and scalability. A month ago, I realized something important I was using React every day, but I wasn’t truly thinking in patterns. That’s when I discovered an amazing playlist by @atapas . Why React Design Patterns Matter React design patterns help you: Write cleaner and more maintainable code Avoid prop drilling & unnecessary re-renders Scale applications without pain Think like a senior engineer, not just a coder Patterns are not about complexity — they’re about clarity and intention. What You’ll Learn From This Playlist This playlist breaks down patterns in a practical and beginner-friendly way, including: Container & Presentational Components Compound Components Render Props Custom Hooks pattern Controlled vs Uncontrolled Components Reusability & separation of concerns Each concept is explained with real-world use cases, not just theory. Where to Watch 📺 You can find the playlist on: Devguide : https://devguide-dev.vercel.app/playlist/PLIJrr73KDmRyQVT__uFZvaVfWPdfyMFHC , YouTube , * A Small but Meaningful Moment * I shared this playlist recently, and Tapas Adhikary himself commented: “Thank you for sharing and the shoutout. The Devguide looks awesome. 🙌” That meant a lot to me — and it motivated me to share this with more developers. Final Thoughts Whether you’re a frontend developer, backend developer, or moving toward full-stack, understanding React design patterns will level you up. Don’t just write React. Design with React. If you’ve already explored these patterns, I’d love to know: Which React pattern helped you the most? * Happy coding * coding #devguide #fullstack #frontend #developer Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . 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https://www.youtube.com/watch?v=zekEqhaPmag | PHP 7 – What changed internally? - Nikita Popov - Forum PHP 2015 - YouTube 정보 보도자료 저작권 문의하기 크리에이터 광고 개발자 약관 개인정보처리방침 정책 및 안전 YouTube 작동의 원리 새로운 기능 테스트하기 © 2026 Google LLC, Sundar Pichai, 1600 Amphitheatre Parkway, Mountain View CA 94043, USA, 0807-882-594 (무료), yt-support-solutions-kr@google.com, 호스팅: Google LLC, 사업자정보 , 불법촬영물 신고 크리에이터들이 유튜브 상에 게시, 태그 또는 추천한 상품들은 판매자들의 약관에 따라 판매됩니다. 유튜브는 이러한 제품들을 판매하지 않으며, 그에 대한 책임을 지지 않습니다. var ytInitialData = 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https://www.algolia.com/fr/lp/rmn-report-2025/ | The Retail Media Networks Report 2025 | Algolia Niket --> Deutsch English français News DevCon2025 | October 1-2 Learn more Algolia Partners Support Login Logout Algolia mark white Algolia logo white Products Search Show users what they're looking for with AI-driven resuts. Search Show users what they're looking for with AI-driven resuts. Recommendations Use behavioral cues to drive higher engagement. Recommendations Use behavioral cues to drive higher engagement. Personalization Show each user what they need across their journey. Personalization Show each user what they need across their journey. Analytics All your insights in one dashboard. Analytics All your insights in one dashboard. Browse Move customers down the funnel with curated category pages. Browse Move customers down the funnel with curated category pages. Agent Studio Create, test, and deploy AI agents, fast. Agent Studio Create, test, and deploy AI agents, fast. 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We got you covered! Brand guidelines Download logo pack Your browser does not support the video tag. Profitez d’une recherche IA guidée par des objectifs métiers. Sponsored listings? Paid search? Display? Download the report to find out which tried and true advertising method works best. Your browser does not support the video tag. Profitez d’une recherche IA guidée par des objectifs métiers. From B2B to CPG, we’ve got insights on the most-used and most effective Retail Media strategies online. Download the report to find out which tried and true advertising method works best. Discover key insights on how brands use retail media The Retail Media Networks: Insights and Emerging Trends report highlights the key factors that shape retail media purchasing decisions, and provides valuable insights for business leaders, digital marketers, and tech experts. The report is based on a study conducted by Algolia's Research & Insights team. Participants from around the globe were asked to share how they purchase retail media and choose retail media solutions. Download your free copy Download your free copy Download your free copy 76% of respondents agreed that sponsored listings and search pages were the most popular retail media solution, showing this tool as an effective means to drive customers toward specific products and inventory. Display banners continue to be a massive driver of high-value traffic, with 60% of respondents utilizing this tried and true method of online advertising. 62% of respondents indicated utilizing Retail Media Platforms, which provide a variety of insights in tools, showing that data monitoring, insights, and analytics tools continue to remain key differentiators when selecting new media technology. Your browser does not support the video tag. Harness the power of AI-driven search with Algolia. 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https://www.fine.dev/blog/ai-coding-guide#tips-for-providing-better-context | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
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Report Abuse Bervianto Leo Pratama Posted on Apr 5, 2025 Exploring .NET Aspire and Adding it to my existing boilerplate # dotnet # devops # programming # docker Microservice Series (6 Part Series) 1 Simple Event-Driven App using Amazon MQ (RabbitMQ) 2 Getting Started with Redis, Elastic Beanstalk, and Razor Pages (.NET 6) ... 2 more parts... 3 Getting Started with gRPC, .NET 6, and Amazon EC2 4 Securing Your Secret Using AWS Systems Manager (Parameter Store) 5 Exploring Azure Queue Storage in .NET 6 Exploring .NET Aspire and Adding it to my existing boilerplate Hi everyone! Glad to see you again! I'm coming up with a new topic, which is still regarding DevOps. Yes, as your guest, it's .NET Aspire! I don't want to take too long for the introduction. Let's get started! Prerequisite .NET Aspire Prerequisite .NET 9.0 Docker (You may use Podman) JetBrains Rider or Your Favourite IDE My Existing Project Note: I will use JetBrains Rider. So, I will install the .NET Aspire Plugin for JetBrains Rider. Another reference for adding .NET Aspire to the existing project. Setup .NET Aspire Project Ensure you already have .NET Aspire templates; if not, please install them using this command. dotnet new install Aspire.ProjectTemplates Enter fullscreen mode Exit fullscreen mode Create a .NET Aspire AppHost project through .NET Aspire templates. dotnet new aspire-apphost -f net9.0 --name BervProject.WebApi.Boilerplate.AppHost Enter fullscreen mode Exit fullscreen mode Now, create a .NET Aspire Service Defaults project through .NET Aspire templates. dotnet new aspire-servicedefaults -f net9.0 --name BervProject.WebApi.Boilerplate.ServiceDefaults Enter fullscreen mode Exit fullscreen mode Let's add the projects to our solutions. dotnet sln add BervProject.WebApi.Boilerplate.AppHost/BervProject.WebApi.Boilerplate.AppHost.csproj BervProject.WebApi.Boilerplate.ServiceDefaults/BervProject.WebApi.Boilerplate.ServiceDefaults.csproj Enter fullscreen mode Exit fullscreen mode Our project solutions will be like this. Well done! Now, let's integrate the existing Web API and add some required services. Integrating the Web API Add the Web API as a reference in our AppHost. dotnet add BervProject.WebApi.Boilerplate.AppHost reference BervProject.WebApi.Boilerplate Enter fullscreen mode Exit fullscreen mode Add Redis package to AppHost. dotnet add BervProject.WebApi.Boilerplate.AppHost package Aspire.Hosting.Redis Enter fullscreen mode Exit fullscreen mode Add Postgres package to AppHost. dotnet add BervProject.WebApi.Boilerplate.AppHost package Aspire.Hosting.PostgreSQL Enter fullscreen mode Exit fullscreen mode Add Azure Storage package to AppHost. dotnet add BervProject.WebApi.Boilerplate.AppHost package Aspire.Hosting.Azure.Storage Enter fullscreen mode Exit fullscreen mode Add Azure Service Bus package to AppHost. dotnet add BervProject.WebApi.Boilerplate.AppHost package Aspire.Hosting.Azure.ServiceBus Enter fullscreen mode Exit fullscreen mode Adding Migration Service Creating the migration service. dotnet new worker -n BervProject.WebApi.Boilerplate.MigrationService -f "net9.0" Enter fullscreen mode Exit fullscreen mode Add to the solution. dotnet sln add BervProject.WebApi.Boilerplate.MigrationService Enter fullscreen mode Exit fullscreen mode Add reference to the API (the source of the migration data) dotnet add BervProject.WebApi.Boilerplate.MigrationService reference BervProject.WebApi.Boilerplate Enter fullscreen mode Exit fullscreen mode Add reference to the service defaults. dotnet add BervProject.WebApi.Boilerplate.MigrationService reference BervProject.WebApi.Boilerplate.ServiceDefaults Enter fullscreen mode Exit fullscreen mode Add Aspire.Npgsql.EntityFrameworkCore.PostgreSQL package to the migration service. dotnet add BervProject.WebApi.Boilerplate.MigrationService package Aspire.Npgsql.EntityFrameworkCore.PostgreSQL Enter fullscreen mode Exit fullscreen mode Update the Program.cs in the migration service. using BervProject.WebApi.Boilerplate.EntityFramework ; using BervProject.WebApi.Boilerplate.MigrationService ; var builder = Host . CreateApplicationBuilder ( args ); builder . AddServiceDefaults (); builder . Services . AddHostedService < Worker >(); builder . Services . AddOpenTelemetry () . WithTracing ( tracing => tracing . AddSource ( Worker . ActivitySourceName )); builder . AddNpgsqlDbContext < BoilerplateDbContext >( "BoilerplateConnectionString" ); var host = builder . Build (); host . Run (); Enter fullscreen mode Exit fullscreen mode Update the Worker.cs in the migration service. using System.Diagnostics ; using BervProject.WebApi.Boilerplate.EntityFramework ; using Microsoft.EntityFrameworkCore ; namespace BervProject.WebApi.Boilerplate.MigrationService ; public class Worker : BackgroundService { public const string ActivitySourceName = "Migrations" ; private static readonly ActivitySource SActivitySource = new ( ActivitySourceName ); private readonly IServiceProvider _serviceProvider ; private readonly IHostApplicationLifetime _hostApplicationLifetime ; public Worker ( IServiceProvider serviceProvider , IHostApplicationLifetime hostApplicationLifetime ) { _serviceProvider = serviceProvider ; _hostApplicationLifetime = hostApplicationLifetime ; } protected override async Task ExecuteAsync ( CancellationToken cancellationToken ) { using var activity = SActivitySource . StartActivity ( "Migrating database" , ActivityKind . Client ); try { using var scope = _serviceProvider . CreateScope (); var dbContext = scope . ServiceProvider . GetRequiredService < BoilerplateDbContext >(); await RunMigrationAsync ( dbContext , cancellationToken ); } catch ( Exception ex ) { activity ?. AddException ( ex ); throw ; } _hostApplicationLifetime . StopApplication (); } private static async Task RunMigrationAsync ( BoilerplateDbContext dbContext , CancellationToken cancellationToken ) { var strategy = dbContext . Database . CreateExecutionStrategy (); await strategy . ExecuteAsync ( async () => { // Run migration in a transaction to avoid partial migration if it fails. await dbContext . Database . MigrateAsync ( cancellationToken ); }); } } Enter fullscreen mode Exit fullscreen mode Add the Migration Service to the AppHost. dotnet add BervProject.WebApi.Boilerplate.AppHost reference BervProject.WebApi.Boilerplate.MigrationService Enter fullscreen mode Exit fullscreen mode Add Microsoft.EntityFrameworkCore to both BervProject.WebApi.Boilerplate.MigrationService and BervProject.WebApi.Boilerplate projects to avoid package version conflicts. <PackageReference Include= "Microsoft.EntityFrameworkCore" Version= "9.0.3" /> Enter fullscreen mode Exit fullscreen mode Final Changes Update the Program.cs in our AppHost. var builder = DistributedApplication . CreateBuilder ( args ); var cache = builder . AddRedis ( "cache" ). WithRedisInsight (); var postgres = builder . AddPostgres ( "postgres" ). WithPgAdmin (); var postgresdb = postgres . AddDatabase ( "postgresdb" ); var serviceBus = builder . AddAzureServiceBus ( "messaging" ). RunAsEmulator (); var storage = builder . AddAzureStorage ( "storage" ). RunAsEmulator (); var blobs = storage . AddBlobs ( "blobs" ); var queues = storage . AddQueues ( "queues" ); var tables = storage . AddTables ( "tables" ); var migration = builder . AddProject < Projects . BervProject_WebApi_Boilerplate_MigrationService >( "migrations" ) . WithReference ( postgresdb , connectionName : "BoilerplateConnectionString" ) . WithExplicitStart (); builder . AddProject < Projects . BervProject_WebApi_Boilerplate >( "apiservice" ) . WithHttpEndpoint () . WithReference ( cache , connectionName : "Redis" ) . WithReference ( postgresdb , connectionName : "BoilerplateConnectionString" ) . WithReference ( blobs , connectionName : "AzureStorageBlob" ) . WithReference ( queues , connectionName : "AzureStorageQueue" ) . WithReference ( tables , connectionName : "AzureStorageTable" ) . WithReference ( serviceBus , connectionName : "AzureServiceBus" ) . WaitFor ( cache ) . WaitFor ( postgresdb ) . WaitFor ( blobs ) . WaitFor ( queues ) . WaitFor ( tables ) . WaitFor ( serviceBus ) . WaitForCompletion ( migration ); builder . Build (). Run (); Enter fullscreen mode Exit fullscreen mode Migrating Connection Strings in the existing API Update Program.cs in BervProject.WebApi.Boilerplate . using System ; using System.IO ; using System.Reflection ; using Autofac.Extensions.DependencyInjection ; using BervProject.WebApi.Boilerplate.ConfigModel ; using BervProject.WebApi.Boilerplate.EntityFramework ; using BervProject.WebApi.Boilerplate.Extenstions ; using BervProject.WebApi.Boilerplate.Services ; using BervProject.WebApi.Boilerplate.Services.Azure ; using Hangfire ; using Hangfire.PostgreSql ; using Microsoft.AspNetCore.Builder ; using Microsoft.EntityFrameworkCore ; using Microsoft.Extensions.Configuration ; using Microsoft.Extensions.DependencyInjection ; using Microsoft.Extensions.Hosting ; using Microsoft.Extensions.Logging ; using NLog.Web ; var builder = WebApplication . CreateBuilder ( args ); builder . Host . UseServiceProviderFactory ( new AutofacServiceProviderFactory ()); builder . Logging . ClearProviders (); builder . Logging . SetMinimumLevel ( LogLevel . Trace ); builder . Logging . AddNLog ( "Nlog.config" ); builder . Logging . AddNLogWeb (); builder . Host . UseNLog (); // settings injection var awsConfig = builder . Configuration . GetSection ( "AWS" ). Get < AWSConfiguration >(); builder . Services . AddSingleton ( awsConfig ); var azureConfig = builder . Configuration . GetSection ( "Azure" ). Get < AzureConfiguration >(); builder . Services . AddSingleton ( azureConfig ); // aws services builder . Services . SetupAWS (); // azure services builder . Services . SetupAzure ( builder . Configuration ); // cron services builder . Services . AddScoped < ICronService , CronService >(); builder . Services . AddHangfire ( x => x . UsePostgreSqlStorage ( opt => { opt . UseNpgsqlConnection ( builder . Configuration . GetConnectionString ( "BoilerplateConnectionString" )); })); builder . Services . AddHangfireServer (); // essential services builder . Services . AddStackExchangeRedisCache ( options => { options . Configuration = builder . Configuration . GetConnectionString ( "Redis" ); }); builder . Services . AddDbContext < BoilerplateDbContext >( options => options . UseNpgsql ( builder . Configuration . GetConnectionString ( "BoilerplateConnectionString" ))); builder . Services . AddHealthChecks (); builder . Services . AddControllers (); builder . Services . AddApiVersioning (); builder . Services . AddSwaggerGen ( options => { var xmlFilename = $" { Assembly . GetExecutingAssembly (). GetName (). Name } .xml" ; options . IncludeXmlComments ( Path . Combine ( AppContext . BaseDirectory , xmlFilename )); }); var app = builder . Build (); // register Consumer var connectionString = builder . Configuration . GetConnectionString ( "AzureServiceBus" ); var queueName = azureConfig . ServiceBus . QueueName ; var topicName = azureConfig . ServiceBus . TopicName ; if (! string . IsNullOrWhiteSpace ( queueName ) && ! string . IsNullOrWhiteSpace ( connectionString )) { var bus = app . Services . GetService < IServiceBusQueueConsumer >(); bus . RegisterOnMessageHandlerAndReceiveMessages (); } if (! string . IsNullOrWhiteSpace ( topicName ) && ! string . IsNullOrWhiteSpace ( connectionString )) { var bus = app . Services . GetService < IServiceBusTopicSubscription >(); bus . RegisterOnMessageHandlerAndReceiveMessages (); } // register essential things if ( app . Environment . IsDevelopment ()) { app . UseDeveloperExceptionPage (); } else { app . UseExceptionHandler ( "/error" ); app . UseHttpsRedirection (); } app . UseRouting (); app . UseAuthorization (); app . MapHealthChecks ( "/healthz" ); app . UseSwagger ( c => { c . RouteTemplate = "api/docs/{documentName}/swagger.json" ; }); app . UseSwaggerUI ( c => { c . SwaggerEndpoint ( "/api/docs/v1/swagger.json" , "My API V1" ); c . RoutePrefix = "api/docs" ; }); app . MapControllers (); app . MapHangfireDashboard (); app . Run (); public partial class Program { } Enter fullscreen mode Exit fullscreen mode Update SetupAzureExtension.cs in BervProject.WebApi.Boilerplate . using Microsoft.Extensions.Configuration ; namespace BervProject.WebApi.Boilerplate.Extenstions ; using Entities ; using BervProject.WebApi.Boilerplate.Services.Azure ; using Services ; using Microsoft.Extensions.DependencyInjection ; using Microsoft.Extensions.Azure ; public static class SetupAzureExtension { public static void SetupAzure ( this IServiceCollection services , ConfigurationManager config ) { services . AddAzureClients ( builder => { builder . AddBlobServiceClient ( config . GetConnectionString ( "AzureStorageBlob" )); builder . AddQueueServiceClient ( config . GetConnectionString ( "AzureStorageQueue" )); builder . AddServiceBusClient ( config . GetConnectionString ( "AzureServiceBus" )); builder . AddTableServiceClient ( config . GetConnectionString ( "AzureStorageTable" )); }); services . AddScoped < IAzureQueueServices , AzureQueueServices >(); services . AddScoped < ITopicServices , TopicServices >(); services . AddScoped < IAzureStorageQueueService , AzureStorageQueueService >(); services . AddScoped < IBlobService , BlobService >(); // add each tables services . AddScoped < IAzureTableStorageService < Note >, AzureTableStorageService < Note >>(); // service bus services . AddSingleton < IServiceBusQueueConsumer , ServiceBusQueueConsumer >(); services . AddSingleton < IServiceBusTopicSubscription , ServiceBusTopicSubscription >(); services . AddTransient < IProcessData , ProcessData >(); services . AddApplicationInsightsTelemetry (); } } Enter fullscreen mode Exit fullscreen mode Running the AppHost Let's run the AppHost! dotnet run --project . \B ervProject.WebApi.Boilerplate.AppHost Enter fullscreen mode Exit fullscreen mode Please wait until all services are running. It may take a long time, depending on your network. Before starting the API, please run the migration service first after the postgres service is running. When there are no errors, your API will run successfully. Let's Compare It with the PR feat: Add .NET Aspire #2827 berviantoleo posted on Apr 05, 2025 View on GitHub Try the API You may try the API with a health check endpoint /healthz . Conclusion Gosh! So tired, it's quite many things that I need to set up for the first time. However, it's satisfying! I notice some unexpected behaviour, for example, the migrating service won't stop after it's finished migrating, especially when running automatically. So, I use a workaround by starting it manually. That's it. If you have any feedback, please let me know! Cheers! Microservice Series (6 Part Series) 1 Simple Event-Driven App using Amazon MQ (RabbitMQ) 2 Getting Started with Redis, Elastic Beanstalk, and Razor Pages (.NET 6) ... 2 more parts... 3 Getting Started with gRPC, .NET 6, and Amazon EC2 4 Securing Your Secret Using AWS Systems Manager (Parameter Store) 5 Exploring Azure Queue Storage in .NET 6 Exploring .NET Aspire and Adding it to my existing boilerplate Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Bervianto Leo Pratama Follow AWS Community Builder | Software Engineer | Focus on topics: Microservices, Cloud Computing, and Cyber Security. Location Bandung, Indonesia Education Bachelor of Science, Computer Science Pronouns He/His/Him Work Software Engineer Joined Aug 9, 2018 More from Bervianto Leo Pratama Enhancing developer experience with Vagrant # vagrant # devops # devex # microservices Exploring Azure Queue Storage in .NET # dotnet # azure # tutorial # azurequeue Mencoba Container Service Back4App # back4app # backend # devops # eksplorasi 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://www.algolia.com/de/products/features/personalization/ | Anpassen der Benutzererfahrung mit der Personalisierung der Suche | Algolia Niket --> Deutsch English français News DevCon2025 | October 1-2 Learn more Unternehmen Partners Einloggen Login Logout Algolia mark white Algolia logo white Lösungen Search Show users what they're looking for with AI-driven resuts. Search Show users what they're looking for with AI-driven resuts. Recommendations Use behavioral cues to drive higher engagement. Recommendations Use behavioral cues to drive higher engagement. Personalization Show each user what they need across their journey. Personalization Show each user what they need across their journey. Analytics All your insights in one dashboard. Analytics All your insights in one dashboard. Browse Move customers down the funnel with curated category pages. Browse Move customers down the funnel with curated category pages. Agent Studio Create, test, and deploy AI agents, fast. Agent Studio Create, test, and deploy AI agents, fast. Generative Experiences Build conversational solutions with retrieval augmented generation (RAG). Generative Experiences Build conversational solutions with retrieval augmented generation (RAG). Ask AI Deliver conversational answers—right from your search bar. Ask AI Deliver conversational answers—right from your search bar. MCP Server Search, analyze, or monitor your index within your agentic workflow. MCP Server Search, analyze, or monitor your index within your agentic workflow. Data Enrichment Modify, enhance, or restructure data as it’s indexed for search. Data Enrichment Modify, enhance, or restructure data as it’s indexed for search. Data Transformation Streamline data preparation and enhance data quality. Data Transformation Streamline data preparation and enhance data quality. Integrations Connect to your existing stack via pre-built libraries and APIs. Integrations Connect to your existing stack via pre-built libraries and APIs. Data Centers Choose from 70+ data centers across 17 regions. Data Centers Choose from 70+ data centers across 17 regions. Security & Compliance Built for peace of mind. Security & Compliance Built for peace of mind. 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We got you covered! Brand guidelines Download logo pack ADVANCED PERSONALIZATION 1:1-Erlebnisse, die Gewinne steigern Schaffen Sie einzigartige Shopping Journeys, die Ihre Nutzer immer wieder zur Conversion führen. Demo Anfordern Kostenlos Starten Personalization redefined Most personalization engines rely on static rules or delayed data. But today’s shoppers change their minds fast — and your experience needs to change with them. Algolia Personalization adjusts dynamically, responding to user signals as they happen. Whether you're helping users discover products or guiding them to purchase, Algolia ensures your experience reflects their evolving intent — not just historical behavior. A full spectrum of personalization Personalize the whole user journey Deliver personalization across searching, browsing, recommendations, and other touchpoints. Tailor your full experience for every user. Automate set-up and optimization Get going in minutes with automatically calculated attributes. Leave ongoing optimization to our algorithm — or fine-tune manually when you want deeper control. Choose the access point that works best for you Developers can run Advanced Personalization from the Dashboard or API — and business users from the Merchandising Studio, making them more self-sufficient. Enrich data with third-party sources Improve customer profiles with data from tools like Segment, Shopify, and Google BigQuery. Our newest feature — now adapting in real time Real-Time Personalization is the latest addition to Algolia’s personalization. It captures user behavior within sessions and adapts experiences in real time — responding to what users are doing right now, not just what they’ve done in the past. Shorten the path to discovery Reduce abandonment with relevance in the moment Maximize conversion by responding to immediate buying signals Surface the most relevant products even when intent moves across categories Keep shoppers engaged as their behavior shifts during a session Turn every click and browse into a conversion opportunity Personalisierung steigert die Profitabilität „Wir haben uns hauptsächlich wegen der Performance für Algolia entschieden, was unsere Bedenken zur Skalierbarkeit ausgeräumt hat. Zudem haben die optimierte Infrastruktur, Storage-Upgrades und die Produkt-Indexierung durch Algolia alle Sorgen hinsichtlich Geschwindigkeit beseitigt.“ Josh Hepworth Head of Technology @ Huckleberry Mehr erfahren „Für einen Händler wie uns, mit mehr als 25.000 Produkten im Katalog, ist eine Lösung, die jede Nutzererfahrung während der Suche verbessert, von enormem Wert. Die integrierte Personalisierung von Algolia ist ein echter Fortschritt im digitalen Zeitalter für Decathlon Singapur .“ Richard Migette Ecommerce Project Leader @ Decathlon Mehr erfahren „Algolia hat uns dabei geholfen, unser Hyperwachstum sehr effektiv zu managen – mit schneller Skalierung auf über 30.000 Produkte im Katalog, einer Steigerung der CTR in den Suchergebnissen um 81 % und einer Vervierfachung der Such-Conversions.“ Luis Aledo User Experience and CRO Manager @ PCComponentes FAQ – Advanced Personalization Wie viel kostet Advanced Personalization? 0 Details finden Sie auf unserer Preisseite. Ist Advanced Personalization datenschutzkonform? 0 Ja, Algolias Dienste entsprechen einer Vielzahl von Normen, Zertifizierungen und Vorschriften. Diese variieren je nach Service. Mehr erfahren . Kann Advanced Personalization auch außerhalb des E-Commerce eingesetzt werden? 0 Ja. Advanced Personalization kann auch für die Personalisierung von Suche, Browsing und Empfehlungen in Medien- oder SaaS-Unternehmen genutzt werden – egal ob B2C oder B2B . Kann Advanced Personalization auch außerhalb des E-Commerce eingesetzt werden? 0 Yes. Personalization applies across search, category pages, homepage recommendations, and even product detail pages—creating a consistent, optimized experience at every step of the customer journey. Kann Advanced Personalization auch außerhalb des E-Commerce eingesetzt werden? 0 Absolutely. Personalization can reorder or prioritize products on category pages based on each visitor’s preferences and behavior—helping them find what they’re looking for faster. Kann Advanced Personalization auch außerhalb des E-Commerce eingesetzt werden? 0 Personalized experiences reduce friction in the customer journey. When users find relevant products more quickly, they’re more likely to convert, repurchase, and stay loyal. Kann Advanced Personalization auch außerhalb des E-Commerce eingesetzt werden? 0 Yes. You can create personalization strategies for different user segments—like new vs. returning visitors, or high-value customers—ensuring the experience aligns with each group’s needs. Kann Advanced Personalization auch außerhalb des E-Commerce eingesetzt werden? 0 Even without login data, we personalize experiences using session-based signals like search terms, clicks, or category visits to surface relevant results from the very first interaction. Kann Advanced Personalization auch außerhalb des E-Commerce eingesetzt werden? 0 Definitely, whether your priority is increasing AOV, boosting discovery, or reducing bounce rates, you can fine-tune personalization strategies to align with your specific KPIs. Probieren Sie die KI-Suche aus, die versteht Demo anfordern Starten Sie kostenlos Lösungen Überblick AI Search AI Browse AI Recommendations Ask AI Intelligent Data Kit Anwendungsfälle Überclick Enterprise Suche Headless commerce Mobile Suche Sprachgesteuerte Suche Bildersuche OEM Bildersuche Entwickler Developer Hub Dokumentation Integrationen Engineering Blog Discord community API status DocSearch Für Open Source Demos GDPR AI Act Integrationen Salesforce Commerce Cloud B2C Shopify Adobe Commerce Netlify Commercetools BigCommerce Verteilt und Sicher Globale infrastruktur Sicherheit & Konformität Azure AWS Branchen Überclick B2C-E-Commerce B2B-E-Commerce Marktplätze SaaS Medien Startups Fashion Tools Search Grader Ecommerce Search Audit Unternehmen Über Algolia Karriere Newsroom Events Leitung Soziale Wirkung Kontact Kontact Kontact Soziales netwerk Entwickler Developer Hub Dokumentation Integrationen Engineering Blog Discord community API status DocSearch Für Open Source Demos GDPR AI Act Branchen Überclick B2C-E-Commerce B2B-E-Commerce Marktplätze SaaS Medien Startups Fashion Tools Search Grader Ecommerce Search Audit Lösungen Überblick AI Search AI Browse AI Recommendations Ask AI Intelligent Data Kit Anwendungsfälle Überclick Enterprise Suche Headless commerce Mobile Suche Sprachgesteuerte Suche Bildersuche OEM Bildersuche Integrationen Salesforce Commerce Cloud B2C Shopify Adobe Commerce Netlify Commercetools BigCommerce Verteilt und Sicher Globale infrastruktur Sicherheit & Konformität Azure AWS Unternehmen Über Algolia Karriere Newsroom Events Leitung Soziale Wirkung Kontact Kontact Kontact Soziales netwerk Algolia mark white ©2026 Algolia - All rights reserved. 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https://www.fine.dev/blog/ai-coding-guide#advice-for-front-end-developers | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
https://www.fine.dev/blog/ai-coding-tools-all#honeycomb | The Top AI Coding Tools and Assistants in 2024 Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back The Top AI Coding Tools and Assistants in 2024 Do you find yourself going crazy with all the different AI coding tools available? There are so many (here we list 32, but more are announced every week), it's hard to cut through the noise and understand which are the most useful AI coding tools. One thing is for certain: in today's fast-evolving software development landscape, AI coding tools are becoming essential for enhancing productivity, streamlining workflows, and improving code quality. Startups and agencies are looking to adopt the best AI coding tool to help them ship faster, better software and gain a competitive edge. This guide reviews 32 AI coding assistants available in 2024, discussing their features, pricing, and accessibility. Of course, we encourage you to check out Fine , the end-to-end AI coding tool designed to slot in to your team's collaborative workflows. Whilst many of the platforms listed focus on one aspect (code gen, testing, etc) - Fine is the AI Coding tool offering it all. Table of Contents Introduction Top 32 AI Coding Tools and Assistants Available for Immediate Use Fine.dev GitHub Copilot ChatGPT Amazon CodeWhisperer Tabnine Replit AI (Ghostwriter) Codiga Sourcery DeepCode (Snyk) CodeWP AIXcoder Cody (Sourcegraph) Figstack Android Studio Bot Amazon CodeGuru Security Mutable AI Ponicode Otter.ai Snyk Cursor Bolt Codium Qodo Void Editor Honeycomb Pear AI Magic AlphaCode Code Llama StableCode Visual Copilot Devin Conclusion FAQs Introduction Artificial Intelligence (AI) has revolutionized software development, with a plethora of coding tools now available to assist developers. Whether it's automating repetitive tasks, suggesting code improvements, or enhancing security, AI coding assistants have something to offer for every level of developer. Top 32 AI Coding Tools and Assistants Here’s a comprehensive list of the top AI coding assistants in 2024, divided into categories based on availability. Available for Immediate Use Fine - End-to-end AI coding assistant for every stage of the dev lifecycle, with full context awareness. Fine learns your codebase via the GitHub integration to minimize errors and maximize usefulness. It can turn issues into PRs; add docs, tests and logs; answer questions about your code; make revisions to PRs or summarize them; review your code and more. Based in the cloud, it's available via mobile as well as desktop. GitHub Copilot – Offers real-time code suggestions using OpenAI Codex. It helps developers write code more efficiently by predicting entire lines or blocks of code based on the context and the developer's intent. GitHub Copilot supports a wide range of programming languages and is integrated into popular development environments like Visual Studio Code, making it accessible and easy to use. Available plans start at $10/month. Pricing : $10/month (individual), $19/month (business) ChatGPT – Versatile AI assistant capable of code generation and debugging. A free version is available, while ChatGPT Plus costs $20/month. ChatGPT doesn't integrate with your codebase, so you'll need to copy and paste between your editor and the site. Pricing : Free, $20/month for Plus Amazon CodeWhisperer – Integrates seamlessly with AWS services, providing real-time code completions. Free tier available; Pro plan starts at $19/user per month. Pricing : Free, $19/user per month for Pro Tabnine – AI-powered code completion with a focus on privacy. Pricing : Free, $12/month for Pro Replit AI (Ghostwriter) – Collaborative cloud-based IDE offering code generation and debugging features, particularly useful for those with no coding experience or already using Replit. Pricing : $10/month for Core, $33/user per month for Teams Codiga – Real-time static code analysis tool with a free tier; Pro plan costs $14/month. Pricing : Free, $14/month for Pro Sourcery – AI code reviewer. Improves code quality through automated refactoring. Uses GPT4-turbo Pricing : Free for open-source, $12/month for Pro DeepCode (Snyk) – Detects security vulnerabilities in real-time. Free for individuals, with team plans starting at $27/month. Pricing : Free for individuals, $27/month per user CodeWP – AI-powered code generator specifically for WordPress. Pricing starts at $18/month. Pricing : Free, $18/month for Pro AIXcoder – Offers intelligent code completion with support for multiple IDEs. Free and custom enterprise plans available. Pricing : Free, custom pricing for enterprises Cody (Sourcegraph) – Supports project-wide code assistance, offering features like code navigation, large-scale search, and contextual help across entire projects, ensuring that developers can maintain consistency and quality across their entire codebase. Pricing : Free option available, paid plans start at $9 per month. Figstack – Assists with code documentation and optimization, priced at $10/month after a free trial. Pricing : $10/month after free trial Android Studio Bot – Available for free as part of Android Studio. Pricing : Free Amazon CodeGuru Security – Helps optimize code security, free for the first 90 days. Post-trial pricing is $10/month. Pricing : $10/month after first 90 days Mutable AI – Creates a wiki for your codebase. Pricing : Free for open source, basic plan starts at $2 per month. Snyk – Offers code and dependency vulnerability detection. Free for individuals; team plans start at $25/month. Pricing : Free for individuals, $27/month for teams Cursor – Cursor is a powerful AI coding assistant designed to streamline the software development process by providing intelligent code completions, contextual code suggestions, and explanations. It supports a wide range of programming languages and integrates smoothly with popular IDEs, making it an efficient tool for both individual developers and teams. Cursor aims to enhance productivity by reducing the time spent on repetitive coding tasks, offering automated code fixes, and facilitating collaboration. Free for basic use; premium features pricing varies. – Free for basic use; premium features pricing varies. Pricing : Varies Bolt – Although commonly described as a Cursor and V0 killer, Bolt seems to be a ChatGPT for front-end development. It's built by Stackblitz, the cloud-based web-development platform that lets you write, run and debug frontend code in your browser. Pricing : Free to start with paid subscriptions available in the app. Codeium – In-IDE AI coding assistant. Offers autocomplete, chat, and inline commands. Pricing : Free plan available, paid plans start at $10 per month. Qodo – AI coding tool that emphasis quality code, helping developers generate, test and review code. Pricing : Free version available, or $19 per month. Enterprise options available. Void Editor – Void describe themselves as an Open-Source alternative to Cursor offering greater privacy. Their logo seems similar to squarespace. Offers the ability to use the tab button to autocomplete the code you're writing - similar to GitHub Copilot. Waitlist access only, no pricing information available. Honeycomb – AI coding tool announced in August 2024 as a YC-backed startup, but the announcement and website have since disappeared. Still viewable on X . Pear AI – AI-powered coding assistant focused on improving development workflows, available at Pear AI. Built as a fork of Continue, which is a fork of VSCode, leading to controversy during their launch. Pricing : Free plan available requiring your own API keys. "Junior" plan for $15 per month includes limited credits for Claude and GPT4o with more credits available for purchase.. Magic – Requires a waitlist to access during the early access phase. AlphaCode – Limited to research and special projects. Code Llama – Open-source, but some hosted services may be restricted. Stable Code Alpha – Available as part of stability.ai membership. Visual Copilot – AI coding assistant for design-to-code. Import designs from Figma and turn into code. Free plan available with 4K context window and 20 code generations; Basic plan $19 per month, Growth plan $39 per month. Devin – Available only in early access; requires joining the waitlist. Conclusion AI coding tools continue to evolve, offering unique features to boost developer productivity. From real-time code suggestions to comprehensive security checks, developers can choose from a variety of options based on their needs and budget. FAQs Q: Are there any free AI coding tools? A: Yes, many tools offer free tiers or trials, including Fine. Q: How can I access Devin or Magic? A: Both tools require joining a waitlist for early access. Q: Are these tools suitable for beginners? A: Yes, many of these tools cater to all skill levels, providing resources and support for new developers. Important note: Information about platforms, their availability, features and pricing, is based on an automated internet search and may be inaccurate or out-of-date. Last updated: 2024-10-10 Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse dev.to staff for The DEV Team Posted on Aug 7, 2025 Congrats to the Algolia MCP Challenge Winners! # devchallenge # algoliachallenge # webdev # ai The wait is over! We are excited to announce the winners of the Algolia MCP Server Challenge . This was a tough one for our judges! From global sanctions indexes to stock analysis platforms to accessibility-focused search engines, we reviewed so many brilliant projects. The submissions from the community demonstrated the remarkable potential of combining Algolia's search capabilities with intelligent agents. Regardless of whether or not you won, we hope you learned a thing or two and are proud of what you accomplished. Thank you to everyone who particiapted! And now, our winners. Congratulations To… Overall Prompt Winner @justin_mc brings the world of competitive Pokemon to the next level with PokéBattle AI Strategist, an intelligent battle strategy assistant that transforms complex battle planning into natural language conversations. Gotta Search 'Em All, MCP!! Justin Mc ・ Jul 28 #devchallenge #algoliachallenge #webdev #ai The multi-index architecture searching across Pokemon, moves, and abilities simultaneously while maintaining relevance showcases exactly what makes AI-powered search so compelling ! Backend Data Optimization Winner @tammibriggs tackles one of development's most persistent challenges with AutoDoc, an AI agent that analyzes recent code commits, detects affected documentation pages, and intelligently suggests updates. Automate Doc Maintenance: Detect and Update Outdated Docs Using Algolia MCP Server, n8n, and LLM Taminoturoko Briggs ・ Jul 24 #devchallenge #algoliachallenge #webdev #ai The integration with n8n for workflow orchestration and GitHub for automated pull requests creates a complete solution that every development team needs! Ultimate User Experience Winner @dev_kiran pushes the boundaries of MCP integration with a modern, AI-powered Algolia MCP Client that connects to the Algolia MCP Server and uses LLMs to run queries, visualize data, and provide intelligent insights through a friendly chat interface. 🔥I Built Custom MCP Client For Algolia🌀 Kiran Naragund ・ Jul 27 #devchallenge #algoliachallenge #webdev #ai The custom theme inspired by Algolia's website and the structured response parsing that transforms Claude's outputs into rich, interactive content showcases exceptional attention to both technical depth and user experience! Our three winners will each receive $1,000 USD, DEV++ Membership , and an exclusive DEV badge. All participants with a valid submission will receive a completion badge for taking on this challenge! Our Sponsor We're incredibly grateful to Algolia , one of our amazing Diamond Sponsors, for making this challenge possible! Their technology enables developers worldwide to build fast, intelligent search experiences—including right here on DEV . We hope they're your first choice when it comes to all things search! What's next? We can't wait to bring you another Algolia Challenge soon! In the mean time, we hope you check out our other challenges and stay updated by following our dedicated challenge tag: # devchallenge Follow This is the official tag for submissions and announcements related to DEV Challenges. We hope you had fun exploring the intersection of AI and search, see you next time! Interested in being a volunteer judge for future challenges? Learn more here ! Top comments (21) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Collapse Expand Jess Lee The DEV Team Jess Lee The DEV Team Jess Lee Follow Building DEV and Forem with everyone here. Interested in the future. Email jess@forem.com Location USA / TAIWAN Pronouns she/they Work Co-Founder & COO at Forem Joined Jul 29, 2016 • Aug 7 '25 Dropdown menu Copy link Hide Congrats on the win @justin_mc , @tammibriggs , and @dev_kiran 👏 👏 👏 Like comment: Like comment: 6 likes Like Comment button Reply Collapse Expand Kiran Naragund Kiran Naragund Kiran Naragund Follow Tech Writer and Moderator @DEV ✦ Full-Stack Developer ✦ Mentor @Exercism ✦ Open-Source Contributor ✦ Email for Collabs :) Email kirannaragund197@gmail.com Location India Education B.Tech in Computer Science & Engineering Pronouns He/Him Work Full Stack Developer Joined Nov 8, 2023 • Aug 8 '25 Dropdown menu Copy link Hide Thank you so much Jess!! Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand Taminoturoko Briggs Taminoturoko Briggs Taminoturoko Briggs Follow I am an enthusiastic Software developer and also a Technical writer. Email godsfirstbriggs@gmail.com Location Nigeria Education University of Porthacourt Work Software development Joined Aug 30, 2021 • Aug 7 '25 Dropdown menu Copy link Hide Thanks! Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Justin Mc Justin Mc Justin Mc Follow Joined Jul 10, 2025 • Aug 7 '25 Dropdown menu Copy link Hide Thank you!!! Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Nikoloz Turazashvili (@axrisi) Follow Founder & CTO at NikoLabs LLC, building Axrisi—an AI-powered browser extension for seamless on-page text processing and productivity. Opened Chicos restaurant in Tbilisi, Georgia. Email turazashvili@gmail.com Location Tbilisi, Georgia Education EXCELIA La Rochelle Pronouns He/Him Work Founder & CTO at NikoLabs LLC and Axrisi Joined May 30, 2025 • Aug 8 '25 Dropdown menu Copy link Hide Joining Jess! Congrats everyone! :) Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Apoorv Gupta Apoorv Gupta Apoorv Gupta Follow Writing bugs at scale, calling them “features.” Location Pune, India Education VIT Vellore Pronouns He/Him Work Software Engineer Joined Jun 28, 2025 • Aug 9 '25 Dropdown menu Copy link Hide congrats @justin_mc , @tammibriggs , @dev_kiran . Great Work Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand James Achuil James Achuil James Achuil Follow Joined Apr 9, 2025 • Aug 8 '25 Dropdown menu Copy link Hide Congratulations @justin_mc , @tammibriggs , and @dev_kiran 👏 👏 👏 Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand Fayaz Fayaz Fayaz Follow Software Engineer 𑁍 Thinker 𑁍 Problem Solver. Interests: AI, Software Development, Web Security, Privacy, Nature, Philosophy, History, Spirituality, Politics, Conversation. Location Bangladesh Education BSc. in Computer Science & Engineering Work Building a new SaaS Joined Nov 12, 2017 • Aug 8 '25 Dropdown menu Copy link Hide Congrats to the winners! @justin_mc , @tammibriggs , and @dev_kiran 🏆 Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand Kiran Naragund Kiran Naragund Kiran Naragund Follow Tech Writer and Moderator @DEV ✦ Full-Stack Developer ✦ Mentor @Exercism ✦ Open-Source Contributor ✦ Email for Collabs :) Email kirannaragund197@gmail.com Location India Education B.Tech in Computer Science & Engineering Pronouns He/Him Work Full Stack Developer Joined Nov 8, 2023 • Aug 8 '25 Dropdown menu Copy link Hide Thanks Fayaz :) Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Varshith V Hegde Varshith V Hegde Varshith V Hegde Follow A simple programmer fond of learning Email varshithvh@gmail.com Location Mangalore Education Mangalore Institute of Technology and Engineering Work Software Engineer@KPIT Joined Jun 30, 2022 • Aug 8 '25 • Edited on Aug 8 • Edited Dropdown menu Copy link Hide Congrats to all the winner @dev_kiran loved your app idea and exceution 😎 Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Kiran Naragund Kiran Naragund Kiran Naragund Follow Tech Writer and Moderator @DEV ✦ Full-Stack Developer ✦ Mentor @Exercism ✦ Open-Source Contributor ✦ Email for Collabs :) Email kirannaragund197@gmail.com Location India Education B.Tech in Computer Science & Engineering Pronouns He/Him Work Full Stack Developer Joined Nov 8, 2023 • Aug 8 '25 Dropdown menu Copy link Hide Thanks Varshith :) Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand KAOUTAR BENHADINE KAOUTAR BENHADINE KAOUTAR BENHADINE Follow Software Engineer Location Morocco Joined Jul 6, 2025 • Aug 7 '25 Dropdown menu Copy link Hide Congrats 👏 👏 👏 Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Kiran Naragund Kiran Naragund Kiran Naragund Follow Tech Writer and Moderator @DEV ✦ Full-Stack Developer ✦ Mentor @Exercism ✦ Open-Source Contributor ✦ Email for Collabs :) Email kirannaragund197@gmail.com Location India Education B.Tech in Computer Science & Engineering Pronouns He/Him Work Full Stack Developer Joined Nov 8, 2023 • Aug 8 '25 Dropdown menu Copy link Hide Thanks :) Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Dilum Darshana Dilum Darshana Dilum Darshana Follow Full Stack Developer (Node.js, React.js and AWS) | Exploring Generative AI Email dilum.dar@gmail.com Location Sri Lanka Work Functional Programmer Joined May 15, 2023 • Aug 8 '25 Dropdown menu Copy link Hide Congratulations winners!!! 👏 Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand Oscar Oscar Oscar Follow full-stack web dev specializing in backend development, content creator, low level programmer (aka Rust and C enjoyer). nvim & arch user Email oscar.gaske.cs@gmail.com Location United States, Virginia Education University of Mary Washington (current student) Work Founding SWE @ Todd Agriscience Joined May 9, 2023 • Aug 8 '25 Dropdown menu Copy link Hide Congratulations to the winners! Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand Chuck Meyer Chuck Meyer Chuck Meyer Follow API driven. DevRel 🥑 at Algolia. Location Columbus, OH Education NKU, WPI Pronouns he/him Work Sr Manager, Developer Relations at Algolia Joined Aug 4, 2021 • Aug 8 '25 Dropdown menu Copy link Hide Way to go! And thanks to everyone who submitted to the Hackathon. I gained a lot of insights on MCP use cases from all of you! Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Taminoturoko Briggs Taminoturoko Briggs Taminoturoko Briggs Follow I am an enthusiastic Software developer and also a Technical writer. Email godsfirstbriggs@gmail.com Location Nigeria Education University of Porthacourt Work Software development Joined Aug 30, 2021 • Aug 7 '25 Dropdown menu Copy link Hide Wow 😯😯🤯. Awesome! Like comment: Like comment: 2 likes Like Comment button Reply View full discussion (21 comments) Some comments may only be visible to logged-in visitors. Sign in to view all comments. Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse The DEV Team Follow The hardworking team behind DEV ❤️ Want to contribute to open source and help make the DEV community stronger? The code that powers DEV is called Forem and is freely available on GitHub. You're welcome to jump in! Contribute to Forem More from The DEV Team Congrats to the AI Agents Intensive Course Writing Challenge Winners! # googleaichallenge # devchallenge # ai # agents Join the Algolia Agent Studio Challenge: $3,000 in Prizes! # algoliachallenge # devchallenge # agents # webdev Congrats to the Xano AI-Powered Backend Challenge Winners! # xanochallenge # backend # api # ai 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . DEV Community © 2016 - 2026. We're a place where coders share, stay up-to-date and grow their careers. Log in Create account | 2026-01-13T08:49:45 |
https://docs.devcycle.com/platform/security-and-guardrails/sdk-visibility | SDK Visibility | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up Home Getting Started Essentials DevCycle Overview Key Features System Architecture Feature Hierarchy Feature Types Platform Feature Flags Experimentation Account Management Security and Guardrails Approval Workflows Audit Log Custom Property Schemas Feature Obfuscation Roles & Permissions SDK Visibility Variable Schemas Testing and QA Extras Examples Platform Security and Guardrails SDK Visibility On this page SDK Visibility Typically, DevCycle Variables are served to any SDK where the identified user matches the configured set of targeting rules. This behaviour is suitable for most applications. However, there are cases where a project may contain server-side Features that should never have their Variables served to client and/or mobile SDKs. It can become cumbersome and risky to ensure Targeting Rules exclude those SDKs in every server-only Feature. For this purpose, DevCycle supports an "SDK Visiblity" setting. Setting this allows for control over which SDK types can receive a Feature. Only SDKs corresponding to the checked types will receive the Feature in the configuration, and Targeting Rules will not be evaluated for unselected platforms. Evaluating a Variable on unselected SDK platforms will result in the default value being served. The setting defaults to "Server" SDKs only on every new Feature, ensuring a safe default that must be explicitly changed to expose a Feature to clients. How to enable To use SDK visibility settings, the feature must first be enabled from the Edit Project page. Once enabled, the setting will appear on the Feature page, and you can control which SDK types can use the Feature. Edit this page Last updated on Jan 9, 2026 Previous Roles & Permissions Next Variable Schemas How to enable DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://www.fine.dev/blog/ai-coding-guide#1-automated-bug-fixes | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
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https://www.fine.dev/blog/ai-coding-guide#incorporating-ai-tools-into-your-workflow | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
https://www.fine.dev/blog/ai-coding-guide#practical-instructions-for-providing-context-to-ai-coding-tools | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
https://docs.devcycle.com/integrations/vscode-extension/ | README | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page DevCycle VSCode Extension Extension Installation DevCycle extension can be installed directly within Visual Studio Code or via the Visual Studio Code Marketplace. Visual Studio Code Marketplace Visit the DevCycle Feature Flags Extension page at Visual Studio Marketplace. Click on the "Install" button. Within Visual Studio Code Search for "DevCycle Feature Flags" in the Extensions page. Click on the "Install" button. Post-installation, you can start utilizing the extension straightaway. No additional configuration is necessary. Feature Overview Current Features View All Feature Flags : The variable view in the extension displays a list of all variables existing within your code and your project. See Code Usages : The variable view also shows you where each of your DevCycle variables resides in your codebase, providing a convenient click-to-navigate feature. Understand Feature Status : Hovering over your DevCycle variables in your code brings up a card detailing information about the variable and the current status of the feature across environments. Requirements Before getting started with DevCycle, make sure you meet the following requirements: You need a DevCycle account. Sign up for a free account here (no credit card required). Extension Settings DevCycle extension contributes the following settings: Devcycle-feature-flags: Debug : Displays debug output for the extension, including what CLI commands are being executed. Default is off. Devcycle-feature-flags: Login On Workspace Open : Automatically logs into DevCycle when a configured workspace is opened. Default is on. Devcycle-feature-flags: Send Metrics : Allows DevCycle to send usage metrics. Default is off. Devcycle-feature-flags: Usages On Workspace Open : Automatically checks for code usages when a configured workspace is opened. Default is on. Upcoming Features We're excited about the future of DevCycle! Many advanced features are under development to further enhance the capabilities of the DevCycle extension. To stay updated on our progress, keep checking our GitHub repository and official website. Edit this page Extension Installation Visual Studio Code Marketplace Within Visual Studio Code Feature Overview Current Features Requirements Extension Settings Upcoming Features DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://docs.devcycle.com/cli-mcp/mcp-reference#local-mcp-server-installation | MCP Reference | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up CLI / MCP Overview CLI CLI Reference CLI User Guides Projects Environments SDK Keys Features Variables Variations Targeting Rules Self-Targeting CLI User Guides MCP MCP Getting Started MCP Reference MCP User Guides Incident Investigation MCP MCP Reference On this page DevCycle MCP Reference This page provides comprehensive documentation for all DevCycle MCP tools. For setup instructions, see MCP Getting Started . Available Tools The DevCycle MCP Server provides comprehensive feature flag management capabilities through tools organized into 6 categories : Feature Management Variable Management Project Management Self-Targeting & Overrides Results & Analytics SDK Installation info Production Safety Tools marked ⚠️ can affect production - confirm before proceeding Tools marked ⚠️⚠️ are destructive - require extra confirmation Feature Management list_features List all features in the current project with optional search and pagination. Parameters: search (optional): Search query to filter features (minimum 3 characters) page (optional): Page number (default: 1) perPage (optional): Items per page (default: 100, max: 1000) sortBy (optional): Sort field ( createdAt , updatedAt , name , key , createdBy , propertyKey ) sortOrder (optional): Sort order ( asc , desc ) staleness (optional): Filter by staleness ( all , unused , released , unmodified , notStale ) createdBy (optional): Filter by creator user ID type (optional): Feature type ( release , experiment , permission , ops ) status (optional): Feature status ( active , complete , archived ) create_feature ⚠️ Create a new feature flag. Parameters: key : Unique feature key (pattern: ^[a-z0-9-_.]+$ ) name : Human-readable name (max 100 chars) description (optional): Feature description (max 1000 chars) type (optional): Feature type ( release , experiment , permission , ops ) tags (optional): Array of tags for organization variations (optional): Array of variations with key, name, and variables configurations (optional): Environment-specific configurations sdkVisibility (optional): SDK visibility settings variables (optional): Array of variables to create or reassociate with this feature controlVariation (optional): The key of the variation that is used as the control for Metrics settings (optional): Feature-level settings configuration update_feature ⚠️ Update an existing feature flag. Parameters: key : Feature key to update name (optional): New name description (optional): New description type (optional): New type tags (optional): New tags variations (optional): Updated variations variables (optional): Updated array of variables for this feature settings (optional): Updated feature-level settings configuration sdkVisibility (optional): Updated SDK visibility settings controlVariation (optional): Updated control variation key for Metrics update_feature_status ⚠️ Update the status of a feature flag. Parameters: key : Feature key status : New status ( active , complete , archived ) staticVariation (optional): Variation to serve if status is complete delete_feature ⚠️⚠️ Delete a feature flag from ALL environments. Parameters: key : Feature key to delete cleanup_feature Fetch the DevCycle Feature Cleanup prompt and return its markdown content to guide safe cleanup of a completed feature and its variables in codebases. Parameters: featureKey : The feature key you plan to clean up (used for context in the prompt) get_feature_audit_log_history Get feature flag audit log history from DevCycle. Returns audit log entities matching the DevCycle API schema with date, a0_user, and changes fields. Parameters: feature_key : Feature key page (optional): Page number for pagination (default: 1) perPage (optional): Number of items per page (default: 100, max: 1000) sortBy (optional): Field to sort by ( createdAt , updatedAt , action , user ) (default: createdAt ) sortOrder (optional): Sort order ( asc , desc ) (default: desc ) startDate (optional): Start date for filtering (ISO 8601 format) endDate (optional): End date for filtering (ISO 8601 format) environment (optional): Environment key to filter by user (optional): User ID to filter by action (optional): Action type to filter by Variable Management list_variables List all variables in the current project. Parameters: search (optional): Search query page (optional): Page number perPage (optional): Items per page create_variable ⚠️ Create a new variable. Parameters: key : Unique variable key (pattern: ^[a-z0-9-_.]+$ ) type : Variable type ( String , Boolean , Number , JSON ) name (optional): Variable name description (optional): Variable description defaultValue (optional): Default value _feature (optional): Associated feature key validationSchema (optional): Validation rules update_variable ⚠️ Update an existing variable. Parameters: key : Variable key to update name (optional): New name description (optional): New description type (optional): New type validationSchema (optional): New validation rules delete_variable ⚠️⚠️ Delete a variable from ALL environments. Parameters: key : Variable key to delete SDK Installation install_devcycle_sdk Fetch DevCycle SDK installation instructions and follow the guide to install the SDK. Includes documentation and examples for using the SDK in your application. Parameters: guide : One of android , android-openfeature , angular , dotnet , dotnet-openfeature , flutter , go , go-openfeature , ios , ios-openfeature , java , java-openfeature , javascript , javascript-openfeature , nestjs , nestjs-openfeature , nextjs , nodejs , nodejs-openfeature , php , php-openfeature , python , python-openfeature , react , react-native , react-openfeature , roku , ruby , ruby-openfeature Project Management list_projects List all projects in the organization. Parameters: search (optional): Search query page (optional): Page number (default: 1) perPage (optional): Items per page (default: 100, max: 1000) sortBy (optional): Sort field ( createdAt , updatedAt , name , key , createdBy ) sortOrder (optional): Sort order ( asc , desc ) createdBy (optional): Filter by creator user ID get_current_project Get details of the currently selected project. Parameters: None select_project Select a project to use for subsequent MCP operations. Returns the current project, its environments, and SDK keys. Parameters: projectKey (optional): Project key to select (if omitted, lists available projects to choose from) Self-Targeting & Overrides get_self_targeting_identity Get current DevCycle identity for self-targeting. Parameters: None update_self_targeting_identity Update DevCycle identity for testing. Parameters: dvc_user_id : DevCycle User ID (use empty string to clear) list_self_targeting_overrides List all active overrides for the current project. Parameters: None set_self_targeting_override ⚠️ Set an override to test a specific variation. Parameters: feature_key : Feature key environment_key : Environment key variation_key : Variation to serve clear_feature_self_targeting_overrides ⚠️ Clear overrides for a specific feature/environment. Parameters: feature_key : Feature key environment_key : Environment key Results & Analytics get_feature_total_evaluations Get total variable evaluations per time period for a specific feature. Parameters: featureKey : Feature key startDate (optional): Start date as Unix timestamp (milliseconds since epoch) endDate (optional): End date as Unix timestamp (milliseconds since epoch) platform (optional): Platform filter for evaluation results variable (optional): Variable key filter for evaluation results environment (optional): Environment key to filter results period (optional): Time aggregation period ( day , hour , month ) sdkType (optional): Filter by SDK type ( client , server , mobile , api ) get_project_total_evaluations Get total variable evaluations per time period for the entire project. Parameters: startDate (optional): Start date as Unix timestamp (milliseconds since epoch) endDate (optional): End date as Unix timestamp (milliseconds since epoch) platform (optional): Platform filter for evaluation results variable (optional): Variable key filter for evaluation results environment (optional): Environment key to filter results period (optional): Time aggregation period ( day , hour , month ) sdkType (optional): Filter by SDK type ( client , server , mobile , api ) Authentication Methods 1. CLI Authentication (Recommended for local development) dvc login sso dvc projects select 2. Environment Variables (Recommended for CI/CD) export DEVCYCLE_CLIENT_ID="your-client-id" export DEVCYCLE_CLIENT_SECRET="your-client-secret" export DEVCYCLE_PROJECT_KEY="your-project-key" Advanced Configuration Local MCP Server Installation For users who prefer to run the DevCycle MCP server locally rather than using the hosted version, you can install and configure the local server: Prerequisites Node.js 18+ installed DevCycle CLI installed globally: npm install -g @devcycle/cli DevCycle account with API credentials or SSO authentication Installation Install the DevCycle CLI which includes the local MCP server: npm install -g @devcycle/cli Authentication Choose one of the following authentication methods: Option 1: CLI Authentication (Recommended for local development) # Authenticate via SSO dvc login sso # Select your project dvc projects select Option 2: Environment Variables (Recommended for CI/CD) export DEVCYCLE_CLIENT_ID="your-client-id" export DEVCYCLE_CLIENT_SECRET="your-client-secret" export DEVCYCLE_PROJECT_KEY="your-project-key" AI Editor Configuration Cursor VS Code Claude Code Claude Desktop Windsurf Codex CLI Gemini CLI Add the following to your ~/.cursor/mcp_settings.json file: { "mcpServers" : { "devcycle" : { "command" : "dvc-mcp" } } } Add the following to your settings.json file: { "mcp.servers" : { "devcycle" : { "command" : "dvc-mcp" } } } Run the following command: claude mcp add --transport stdio devcycle dvc-mcp Locate and edit your Claude Desktop configuration file: macOS : ~/Library/Application Support/Claude/claude_desktop_config.json Windows : %APPDATA%\Claude\claude_desktop_config.json Add the following configuration: { "mcpServers" : { "devcycle" : { "command" : "dvc-mcp" } } } In Windsurf Settings → Cascade → Manage MCPs → View raw config: { "mcpServers" : { "devcycle" : { "command" : "dvc-mcp" } } } Locate and edit your configuration file at ~/.codex/config.toml : [mcp_servers.devcycle] command = "dvc-mcp" For more details, see the OpenAI Codex MCP documentation . Locate and edit your configuration file at ~/.gemini/settings.json : { "mcpServers" : { "devcycle" : { "command" : "dvc-mcp" } } } For more details, see the Gemini CLI MCP documentation . Error Handling The MCP server returns structured error responses: { "errorType" : "AUTHENTICATION_ERROR" , "errorMessage" : "401 Unauthorized" , "toolName" : "list_features" , "suggestions" : [ "Re-authenticate with DevCycle (run \"dvc login sso\" for CLI for local MCP or re-login through OAuth for remote MCP)" , "Verify your API credentials are correct" , "Check if your token has expired" ] , "timestamp" : "2025-07-01T00:00:00.000Z" } Fields: errorType : One of AUTHENTICATION_ERROR , PERMISSION_ERROR , RESOURCE_NOT_FOUND , VALIDATION_ERROR , SCHEMA_VALIDATION_ERROR , RATE_LIMIT_ERROR , NETWORK_ERROR , PROJECT_ERROR , UNKNOWN_ERROR . errorMessage : Human-readable error description. toolName : The MCP tool that produced the error. suggestions : Remediation steps tailored to the error type. timestamp : ISO 8601 timestamp when the error was generated. Common error scenarios: Authentication failures : Check credentials and project configuration API rate limits : Implement retry logic in your automation Validation errors : Ensure parameters meet requirements (patterns, lengths, etc.) Permission errors : Verify your API key has necessary permissions Development & Local Testing Running from Source # Clone the repository git clone https://github.com/DevCycleHQ/cli.git cd cli # Install dependencies yarn install # Build the project yarn build # Run the MCP server node dist/mcp/index.js Testing with AI Assistants For local testing, update your AI assistant configuration to point to the local build: { "mcpServers" : { "devcycle" : { "command" : "node" , "args" : [ "/path/to/cli/dist/mcp/index.js" ] } } } Debug Logging The MCP server logs all operations to stderr, which can be viewed in: Cursor: Developer Tools console Claude Desktop: Log files in the application support directory Environment Variables for Development # Enable verbose logging export DEBUG=1 # Use specific DevCycle API endpoint export DEVCYCLE_API_URL="https://api.devcycle.com" Getting Help GitHub Issues : GitHub Issues General Documentation : DevCycle Docs DevCycle Community : Discord Support : Contact Support Edit this page Last updated on Jan 9, 2026 Previous MCP Getting Started Next Incident Investigation Available Tools Feature Management Variable Management SDK Installation Project Management Self-Targeting & Overrides Results & Analytics Authentication Methods 1. CLI Authentication (Recommended for local development) 2. Environment Variables (Recommended for CI/CD) Advanced Configuration Local MCP Server Installation Error Handling Development & Local Testing Running from Source Testing with AI Assistants Debug Logging Environment Variables for Development Getting Help DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://vibe.forem.com/dipti_moryani_08e62702314/fields-not-supported-for-clustering-in-tableau-1ln5 | Fields Not Supported for Clustering in Tableau - Vibe Coding Forem Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account Vibe Coding Forem Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Dipti Moryani Posted on Dec 23, 2025 Fields Not Supported for Clustering in Tableau # ai # database In today’s data-driven world, simply visualizing data is no longer enough. Business leaders want answers to deeper questions: Which customers behave similarly? Which products belong to the same performance group? Which regions show comparable growth patterns? This is where clustering becomes a powerful analytical technique. Tableau makes advanced clustering accessible—even for users without a strong statistical background—by embedding it directly into its visual analytics workflow. In this article, we will explore: What clustering is and why it matters How Tableau implements clustering Step-by-step clustering using Tableau How to interpret cluster quality and statistics Practical business use cases Limitations and best practices Let’s dive in. What Is Clustering? Clustering is an unsupervised machine learning technique used to group similar observations or data points based on shared characteristics. Unlike classification, clustering does not rely on predefined labels. Instead, it discovers natural groupings hidden within the data. A Simple Business Example Consider a car manufacturer analyzing customer preferences: Cluster 1: Customers looking for small cars under $6,000 Cluster 2: Customers interested in mid-range sedans Cluster 3: Customers willing to spend $30,000+ on premium SUVs By identifying these clusters, the manufacturer can: Optimize product design Tailor marketing campaigns Improve pricing strategies Forecast demand more accurately Clustering helps businesses move from intuition-based decisions to data-backed segmentation. How Tableau Performs Clustering Tableau uses the K-means clustering algorithm, one of the most widely used clustering methods. How K-means Works (Conceptually) You specify the number of clusters (k) Tableau places k centroids (center points) Each data point is assigned to the nearest centroid Centroids are recalculated based on assigned points Steps 3 and 4 repeat until clusters stabilize The objective is to minimize the total distance between data points and their respective cluster centroids. The beauty of Tableau is that all of this happens behind the scenes—allowing analysts to focus on interpretation rather than computation. Getting Started: Preparing the Dataset To demonstrate clustering, download the sample dataset provided in the link you referenced (for example, a flower dataset containing petal length, petal width, and species). Once downloaded: Open Tableau Desktop Load the dataset Review the available dimensions and measures The dataset contains measurements of flowers across three species, making it ideal for clustering demonstrations. Creating the Initial Visualization Let’s begin by visualizing the relationship between two measures: Petal Length Petal Width Steps: Drag Petal Length to Columns Drag Petal Width to Rows By default, Tableau aggregates measures, resulting in a single point. To see individual observations: Go to Analysis in the top menu Uncheck Aggregate Measures You should now see a scatter plot representing individual data points. This visual foundation is critical—clustering in Tableau always starts from an existing visualization. Applying Clustering in Tableau Now comes the fun part. Steps to Add Clusters: Open the Analytics pane Drag Cluster onto the scatter plot Drop it anywhere in the view Tableau automatically: Chooses an initial number of clusters Uses the measures present in the view Assigns each point to a cluster At this stage, Tableau gives you a working model—but not necessarily the best one. Customizing Clusters Tableau allows full control over clustering behavior. Adjusting the Number of Clusters Click the Clusters pill Increase or decrease the number of clusters Observe how cluster boundaries change Selecting Variables for Clustering You can explicitly define which measures Tableau should use: Drag additional measures into the clustering configuration Remove irrelevant measures to avoid noise This flexibility allows you to align clusters with specific business questions. Understanding Cluster Quality: Model Description Clustering is only useful if it’s statistically meaningful. Tableau provides transparency through the “Describe Clusters” option: Click the cluster pill Select Describe Clusters This opens a detailed summary including: Variables used Cluster centers Statistical significance metrics Let’s break down the two most important metrics. Key Statistical Metrics Explained F-Statistic (F-Ratio) The F-statistic measures how well a variable distinguishes between clusters. F=Between-Group VariabilityWithin-Group VariabilityF = \frac{\text{Between-Group Variability}}{\text{Within-Group Variability}}F=Within-Group VariabilityBetween-Group Variability Higher F-values indicate stronger differentiation Variables with low F-values contribute little to cluster separation P-Value The p-value measures statistical significance: A low p-value means the variable significantly differentiates clusters Typically, p-values below 0.05 are considered meaningful Together, F-statistic and p-value help validate whether clusters are meaningful—or just visually appealing noise. Saving Clusters for Further Analysis Clusters don’t have to remain temporary. You can: Drag the Cluster field from the Marks card Drop it into Dimensions This converts clusters into a reusable group that can be: Filtered Used in dashboards Combined with other dimensions Analyzed across different views Fields Not Supported for Clustering in Tableau Tableau restricts certain fields from clustering to preserve statistical integrity: Dates Bins Sets Table Calculations Blended Calculations Ad-hoc Calculations Parameters Generated Latitude/Longitude Understanding these limitations helps avoid confusion when configuring clusters. A Second Example: Clustering Countries Using World Indicators Tableau’s World Indicators sample dataset is perfect for real-world clustering. Example Scenario: Cluster countries based on: Life expectancy Population over age 65 Urban population percentage Steps: Open World Indicators sample workbook Create a map or scatter plot using these measures Apply clustering from the Analytics pane Insights You Can Derive: Identify aging economies Spot developing vs developed regions Compare healthcare and urbanization trends You can also: Select a cluster Switch to a text table View all countries belonging to that cluster This makes clustering highly actionable for policy analysis, market entry decisions, and global strategy. Business Use Cases of Clustering in Tableau Clustering can be applied across industries: Marketing: Customer segmentation and personalization Finance: Risk profiling and portfolio grouping Healthcare: Patient stratification Retail: Product and store performance analysis Operations: Supplier and logistics optimization The key is not just creating clusters—but interpreting and acting on them. Best Practices for Effective Clustering Start with clear business questions Normalize data when scales vary widely Avoid too many variables at once Validate clusters using statistical metrics Always interpret clusters in business context Clustering is exploratory by nature—iteration is expected. Conclusion Clustering in Tableau bridges the gap between advanced analytics and business usability. With just a few drag-and-drop actions, you can uncover hidden structures, segment data meaningfully, and generate insights that directly support decision-making. While this article covered only a few scenarios, the true power of clustering lies in experimentation and interpretation. Try different datasets, vary measures, and question the patterns you observe. Keep exploring. Keep practicing. And most importantly—let your data tell its story. Happy Clustering! At Perceptive Analytics, our mission is “to enable businesses to unlock value in data.” For over 20 years, we’ve partnered with more than 100 clients—from Fortune 500 companies to mid-sized firms—to solve complex data analytics challenges. Our services include delivering end-to-end tableau consulting services and working as a trusted Power BI Consulting Company , turning data into strategic insight. We would love to talk to you. Do reach out to us. Top comments (1) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Collapse Expand Daniel Algo Daniel Algo Daniel Algo Follow Joined Dec 22, 2025 • Dec 23 '25 Dropdown menu Copy link Hide Hi. I know about you on dev.co Now I am actively looking for a collaborator who can make an extra income with me. If you are interested in this opportunity, please let me know via whatsapp. +1 (415) 966-0362 Thank you. 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Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Dipti Moryani Follow Real Life Case Studies on Data and AI Location New York Pronouns She/her Work Digital Marketeer Joined Jul 30, 2025 More from Dipti Moryani Example: Clustering Flower Species Data # webdev # programming # ai # javascript Understanding Cluster Quality: Model Description # webdev # programming # ai # tutorial Assumptions for Moderation Analysis # webdev # programming # ai # tutorial 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV Vibe Coding Forem — Discussing AI software development, and showing off what we're building. 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https://docs.devcycle.com/integrations/feature-importer/ | DevCycle Feature Flag Importer | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page DevCycle Feature Flag Importer DevCycle's Feature Flag Importer is designed to import resources from other Feature Flag providers. The Importer is intended to be run on a single Project and will create or update a Project with the same key containing Environments, Features, and Variables. info The Feature Importer script is fully open-source. Check it out here: https://github.com/devcyclehq/feature-importer Tutorial Video Setup Clone repo from here . Run npm install to install dependencies. Setup configuration file . Run npm start to start an import. Configuration File The Feature Importer can be configured using environment variables or a JSON config file. By default the config is read from config.json in the project root, this can be overwritten using CONFIG_FILE_PATH . info The Feature Importer only supports LaunchDarkly API Version 20220603 . Please select this version when creating an API access token in LaunchDarkly. Required ldAccessToken : string LaunchDarkly access token. Used for pulling Feature Flags. Equivalent env var: LD_ACCESS_TOKEN dvcClientId : string DevCycle client ID. Used for fetching API credentials. Equivalent env var: DVC_CLIENT_ID dvcClientSecret : string DevCycle client secret. Used for fetching API credentials. Equivalent env var: DVC_CLIENT_SECRET sourceProjectKey : string LaunchDarkly's Project key. Resources will be pulled from this Project. Equivalent env var: SOURCE_PROJECT_KEY Optional targetProjectKey : string A DevCycle Project key. Resources will be created within this Project. A Project will be created with this key if it does not already exist. If not specified, the target Project key will be used Equivalent env var: TARGET_PROJECT_KEY includeFeatures : string[] An array of LD Feature Flag keys to be imported. By default, the Importer will attempt to migrate all Features. Equivalent env var: INCLUDE_FEATURES excludeFeatures : string[] An array of LD Feature Flag keys to be skipped when importing. Equivalent env var: EXCLUDE_FEATURES overwriteDuplicates : boolean If true, when the Importer encounters a duplicate resource it will be overwritten. By default, duplicates will be skipped. Equivalent env var: OVERWRITE_DUPLICATES operationMap : Map<string, string> A map of LD operations to map to DevCycle operations DevCycle operations: = , != , > , < , >= , <= , contain , !contain , exist , !exist Equivalent env var: OPERATION_MAP Sample config.json file: { "ldAccessToken" : "api-key" , "dvcClientId" : "clientId" , "dvcClientSecret" : "clientSecret" , "sourceProjectKey" : "project-key" , "includeFeatures" : [ "feat-1" , "feat-2" ] , "excludeFeatures" : [ ] , "overwriteDuplicates" : false , "operationMap" : { "startsWith" : "contain" , "endsWith" : "contain" } } Sample .env file: LD_ACCESS_TOKEN="api-key" DVC_CLIENT_ID="clientId" DVC_CLIENT_SECRET="clientSecret" SOURCE_PROJECT_KEY="project-key" INCLUDE_FEATURES=[feat-1,feat-2] EXCLUDE_FEATURES=[] OVERWRITE_DUPLICATES=false OPERATION_MAP='{"endsWith":"contain","startsWith":"contain"}' Code Migration Migrating Code from LaunchDarkly In LaunchDarkly, the primary identifier is key , in DVC the equivalent value should be passed as user_id DVC supports the following top-level properties on the user object: see DVC User Object . Any other properties used for Targeting should be passed within the customData map. If you are passing a date to be used with LD's before/after operators, the value should be converted to a Long when passed to DVC. The Importer will convert before & after operators to < & > in DVC. DVC doesn't support Targeting by the top-level isAnonymous property. If you are using LD's Targeting with the anonymous attribute, make sure to include an anonymous property in the user's customData Contributing to DevCycle or creating a new Integration: If you would like to contribute to an existing integration or tool, all of DevCycle's tools and integrations are open source on the DevCycle github repository. Further, if you'd like to create a new tool or integration, a great starting point is DevCycle's Management API which allows you to modify and interact with Features and more within a DevCycle Project, as well as the DevCycle Bucketing API which is used to give users Features and Variables (as used within the DevCycle SDKs!) Edit this page Last updated on Jan 9, 2026 Tutorial Video Setup Configuration File Code Migration Migrating Code from LaunchDarkly DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://www.python.org/downloads/release/python-3150a3/ | Python Release Python 3.15.0a3 | Python.org Notice: While JavaScript is not essential for this website, your interaction with the content will be limited. Please turn JavaScript on for the full experience. Skip to content ▼ Close Python PSF Docs PyPI Jobs Community ▲ The Python Network Donate ≡ Menu Search This Site GO A A Smaller Larger Reset Socialize LinkedIn Mastodon Chat on IRC Twitter About Applications Quotes Getting Started Help Python Brochure Downloads All releases Source code Windows macOS Android Other Platforms License Alternative Implementations Documentation Docs Audio/Visual Talks Beginner's Guide FAQ Non-English Docs PEP Index Python Books Python Essays Community Diversity Mailing Lists IRC Forums PSF Annual Impact Report Python Conferences Special Interest Groups Python Logo Python Wiki Code of Conduct Community Awards Get Involved Shared Stories Success Stories Arts Business Education Engineering Government Scientific Software Development News Python News PSF Newsletter PSF News PyCon US News News from the Community Events Python Events User Group Events Python Events Archive User Group Events Archive Submit an Event Python 3.15.0a3 Release date: Dec. 16, 2025 This is an early developer preview of Python 3.15 Major new features of the 3.15 series, compared to 3.14 Python 3.15 is still in development. This release, 3.15.0a3, is the third of seven planned alpha releases. Alpha releases are intended to make it easier to test the current state of new features and bug fixes and to test the release process. During the alpha phase, features may be added up until the start of the beta phase (2026-05-05) and, if necessary, may be modified or deleted up until the release candidate phase (2026-07-28). Please keep in mind that this is a preview release and its use is not recommended for production environments. Many new features for Python 3.15 are still being planned and written. Among the new major new features and changes so far: PEP 799 : A new high-frequency, low-overhead, statistical sampling profiler and dedicated profiling package PEP 686 : Python now uses UTF-8 as the default encoding PEP 782 : A new PyBytesWriter C API to create a Python bytes object Improved error messages (Hey, fellow core developer, if a feature you find important is missing from this list, let Hugo know.) The next pre-release of Python 3.15 will be 3.15.0a4, currently scheduled for 2026-01-13. More resources Online documentation PEP 790 , 3.15 release schedule Report bugs at https://github.com/python/cpython/issues Help fund Python directly (or via GitHub Sponsors ) and support the Python community And now for something completely different Instantly the captain ran forward, and in a loud voice commanded his crew to desist from hoisting the cutting-tackles, and at once cast loose the cables and chains confining the whales to the ship. “What now?” said the Guernsey-man, when the Captain had returned to them. “Why, let me see; yes, you may as well tell him now that—that—in fact, tell him I’ve diddled him, and (aside to himself) perhaps somebody else.” Enjoy the new release Thanks to all of the many volunteers who help make Python Development and these releases possible! 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Full Changelog Files macOS Download macOS installer Windows Download Python install manager Source release Download XZ compressed source tarball Version Operating System Description MD5 Checksum SHA256 Checksum File Size Sigstore SBOM Gzipped source tarball Source release 2d5db2f28324577846fad0fda7389994 n/a 40.6 MB .sigstore SPDX XZ compressed source tarball Source release d6ee7c74b9618520e88ec175f5539623 n/a 33.1 MB .sigstore SPDX Android embeddable package (aarch64) Android 6ea85f675dfd621ffbd20358a407238b n/a 20.9 MB .sigstore Android embeddable package (x86_64) Android f5aee27a4aba76592c212a6ef78c3593 n/a 21.2 MB .sigstore macOS installer macOS for macOS 10.15 and later 6d465ce19df3664f12fd19dfc18fa624 n/a 86.1 MB .sigstore Windows installer (64-bit) Windows Recommended b1aa535849569453127c2935b27b42c6 n/a 39.4 MB .sigstore SPDX Windows installer (32-bit) Windows 4d343f22b2fa2811b58b09054b1d16f4 n/a 38.0 MB .sigstore SPDX Windows installer (ARM64) Windows Experimental 1f279eeea133c7447eafef57e474a881 n/a 38.6 MB .sigstore SPDX Windows embeddable package (64-bit) Windows d99611ff8b887880885d10d796a57652 n/a 12.0 MB .sigstore SPDX Windows embeddable package (32-bit) Windows b0e29af281a5d37e521eea1d7a21da16 n/a 10.7 MB .sigstore SPDX Windows embeddable package (ARM64) Windows 07d3f41d0e25e1c220e3241e20091ef0 n/a 11.3 MB .sigstore SPDX Windows release manifest Windows Install with 'py install 3.15' 093048797aca9f65de6266059a518150 n/a 15.3 KB .sigstore ▲ Back to Top About Applications Quotes Getting Started Help Python Brochure Downloads All releases Source code Windows macOS Android Other Platforms License Alternative Implementations Documentation Docs Audio/Visual Talks Beginner's Guide FAQ Non-English Docs PEP Index Python Books Python Essays Community Diversity Mailing Lists IRC Forums PSF Annual Impact Report Python Conferences Special Interest Groups Python Logo Python Wiki Code of Conduct Community Awards Get Involved Shared Stories Success Stories Arts Business Education Engineering Government Scientific Software Development News Python News PSF Newsletter PSF News PyCon US News News from the Community Events Python Events User Group Events Python Events Archive User Group Events Archive Submit an Event Contributing Developer's Guide Issue Tracker python-dev list Core Mentorship Report a Security Issue ▲ Back to Top Help & General Contact Diversity Initiatives Submit Website Bug Status Copyright ©2001-2026. 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https://www.algolia.com/fr/products/ai-search | Recherche basée sur l’IA par Algolia | Algolia Niket --> Deutsch English français News DevCon2025 | October 1-2 Learn more Algolia Partners Support Login Logout Algolia mark white Algolia logo white Products Search Show users what they're looking for with AI-driven resuts. Search Show users what they're looking for with AI-driven resuts. Recommendations Use behavioral cues to drive higher engagement. Recommendations Use behavioral cues to drive higher engagement. Personalization Show each user what they need across their journey. Personalization Show each user what they need across their journey. Analytics All your insights in one dashboard. Analytics All your insights in one dashboard. Browse Move customers down the funnel with curated category pages. Browse Move customers down the funnel with curated category pages. Agent Studio Create, test, and deploy AI agents, fast. Agent Studio Create, test, and deploy AI agents, fast. 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We got you covered! Brand guidelines Download logo pack AI Search Une meilleure expérience de recherche La recherche basée sur l’IA la plus facile à utiliser. Demandez une démo Commencez maintenant Plus de 18 000 clients dans 150+ pays font confiance à Algolia Découvrez Algolia en action La solution tout-en-un pour la recherche IA Built for flexibility and scale, Algolia adapts to your architecture, workflows, and commercial goals. Facile à utiliser Implémentez nos API en quelques minutes et gardez la maîtrise de vos classements. See all industries Ultra-rapide Une recherche à la vitesse de frappe, avec la solution d’IA la plus rapide du marché. See all use cases Scalable Bénéficiez d’une infrastructure capable de gérer 30 milliards d’enregistrements et près de 1,7 trillion de recherches par an, avec une disponibilité de 99,999 %. See all departments Unsurpassed relevance drives revenue Deliver instantly relevant results with a hybrid keyword and vector retrieval engine that understands user intent and natural language. Real-time personalization adds another layer of intelligence so every visitor finds exactly what they’re looking for. Personnalisez la curation Donnez à vos équipes métier le pouvoir de façonner l’expérience de recherche. Grâce à nos outils de merchandising, créez vos campagnes et influencez le classement des résultats. Personnalisez la curation Donnez à vos équipes métier le pouvoir de façonner l’expérience de recherche. Grâce à nos outils de merchandising, créez vos campagnes et influencez le classement des résultats. Personnalisez la curation Donnez à vos équipes métier le pouvoir de façonner l’expérience de recherche. Grâce à nos outils de merchandising, créez vos campagnes et influencez le classement des résultats. Personnalisez la curation Donnez à vos équipes métier le pouvoir de façonner l’expérience de recherche. Grâce à nos outils de merchandising, créez vos campagnes et influencez le classement des résultats. Des fonctionnalités puissantes Everything you need to deploy AI-powered search. Crawler Extrayez et enrichissez vos contenus automatiquement. InstantSearch Concevez une expérience optimale grâce à une suite de composants UI prêts à l’emploi. Analytics Comprenez vos utilisateurs et révélez de nouvelles opportunités. A/B Testing Identifiez les stratégies de pertinence les plus performantes. Data Transformation Simplifiez la préparation de vos données et améliorez leur qualité. Rules Optimisez le classement pour des requêtes spécifiques. Search API Une recherche performante, quel que soit votre stack technologique. Guides d'achat Transformez instantanément votre catalogue produit en guides d’achat générés par IA. Custom Ranking Intégrez vos priorités business dans le classement. Merchandising Mettez en avant vos campagnes promotionnelles. Intégrations Indexez vos contenus depuis n’importe quelle source. Autocomplete Guidez vos utilisateurs avec une saisie prédictive et tolérante aux fautes. Intégrations fiables & partenariats Mise en place rapide grâce à des intégrations prêtes à l’emploi sur les plateformes les plus populaires. See all integrations Des solutions adaptées à tous les secteurs Add conversational search to your search bar, or build entirely new retrieval solutions powered by vector embeddings and LLMs. E-commerce B2C 0 Créez des expériences de recherche et de navigation personnalisées et flexibles, avec des recommandations adaptées à vos clients. En savoir plus sur le B2C e-commerce E-commerce B2B 0 Indexez votre catalogue, rendez-le opérationnel pour vos acheteurs et boostez vos conversions. En savoir plus sur le B2B e-commerce Marketplaces 0 Concevez des expériences de recherche performantes à grande échelle, tout en réduisant le temps de développement. En savoir plus sur les marketplaces MCP Server 0 Indexez vos contenus et offrez une meilleure découverte à vos utilisateurs. En savoir plus sur les médias FAQ – Algolia AI Search Qu’est-ce que l’AI Search et comment ça fonctionne ? 0 Algolia AI Search est une solution cloud, API-first, qui combine IA et recherche par mots-clés (NLP, autocomplete, tolérance aux fautes, similarité cosinus…). Elle comprend l’intention des utilisateurs et propose des résultats rapides et pertinents. Quelle est sa rapidité ? 0 Extrêmement rapide : 1 à 20 millisecondes par requête, soit jusqu’à 200x plus vite que la concurrence. Quels types de contenus peut-on indexer ? 0 Catalogues produits, articles de blog, FAQ, médias, images, données issues d’API… L’IA comprend aussi bien les mots que leur sens, sur des contenus courts ou longs. Pourquoi Algolia est différent des autres moteurs de recherche ? 0 Contrairement aux solutions opaques , Algolia associe une architecture API-first, des outils développeurs puissants, une scalabilité mondiale et des contrôles fins de pertinence. Vous gardez une visibilité et un contrôle total. Quelles fonctionnalités clés ? 0 Recherche sémantique, réglage de la pertinence par IA, embeddings vectoriels, matching hybride, personnalisation en temps réel, re-ranking dynamique, support multilingue. Pour quels secteurs ? 0 E-commerce, SaaS, médias, finance, marketplaces, mobile, headless commerce, recherche vocale, recherche d’images… Algolia optimise découverte, pertinence et conversion partout. Comment Algolia améliore les conversions ? 0 En proposant des résultats plus pertinents, plus vite. Moins de frictions, moins de “zéro résultat”, plus d’engagement et plus de ventes . Algolia gère-t-elle plusieurs langues ? 0 Oui, l’IA est multilingue, capable de comprendre l’intention à travers des dizaines de langues , sans créer une recherche par région. La personnalisation, comment ça marche ? 0 Algolia Personalization analyse les actions (clics, vues, achats) pour générer des profils d’affinité, appliqués en temps réel à la recherche, sans sacrifier la pertinence. Une personnalisation avancée est également disponible. Faut-il coder pour l’utiliser ? 0 Les développeurs disposent d’API et SDK flexibles, tandis que les équipes métier utilisent un tableau de bord low-code/no-code pour gérer la pertinence et analyser les performances. Est-ce scalable pour les sites à fort trafic ? 0 Oui. Algolia est conçue pour des sites à fort trafic, avec des temps de réponse inférieurs à 50 ms, même à grande échelle. Est-ce que la mise en place est rapide ? 0 En quelques minutes via nos API ou dashboard . Développeurs et non-techniciens peuvent personnaliser la pertinence, gérer le merchandising et suivre la performance facilement. Comment mesurer et optimiser ? 0 Avec notre dashboard Analytics intégré : taux de clics, requêtes sans résultat, impact de la personnalisation, A/B tests, règles de merchandising… tout pour optimiser en continu. Puis-je tester Algolia AI Search ? 0 Oui, via un essai gratuit : utilisez vos propres données ou nos exemples, testez toutes les fonctionnalités et mesurez l’impact avant de vous engager. Créez les meilleures expériences de recherche et de navigation Obtenir une démo Commencez gratuitement Solutions Aperçu AI Search AI Browse AI Recommendations Ask AI Intelligent Data Kit Cas d'usage Aperçu Recherche Enterprise Ecommerce headless Recherche mobile Recherche vocale Recherche d'image OEM Recherche d'image Développeurs Developer Hub Documentation Intégrations Engineering blog Communauté Discord Status d'API DocSearch Pour Open Source Demos GDPR AI Act Intégrations Salesforce Commerce Cloud B2C Shopify Adobe Commerce Netlify Commercetools BigCommerce Distribué & sécurisé Infrastructure mondiale Sécurité & conformité Azure AWS Industries Aperçu Ecommerce B2C Ecommerce B2B Marketplaces SaaS Média Startups Fashion Tools Search Grader Ecommerce Search Audit Algolia À propos Carrières Newsroom Évènements Équipe dirigeante Impact social Contact us Anti-Modern Slavery Statement Awards Réseaux sociaux Développeurs Developer Hub Documentation Intégrations Engineering blog Communauté Discord Status d'API DocSearch Pour Open Source Demos GDPR AI Act Industries Aperçu Ecommerce B2C Ecommerce B2B Marketplaces SaaS Média Startups Fashion Tools Search Grader Ecommerce Search Audit Solutions Aperçu AI Search AI Browse AI Recommendations Ask AI Intelligent Data Kit Cas d'usage Aperçu Recherche Enterprise Ecommerce headless Recherche mobile Recherche vocale Recherche d'image OEM Recherche d'image Intégrations Salesforce Commerce Cloud B2C Shopify Adobe Commerce Netlify Commercetools BigCommerce Distribué & sécurisé Infrastructure mondiale Sécurité & conformité Azure AWS Algolia À propos Carrières Newsroom Évènements Équipe dirigeante Impact social Contact us Anti-Modern Slavery Statement Awards Réseaux sociaux Algolia mark white ©2026 Algolia - All rights reserved. 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https://docs.devcycle.com/integrations/bitbucket/feature-usage-action/ | Bitbucket: Feature Flag Code Usages | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page Bitbucket: Feature Flag Code Usages Get the integration on the Bitbucket Marketplace Bitbucket Pipelines Pipe: DevCycle Code References Code References of DevCycle Variables pushed to DevCycle With this Bitbucket pipeline, your DevCycle dashboard will be updated to display code snippets for each DevCycle variable usage within your project. Note: This is intended to run when pushing changes to your main branch Example Output YAML Definition Add the following snippet to your bitbucket-pipelines.yml file: branches : main : - step : script : - pipe : devcyclehq/bitbucket - code - refs - pipe : 1.0.3 variables : PROJECT_KEY : '<string>' CLIENT_ID : '<string>' CLIENT_SECRET : '<string>' Variables To add variables to be used in the bitbucket-pipelines.yml , an admin must add Repository Variables in Repository Settings > Repository Variables , and then add all necessary variables as secured variables Variable Usage PROJECT_KEY (*) Your DevCycle project key, see Projects CLIENT_ID (*) Your organization's API client ID, see Organization Settings CLIENT_SECRET (*) Your organization's API client secret, see Organization Settings (*) = required variable. Prerequisites Create a new Project & a new Feature Grab the PROJECT_KEY in Projects , and find your specific project name & key Grab the CLIENT_ID in Settings , under API AUTHENTICATION Grab the CLIENT_SECRET in Settings , under API AUTHENTICATION Examples Example: - pipe : devcyclehq/bitbucket - code - refs - pipe : 1.0.3 variables : PROJECT_KEY : $PROJECT_KEY CLIENT_ID : $CLIENT_ID CLIENT_SECRET : $CLIENT_SECRET Support The pipe is maintained by [email protected] . If you are reporting an issue, please include: the version of the pipe relevant logs and error messages steps to reproduce Edit this page Example Output YAML Definition Variables Prerequisites Examples Support DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://docs.devcycle.com/integrations/slack/ | Integration for Slack | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page Integration for Slack This guide explains how to set up and use the DevCycle App for Slack. You can use the DevCycle App for Slack to keep track of and monitor feature flags within your team's Slack workspace. Setup Navigate to the Settings page in the DevCycle Dashboard, click on Integrations in the side navigation bar, and click View on the Integration for Slack. Click on the Add to Slack button and connect the DevCycle App for Slack with your workspace. Once the DevCycle app has been added, you can now subscribe to Feature changes on a project-level or by individual Feature(s). First, navigate to the Slack channel where you would like to have the Slack messages to be posted. To add a subscription for project-level changes, use the command /devcycle subscribe project-key . To find a project's respective key, go to your organization's Project Settings page and copy the key under the Project name. To add a subscription for individual feature changes, use the command /devcycle subscribe project-key [-f feature-key] . To add a subscription for a project or feature changes on a specific environment, add the flag [-e environment-key] to the command. For example, All Project changes for the specified Environment: /devcycle subscribe project-key [-e environment-key] All Feature changes that impact the specified Environment: /devcycle subscribe project-key [-f feature-key] [-e environment-key] Once you've susbcribed, you're all set! Go ahead and make some changes to a Feature then check your Slack Channel for notifications. Example Slack Message The View Project [Name] button will take you to the project that the Feature belongs to. The View Changes on Feature will take you to the Audit Log entry with more details about the Feature modification. Slack Commands /devcycle [ subscribe | unsubscribe | list | help ] /dvc [ subscribe | unsubscribe | list | help ] Private Channels To use the DevCycle Integration for Slack in private channels, you must invite the DevCycle App to the channel. Uninstall the DevCycle App for Slack In the case that you connected the DevCycle app for Slack to the wrong workspace or would like to remove it, please follow the instructions in this Slack Help Center article . Edit this page Last updated on Jan 9, 2026 Setup Example Slack Message Slack Commands Private Channels Uninstall the DevCycle App for Slack DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://docs.devcycle.com/integrations/gitlab/pr-insights-action/ | GitLab: Feature Flag Change Insights on Merge Request | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page GitLab: Feature Flag Change Insights on Merge Request Get the integration here: https://gitlab.com/devcycle/devcycle-pr-insights-ci-cd Overview With this GitLab CI/CD pipeline, information on which DevCycle features have been added or removed in a code change will be shown directly on each Merge Request as a comment. Note: This is intended to run for all merge requests. Example Output Access Tokens This pipeline uses the GitLab API to add comments to a Merge Requests. To do this, it requires a GitLab access token with the api scope. GitLab provides different types of access tokens, but we recommend using a project access token for this pipeline. If your GitLab plan does not support project access tokens, a personal access token can be used instead. To create a project access token, follow the instuctions outlined in the GitLab documentation . To create a personal access token, follow the instuctions outlined in the GitLab documentation . The access token should be stored as a GitLab CI/CD variable in your project settings. To do this, navigate to your project settings, select CI/CD from the left sidebar, and then select Variables . From here, you can add a new variable with the name GITLAB_ACCESS_TOKEN and the value of your access token. Usage Create a new .gitlab-ci.yml file in your GitLab repository or update the existing one. Add the code_usages stage and paste the following code into a code_usages: pr_insights : stage : pr_insights image : devcycleinfra/devcycle - pr - insights - gitlab : latest script : - git fetch origin $CI_MERGE_REQUEST_TARGET_BRANCH_NAME - git fetch origin $CI_MERGE_REQUEST_SOURCE_BRANCH_NAME - node /devcycle - pr - insights - ci - cd rules : - if : $CI_PIPELINE_SOURCE == 'merge_request_event' Your DevCycle API credentials and project token are required to update the DevCycle dashboard. When referencing your API client ID and secret, we recommend using GitLab CI/CD variables to store your credentials securely. Environment Variables Your DevCycle API credentials and project token are required to update the DevCycle dashboard. These should be set as environment variables in your GitLab project settings: Variable Description DEVCYCLE_PROJECT_KEY Your DevCycle project key, see Projects DEVCYCLE_CLIENT_ID Your organization's API client ID, see Organization Settings DEVCYCLE_CLIENT_SECRET Your organization's API client secret, see Organization Settings When setting these environment variables, we recommend you protect them to ensure they're only exposed to protected branches or tags and mask them to hide their values in job logs. Usage Set the necessary environment variables in your GitLab project settings as described above. Add the provided .gitlab-ci.yaml to your project root. Push your changes. The pipeline should run automatically on any merge request. Support For any issues, feedback, or questions, please feel free to open an issue on this repository. Edit this page Overview Example Output Access Tokens Usage Environment Variables Usage Support DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://docs.devcycle.com/integrations/github/pr-insights-action/ | GitHub: Feature Flag Change Insights on Pull Request | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page GitHub: Feature Flag Change Insights on Pull Request Get the integration on the GitHub Marketplace Overview With this Github action, information on which DevCycle features have been added or removed in a code change will be shown directly on each Pull Request as a comment. Note: This is intended for pull_request workflow events Example Output Usage Create a new Actions workflow in your GitHub repository (e.g. devcycle-insights.yml) in the .github/workflows directory. In your new file, paste the following code: on : pull_request jobs : dvc-feature-flag-insights : runs-on : ubuntu - latest name : Generate DevCycle PR Insights steps : - uses : actions/checkout@v4 with : fetch-depth : 0 - uses : DevCycleHQ/feature - flag - pr - insights - [email protected] with : # Token generated by GitHub github-token : $ { { secrets.GITHUB_TOKEN } } # Your DevCycle project key project-key : your - project - key # Your organization's DevCycle API client ID & secret client-id : $ { { secrets.DVC_CLIENT_ID } } client-secret : $ { { secrets.DVC_CLIENT_SECRET } } We recommend including your DevCycle API credentials and project token as inputs. If included, the PR comment will be enriched with Feature Flag data from DevCycle. When referencing your API client ID and secret, we recommend using GitHub Secrets to store your credentials securely. Inputs input required description github-token yes The GitHub Actions token e.g. secrets.GITHUB_TOKEN project-key no Your DevCycle project key, see Projects client-id no Your organization's API client ID, see Organization Settings client-secret no Your organization's API client secret, see Organization Settings only-comment-on-change no If true, comments will only be added to PRs when changes to DevCycle variables are detected. Defaults to false. Configuration The patterns used to identify references to variables in your code are fully customizable. This action uses the DevCycle CLI under the hood, for details on how to configure the pattern matcher see the CLI configuration . Edit this page Overview Example Output Usage Inputs Configuration DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close # analysis Follow Hide Scene and theme analyses Create Post Older #analysis posts 1 2 3 4 5 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu Red Team vs. Blue Team: Strategies for Advanced Penetration Testing Emanuele Balsamo Emanuele Balsamo Emanuele Balsamo Follow for CyberPath Jan 2 Red Team vs. Blue Team: Strategies for Advanced Penetration Testing # redteam # blueteam # overview # analysis Comments Add Comment 10 min read Hunting Coyote: A Full Analysis of the 0LIONW0 AutoIt3 RAT Mehmet Ali Mehmet Ali Mehmet Ali Follow Dec 3 '25 Hunting Coyote: A Full Analysis of the 0LIONW0 AutoIt3 RAT # security # reverse # malware # analysis 2 reactions Comments Add Comment 17 min read Unlocking TrendRadar: AI-Powered News Analysis ESROM BERHANE ESROM BERHANE ESROM BERHANE Follow Nov 22 '25 Unlocking TrendRadar: AI-Powered News Analysis # news # programming # ai # analysis Comments Add Comment 1 min read Project Structure in Umami codebase - Part 1.2 Ramu Narasinga Ramu Narasinga Ramu Narasinga Follow Nov 19 '25 Project Structure in Umami codebase - Part 1.2 # umami # analysis # opensource # projectstructure Comments Add Comment 2 min read Project Structure in Umami codebase - Part 1.0 Ramu Narasinga Ramu Narasinga Ramu Narasinga Follow Nov 17 '25 Project Structure in Umami codebase - Part 1.0 # umami # analysis # opensource # projectstructure Comments Add Comment 2 min read AI Overviews are cutting web traffic in half KahWee Teng KahWee Teng KahWee Teng Follow Oct 20 '25 AI Overviews are cutting web traffic in half # seo # google # ai # analysis Comments 1 comment 2 min read Building a Bitcoin Service with ICP and Fetch.ai Kshipra Kshipra Kshipra Follow Sep 4 '25 Building a Bitcoin Service with ICP and Fetch.ai # github # analysis # blog Comments Add Comment 3 min read First Steps into Darkness: My First Malware Analysis Experience Ali Aryani Ali Aryani Ali Aryani Follow Oct 7 '25 First Steps into Darkness: My First Malware Analysis Experience # malware # analysis # programming # security Comments Add Comment 3 min read Driving Ghidra Static Analysis with Local LLMs (LM Studio + GhidraMCP Setup) sumeshi_kun sumeshi_kun sumeshi_kun Follow Aug 18 '25 Driving Ghidra Static Analysis with Local LLMs (LM Studio + GhidraMCP Setup) # ghidra # malware # analysis # llm Comments Add Comment 3 min read Pandas Skills: From DataFrame Accessors to Sales Data Business Insights Labby Labby Labby Follow for LabEx Aug 13 '25 Pandas Skills: From DataFrame Accessors to Sales Data Business Insights # pandas # python # data # analysis Comments Add Comment 1 min read Bridging Blockchain Technologies: Fetch.ai and ICP Integration Kshipra Kshipra Kshipra Follow Aug 6 '25 Bridging Blockchain Technologies: Fetch.ai and ICP Integration # github # analysis # blog Comments Add Comment 3 min read Recognizing Actor Boundaries Through Domain-Driven Design: Lessons from a Shared WorkLog Augusts Bautra Augusts Bautra Augusts Bautra Follow Jul 4 '25 Recognizing Actor Boundaries Through Domain-Driven Design: Lessons from a Shared WorkLog # rails # actor # analysis # ddd Comments Add Comment 2 min read Why Does Q-Learning Algorithm Converge? MasterOfMagicMatrices MasterOfMagicMatrices MasterOfMagicMatrices Follow May 19 '25 Why Does Q-Learning Algorithm Converge? # rl # algorithms # analysis # math Comments Add Comment 12 min read Sentiment Analysis for Mobile Apps Sushan Dristi Sushan Dristi Sushan Dristi Follow Jun 21 '25 Sentiment Analysis for Mobile Apps # sentiment # analysis # user # negative Comments Add Comment 7 min read Review Qodana static code analysis and SCA/SBOM license audit Daniel Marques Daniel Marques Daniel Marques Follow Jun 12 '25 Review Qodana static code analysis and SCA/SBOM license audit # qodana # sca # sbom # analysis 2 reactions Comments Add Comment 3 min read SteamOS vs. Windows: A Comparative Analysis for Gaming Shixian Sheng Shixian Sheng Shixian Sheng Follow May 31 '25 SteamOS vs. Windows: A Comparative Analysis for Gaming # windows # gaming # steamos # analysis Comments Add Comment 3 min read How Much Does It Really Cost to Run a Voice-AI Agent at Scale? Roman Piacquadio Roman Piacquadio Roman Piacquadio Follow for Cloud(x); May 20 '25 How Much Does It Really Cost to Run a Voice-AI Agent at Scale? # ai # voice # cost # analysis 10 reactions Comments 2 comments 13 min read DeFi Isn’t Dead - But Most Tokens Are Martin Call Martin Call Martin Call Follow May 1 '25 DeFi Isn’t Dead - But Most Tokens Are # web3 # cryptocurrency # tokenomics # analysis 4 reactions Comments Add Comment 2 min read IE Browser Compatibility View Settings Data Analysis Excalibra Excalibra Excalibra Follow Jan 23 '25 IE Browser Compatibility View Settings Data Analysis # ie # browser # analysis # compatability 1 reaction Comments Add Comment 2 min read Script for checking Windows baselines Excalibra Excalibra Excalibra Follow Jan 22 '25 Script for checking Windows baselines # powershell # script # analysis # report 2 reactions Comments 1 comment 15 min read The AI-Augmented Analyst Anthony Clemons Anthony Clemons Anthony Clemons Follow Dec 11 '24 The AI-Augmented Analyst # datascience # analysis # datanalysis Comments Add Comment 7 min read [Python] A Script for Processing and Analysing Bilibili Video Comments and Bullet Chats Excalibra Excalibra Excalibra Follow Dec 31 '24 [Python] A Script for Processing and Analysing Bilibili Video Comments and Bullet Chats # python # script # analysis # data 2 reactions Comments 1 comment 7 min read Web scraping and analysing foreign languages data Gabriel Rowan Gabriel Rowan Gabriel Rowan Follow Dec 23 '24 Web scraping and analysing foreign languages data # python # pandas # analysis # webscraping 1 reaction Comments Add Comment 4 min read A Comparison of Top Private and Browser Based SQL on CSV Tools Nitin Bansal Nitin Bansal Nitin Bansal Follow Nov 24 '24 A Comparison of Top Private and Browser Based SQL on CSV Tools # csv # sql # private # analysis 1 reaction Comments Add Comment 4 min read How to Style Your Notebook for Data Analysis: A Guide with Heart Attack Prediction Example Edwin Kinyao Edwin Kinyao Edwin Kinyao Follow Oct 13 '24 How to Style Your Notebook for Data Analysis: A Guide with Heart Attack Prediction Example # jupyter # analysis # markdown # datascience Comments Add Comment 4 min read loading... trending guides/resources Project Structure in Umami codebase - Part 1.0 Red Team vs. Blue Team: Strategies for Advanced Penetration Testing Unlocking TrendRadar: AI-Powered News Analysis Hunting Coyote: A Full Analysis of the 0LIONW0 AutoIt3 RAT 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://docs.devcycle.com/integrations/github/feature-usage-action/ | GitHub: Feature Flag Code Usages | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page GitHub: Feature Flag Code Usages Get the integration on the GitHub Marketplace Overview With this Github action, your DevCycle dashboard will be updated to display code snippets for each DevCycle variable usage within your project. Note: This is intended to run when pushing changes to your main branch Example Output Usage Create a new Actions workflow in your GitHub repository (e.g. devcycle-usages.yml) in the .github/workflows directory. In your new file, paste the following code: on : push : branches : [ main ] jobs : dvc-code-usages : runs-on : ubuntu - latest name : Fetch DevCycle Code Usages steps : - uses : actions/checkout@v3 with : fetch-depth : 0 - uses : DevCycleHQ/feature - flag - code - usage - [email protected] with : github-token : $ { { secrets.GITHUB_TOKEN } } client-id : $ { { secrets.DVC_CLIENT_ID } } client-secret : $ { { secrets.DVC_CLIENT_SECRET } } project-key : app - devcycle - com Your DevCycle API credentials and project token are required to update the DevCycle dashboard. When referencing your API client ID and secret, we recommend using GitHub Secrets to store your credentials securely. Inputs input required description github-token yes The GitHub Actions token e.g. secrets.GITHUB_TOKEN project-key yes Your DevCycle project key, see Projects client-id yes Your organization's API client ID, see Organization Settings client-secret yes Your organization's API client secret, see Organization Settings Configuration The patterns used to identify references to variables in your code are fully customizable. This action uses the DevCycle CLI under the hood, for details on how to configure the pattern matcher see the CLI configuration . Edit this page Overview Example Output Usage Inputs Configuration DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://docs.devcycle.com/integrations/jira/ | DevCycle Feature Flag Management for Jira | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page DevCycle Feature Flag Management for Jira Setup Jira Marketplace Listing Install DevCycle Feature Flag Management for Jira Jira Integration Setup Setup DevCycle Feature Flag Management for Jira DevCycle for Jira streamlines your workflow by enabling the linking of Jira tickets directly to features within DevCycle, making the feature status easily viewable within Jira. Feature development teams often utilize a diverse array of tools, from project management and code repositories to feature management tools. However, these tools often contain siloed information, making it a daunting task to track the exact status of a feature in the development lifecycle. DevCycle for Jira is the solution to this problem. It establishes a two-way synchronization between Jira, the leading project management tool, and DevCycle, the top feature management tool. With DevCycle for Jira enabled, teams can quickly identify which feature flags are tied to their tickets and understand their current configuration and status. This results in smoother standups, code reviews, QA, and planning processes. See Your Feature's Status in Jira Link your Feature Flags to Jira effortlessly and monitor their status directly in Jira. Connect Your Tickets in DevCycle Integrate your Jira Ticket IDs with any Feature in DevCycle. This flexibility allows you to associate one ticket with multiple features and vice versa, according to your project needs. More Info DevCycle for Jira equips your team to comprehend the full context of every ticket in Jira, simplifying the task of finding the Jira context within DevCycle. This integration allows for a quicker understanding of the current status of all tasks, enabling you to develop your features faster and with more confidence. Simply input the Jira ticket numbers on your feature to connect existing Features to Jira tickets. You can view the status of a feature in every environment through a single connection in Jira. Associate one Jira ticket with numerous DevCycle Features, or link numerous Jira tickets to one DevCycle feature, providing a flexible view of your work. Note that each DevCycle project can only be connected to a single Jira project. Contributing to DevCycle or creating a new Integration: If you would like to contribute to an existing integration or tool, all of DevCycle's tools and integrations are open source on the DevCycle github repository. Further, if you'd like to create a new tool or integration, a great starting point is DevCycle's Management API which allows you to modify and interact with features and more within a devcycle project, as well as the DevCycle Bucketing API which is used to give users features and variables (as used within the DevCycle SDKs!) Edit this page Last updated on Jan 9, 2026 Setup See Your Feature's Status in Jira Connect Your Tickets in DevCycle More Info DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://dev.to/pockit_tools/pnpm-vs-npm-vs-yarn-vs-bun-the-2026-package-manager-showdown-51dc#disk-usage-where-pnpm-shines | pnpm vs npm vs yarn vs Bun: The 2026 Package Manager Showdown - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse HK Lee Posted on Jan 9 • Originally published at pockit.tools pnpm vs npm vs yarn vs Bun: The 2026 Package Manager Showdown # bunjs # pnpm # yarn # npm Every JavaScript project starts with a choice: which package manager? For years, it was npm by default. Then yarn promised faster installs. Then pnpm claimed to save gigabytes of disk space. And now Bun's built-in package manager claims to make everything else obsolete. But here's what no one tells you: the "best" package manager depends entirely on your specific use case, and blindly following benchmarks can lead you astray. A package manager that's perfect for a solo developer's side project might be terrible for a 500-package monorepo—and vice versa. This guide cuts through the marketing hype. After extensive testing across different project sizes and configurations in January 2026, here's what actually matters for each package manager, when to use it, and how to migrate if you need to. 📌 Version Note: This comparison covers npm 11.x, yarn 4.x (Berry), pnpm 10.x, and Bun 1.3 as of January 2026. The Quick Verdict If you're in a hurry, here's the short version: Use Case Recommended Why Solo/small projects Bun Fastest by far, simplest setup Large monorepos pnpm Best disk efficiency, workspace support Enterprise/legacy npm Maximum compatibility, no surprises Yarn ecosystem yarn 4 PnP mode, excellent plugins Performance at scale pnpm or Bun Both excel, pnpm more mature Now let's dive into why. The Contenders: 2026 State of Play npm 11.x Status: Still the default, ships with Node.js Latest: npm 11.7.0 (December 2025) Philosophy: Compatibility over innovation Key Strength: Works everywhere, always npm has evolved significantly. The node_modules structure is now more optimized, and features like npm audit have become industry standards. But npm's conservative approach means it's rarely the fastest or most efficient—it's just the most reliable. yarn 4.x (Berry) Status: Complete rewrite from yarn 1.x Latest: yarn 4.12.0 (January 2026) Philosophy: Innovation through Plug'n'Play (PnP) Key Strength: Zero-installs, plugin architecture Yarn Berry is essentially a different product from yarn 1. The Plug'n'Play feature eliminates node_modules entirely, instead using a .pnp.cjs file that maps imports directly to zip archives. It's radical—and divisive. pnpm 10.x Status: The "smart" alternative Latest: pnpm 10.27.0 (December 2025) Philosophy: Efficiency without breaking compatibility Key Strength: Content-addressable storage, true deduplication pnpm's approach is elegant: store all packages once in a global content-addressable store, then use hard links to make them appear in each project's node_modules . You get the compatibility of the traditional node_modules structure with massive disk savings. Bun 1.3 Package Manager Status: The new challenger Latest: Bun 1.3.0 (January 1, 2026) Philosophy: Speed above all else Key Strength: Native speed, zero configuration, full-stack capabilities Bun isn't just a package manager—it's a complete JavaScript runtime. Bun 1.3 introduced full-stack development features, unified database APIs, and further performance improvements. Its bun install command is often 10-30x faster than npm for cold installs. Benchmark Results: Cold Install Performance Let's start with what everyone cares about—raw speed. We tested each package manager on the same projects with cleared caches: Small Project (50 dependencies) Project: Typical React + TypeScript starter Dependencies: 50 direct, ~400 total Cold Install Times (cleared cache): ┌────────────┬──────────┬────────────┐ │ Manager │ Time │ vs npm │ ├────────────┼──────────┼────────────┤ │ bun │ 0.8s │ 18x faster │ │ pnpm │ 4.2s │ 3.4x faster│ │ yarn │ 6.8s │ 2.1x faster│ │ npm │ 14.3s │ baseline │ └────────────┴──────────┴────────────┘ Enter fullscreen mode Exit fullscreen mode Medium Project (200 dependencies) Project: Next.js 15 app with common libraries Dependencies: 200 direct, ~1,200 total Cold Install Times (cleared cache): ┌────────────┬──────────┬────────────┐ │ Manager │ Time │ vs npm │ ├────────────┼──────────┼────────────┤ │ bun │ 2.1s │ 22x faster │ │ pnpm │ 12.4s │ 3.7x faster│ │ yarn │ 18.2s │ 2.5x faster│ │ npm │ 46.1s │ baseline │ └────────────┴──────────┴────────────┘ Enter fullscreen mode Exit fullscreen mode Large Monorepo (15 packages, 800 dependencies) Project: Turborepo monorepo with 15 packages Dependencies: 800 direct, ~3,500 total Cold Install Times (cleared cache): ┌────────────┬──────────┬────────────┐ │ Manager │ Time │ vs npm │ ├────────────┼──────────┼────────────┤ │ bun │ 4.8s │ 28x faster │ │ pnpm │ 28.6s │ 4.7x faster│ │ yarn │ 52.3s │ 2.6x faster│ │ npm │ 134.2s │ baseline │ └────────────┴──────────┴────────────┘ Enter fullscreen mode Exit fullscreen mode Key Insight: Bun's lead actually increases with project size. For monorepos, the difference is staggering. Cached/Warm Install Performance But cold installs aren't the whole story. Most of the time, you're installing with some level of caching: Warm Install (lockfile exists, some cache): ┌────────────┬──────────────┬──────────────┐ │ Manager │ Small (50) │ Large (800) │ ├────────────┼──────────────┼──────────────┤ │ bun │ 0.3s │ 1.2s │ │ pnpm │ 1.1s │ 8.4s │ │ yarn (PnP) │ 0.0s* │ 0.0s* │ │ npm │ 3.2s │ 24.6s │ └────────────┴──────────────┴──────────────┘ * Yarn PnP with zero-installs commits dependencies to repo Enter fullscreen mode Exit fullscreen mode Yarn's Zero-Installs Trick: With PnP mode and zero-installs, yarn commits your dependencies directly to the repository. CI/CD runs need zero install time—they just yarn and go. The tradeoff? Your repo size increases significantly. Disk Usage: Where pnpm Shines Raw speed is one thing, but what about your hard drive? Single Project Disk Usage Same 200-dependency project: ┌────────────┬──────────────┬──────────────┐ │ Manager │ node_modules │ vs npm │ ├────────────┼──────────────┼──────────────┤ │ npm │ 487 MB │ baseline │ │ yarn │ 502 MB │ +3% │ │ pnpm │ 124 MB* │ -75% │ │ bun │ 461 MB │ -5% │ └────────────┴──────────────┴──────────────┘ * pnpm uses hard links to global store Enter fullscreen mode Exit fullscreen mode Multiple Projects (Same Dependencies) Here's where pnpm's architecture pays off. If you have 10 projects using React 19: 10 Projects with overlapping dependencies: ┌────────────┬──────────────┬──────────────┐ │ Manager │ Total Disk │ vs npm │ ├────────────┼──────────────┼──────────────┤ │ npm │ 4.87 GB │ baseline │ │ yarn │ 5.02 GB │ +3% │ │ pnpm │ 612 MB │ -87% │ │ bun │ 4.61 GB │ -5% │ └────────────┴──────────────┴──────────────┘ Enter fullscreen mode Exit fullscreen mode pnpm stores each unique package version exactly once. Every project links to that single copy. If you work on many projects, pnpm can save tens of gigabytes. Bun's Approach: Bun uses a global cache but still creates full node_modules directories. It's faster than npm/yarn but doesn't achieve pnpm's deduplication. Monorepo Support Compared Monorepos have become the default for many organizations. Here's how each manager handles them: Workspace Configuration npm (workspaces): // package.json { "workspaces" : [ "packages/*" , "apps/*" ] } Enter fullscreen mode Exit fullscreen mode yarn (workspaces): // package.json { "workspaces" : [ "packages/*" , "apps/*" ] } Enter fullscreen mode Exit fullscreen mode pnpm (pnpm-workspace.yaml): # pnpm-workspace.yaml packages : - ' packages/*' - ' apps/*' Enter fullscreen mode Exit fullscreen mode Bun (workspaces): // package.json { "workspaces" : [ "packages/*" , "apps/*" ] } Enter fullscreen mode Exit fullscreen mode Workspace Features Comparison Feature npm yarn pnpm Bun Workspace protocol ( workspace:* ) ❌ ✅ ✅ ✅ Selective dependency installation ❌ ✅ ✅ ✅ Parallel task execution ❌ ✅ ✅ ✅ Cross-workspace linking Basic Good Excellent Good Hoisting control Limited Full Full Limited Filtering ( --filter ) ❌ ✅ ✅ ❌ The Bottom Line: pnpm and yarn are the clear leaders for monorepo management. npm's workspace support is functional but basic. Bun's is improving rapidly but still catching up. Real-World Monorepo Performance We tested a Turborepo setup with 15 packages: Task: Install + Build all packages ┌────────────┬──────────────┬──────────────┐ │ Manager │ Install │ Full Build │ ├────────────┼──────────────┼──────────────┤ │ pnpm │ 28.6s │ 142s │ │ bun │ 4.8s │ 138s │ │ yarn │ 52.3s │ 156s │ │ npm │ 134.2s │ 198s │ └────────────┴──────────────┴──────────────┘ Enter fullscreen mode Exit fullscreen mode Interesting: Bun's install speed advantage shrinks when you include build time. The build phase dominates, making the install speed difference less impactful for CI/CD overall. Security Features Security has become a first-class concern. Here's how each manager handles it: Audit Capabilities Feature npm yarn pnpm Bun audit command ✅ Native ✅ Plugin ✅ Native ❌ Auto-fix vulnerabilities ✅ ✅ ✅ ❌ Advisory database npm registry npm registry npm registry - SBOM generation ✅ ✅ Plugin ✅ ❌ Critical Note: Bun currently lacks built-in security auditing. For production applications, you'll need third-party tools like Snyk or Socket. Lockfile Security All four managers use lockfiles to ensure reproducible installs: npm: package-lock.json (JSON) yarn: yarn.lock (custom format) pnpm: pnpm-lock.yaml (YAML) Bun: bun.lockb (binary) Bun's Binary Lockfile: Bun's bun.lockb is binary for speed. While this makes installs faster, it's not human-readable and can't be easily diffed in code review. Bun offers bun.lock (text) as an alternative, but it's not the default. Supply Chain Protection Feature npm yarn pnpm Bun Signature verification ✅ ✅ ✅ ❌ Strict peer dependencies Optional Optional Default Optional .npmrc security options Full Limited Full Limited Network isolation mode ❌ ✅ ✅ ❌ Compatibility Reality Check Here's what nobody talks about: not every package works perfectly with every manager. Known Compatibility Issues (January 2026) pnpm: Some packages break with strict node_modules structure Workaround: shamefully-hoist=true in .npmrc Most major packages now support pnpm natively yarn PnP: Many packages still don't support PnP mode Workaround: nodeLinker: node-modules falls back to traditional structure Adoption is improving but still incomplete Bun: ~98% npm compatibility (up from 95% in 2025) Some native modules still have issues Workaround: Use --backend=copyfile for problematic packages Framework Compatibility Framework npm yarn pnpm Bun Next.js 15 ✅ ✅ ✅ ✅ Remix ✅ ✅ ✅ ⚠️ Nuxt 4 ✅ ✅ ✅ ✅ Angular 19 ✅ ⚠️ ✅ ⚠️ SvelteKit ✅ ✅ ✅ ✅ Astro 5 ✅ ✅ ✅ ✅ ⚠️ = Works but some edge cases or extra configuration needed CI/CD Performance For many teams, CI/CD time is where package manager choice really matters: GitHub Actions Benchmark # Same workflow, different package managers # Node.js 22, ubuntu-latest, clean cache ┌────────────┬──────────────┬──────────────┬──────────────┐ │ Manager │ Install │ Cache Hit │ Total Job │ ├────────────┼──────────────┼──────────────┼──────────────┤ │ npm │ 48s │ 12s │ 2m 34s │ │ yarn │ 21s │ 8s │ 2m 15s │ │ yarn (PnP) │ 18s │ 0s* │ 2m 02s │ │ pnpm │ 14s │ 4s │ 2m 08s │ │ bun │ 3s │ 1s │ 1m 52s │ └────────────┴──────────────┴──────────────┴──────────────┘ * Zero-installs : dependencies committed to repo Enter fullscreen mode Exit fullscreen mode Docker Build Performance # Multi-stage build comparison ┌────────────┬──────────────┬──────────────┐ │ Manager │ Layer Cache │ No Cache │ ├────────────┼──────────────┼──────────────┤ │ npm │ 18s │ 52s │ │ pnpm │ 8s │ 24s │ │ bun │ 2s │ 6s │ └────────────┴──────────────┴──────────────┘ Enter fullscreen mode Exit fullscreen mode The Docker Secret: Bun's speed advantage is even more pronounced in Docker because its binary includes the runtime—no need to install Node.js separately. Migration Guides Ready to switch? Here's how: npm → pnpm Install pnpm: npm install -g pnpm Enter fullscreen mode Exit fullscreen mode Import existing lockfile: pnpm import Enter fullscreen mode Exit fullscreen mode Delete old files: rm -rf node_modules package-lock.json Enter fullscreen mode Exit fullscreen mode Install: pnpm install Enter fullscreen mode Exit fullscreen mode Update scripts (if needed): // package.json - usually works as-is { "scripts" : { "dev" : "next dev" , // no change needed "build" : "next build" } } Enter fullscreen mode Exit fullscreen mode npm → Bun Install Bun: curl -fsSL https://bun.sh/install | bash Enter fullscreen mode Exit fullscreen mode Remove old files: rm -rf node_modules package-lock.json Enter fullscreen mode Exit fullscreen mode Install: bun install Enter fullscreen mode Exit fullscreen mode Update scripts for Bun runtime (optional): { "scripts" : { "dev" : "bun run --bun next dev" , "build" : "bun run next build" } } Enter fullscreen mode Exit fullscreen mode yarn 1.x → yarn 4.x (Berry) # Enable corepack (Node.js 16+) corepack enable # Set yarn version yarn set version stable # Migrate configuration yarn config set nodeLinker node-modules # for compatibility # Install yarn install Enter fullscreen mode Exit fullscreen mode Rollback Plan If migration causes issues: # Keep your old lockfile backed up! cp package-lock.json package-lock.json.backup # To rollback: rm -rf node_modules bun.lockb pnpm-lock.yaml yarn.lock mv package-lock.json.backup package-lock.json npm install Enter fullscreen mode Exit fullscreen mode When to Use What: Decision Framework Use npm when: ✅ Maximum compatibility is required ✅ Team is unfamiliar with alternatives ✅ Legacy project with many native dependencies ✅ Corporate environment with strict tooling policies ✅ You want "it just works" Use yarn when: ✅ You need Plug'n'Play zero-installs ✅ You want the plugin ecosystem ✅ Your team is already yarn experts ✅ You need advanced workspace features ✅ Offline-first development is important Use pnpm when: ✅ Disk space is a concern ✅ You have many projects with overlapping dependencies ✅ Large monorepo with complex dependencies ✅ You want speed without sacrificing compatibility ✅ Strict dependency isolation matters Use Bun when: ✅ Speed is the absolute priority ✅ You're starting a new project ✅ CI/CD time is a major cost ✅ You're building Node.js APIs or scripts ✅ You want a unified runtime + package manager The Hidden Costs Nobody Mentions Before you switch, consider: Learning Curve npm → pnpm: Minimal. Almost drop-in. npm → yarn 4: Moderate. PnP mode requires understanding. npm → Bun: Low for package management, higher if using Bun runtime. Tooling Compatibility IDE support: All four work with VS Code, JetBrains, etc. CI/CD templates: npm has the most, Bun the least ready-made Docker images: npm/yarn are everywhere, pnpm common, Bun less common Team Onboarding The fastest package manager doesn't help if it slows down your team. Consider: How comfortable is your team with the new tool? Are your documentation and scripts updated? Have you tested the entire development workflow? Future Outlook: 2026 and Beyond npm: Will remain the default. Focus on incremental improvements. yarn: Continuing to push PnP adoption. Better monorepo support coming. pnpm: Rapid growth in enterprise. Becoming the "safe modern choice." Bun: Aggressive development. Aiming for 100% npm compatibility. May become the default for new projects by 2027. The ecosystem is fragmenting in healthy ways. Competition drives innovation—and all four managers are better for it. Conclusion: There's No Wrong Choice (Mostly) After extensive testing, here's the honest truth: all four package managers work fine for most projects. The performance differences, while measurable, rarely matter for small-to-medium projects. Where choice matters: Monorepos: pnpm or yarn CI/CD-heavy workflows: Bun or pnpm Disk-constrained systems: pnpm Maximum compatibility: npm Bleeding edge: Bun The most important thing isn't which package manager you choose—it's that you choose consistently across your projects and team. Switching between managers constantly creates more friction than any speed difference could justify. My recommendation for 2026: New projects: Try Bun. It's fast enough to justify the minor compatibility risks. Existing projects: Consider pnpm if you're feeling pain. Otherwise, npm is fine. Enterprise monorepos: pnpm is the safe, powerful choice. Benchmarks conducted January 2026 on M3 MacBook Pro with Node.js 22.x. Results will vary based on hardware, network, and project specifics. Always test with your own codebase before making decisions. 🚀 Explore More: This article is from the Pockit Blog . If you found this helpful, check out Pockit.tools . It’s a curated collection of offline-capable dev utilities. Available on Chrome Web Store for free. Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse HK Lee Follow solo web developer Joined Dec 26, 2025 Trending on DEV Community Hot I Am 38, I Am a Nurse, and I Have Always Wanted to Learn Coding # career # learning # beginners # coding Top 7 Featured DEV Posts of the Week # top7 # discuss What was your win this week??? # weeklyretro # discuss 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://docs.devcycle.com/integrations/terraform/ | Terraform | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page Terraform DevCycle Terraform Provider is a Terraform provider that provides a way to manage DevCycle projects, features, variables, variations, and environments. It also provides the ability to receive DevCycle Variable values during the apply step, and feature flag the behaviour of other Terraform configuration. Setup Usage Setup Install the Terraform Provider terraform { required_providers { devcycle = { source = "DevCycleHQ/devcycle" version = "1.0.0" } } } provider "devcycle" { client_id = "your-client-id" client_secret = "your-client-secret" server_sdk_token = "project-specific-server-sdk-token" } Set up DevCycle API Credentials All actions by the Terraform provider are scoped at the organization root level. This requires DevCycle API authorization. Your DevCycle organization's client ID and secret must be provided. They can be obtained from the settings page of the DevCycle dashboard. There are several ways to provide these credentials: Directly in the Terraform configuration This is not recommended, as your credentials will be visible in your Terraform state. But if you are securely controlling access to the state file, then this can be used safely. Set the client_id and client_secret fields in the provider.devcycle block in your Terraform configuration to the respective values. These will be used to get an OAuth2 access token at the time of use - this value is not stored in the Terraform state file for security. For the server_sdk_token field, this is scoped to a single DevCycle project. This should be the project you want to control your resources from. The server sdk token can be found in the same settings page as the client id and secret - but you need to select the right project first. Environment Variables Set the following environment variables: $ export DEVCYCLE_CLIENT_ID=<your client id> $ export DEVCYCLE_CLIENT_SECRET=<your client secret> $ export DEVCYCLE_SERVER_TOKEN=<your server token> Terraform will use these as a fallback from the provider passed variables. Meaning the values in the configuration block will take precedence over the environment variables. Usage For more detailed documentation on the DevCycle Terraform provider, see the DevCycle Terraform Provider Documentation After configuration of the provider - let's use the provider to create a DevCycle project. Create a Project resource "devcycle_project" "example" { name = "Example DevCycle Project" key = "example-devcycle-project" description = "Terraform example project" } Running terraform apply will create this project - and you can see it in the DevCycle dashboard. This creates a bare project - with no features or variables. To add those, lets create a feature. Create a Feature resource "devcycle_feature" "example" { project_id = devcycle_project.example.id name = "Example Feature" key = "example-feature" description = "Terraform example feature" type = "experiment" tags = ["terraform"] } This feature takes in the project id as a variable - we're passing in the id exported from the devcycle_project resource to make it simple. But if you wanted to create a feature for an already existing project that isn't managed by terraform - feel free to use the human readable project key instead. Anywhere you see id or key in the configuration, you can use either the human readable key or the id, as the API manages the conversion between the two. The feature resource can create and manage the variations and variables attached to the feature - but it's not recommended to manage the variables if you don't need to in the same block, and to instead use the devcycle_variable resource. Create a Variable resource "devcycle_variable" "example" { name = "Terraform Example Variable" key = "example-variable" description = "Terraform created variable" type = "Boolean" feature_id = devcycle_feature.example.id project_id = devcycle_project.example.id } After creating the variable - you can either read from the existing environments auto-created on project creation: development , staging , production or create a new one. Getting SDK Keys Using the data block - you can read the SDK keys that get generated for the environment to change keys automatically in your Terraform configuration. SDK Keys are prefixed by their type, client , mobile , or server . data "devcycle_environment" "test" { key = "development" project_key = devcycle_project.example.key } output "development_sdk_keys" { value = data.devcycle_environment.test.sdk_keys } Evaluating Variables One of the major features that this provider provides is the ability to evaluate variables. This is done by using the typed data blocks for the type of variable desired. data "devcycle_evaluated_variable_boolean" "create-resource" { default_value = false id = devcycle_variable.example.id user = { id = "user-id" } } This evaluation can then be accessed via data.devcycle_evaluated_variable_boolean.create-resource.value . The default value will be returned if there is no returned value from no matching variation. There is another data block for each type of variable that can be evaluated (JSON, Boolean, String, Number) Each is typed explicitly because a variable type cannot be changed after creation. Contributing to DevCycle or creating a new Integration: If you would like to contribute to an existing integration or tool, all of DevCycle's tools and integrations are open source on the DevCycle github repository. Further, if you'd like to create a new tool or integration, a great starting point is DevCycle's Management API which allows you to modify and interact with features and more within a devcycle project, as well as the DevCycle Bucketing API which is used to give users features and variables (as used within the DevCycle SDKs!) Edit this page Last updated on Jan 9, 2026 Setup Install the Terraform Provider Set up DevCycle API Credentials Usage Create a Project Create a Feature Create a Variable Getting SDK Keys Evaluating Variables DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://www.fine.dev/blog/ai-coding-guide#choosing-the-right-llm-for-your-needs | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
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https://www.fine.dev/blog/ai-coding-guide#3-copy-pasting-relevant-code-into-chatgpt-and-claude | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
https://www.fine.dev/blog/ai-coding-guide#use-cases-for-ai-in-coding | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
https://www.python.org/downloads/release/python-31311/ | Python Release Python 3.13.11 | Python.org Notice: While JavaScript is not essential for this website, your interaction with the content will be limited. Please turn JavaScript on for the full experience. Skip to content ▼ Close Python PSF Docs PyPI Jobs Community ▲ The Python Network Donate ≡ Menu Search This Site GO A A Smaller Larger Reset Socialize LinkedIn Mastodon Chat on IRC Twitter About Applications Quotes Getting Started Help Python Brochure Downloads All releases Source code Windows macOS Android Other Platforms License Alternative Implementations Documentation Docs Audio/Visual Talks Beginner's Guide FAQ Non-English Docs PEP Index Python Books Python Essays Community Diversity Mailing Lists IRC Forums PSF Annual Impact Report Python Conferences Special Interest Groups Python Logo Python Wiki Code of Conduct Community Awards Get Involved Shared Stories Success Stories Arts Business Education Engineering Government Scientific Software Development News Python News PSF Newsletter PSF News PyCon US News News from the Community Events Python Events User Group Events Python Events Archive User Group Events Archive Submit an Event Python 3.13.11 Release date: Dec. 5, 2025 This is the eleventh maintenance release of Python 3.13 Note: This is Python 3.13.11, a maintenance release for Python 3.13. Python 3.14 is now the latest feature release series of Python 3. Get the latest release of 3.14.x here . Python 3.13.11 is the eleventh maintenance release of 3.13. This is an expedited release to fix the following regressions: gh-142206 : Exceptions in multiprocessing in running programs while upgrading Python. gh-142218 : Segmentation faults and assertion failures in insertdict. gh-140797 : Crash when using multiple capturing groups in re.Scanner And these security fixes: gh-142145 : Remove quadratic behavior in node ID cache clearing ( CVE-2025-12084 ) gh-119451 : Fix a potential denial of service in http.client gh-119452 : Fix a potential virtual memory allocation denial of service in http.server Major new features of the 3.13 series, compared to 3.12 Some of the new major new features and changes in Python 3.13 are: New features A new and improved interactive interpreter , based on PyPy 's, featuring multi-line editing and color support, as well as colorized exception tracebacks . An experimental free-threaded build mode , which disables the Global Interpreter Lock, allowing threads to run more concurrently. The build mode is available as an experimental feature in the Windows and macOS installers as well. A preliminary, experimental JIT , providing the ground work for significant performance improvements. The locals() builtin function (and its C equivalent) now has well-defined semantics when mutating the returned mapping , which allows debuggers to operate more consistently. A modified version of mimalloc is now included, optional but enabled by default if supported by the platform, and required for the free-threaded build mode. Docstrings now have their leading indentation stripped , reducing memory use and the size of .pyc files. (Most tools handling docstrings already strip leading indentation.) The dbm module has a new dbm.sqlite3 backend that is used by default when creating new files. The minimum supported macOS version was changed from 10.9 to 10.13 (High Sierra) . Older macOS versions will not be supported going forward. WASI is now a Tier 2 supported platform . Emscripten is no longer an officially supported platform (but Pyodide continues to support Emscripten). iOS is now a Tier 3 supported platform . Android is now a Tier 3 supported platform . Typing Support for type defaults in type parameters . A new type narrowing annotation , typing.TypeIs . A new annotation for read-only items in TypeDicts . A new annotation for marking deprecations in the type system . Removals and new deprecations PEP 594 (Removing dead batteries from the standard library) scheduled removals of many deprecated modules: aifc , audioop , chunk , cgi , cgitb , crypt , imghdr , mailcap , msilib , nis , nntplib , ossaudiodev , pipes , sndhdr , spwd , sunau , telnetlib , uu , xdrlib , lib2to3 . Many other removals of deprecated classes, functions and methods in various standard library modules. C API removals and deprecations . (Some removals present in alpha 1 were reverted in alpha 2, as the removals were deemed too disruptive at this time.) New deprecations , most of which are scheduled for removal from Python 3.15 or 3.16. For more details on the changes to Python 3.13, see What's new in Python 3.13 . More resources Online Documentation PEP 719 , 3.13 Release Schedule Report bugs at https://github.com/python/cpython/issues . Help fund Python directly (or via GitHub Sponsors ), and support the Python community . 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https://docs.devcycle.com/integrations/openfeature/ | OpenFeature | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page OpenFeature OpenFeature is an open standard that provides a vendor-agnostic, community-driven SDKs for Feature Flagging that works natively with DevCycle. DevCycle supports a wide range of OpenFeature Compatible SDKs , all of which have native integrations with OpenFeature. What is OpenFeature? OpenFeature is a standardization initiative that creates a common interface for Feature Flag operations across different platforms and vendors. Think of it as a universal adapter for Feature Flags. You can write your code once using OpenFeature's specifications, and switch between different Feature Flag providers (like DevCycle) without rewriting your code. This standardization is particularly valuable if you're working in a multi-vendor environment, migrating between Feature Flag platforms, or want to future-proof your codebase against vendor changes. DevCycle provides fully-featured OpenFeature providers for both client-side and server-side applications, giving you the flexibility of the OpenFeature standard combined with DevCycle's advanced Feature management capabilities. OpenFeature Compatible SDKs Client-Side JavaScript React Angular iOS Android Server-Side Node.js Go Java .NET / C# Python PHP Ruby NestJS Core Concepts OpenFeature's architecture is built around several key components that work together to provide a flexible and extensible Feature Flagging system. Evaluation API The Evaluation API is your primary interface for retrieving Feature Flags in your application. It's the backbone of all SDKs and provides standard methods for retrieving Feature, Variable and Variation data from your Feature Flag Provider. These APIs and methods abstracts away the complexity of communicating with your underlying Feature Flag service(s). When you integrate DevCycle through OpenFeature, your application code will use OpenFeature's standardized methods and DevCycle will handle the logic of Flag resolution, user targeting, and configuration management. Providers Providers act as the bridge between OpenFeature's SDKs and Feature Flag platforms. Providers can wrap around an existing vendor's SDK, interact directly with OpenFeature's REST APIs, or parse a locally stored file to obtain Flag evaluations. They will translate arguments supplied via OpenFeature to the vendor's SDK and vice versa. Example: Using setProvider / setProviderAndWait to set up the DevCycleProvider on the JavaScript OpenFeature SDK. import DevCycleProvider from '@devcycle/openfeature-web-provider' import { OpenFeature } from '@openfeature/web-sdk' // Initialize the DevCycle Provider const devcycleProvider = new DevCycleProvider ( '<DEVCYCLE_CLIENT_SDK_KEY>' ) // Set the DevCycleProvider for OpenFeature await OpenFeature . setProviderAndWait ( devcycleProvider ) // Get the OpenFeature client const openFeatureClient = OpenFeature . getClient ( ) DevCycle's OpenFeature provider is built on top of our native SDKs, giving you access to all of DevCycle's capabilities including Realtime Updates, EdgeDB, and Advanced Targeting, through the OpenFeature standard interface. Evaluation Context Evaluation Context represents the user and environmental information that influences which Features and Variations a user receives. This may include user identifiers, device data, and any other custom properties relevant to your target audience. You can set context globally for your entire application or pass it dynamically with each Flag evaluation. OpenFeature's context model maps directly to DevCycle's User Properties , enabling granular audience segmentation based on user or device-based properties as well as other environmental factors. This ensures your Targeting Rules work identically whether you're using DevCycle's native SDKs or the OpenFeature interface. Example: Setting the Evaluation Context on the JavaScript OpenFeature SDK. // Get the OpenFeature client const openFeatureClient = OpenFeature . getClient ( ) // Set the OpenFeature evaluation context openFeatureClient . setContext ( { user_id : 'your_unique_id' , email : ' [email protected] ' , name : 'name' , language : 'en' , country : 'CA' , appVersion : '1.0.11' , appBuild : 1000 , customData : { key : 'value' } , privateCustomData : { key : 'value' } , } ) OpenFeature's setContext method maps to identifyUser on DevCycle. await devCycleClient . identifyUser ( { user_id : 'your_unique_id' , email : ' [email protected] ' , name : 'name' , language : 'en' , country : 'CA' , appVersion : '1.0.11' , appBuild : 1000 , customData : { key : 'value' } , privateCustomData : { key : 'value' } , } ) Hooks Hooks provide extension points throughout the Flag (Variable) Evaluation lifecycle, allowing you to inject custom logic before, during, or after Flag Evaluations. Common use cases include adding telemetry, enriching evaluation context with additional data, implementing custom caching strategies, or validating Flag values before they're returned to your application. DevCycle Server SDKs support hooks, allowing you to hook into the lifecycle of a Variable Evaluation to execute code and build custom integrations before and after execution of the evaluation. Example: const client = new DevCycleClient ( 'token' ) client . addHook ( new EvalHook ( ( context ) => { // before hook } , ( context , variableDetails ) => { // after hook } , ( context , variableDetails ) => { // onFinally hook } , ( context , variableDetails ) => { // error hook } , ) , ) Tracking Tracking in OpenFeature links Metrics or Key Performance Indicators (KPIs) to Feature Flag Evaluation contexts. This connection allows teams to assess how Feature releases affect their Organization, whether it's making a positive or negative impact and if they can iterate off of it. Tracking Metrics are crucial to Feature Experimentation and A/B testing. Implementing Tracking in DevCycle can be done by adding Custom Events to your application in order to capture specific events that you'd want to measure, and setting up Metrics on your Feature in order to view, compare and analyze the results of your Feature releases. Example: Implementing a Track event on the JavaScript OpenFeature SDK. openFeatureClient . track ( 'custom-event' , { target : 'event-target' , value : 100 , metaDataField : 'value' , } ) OpenFeature's track method maps to DevCycle's track method. devcycleClient . track ( { type : 'custom-event' , target : 'event-target' , value : 100 , metaData : { metaDataField : 'value' , } , } ) OpenFeature Remote Evaluation API The OpenFeature Remote Evaluation API (OFREP) is a new open standard API interface for Feature Flagging that allows the use of generic providers to connect to any Feature Flag management systems that supports the protocol. Note: the standard is in its very early stages and is subject to change. See our Bucketing API Documentation for more information on how to consume the OpenFeature Remote Evaluation API with DevCycle. Edit this page Last updated on Jan 9, 2026 What is OpenFeature? OpenFeature Compatible SDKs Client-Side Server-Side Core Concepts Evaluation API Providers Evaluation Context Hooks Tracking OpenFeature Remote Evaluation API DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://www.fine.dev/blog/ai-coding-guide#1-fine | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
https://docs.devcycle.com/platform/extras/webhooks/ | Webhooks | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up Home Getting Started Essentials DevCycle Overview Key Features System Architecture Feature Hierarchy Feature Types Platform Feature Flags Experimentation Account Management Security and Guardrails Testing and QA Extras Custom Domains Feature Opt-In Self-Hosted Feature Flags with DevCycle Webhooks Examples Platform Extras Webhooks On this page Webhooks This topic explains how to create and use Webhooks in DevCycle. Webhooks allow you to build your own integrations that subscribe to Feature changes in DevCycle. Use Webhooks to update external ticket trackers, notify teammates of new features, targeting changes, and more. Outbound Webhooks Creating a Webhook To create a Webhook: Navigate to the Integrations page. Navigate to the "Webhooks" section and click + New Webhook . The "Create a Webhook" modal appears. Give the Webhook a human-readable name. (Optional) Give the Webhook a description. Enter a Payload URL. Click Create Webhook . From there, you will be taken to the Webhook details page. Select if you'd like events sent for all Features in your project or a single Feature. Select which events will be sent through the Webhook. Click Save . Example Payload Below is the type definition for the payload that gets sent to the Webhook url: /** * The 'newContents' and 'previousContents' type is a subset of the resource * that was changed */ export type AuditLogChange = { type : string newContents : Record < string , unknown > | null previousContents : Record < string , unknown > | null _environments ? : string [ ] metadata ? : Record < string , unknown > } type User = { name ? : string email : string } type WebhookPayload = { /** * An array of types that were triggered, the 'changes' property * should have all these events in this array */ events : string [ ] /** * The key of the Feature */ key ? : string /** * The key of the project */ projectId : string /** * The version of the payload so we can have different versions * in the future */ version : string /** * The changes that were made */ changes : AuditLogChange [ ] /** * The date this Webhook triggered the URL on */ date : Date /** * The user that triggered the change */ user : User } For example, a user edits a Feature’s key and adds a new variable, the data posted to the user’s Webhook URL would be: { "events" : [ "modifiedFeature" , "addedVariable" ] , "key" : "feature-key" , "date" : "2024-01-16T18:30:42.796Z" , "user" : { "name" : "Jason" , "email" : " [email protected] " } , "version" : "1" "changes" : [ { "type" : "modifiedFeature" , "newContents" : { "key" : "new-feature-key" } , "previousContents" : { "key" : "feature-key" } } , { "type" : "addedVariable" , "newContents" : { ... // new variable object } , "previousContents" : null } , ] } Testing a Webhook To test a Webhook: Navigate to the Integrations page. Navigate to the "Webhooks" section. Click the expand arrows next to the Add integration button. Navigate to the Test section of the Webhook details page. Click Test Connection to verify the Webhook Url is accessible. The API response will be displayed below. Deleting a Webhook To delete a Webhook: Navigate to the Integrations page. Navigate to the "Webhooks" section. Click on the Webhook that you wish to delete. Navigate to the Settings section of the Webhook details page. Click Delete Webhook . A confirmation modal will appear. Click Delete . Inbound Webhooks (Coming Soon) This feature will allow the user to create Webhook urls for certain actions, like turning on/off a Feature in production. If you would like this feature, contact [email protected] ! Edit this page Last updated on Jan 9, 2026 Previous Self-Hosted Feature Flags with DevCycle Next Examples Outbound Webhooks Creating a Webhook Example Payload Testing a Webhook Deleting a Webhook Inbound Webhooks (Coming Soon) DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Follow User actions Er. Bhupendra Ex-Software Developer at Nagarro || Java developer || SpringBoot || SpringJPA || Spring Security Location Bangalore Joined Joined on Dec 13, 2021 github website twitter website Education Btech CSE Pronouns He More info about @bhu_kalki Badges Four Year Club This badge celebrates the longevity of those who have been a registered member of the DEV Community for at least four years. Got it Close Three Year Club This badge celebrates the longevity of those who have been a registered member of the DEV Community for at least three years. Got it Close Two Year Club This badge celebrates the longevity of those who have been a registered member of the DEV Community for at least two years. Got it Close One Year Club This badge celebrates the longevity of those who have been a registered member of the DEV Community for at least one year. Got it Close Post 38 posts published Comment 0 comments written Tag 0 tags followed POST ALL USES IN BRINGBOOT Er. Bhupendra Er. Bhupendra Er. Bhupendra Follow Jan 7 POST ALL USES IN BRINGBOOT # api # java # springboot # tutorial Comments Add Comment 2 min read @RequestBody in SpringBoot Er. Bhupendra Er. Bhupendra Er. Bhupendra Follow Jan 7 @RequestBody in SpringBoot # backend # java # springboot Comments Add Comment 4 min read ALL TYPE OF GET USES IN SPRING BOOT Er. Bhupendra Er. Bhupendra Er. Bhupendra Follow Jan 7 ALL TYPE OF GET USES IN SPRING BOOT # api # java # springboot # tutorial Comments Add Comment 5 min read @GetMapping vs @RequestMapping Er. Bhupendra Er. Bhupendra Er. Bhupendra Follow Jan 7 @GetMapping vs @RequestMapping Comments Add Comment 3 min read @GetMapping in SpingBoot Er. Bhupendra Er. Bhupendra Er. 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Bhupendra Follow Jan 3 REST API Best Practices # api # architecture # backend Comments Add Comment 1 min read JAVA DEVELOPER INTERVIEW QUESTION: Er. Bhupendra Er. Bhupendra Er. Bhupendra Follow Jan 3 JAVA DEVELOPER INTERVIEW QUESTION: # webdev # interview # java # programming Comments Add Comment 1 min read Spring Security Implementation Overview asked in 3 yoe interview Er. Bhupendra Er. Bhupendra Er. Bhupendra Follow Dec 30 '25 Spring Security Implementation Overview asked in 3 yoe interview # webdev # interview # java # programming Comments Add Comment 1 min read REST API Implementation asked in 3 yoe java developer Er. Bhupendra Er. Bhupendra Er. Bhupendra Follow Dec 30 '25 REST API Implementation asked in 3 yoe java developer # java # restapi # interview Comments Add Comment 1 min read BEST INTRODUCTION FOR 3YOE IN JAVA DEVELOPER Er. Bhupendra Er. Bhupendra Er. 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https://docs.devcycle.com/integrations/google-analytics-4/ | Sending DevCycle Data as a Custom Event to Google Analytics 4 (GTM Specific) | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page Sending DevCycle Data as a Custom Event to Google Analytics 4 (GTM Specific) Transition from Google Optimize This guide enables you to integrate DevCycle feature flags with Google Analytics 4 (GA4) for A/B testing and experimentation using Google Tag Manager (GTM). If you are a former Google Optimize customer transitioning to GA4, this guide is specific to GTM implementations. GTM Elements: Tags, Variables, and Triggers Below is a description of Google Tag Manager's tags, variables, and triggers. For more in-depth understanding, consult Google's official documentation . Tags execute specified functionality, such as sending data to GA4 or initializing DevCycle. Variables serve as placeholders for predefined values, which in this guide store the feature and variation data. Triggers are conditions that, when met, execute actions defined in Tags. Google Tag Manager (GTM) Configuration Step 1: Create a New Tag for DevCycle Initialization and Feature Flag Configuration Values Navigate to your GTM workspace and access the "Tags" section. Create a new tag and name it "DevCycle Initialization & Feature Flag Configuration Values". Choose "Custom HTML" for "Tag Configuration". Insert a script to push a custom event named set_user_properties (or any name of your choosing) to the dataLayer with the parameters: featureName: {{featureName}} and variation: {{variation}} . This script can be found below. < script > let user = { isAnonymous : true } ; let devcycleOptions = { logLevel : "debug" } ; let devcycleClient = DevCycle . initializeDevCycle ( "<SDK_KEY>" , // Replace with your specific DevCycle SDK Key user , devcycleOptions ) ; devcycleClient . onClientInitialized ( ) . then ( function ( ) { let features = devcycleClient . allFeatures ( ) ; pushData ( features ) ; } ) ; function pushData ( featuresConfig ) { let arr = [ ] ; // JSON to Array for ( let i in featuresConfig ) { arr . push ( [ i , featuresConfig [ i ] ] ) ; } // Push to dataLayer for ( let j = 0 ; j < arr . length ; j ++ ) { let featureName = arr [ j ] [ 0 ] . replaceAll ( "-" , "_" ) ; let currentVariation = arr [ j ] [ 1 ] [ "variationName" ] . replaceAll ( "-" , "_" ) ; window . dataLayer . push ( { event : "set_user_properties" , // Can be any event name you want featureName : featureName , variationName : currentVariation , } ) ; } } < / script > For “Triggering", select the “Window Loaded” option as the firing trigger. Step 2: Configure GTM Variables Navigate to the “Variable” section. In “User-Defined Variables", create a new variable. Choose “Data Layer Variable” for "Variable Type". Enter “featureName” for "Data Layer Variable Name". Repeat to create another variable and name it “variationName". Step 3: Create Tag to Send Custom Events Option 1: Setup via Google Tag In your GTM workspace, navigate to "Tags" and create a new one. Name it "GA4_Custom_User_Properties". Select "Google Tag" for "Tag Configuration". Provide your Tag ID for your Google Analytics instance. Under "Shared event settings", add a new Parameter with the featureName variable you created as the "Event Parameter", and your variationName variable as the "value". Option 2: Send Custom Events to Google Analytics 4 In your GTM workspace, navigate to "Tags" and create a new one. Name it "GA4_Custom_User_Properties". Select "GA4 Event" for "Tag Configuration." In "Configuration Tag", choose your existing GA4 Configuration Tag. Input set_user_properties for "Event Name" (or the event name you chose). Step 4: Define Trigger for the new Tag Within the tag you just setup, create a new "Firing Trigger" in "Triggering". Create a new trigger and set the trigger type to "Custom Event" or to another trigger of your choice. Name the event (if applicable) as set_user_properties (Or the event name you chose in your custom HTML). Step 5: Publish Changes Before hitting "Submit", it's crucial to validate that your configurations are working as intended. Use GTM's "Preview" mode for this. How to Validate your setup with GTM's Preview Mode Click on "Preview" at the top right of the GTM interface. This will open a new browser tab, where you'll navigate to your website. Perform actions that should trigger the tag you've configured. Check the GTM Preview pane that appears at the bottom of your website. It should show the tags that are fired upon your actions. Specifically, confirm that your DevCycle feature and variation data is correctly passed to GA4 tags. When you've confirmed that your data is being passed in correctly, publish your changes by clicking on "Submit"! Google Analytics 4 Configuration Reporting in Google Analytics 4 Navigate to "Reports" > "Library" > "New Report". Choose the metric for analysis under "Event Metric". Select the feature property under "Dimension," e.g., DevCycle_featureNameA . If the dimension doesn't exist: Go to "Admin" > "Custom definitions" > "Create custom dimension". Set the scope to Event and name the event parameter according to your feature. Contributing to DevCycle or Creating a New Integration: DevCycle's tools and integrations are open source and can be found on the DevCycle GitHub repository . For new integrations, refer to DevCycle's Management API and DevCycle Bucketing API . Edit this page Last updated on Jan 9, 2026 Transition from Google Optimize GTM Elements: Tags, Variables, and Triggers Google Tag Manager (GTM) Configuration Step 1: Create a New Tag for DevCycle Initialization and Feature Flag Configuration Values Step 2: Configure GTM Variables Step 3: Create Tag to Send Custom Events Step 4: Define Trigger for the new Tag Step 5: Publish Changes Google Analytics 4 Configuration Reporting in Google Analytics 4 DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://docs.devcycle.com/integrations/rollbar/ | Rollbar | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page Rollbar Rollbar is a tool used for error logging and real-time performance tracking for your applications. Rollbar provides you with the ability to capture detailed information on errors to help diagnose and resolve issues faster. Enrich your logs further by including DevCycle Feature data into your error logging. The DevCycle Rollbar integration enhances error tracking by adding feature configuration data directly to your Rollbar error logs. By sending DevCycle Feature and Variable data from the DevCycle SDKs to Rollbar, developers can gain valuable insights into the specific feature configuration that was delivered to a user during an error. Configuration Including DevCycle Features in your Rollbar Config Include DevCycle Feature data to the initialization of Rollbar to allow all Rollbar errors to be populated with the specific DevCycle feature configuration at that time of the error. The exact DevCycle data and format that you pass to Rollbar can be easily configured, so feel free to experiment with the data that's available on your SDK. In our example below, we supply all Features and Variables that the user/device received to the Rollbar config. Steps : Get all Features and/or all Variables from the DevCycle SDK. Create a custom field called devCycleSettings within your Rollbar config payload. Add your Features and Variables to the devCycleSettings object. import { Provider , useRollbar } from '@rollbar / react import { useDevCycleClient , useIsDevCycleInitialized , useVariableValue , withDevCycleProvider } from '@devcycle/react-client-sdk' ... function MyComponent ( ) { const devCycleClient = useDevCycleClient ( ) const devCycleFeatures = devCycleClient . allFeatures ( ) const devCycleVariables = devCycleClient . allVariables ( ) const rollbarConfig = { accessToken : 'YOUR_ROLLBAR_CLIENT_ACCESS_TOKEN' , captureUncaught : true , captureUnhandledRejections : true , environment : 'production' , payload : { custom : { devCycleSettings : { features : devCycleFeatures , // this will send all DevCycle features in the error payload to Rollbar variables : devCycleVariables // this will send all DevCycle variables in the error payload to Rollbar } } } } return ( < Provider config = { rollbarConfig } > < TestError /> </ Provider > } function App ( ) { const devcycleReady = useIsDevCycleInitialized ( ) if ( ! devcycleReady ) return < div > < h1 > DevCycle is not ready! Loading State... </ h1 > </ div > return ( < > < div > < Router > < Routes > < Route path = " / " element = { < MyComponent /> } /> </ Routes > </ Router > </ div > </ > ) } export default withDevCycleProvider ( { sdkKey : 'YOUR_DEVCYCLE_SDK_KEY' , user : { user_id : 'USER_ID' , isAnonymous : false } } ) ( App ) Including DevCycle Features on Specific Errors Rollbar allows you to define extra properties for an error. Instead of providing all Feature data on initialization, you may want to supply DevCycle Feature data to specific errors of you choice. In our example below, we're using DevCycle to determine whether a user should receive a new Feature with new behavior or the existing old behavior. If there is an error running any of those behaviors, we're logging an error to Rollbar and supplying all DevCycle Features to the error as an extra property. Steps : Get all Features and/or all Variables from the DevCycle SDK. In your rollbar.error properties, add a custom field (ex: devCycleFeature ) containing your Feature or Variable data. Example: const rollbar = useRollbar ( ) ; const variableValue = useVariableValue ( 'variable_key' , false ) try { if ( variableValue ) { testNewBehavior ( ) } else { oldBehavior ( ) } } catch ( error ) { if ( variableValue ) { const devcycleClient = useDevCycleClient ( ) const features = devcycleClient . allFeatures ( ) rollbar . error ( error , { devCycleFeature : { name : 'New Feature' , id : features [ 'feature-key' ] [ '_id' ] } } ) } } Service Links Rollbar service links allow you to create links that connect directly with DevCycle, to provide easy access to Features and Variables from the Rollbar interface. To learn how to create service links for DevCycle, visit the Rollbar docs here . Edit this page Last updated on Jan 9, 2026 Configuration Including DevCycle Features in your Rollbar Config Including DevCycle Features on Specific Errors Service Links DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://www.algolia.com/de/products/ai-search | Algolia KI-Suche | Algolia Niket --> Deutsch English français News DevCon2025 | October 1-2 Learn more Unternehmen Partners Einloggen Login Logout Algolia mark white Algolia logo white Lösungen Search Show users what they're looking for with AI-driven resuts. Search Show users what they're looking for with AI-driven resuts. Recommendations Use behavioral cues to drive higher engagement. Recommendations Use behavioral cues to drive higher engagement. Personalization Show each user what they need across their journey. Personalization Show each user what they need across their journey. Analytics All your insights in one dashboard. Analytics All your insights in one dashboard. Browse Move customers down the funnel with curated category pages. Browse Move customers down the funnel with curated category pages. Agent Studio Create, test, and deploy AI agents, fast. Agent Studio Create, test, and deploy AI agents, fast. Generative Experiences Build conversational solutions with retrieval augmented generation (RAG). Generative Experiences Build conversational solutions with retrieval augmented generation (RAG). Ask AI Deliver conversational answers—right from your search bar. Ask AI Deliver conversational answers—right from your search bar. MCP Server Search, analyze, or monitor your index within your agentic workflow. MCP Server Search, analyze, or monitor your index within your agentic workflow. Data Enrichment Modify, enhance, or restructure data as it’s indexed for search. Data Enrichment Modify, enhance, or restructure data as it’s indexed for search. Data Transformation Streamline data preparation and enhance data quality. Data Transformation Streamline data preparation and enhance data quality. Integrations Connect to your existing stack via pre-built libraries and APIs. Integrations Connect to your existing stack via pre-built libraries and APIs. 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Branchen Ecommerce Ecommerce B2B Commerce B2B Commerce Fashion Fashion Grocery Grocery Media Media Marketplaces Marketplaces SaaS SaaS Higher Education Higher Education Documentation search Documentation search Enterprise search Enterprise search Headless commerce Headless commerce Image search Image search Mobile & App search Mobile & App search Retail Media Network Retail Media Network Site search Site search Visual search Visual search Voice search Voice search Digital Experience Digital Experience Ecommerce Ecommerce Engineering Engineering Merchandising Merchandising Product Management Product Management Preise Entwickler GET STARTED Developer Hub Developer Hub Dokumentation Dokumentation Integrationen Integrationen UI-Komponenten UI-Komponenten Autocomplete Autocomplete RESOURCES Code Exchange Code Exchange Engineering Blog Engineering Blog MCP MCP Discord Discord Webinars & Events Webinars & Events QUICK LINKS Schnellstartanleitung Schnellstartanleitung Für Open Source Für Open Source API Status API Status Support Support Resources INSPIRATION Algolia Blog Algolia Blog Resource Center Resource Center Kundengeschichten Kundengeschichten Webinars & Events Webinars & Events Newsroom Newsroom LEARN Customer Hub Customer Hub What's New What's New AI Search Grader AI Search Grader Documentation Documentation Algolia Academy Algolia Academy Professional Services Professional Services Quick Access Unternehmen Partners Einloggen Login Logout Request demo Get started Search Algolia Close Request demo Get started Other Types Filter --> Clear All Filters Filters Looking for our logo? We got you covered! Brand guidelines Download logo pack KI-Suche Ein besseres Sucherlebnis KI-Suche, die einfach zu bedienen ist. Demo anfordern Starten Sie jetzt Über 17.000 Kunden in über 150 Ländern vertrauen auf Algolia In Aktion sehen Die zentrale Anlaufstelle für die KI-Suche Built for flexibility and scale, Algolia adapts to your architecture, workflows, and commercial goals. Benutzerfreundlich Implementieren Sie unsere APIs in wenigen Minuten und erhalten Sie einfach Kontrolle über die Rankings. See all industries Schnell Suchen Sie so schnell wie Sie schreiben, mit der schnellsten KI-Suche für Unternehmen, die wir kennen. See all use cases Skalierbar Arbeiten Sie mit einem Partner zusammen, der 30 Milliarden Datensätze und fast 2 Milliarden Suchanfragen pro Jahr mit einer Verfügbarkeit von 99,999 % verarbeitet. See all departments Unsurpassed relevance drives revenue Deliver instantly relevant results with a hybrid keyword and vector retrieval engine that understands user intent and natural language. Real-time personalization adds another layer of intelligence so every visitor finds exactly what they’re looking for. Individuelle Kuration Geben Sie Ihren Business-Teams die Möglichkeit, das Sucherlebnis aktiv zu gestalten. Nutzen Sie unsere Merchandising-Tools, um Kampagnen zu kuratieren und Rankings zu beeinflussen. Individuelle Kuration Geben Sie Ihren Business-Teams die Möglichkeit, das Sucherlebnis aktiv zu gestalten. Nutzen Sie unsere Merchandising-Tools, um Kampagnen zu kuratieren und Rankings zu beeinflussen. Individuelle Kuration Geben Sie Ihren Business-Teams die Möglichkeit, das Sucherlebnis aktiv zu gestalten. Nutzen Sie unsere Merchandising-Tools, um Kampagnen zu kuratieren und Rankings zu beeinflussen. Individuelle Kuration Geben Sie Ihren Business-Teams die Möglichkeit, das Sucherlebnis aktiv zu gestalten. Nutzen Sie unsere Merchandising-Tools, um Kampagnen zu kuratieren und Rankings zu beeinflussen. Mehr Möglichkeiten Everything you need to deploy AI-powered search. Crawler Extrahieren und bereichern Sie Ihre Inhalte automatisch. InstantSearch Gestalten Sie das perfekte Erlebnis und erfassen Sie Ereignisse mit einer Vielzahl von fertigen UI-Komponenten. Analysen Verstehen Sie Ihre Nutzer, um verborgene Möglichkeiten aufzudecken. A/B-Tests Wählen Sie die leistungsfähigsten Relevanzstrategien. Data Transformations Optimieren Sie die Datenaufbereitung und verbessern Sie die Qualität. Regeln Optimieren Sie das Ranking für bestimmte Suchanfragen. Search API Flexible Suche – für jedes Tech-Stack und jedes Frontend. Shopping Guides KI, die jeden Produktkatalog sofort in Einkaufsführer verwandelt. Benutzerdefiniertes Ranking Fügen Sie Ihrem Ranking geschäftliche Relevanz hinzu. Merchandising Kuratieren Sie Ergebnisse für Werbekampagnen. Integrationen Indexierung von Inhalten aus beliebigen Quellen. Automatische Vervollständigung Leiten Sie Nutzer mit der Tippfehler-toleranten Autovervollständigung zu den richtigen Inhalten. Verlässliche Integrationen & Partnerschaften Schnelle Implementierung dank fertiger Integrationen für beliebte Plattformen. See all integrations Für eine Vielzahl von Branchen Add conversational search to your search bar, or build entirely new retrieval solutions powered by vector embeddings and LLMs. B2C-E-Commerce 0 Erstellen Sie personalisierte, flexible Search & Discovery-Erlebnisse für den E-Commerce, die Ihre Kunden lieben werden. Lesen Sie mehr über B2C-E-Commerce B2B-E-Commerce 0 Indizieren Sie Ihren Katalog und lassen Sie ihn für Ihre Käufer arbeiten. Steigern Sie die Konversion. Lesen Sie mehr über B2B-E-Commerce Marktplätze 0 Entwickeln Sie leistungsstarke Sucherlebnisse in großem Maßstab und reduzieren Sie gleichzeitig die Entwicklungszeit. Lesen Sie mehr über Marktplätze Medien 0 Indizieren Sie Ihre Inhalte und lassen Sie sie für Ihre Nutzer arbeiten. Lesen Sie mehr über Medien Algolia KI-Suche – FAQ Was ist AI Search und wie funktioniert es? 0 Algolia AI Search ist eine cloudbasierte, API-first Suchlösung, die KI- und Keyword-Technologien kombiniert, wie Natural Language Processing, Autovervollständigung, Fehlertoleranz und Kosinus-Ähnlichkeit, um ein Sucherlebnis zu liefern, das die Nutzerintention versteht und schnelle, hochrelevante Ergebnisse zurückgibt. Wie schnell ist die Algolia KI-Suche? 0 Richtig schnell. Die meisten Suchanfragen werden innerhalb von 1 bis 20 Millisekunden bearbeitet. Das ist bis zu 200-mal schneller als bei unseren Wettbewerbern. Die Abfrageleistung hängt jedoch von vielen Faktoren ab, von der Größe der Daten, die Sie durchsuchen, bis hin zu der Frage, wie rechenintensiv eine Abfrage ist. So sind beispielsweise Funktionen wie Geosortierung und Unterscheidbarkeit „teurer“. Erfahren Sie hier mehr. Welche Arten von Inhalten oder Daten kann AI Search verarbeiten? 0 Unsere AI Search API kann jeden Inhalt in den gehosteten Index aufnehmen – Produktkataloge, Blogbeiträge, Hilfeartikel, Medien, Bilder oder API-basierte Daten. Sie versteht sowohl Keywords als auch Bedeutungen und funktioniert daher gut mit allen Arten von Daten, von kurzen Titeln bis zu langen Dokumenten. Erfahren Sie mehr hier . Wie unterscheidet sich Algolia von anderen KI-Suchmaschinen? 0 Im Gegensatz zu vielen Wettbewerbern kombiniert Algolia eine API-first Architektur mit starken Entwickler-Tools, globaler Skalierbarkeit und detaillierten Relevanzkontrollen. Anstatt eines Black-Box-Ansatzes gibt Algolia volle Transparenz und Kontrolle, sodass Sie Ergebnisse an Ihre Ziele anpassen können und trotzdem die Vorteile der KI nutzen. Was sind die Hauptfunktionen von Algolia AI Search? 0 Kernfunktionen sind semantische Suche, KI-gestützte Relevanzoptimierung, Vektor-Embeddings, hybride Keyword- und Vektor-Suche, Echtzeit-Personalisierung, dynamisches Re-ranking und mehrsprachige Suche. Für welche Branchen und Anwendungsfälle ist Algolias AI Search am besten geeignet? 0 E-Commerce, SaaS, Medien, Mode, Finanzwesen, Marktplätze, Enterprise, Mobile Apps, Headless Commerce, Voice Search und Bildersuche sind nur einige Bereiche, in denen Algolia schnellere Entdeckung, bessere Relevanz und höhere Conversion Rates ermöglicht. Wie verbessert Algolias AI Search die Conversion Rates? 0 Durch schnellere und relevantere Ergebnisse reduziert Algolia Reibung im Kaufprozess. Nutzer finden schneller, was sie suchen, haben weniger „Keine Ergebnisse“-Seiten und erleben ein insgesamt besseres Sucherlebnis – was zu mehr Engagement und höheren Conversion Rates führt. Kann Algolia AI Search mehrere Sprachen verarbeiten? 0 Ja. Unsere AI Search API ist sprachunabhängig und darauf trainiert, Bedeutung und Intention in Dutzenden von Sprachen zu verstehen. So können Unternehmen weltweit agieren, ohne für jede Region eine eigene Suche aufzubauen. Wie funktioniert Personalisierung in Algolia AI Search? 0 Algolia erfasst Nutzeraktionen wie Klicks, Ansichten oder Käufe und übersetzt sie in Affinitätsprofile. Diese Profile verbessern Suchergebnisse in Echtzeit, nach textueller und geschäftlicher Relevanz. Für weitergehende Automatisierung erstellt und wendet die Advanced Personalization Pipeline diese Profile automatisch an. Ist für die Implementierung von AI Search Programmieraufwand nötig? 0 Entwickler können die Suche mit APIs und SDKs integrieren und haben volle Flexibilität. Für nicht-technische Teams steht ein Low-Code/No-Code Dashboard bereit, mit dem Relevanzregeln angepasst, Performance analysiert und Änderungen einfach umgesetzt werden können. Ist Algolia für stark frequentierte Websites skalierbar? 0 Ja. Die global verteilte Infrastruktur von Algolia ist so konzipiert, dass sie Antwortzeiten unter 50 ms liefert – selbst für Seiten mit Millionen von Anfragen pro Tag. Wie einfach ist die Implementierung von AI Search? 0 Algolia lässt sich in wenigen Minuten mit APIs oder Dashboard implementieren. Entwickler haben volle Anpassungsfreiheit, während nicht-technische Teams Rankings steuern, Merchandising-Regeln einrichten und Performance überwachen können – ganz ohne Coding. Wie kann ich AI Search messen und optimieren? 0 Das integrierte Analytics-Dashboard von Algolia zeigt wichtige Kennzahlen wie Query-Performance, Klickrate, Zero-Result-Suchen und den Einfluss der Personalisierung. Von dort können Sie Relevanz feinjustieren, A/B-Tests durchführen und Merchandising-Regeln anwenden. Wie kann ich Algolia AI Search ausprobieren? 0 Sie können mit einer kostenlosen Testversion starten, Ihre eigenen Daten hochladen oder Beispieldaten nutzen. So können Sie die Funktionen der Plattform testen, Performance messen und das Nutzererlebnis bewerten, bevor Sie sich entscheiden. Probieren Sie die KI-Suche aus, die versteht Demo anfordern Starten Sie kostenlos Lösungen Überblick AI Search AI Browse AI Recommendations Ask AI Intelligent Data Kit Anwendungsfälle Überclick Enterprise Suche Headless commerce Mobile Suche Sprachgesteuerte Suche Bildersuche OEM Bildersuche Entwickler Developer Hub Dokumentation Integrationen Engineering Blog Discord community API status DocSearch Für Open Source Demos GDPR AI Act Integrationen Salesforce Commerce Cloud B2C Shopify Adobe Commerce Netlify Commercetools BigCommerce Verteilt und Sicher Globale infrastruktur Sicherheit & Konformität Azure AWS Branchen Überclick B2C-E-Commerce B2B-E-Commerce Marktplätze SaaS Medien Startups Fashion Tools Search Grader Ecommerce Search Audit Unternehmen Über Algolia Karriere Newsroom Events Leitung Soziale Wirkung Kontact Kontact Kontact Soziales netwerk Entwickler Developer Hub Dokumentation Integrationen Engineering Blog Discord community API status DocSearch Für Open Source Demos GDPR AI Act Branchen Überclick B2C-E-Commerce B2B-E-Commerce Marktplätze SaaS Medien Startups Fashion Tools Search Grader Ecommerce Search Audit Lösungen Überblick AI Search AI Browse AI Recommendations Ask AI Intelligent Data Kit Anwendungsfälle Überclick Enterprise Suche Headless commerce Mobile Suche Sprachgesteuerte Suche Bildersuche OEM Bildersuche Integrationen Salesforce Commerce Cloud B2C Shopify Adobe Commerce Netlify Commercetools BigCommerce Verteilt und Sicher Globale infrastruktur Sicherheit & Konformität Azure AWS Unternehmen Über Algolia Karriere Newsroom Events Leitung Soziale Wirkung Kontact Kontact Kontact Soziales netwerk Algolia mark white ©2026 Algolia - All rights reserved. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Follow User actions Davide de Paolis Trouble maker and Problem solver ⚙️🔧 Loves simplicity, hates bullshit 💩. Productivity obsessed, avid learner 🖥🚀 Sport and outdoor freak 🧗⛰ Metalhead 🎸🤘 Father of 2 👨👩👦👦 Opinions are my own Location Hamburg Joined Joined on Jan 28, 2019 Personal website https://instagram.com/ddepaolis.solving.problems github website Work Engineering Manager @ sevDesk Kubernetes Awarded to the top Kubernetes author each week Got it Close Six Year Club This badge celebrates the longevity of those who have been a registered member of the DEV Community for at least six years. Got it Close 5 Top 7 Awarded for having a post featured in the weekly "must-reads" list. 🙌 Got it Close Writing Debut Awarded for writing and sharing your first DEV post! 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You've written at least one post per week for 8 consecutive weeks. Unlock the 16-week badge next! Got it Close 4 Week Writing Streak You've posted at least one post per week for 4 consecutive weeks! Got it Close Show all 22 badges More info about @dvddpl Organizations AWS Community Builders Skills/Languages Javascript, Node.js, Serverless, AWS, React, Toastmaster, Currently learning AWS Solution Architect Associate certification Post 193 posts published Comment 526 comments written Tag 9 tags followed Pin Pinned 🌊 Be the Rising Tide: The Multiplying Effect of Lifting (and Pushing) Others Davide de Paolis Davide de Paolis Davide de Paolis Follow Sep 11 '25 🌊 Be the Rising Tide: The Multiplying Effect of Lifting (and Pushing) Others # leadership # outofthecomfortzone # engineeringmanager # personalgrowth Comments Add Comment 3 min read What mass layoffs, cancelled projects and chinese farmers can teach us about resiliency. Davide de Paolis Davide de Paolis Davide de Paolis Follow Nov 10 '22 What mass layoffs, cancelled projects and chinese farmers can teach us about resiliency. # mentalhealth # softwareengineering # career # leadership 63 reactions Comments 5 comments 6 min read Make mistakes, and ask questions. It's OK! Davide de Paolis Davide de Paolis Davide de Paolis Follow Mar 2 '22 Make mistakes, and ask questions. It's OK! # productivity # careeradvice # mentalhealth # blamelessculture 17 reactions Comments Add Comment 9 min read The screw and the hammer: Love the problems, not your solutions. Davide de Paolis Davide de Paolis Davide de Paolis Follow Nov 11 '21 The screw and the hammer: Love the problems, not your solutions. # productivity # career # personaldevelopment # beginners 93 reactions Comments 10 comments 6 min read Strive for simplicity Davide de Paolis Davide de Paolis Davide de Paolis Follow Jan 6 '20 Strive for simplicity # career # softwaredevelopment # architecture # coding 50 reactions Comments 9 comments 4 min read Road To Compliance: Will Your Internal Users Hate Your Platform Team? Davide de Paolis Davide de Paolis Davide de Paolis Follow Jan 10 Road To Compliance: Will Your Internal Users Hate Your Platform Team? # compliance # aws # serverless # publicspeaking Comments 1 comment 12 min read Want to connect with Davide de Paolis? Create an account to connect with Davide de Paolis. You can also sign in below to proceed if you already have an account. Create Account Already have an account? Sign in The Culture Map: Book review and reflections from an EM Leading International Teams Davide de Paolis Davide de Paolis Davide de Paolis Follow Jan 6 The Culture Map: Book review and reflections from an EM Leading International Teams # leadership # bookscorner # growthmindset # collaboration Comments Add Comment 8 min read Vibecoding in Between Meetings: Learning MCP Servers and Building a PoC ( with AWS Kiro) Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Dec 19 '25 Vibecoding in Between Meetings: Learning MCP Servers and Building a PoC ( with AWS Kiro) # ainativedevelopment # mcpservers # aws # agenticai Comments Add Comment 3 min read My 2025 Year in Review - Conferences, Community, and Growth as an AWS Community Builder and Leader Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Dec 16 '25 My 2025 Year in Review - Conferences, Community, and Growth as an AWS Community Builder and Leader # awscommunitybuilder # yearlyretrospective # aws # publicspeaking 3 reactions Comments Add Comment 7 min read From Nagging to Holding People Accountable Davide de Paolis Davide de Paolis Davide de Paolis Follow Dec 15 '25 From Nagging to Holding People Accountable # growthmindset # readingsuggestions # leadership # accountability Comments 2 comments 7 min read Am I doing the best I can? Thoughts about talent, mediocrity, expectations and success. Davide de Paolis Davide de Paolis Davide de Paolis Follow Nov 24 '25 Am I doing the best I can? Thoughts about talent, mediocrity, expectations and success. # personalgrowth # talent # growthmindset # leadership 1 reaction Comments 3 comments 6 min read Serverless vs Kubernetes: The Final Showdown - Why we decided to hold this talk and where to go from here Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Nov 19 '25 Serverless vs Kubernetes: The Final Showdown - Why we decided to hold this talk and where to go from here # serverless # kubernetes # publicspeaking # architecture 4 reactions Comments 1 comment 4 min read Know your tendencies - Questioning yourself (and others)- The 4 Tendencies Framework Davide de Paolis Davide de Paolis Davide de Paolis Follow Oct 22 '25 Know your tendencies - Questioning yourself (and others)- The 4 Tendencies Framework # personalgrowth # mentalhealth # growthmindset # leadership 1 reaction Comments Add Comment 5 min read Reflections from InfoQ Munich 2025 -Platforms, People, and the Path to Sovereignty Davide de Paolis Davide de Paolis Davide de Paolis Follow Oct 17 '25 Reflections from InfoQ Munich 2025 -Platforms, People, and the Path to Sovereignty # techtalks # infoqdevsummit # publicspeaking # conferences Comments Add Comment 8 min read Discomfort isn’t the enemy: Lessons from 3 times I cried at work Davide de Paolis Davide de Paolis Davide de Paolis Follow Oct 13 '25 Discomfort isn’t the enemy: Lessons from 3 times I cried at work # leadership # impostersyndrome # mentalhealth # careerdevelopment 1 reaction Comments Add Comment 5 min read Easy way to configure your kubeconfig and to debug your your EKS Cluster Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Feb 2 '25 Easy way to configure your kubeconfig and to debug your your EKS Cluster # kubernetes # aws # debugging # devops 1 reaction Comments Add Comment 5 min read The easiest way to set up and configure your AWS CLI Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Feb 1 '25 The easiest way to set up and configure your AWS CLI # credentials # aws # cli # cloudengineering 6 reactions Comments Add Comment 4 min read Clean up S3 with Batch Operations, Tags and Lifecycle policies, they said. It will be cheaper, they said. Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Jan 14 '25 Clean up S3 with Batch Operations, Tags and Lifecycle policies, they said. It will be cheaper, they said. # aws # learning # storage # finops 3 reactions Comments Add Comment 9 min read My 2024 (and 3rd year) as an AWS Community Builder Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Jan 1 '25 My 2024 (and 3rd year) as an AWS Community Builder # awscommunitybuilders # publicspeaking # aws 3 reactions Comments Add Comment 6 min read I failed the AWS Solutions Architect Associate exam, so what?! Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Feb 28 '24 I failed the AWS Solutions Architect Associate exam, so what?! # aws # solutionsarchitect # certification # career 52 reactions Comments 24 comments 5 min read my 2023 in review: sad, mad, sad, glad, glad, glad 😓😤🥳! Davide de Paolis Davide de Paolis Davide de Paolis Follow Dec 31 '23 my 2023 in review: sad, mad, sad, glad, glad, glad 😓😤🥳! # career # mentalhealth # mindfulness # personalgrowth 4 reactions Comments 2 comments 8 min read I have read it so you don't have to - Rich Dad, Poor Dad, by R.T. Kiyosaki Davide de Paolis Davide de Paolis Davide de Paolis Follow Dec 29 '23 I have read it so you don't have to - Rich Dad, Poor Dad, by R.T. Kiyosaki # personalgrowth # hustle # career # books 6 reactions Comments Add Comment 6 min read Bragging like a STAR ⭐️! Boost your confidence and be more convincing during salary negotiations and job interviews. Davide de Paolis Davide de Paolis Davide de Paolis Follow Nov 23 '23 Bragging like a STAR ⭐️! Boost your confidence and be more convincing during salary negotiations and job interviews. # career # learning # mentalhealth # motivation 6 reactions Comments 3 comments 10 min read playing around with PartyRock! a new Bedrock playground Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Nov 17 '23 playing around with PartyRock! a new Bedrock playground # partyrockplayground # aws # generativeai 2 reactions Comments Add Comment 2 min read I spoke at AWS Community Day in Munich: how I prepared and how it went. Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Sep 19 '23 I spoke at AWS Community Day in Munich: how I prepared and how it went. # career # publicspeaking # aws # community 47 reactions Comments 14 comments 11 min read There is no right or wrong, as long as it works - Actually, how you use DynamoDB makes a lot of difference! Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders May 31 '23 There is no right or wrong, as long as it works - Actually, how you use DynamoDB makes a lot of difference! # serverless # dynamodb # bestpractices # techlead 12 reactions Comments 1 comment 14 min read Be the rising tide - grow as a leader or engineer by helping others Davide de Paolis Davide de Paolis Davide de Paolis Follow Apr 28 '23 Be the rising tide - grow as a leader or engineer by helping others # leadership # careeradvice # softwareengineering # learning 7 reactions Comments Add Comment 4 min read Understanding the desiredCount and autoscaling behaviour of AWS Fargate - Fuckups 🤦♂️ and learnings 🤓 Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Apr 27 '23 Understanding the desiredCount and autoscaling behaviour of AWS Fargate - Fuckups 🤦♂️ and learnings 🤓 # techlead # serverless # aws # mentalhealth 27 reactions Comments 3 comments 6 min read Fixing Error spawnSync bash ENOENT occurring during CDK Lambda Bundling on CI pipeline Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Apr 14 '23 Fixing Error spawnSync bash ENOENT occurring during CDK Lambda Bundling on CI pipeline # debugging # continuosintegration # node # lambda 8 reactions Comments 2 comments 3 min read Software Engineering is about trade-offs: make sure you have options! (architecture patterns comparison) Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Mar 20 '23 Software Engineering is about trade-offs: make sure you have options! (architecture patterns comparison) # aws # career # techlead # serverless 72 reactions Comments 2 comments 8 min read My first 6 months as AWS Community Builder: boosting my career and fighting Imposter Syndrome! Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Mar 3 '23 My first 6 months as AWS Community Builder: boosting my career and fighting Imposter Syndrome! # career # techlead # community # knowledgesharing 20 reactions Comments 1 comment 8 min read How to Filter Unique values in CloudWatch Insights (+ 3 other query snippets) Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Mar 1 '23 How to Filter Unique values in CloudWatch Insights (+ 3 other query snippets) # debugging # aws # techlead # beginners 20 reactions Comments Add Comment 3 min read Working on legacy code-bases can make us better developers, here is why. Davide de Paolis Davide de Paolis Davide de Paolis Follow Feb 15 '23 Working on legacy code-bases can make us better developers, here is why. # mentalhealth # softwaredevelopment # technicalleadership # career 8 reactions Comments 1 comment 6 min read Disaster Recovery Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Feb 10 '23 Disaster Recovery Cheat-sheet/Write-up # aws # analytics # techlead # solutionsarchitect 4 reactions Comments Add Comment 4 min read AWS Security in the Cloud Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Feb 6 '23 AWS Security in the Cloud Cheat-sheet/Write-up # aws # analytics # techlead # solutionsarchitect 4 reactions Comments Add Comment 8 min read AWS Mobile and Web apps Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow Feb 5 '23 AWS Mobile and Web apps Cheat-sheet/Write-up # aws # analytics # techlead # solutionsarchitect Comments Add Comment 2 min read AWS AI and ML stack Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Feb 4 '23 AWS AI and ML stack Cheat-sheet/Write-up # aws # analytics # techlead # solutionsarchitect 7 reactions Comments Add Comment 3 min read AWS Costs and Budgets Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Feb 3 '23 AWS Costs and Budgets Cheat-sheet/Write-up # aws # analytics # techlead # solutionsarchitectuting 4 reactions Comments Add Comment 4 min read AWS Digest nr 8 Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Feb 2 '23 AWS Digest nr 8 # aws # techlead # serverless # softwaredevelopment 3 reactions Comments Add Comment 2 min read AWS Logging, Monitoring and Auditing Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Feb 1 '23 AWS Logging, Monitoring and Auditing Cheat-sheet/Write-up # aws # analytics # techlead # solutionsarchitect 5 reactions Comments 1 comment 7 min read AWS Serverless Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Jan 25 '23 AWS Serverless Cheat-sheet/Write-up # aws # analytics # techlead # solutionsarchitect 7 reactions Comments Add Comment 12 min read AWS Datastores and Analytics Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Jan 23 '23 AWS Datastores and Analytics Cheat-sheet/Write-up # aws # analytics # techlead # solutionsarchitect 4 reactions Comments Add Comment 8 min read AWS Databases Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Jan 20 '23 AWS Databases Cheat-sheet/Write-up # aws # database # techlead # solutionsarchitect 8 reactions Comments Add Comment 11 min read Get your self-esteem crushed with AWS Certifications and Badges! Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Jan 18 '23 Get your self-esteem crushed with AWS Certifications and Badges! # aws # techlead # serverless # personalgrowth 10 reactions Comments Add Comment 5 min read Send slack notifications with AWS Chatbot Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Jan 13 '23 Send slack notifications with AWS Chatbot # aws # serverless # techlead # monitoring 10 reactions Comments 2 comments 6 min read 2023 - This year go for Smart Goals and Habits Davide de Paolis Davide de Paolis Davide de Paolis Follow Jan 9 '23 2023 - This year go for Smart Goals and Habits # mentalhealth # personalgrowth # career # productivity 8 reactions Comments Add Comment 8 min read my 2022 was not as bad as I thought after reading all other people's yearly retrospectives Davide de Paolis Davide de Paolis Davide de Paolis Follow Jan 8 '23 my 2022 was not as bad as I thought after reading all other people's yearly retrospectives # career # mentalhealth # agile # personaldevelopment 5 reactions Comments 6 comments 9 min read AWS Storage Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Dec 13 '22 AWS Storage Cheat-sheet/Write-up # aws # storage # techlead # solutionsarchitect 11 reactions Comments Add Comment 17 min read AWS Virtual Private Cloud (VPC) Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Dec 9 '22 AWS Virtual Private Cloud (VPC) Cheat-sheet/Write-up # aws # vpc # techlead # solutionsarchitect 9 reactions Comments Add Comment 11 min read AWS Organizations and Control Tower Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Dec 7 '22 AWS Organizations and Control Tower Cheat-sheet/Write-up # aws # cloudcompute # techlead # solutionsarchitect 8 reactions Comments Add Comment 4 min read AWS Elastic Load Balancing and Autoscaling Cheat-sheet/Write-up Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Nov 29 '22 AWS Elastic Load Balancing and Autoscaling Cheat-sheet/Write-up # aws # cloudcompute # techlead # solutionsarchitect 6 reactions Comments Add Comment 10 min read Reflecting on my Path: What becoming a Toastmaster meant for me Davide de Paolis Davide de Paolis Davide de Paolis Follow Nov 28 '22 Reflecting on my Path: What becoming a Toastmaster meant for me # toastmasters # publicspeaking # personalgrowth # learning 8 reactions Comments 2 comments 6 min read AWS Digest nr 7 Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Nov 25 '22 AWS Digest nr 7 # aws # techlead # serverless # softwaredevelopment 5 reactions Comments Add Comment 2 min read Enable AWS CLI Completer for increased productivity Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Nov 21 '22 Enable AWS CLI Completer for increased productivity # aws # awscommunitybuilder # productivity # tips 19 reactions Comments 6 comments 2 min read Serverless Summit Day One Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Nov 17 '22 Serverless Summit Day One # techlead # aws # serverless # solutionsarchitect 8 reactions Comments 2 comments 2 min read Clever coding tricks ( that we don't need ) Davide de Paolis Davide de Paolis Davide de Paolis Follow Nov 16 '22 Clever coding tricks ( that we don't need ) # rant # techlead # javascript # refactoring 27 reactions Comments 7 comments 2 min read Drop those credentials, use Single-Sign-On Login instead Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Nov 16 '22 Drop those credentials, use Single-Sign-On Login instead # aws # serverless # security # accessmanagement 23 reactions Comments 4 comments 8 min read AWS Digest nr 6 Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Nov 14 '22 AWS Digest nr 6 # aws # techlead # serverless # softwaredevelopment 3 reactions Comments Add Comment 3 min read Focus on the positive, pt2 - How do you stay positive, when you lose? Davide de Paolis Davide de Paolis Davide de Paolis Follow Nov 13 '22 Focus on the positive, pt2 - How do you stay positive, when you lose? # publicspeaking # mentalhealth # motivation # writing 6 reactions Comments 1 comment 3 min read Focus on the positive! - No! the world needs more pessimists! Davide de Paolis Davide de Paolis Davide de Paolis Follow Nov 12 '22 Focus on the positive! - No! the world needs more pessimists! # publicspeaking # mentalhealth # funny # writing 4 reactions Comments 3 comments 4 min read From PHP monolith to serverless: multi-stack cross-account canary rollout Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Nov 1 '22 From PHP monolith to serverless: multi-stack cross-account canary rollout # techlead # aws # serverless # refactoring 10 reactions Comments 2 comments 8 min read AWS Digest nr 5 Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Nov 1 '22 AWS Digest nr 5 # aws # techlead # serverless # softwaredevelopment Comments Add Comment 4 min read AWS Digest nr 4 Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Oct 31 '22 AWS Digest nr 4 # aws # techlead # serverless # softwaredevelopment 3 reactions Comments Add Comment 2 min read How to create diagrams of your Architecture Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Oct 24 '22 How to create diagrams of your Architecture # aws # techlead # solutionsarchitect # tools 40 reactions Comments 2 comments 3 min read AWS Digest nr 3 Davide de Paolis Davide de Paolis Davide de Paolis Follow for AWS Community Builders Oct 19 '22 AWS Digest nr 3 # aws # techlead # serverless # softwaredevelopment 1 reaction Comments Add Comment 3 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://www.fine.dev/blog/ai-coding-guide#practical-tips-backend | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
https://docs.devcycle.com/integrations/bitbucket/pr-insights-action/ | Bitbucket: Feature Flag Change Insights on Pull Request | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page Bitbucket: Feature Flag Change Insights on Pull Request Get the integration on the Bitbucket Marketplace Bitbucket Pipelines Pipe: DevCycle PR Insights With this Bitbucket pipeline, information on which DevCycle features have been added or removed in a code change will be shown directly on each Pull Request as a comment. Note: This is intended for pull-requests workflow events Example Output YAML Definition Add the following snippet to your bitbucket-pipelines.yml file: pull-requests : '*' : - step : script : - pipe : devcyclehq/devcycle - pr - insights - pipe : 1.2.1 variables : USER_NAME : '<string>' PASSWORD : '<string>' # PROJECT_KEY: '<string>' # Optional. # CLIENT_ID: '<string>' # Optional. # CLIENT_SECRET: '<string>' # Optional. We recommend including your DevCycle API credentials and project token as inputs. If included, the PR comment will be enriched with Feature Flag data from DevCycle. Variables To add variables to be used in the bitbucket-pipelines.yml, an admin must add Repository Variables in Repository Settings > Repository Variables, and then add all necessary variables as secured variables Variable Description USER_NAME (*) Your bitbucket username PASSWORD (*) Your generated app password PROJECT_KEY Your DevCycle project key, see Projects CLIENT_ID Your organization's API client ID, see Organization Settings CLIENT_SECRET Your organization's API client secret, see Organization Settings (*) = required variable. Prerequisites Create a new Project & a new Feature Generate a new App Password Select write permissions under Pull Requests , and create the password Grab your username, can easily find it in Personal Settings Optional Prerequisites Grab the PROJECT_KEY in Projects , and find your specific project name & key Grab the CLIENT_ID in Settings , under API AUTHENTICATION Grab the CLIENT_SECRET in Settings , under API AUTHENTICATION Examples Example: pull-requests : '*' : - step : script : - pipe : devcyclehq/devcycle - pr - insights - pipe : 1.2.1 variables : USER_NAME : $BITBUCKET_USER_NAME PASSWORD : $BITBUCKET_APP_PASSWORD PROJECT_KEY : $DEVCYCLE_PROJECT_KEY CLIENT_ID : $DEVCYCLE_CLIENT_ID CLIENT_SECRET : $DEVCYCLE_CLIENT_SECRET Configuration The patterns used to identify references to variables in your code are fully customizable. This action uses the DevCycle CLI under the hood, for details on how to configure the pattern matcher see the CLI configuration . Support The pipe is maintained by [email protected] . If you are reporting an issue, please include: the version of the pipe relevant logs and error messages steps to reproduce Edit this page Example Output YAML Definition Variables Prerequisites Examples Configuration Support DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Amazon Web Services Follow Hide Amazon Web Services (AWS) is a collection of web services for computing, storage, machine learning, security, and more There are over 200+ AWS services as of 2023. Create Post submission guidelines Articles which primary focus is AWS are permitted to used the #aws tag. Older #aws posts 10 11 12 13 14 15 16 17 18 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu Your 2026 Cloud Computing Roadmap: Start Here Technical Monish Technical Monish Technical Monish Follow Dec 31 '25 Your 2026 Cloud Computing Roadmap: Start Here # aws # cloudcomputing # roadmap # career Comments Add Comment 5 min read Building an Adaptive Learning Agent Using Kiro IDE- agentic-ai-edtech Asim Shaikh Asim Shaikh Asim Shaikh Follow Dec 30 '25 Building an Adaptive Learning Agent Using Kiro IDE- agentic-ai-edtech # aws # kiroide # ai # hackathon Comments Add Comment 2 min read How We Migrated an AWS Amplify GraphQL Backend to CDK (Without a Rewrite) StackOps AI StackOps AI StackOps AI Follow Dec 30 '25 How We Migrated an AWS Amplify GraphQL Backend to CDK (Without a Rewrite) # architecture # aws # devops Comments Add Comment 4 min read I fired my server manager: Running Python on AWS Lambda (Serverless) Eric Rodríguez Eric Rodríguez Eric Rodríguez Follow Dec 30 '25 I fired my server manager: Running Python on AWS Lambda (Serverless) # aws # python # serverless # lambda Comments Add Comment 1 min read Prompts que construyen: cómo dominar Amazon Q Developer en tu día a día Bianca Torres Bianca Torres Bianca Torres Follow Jan 5 Prompts que construyen: cómo dominar Amazon Q Developer en tu día a día # aws # devops # ai # promptengineering Comments Add Comment 3 min read Day 29: Establishing Secure Communication Between Public and Private VPCs via VPC Peering Thu Kha Kyawe Thu Kha Kyawe Thu Kha Kyawe Follow Dec 30 '25 Day 29: Establishing Secure Communication Between Public and Private VPCs via VPC Peering # aws # 100daysofcloudaws Comments Add Comment 3 min read My Perspective on AWS Security Hub for DevSecOps andre aliaman andre aliaman andre aliaman Follow Dec 31 '25 My Perspective on AWS Security Hub for DevSecOps # aws # devops # cybersecurity # productivity Comments Add Comment 2 min read Building Dreim Paper: A YouTube Shorts Pipeline for Reddit Poetry Matthew Reid Matthew Reid Matthew Reid Follow Dec 29 '25 Building Dreim Paper: A YouTube Shorts Pipeline for Reddit Poetry # webdev # automation # ai # aws Comments Add Comment 5 min read A Guide to AWS CloudFront Invalidations nithish rodrigo nithish rodrigo nithish rodrigo Follow Jan 3 A Guide to AWS CloudFront Invalidations # aws # cloudfront # invalidation # s3 2 reactions Comments Add Comment 2 min read AWS Organizations: The Easy Way Adedoyin Adedoyin Adedoyin Follow Dec 29 '25 AWS Organizations: The Easy Way # aws # productivity # tutorial Comments Add Comment 2 min read A Clean AWS Architecture for Authenticated Full-Stack Serverless Apps AwsKnowledgeHub AwsKnowledgeHub AwsKnowledgeHub Follow Jan 3 A Clean AWS Architecture for Authenticated Full-Stack Serverless Apps # aws # serverless # architecture # webdev Comments Add Comment 2 min read Moving VMware to AWS Just Got a Whole Lot Easier WILLIAM OBIANA WILLIAM OBIANA WILLIAM OBIANA Follow Dec 30 '25 Moving VMware to AWS Just Got a Whole Lot Easier # aws # cloud # ai # devops 1 reaction Comments Add Comment 2 min read Kiro: Instant Finance & Expense Tracking Shreya Nalawade Shreya Nalawade Shreya Nalawade Follow Jan 4 Kiro: Instant Finance & Expense Tracking # aws # ai # serverless # kiro 6 reactions Comments Add Comment 4 min read Define Methods of Deploying and Operating in the AWS Cloud Ntombizakhona Mabaso Ntombizakhona Mabaso Ntombizakhona Mabaso Follow for AWS Community Builders Jan 3 Define Methods of Deploying and Operating in the AWS Cloud # aws # cloud # cloudcomputing # cloudpractitioner 2 reactions Comments Add Comment 3 min read 🚀 Terraform Day 24: Highly Available Web Application on AWS (ALB + ASG + Private Subnets) Jeeva Jeeva Jeeva Follow Dec 30 '25 🚀 Terraform Day 24: Highly Available Web Application on AWS (ALB + ASG + Private Subnets) # architecture # devops # aws # terraform Comments Add Comment 2 min read Publish Jekyll on Amazon Linux2 on EC2 dss99911 dss99911 dss99911 Follow Dec 31 '25 Publish Jekyll on Amazon Linux2 on EC2 # tools # jekyll # aws # ec2 Comments Add Comment 3 min read 🔒 Building a Secure AWS Environment with Terraform + AWS CloudShell LaTerral Williams LaTerral Williams LaTerral Williams Follow Dec 29 '25 🔒 Building a Secure AWS Environment with Terraform + AWS CloudShell # security # devops # aws # terraform 1 reaction Comments Add Comment 6 min read 🛡️ Building a Cloud Security Dashboard with AWS Athena + QuickSight (Beginner Friendly) LaTerral Williams LaTerral Williams LaTerral Williams Follow Dec 29 '25 🛡️ Building a Cloud Security Dashboard with AWS Athena + QuickSight (Beginner Friendly) # analytics # beginners # security # aws Comments Add Comment 8 min read pydynox: A fast DynamoDB ORM for Python with a Rust core. Leandro Damascena Leandro Damascena Leandro Damascena Follow Jan 1 pydynox: A fast DynamoDB ORM for Python with a Rust core. # python # dynamodb # rust # aws 1 reaction Comments Add Comment 3 min read 🚀AWS Community Builder 2026: Your Golden Ticket to Cloud Stardom! Harshal Pagar Harshal Pagar Harshal Pagar Follow Dec 29 '25 🚀AWS Community Builder 2026: Your Golden Ticket to Cloud Stardom! # aws # awscommunity # awscommunitybuilders Comments Add Comment 7 min read Mastering Serverless Data Pipelines: AWS Step Functions Best Practices for 2026 Jubin Soni Jubin Soni Jubin Soni Follow Dec 30 '25 Mastering Serverless Data Pipelines: AWS Step Functions Best Practices for 2026 # aws # serverless # stepfunctions # dataengineering Comments Add Comment 5 min read EP 5: Autoscaling - Building Systems that Breathe Hrishikesh Dalal Hrishikesh Dalal Hrishikesh Dalal Follow Dec 29 '25 EP 5: Autoscaling - Building Systems that Breathe # webdev # kubernetes # aws Comments Add Comment 3 min read Stop Writing User Stories for AI. Start Writing Specs. Jacques Montagne Jacques Montagne Jacques Montagne Follow Dec 29 '25 Stop Writing User Stories for AI. Start Writing Specs. # genai # architecture # productivity # aws Comments Add Comment 3 min read Remote backend and State locking using S3 in terraform. Rohan Nalawade Rohan Nalawade Rohan Nalawade Follow Dec 29 '25 Remote backend and State locking using S3 in terraform. # terraform # aws # infrastructureascode # devops Comments Add Comment 2 min read 🚀 Terraform Day 23: Production-Grade Monitoring & Observability on AWS (Serverless) Jeeva Jeeva Jeeva Follow Dec 29 '25 🚀 Terraform Day 23: Production-Grade Monitoring & Observability on AWS (Serverless) # serverless # aws # terraform # monitoring Comments Add Comment 2 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Amazon Web Services Follow Hide Amazon Web Services (AWS) is a collection of web services for computing, storage, machine learning, security, and more There are over 200+ AWS services as of 2023. Create Post submission guidelines Articles which primary focus is AWS are permitted to used the #aws tag. Older #aws posts 12 13 14 15 16 17 18 19 20 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu Introduction to AWS AI Concepts: A Beginner's Guide Annysah Annysah Annysah Follow for AWS Community Builders Dec 29 '25 Introduction to AWS AI Concepts: A Beginner's Guide # ai # aws # beginners 2 reactions Comments Add Comment 5 min read How I Stopped Over-Thinking and Started Shipping Brandon Charest Brandon Charest Brandon Charest Follow Dec 29 '25 How I Stopped Over-Thinking and Started Shipping # programming # rust # beginners # aws Comments Add Comment 3 min read Understand AWS Cloud Security, Governance, and Compliance Concepts Ntombizakhona Mabaso Ntombizakhona Mabaso Ntombizakhona Mabaso Follow for AWS Community Builders Jan 2 Understand AWS Cloud Security, Governance, and Compliance Concepts # aws # cloud # cloudcomputing # cloudpractitioner 1 reaction Comments Add Comment 3 min read 🚀 Terraform Day 22: Secure Two-Tier Architecture on AWS (EC2 + RDS) Jeeva Jeeva Jeeva Follow Dec 28 '25 🚀 Terraform Day 22: Secure Two-Tier Architecture on AWS (EC2 + RDS) # architecture # security # aws # terraform Comments Add Comment 2 min read Amazon Nova 2: AWS's Reasoning Models That Actually Get Production Needs! Ahmed Adel Ahmed Adel Ahmed Adel Follow Dec 28 '25 Amazon Nova 2: AWS's Reasoning Models That Actually Get Production Needs! # cloud # ai # aws # machinelearning 5 reactions Comments Add Comment 3 min read Optimized Image Hosting: Why I Integrated Cloudflare R2 with my AWS Backend Yasith wijesuriya Yasith wijesuriya Yasith wijesuriya Follow Dec 28 '25 Optimized Image Hosting: Why I Integrated Cloudflare R2 with my AWS Backend # aws # cloudflare # serverless # architecture Comments Add Comment 2 min read Simple Service Relationships in EventBridge Roger Chi Roger Chi Roger Chi Follow for AWS Community Builders Jan 12 Simple Service Relationships in EventBridge # aws # serverless # eventdriven # awscdk 2 reactions Comments Add Comment 8 min read Variables in Terraform: My learnings. Rohan Nalawade Rohan Nalawade Rohan Nalawade Follow Dec 28 '25 Variables in Terraform: My learnings. # terraform # aws # cloud # devops Comments Add Comment 3 min read Comparing Cloud AI Platforms in 2025: Bedrock, Azure OpenAI, and Gemini Swati Tyagi Swati Tyagi Swati Tyagi Follow Dec 28 '25 Comparing Cloud AI Platforms in 2025: Bedrock, Azure OpenAI, and Gemini # aws # bedrock # openai # gemini Comments Add Comment 2 min read Turning License Changes into Opportunity Eyal Estrin Eyal Estrin Eyal Estrin Follow for AWS Community Builders Dec 29 '25 Turning License Changes into Opportunity # productivity # opensource # aws # tooling 1 reaction Comments Add Comment 7 min read Getting started with GPU Programming on an EC2! Sreekesh Iyer Sreekesh Iyer Sreekesh Iyer Follow for AWS Community Builders Dec 30 '25 Getting started with GPU Programming on an EC2! # cuda # gpu # aws # nvidia 6 reactions Comments Add Comment 5 min read Create a Professional Certification Quiz with Kiro Olivier Lemaitre Olivier Lemaitre Olivier Lemaitre Follow Jan 11 Create a Professional Certification Quiz with Kiro # aws # certification # programming # genai 2 reactions Comments Add Comment 10 min read Understand the AWS Shared Responsibility Model Ntombizakhona Mabaso Ntombizakhona Mabaso Ntombizakhona Mabaso Follow for AWS Community Builders Jan 1 Understand the AWS Shared Responsibility Model # aws # cloud # cloudcomputing # cloudpractitioner Comments Add Comment 3 min read DEV Track Spotlight: Compile blazing-fast MCP servers in Rust (DEV405) Gunnar Grosch Gunnar Grosch Gunnar Grosch Follow for AWS Jan 1 DEV Track Spotlight: Compile blazing-fast MCP servers in Rust (DEV405) # aws # ai # mcp # rust 1 reaction Comments Add Comment 6 min read From ClickOps to DevOps: My First Infrastructure as Code Project with Terraform Rohan Nalawade Rohan Nalawade Rohan Nalawade Follow Dec 27 '25 From ClickOps to DevOps: My First Infrastructure as Code Project with Terraform # cloud # aws # devops # terraform Comments Add Comment 3 min read Goodbye Localhost: Why I deployed my site on raw S3 instead of Netlify Eric Rodríguez Eric Rodríguez Eric Rodríguez Follow Dec 27 '25 Goodbye Localhost: Why I deployed my site on raw S3 instead of Netlify # aws # webdev # beginners # s3 Comments Add Comment 2 min read 5 Dockerfile Misconfigurations You Should Avoid Haripriya Veluchamy Haripriya Veluchamy Haripriya Veluchamy Follow Jan 11 5 Dockerfile Misconfigurations You Should Avoid # docker # containers # devops # aws 1 reaction Comments Add Comment 2 min read AWS Terraform Blue-Green Deployment Using Elastic Beanstalk Amit Kushwaha Amit Kushwaha Amit Kushwaha Follow Jan 11 AWS Terraform Blue-Green Deployment Using Elastic Beanstalk # devops # aws # learning # kubernetes 6 reactions Comments Add Comment 7 min read Native Amazon EKS Backups with AWS Backup saifeddine Rajhi saifeddine Rajhi saifeddine Rajhi Follow for AWS Community Builders Dec 29 '25 Native Amazon EKS Backups with AWS Backup # aws # eks # backup # kubernetes 3 reactions Comments Add Comment 4 min read Building an AI Document Processing Pipeline on AWS (Textract + Bedrock) Vasil Shaikh Vasil Shaikh Vasil Shaikh Follow Dec 27 '25 Building an AI Document Processing Pipeline on AWS (Textract + Bedrock) # aws # ai # serverless # machinelearning Comments Add Comment 5 min read Why We Didn’t Move to EKS (Yet): Choosing ECS Over Kubernetes in Production Amaan Ul Haq Siddiqui Amaan Ul Haq Siddiqui Amaan Ul Haq Siddiqui Follow Dec 28 '25 Why We Didn’t Move to EKS (Yet): Choosing ECS Over Kubernetes in Production # architecture # kubernetes # aws # devops 5 reactions Comments Add Comment 3 min read Bridging the Gap: Adding a `dev` Subdomain to Your Production EC2 Environment Ajit Kumar Ajit Kumar Ajit Kumar Follow Dec 27 '25 Bridging the Gap: Adding a `dev` Subdomain to Your Production EC2 Environment # docker # aws # devops # tutorial Comments Add Comment 3 min read From Shared Infrastructure to Scalable Platforms: De-Risking Our Vertical Technology Roadmap Jotheeswaran Jotheeswaran Jotheeswaran Follow Dec 27 '25 From Shared Infrastructure to Scalable Platforms: De-Risking Our Vertical Technology Roadmap # architecture # aws # devops Comments Add Comment 3 min read 👤 AWS 116: Who Goes There? - Creating Your First IAM User Hritik Raj Hritik Raj Hritik Raj Follow Dec 27 '25 👤 AWS 116: Who Goes There? - Creating Your First IAM User # aws # iam # security # 100daysofcloud Comments Add Comment 3 min read Serverless on Kubernetes with KNative and EKS Victor Manuel Pinzon Victor Manuel Pinzon Victor Manuel Pinzon Follow Dec 26 '25 Serverless on Kubernetes with KNative and EKS # knative # kubernetes # eks # aws Comments Add Comment 3 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://docs.devcycle.com/integrations/vercel-edge-config/ | Vercel Edge Config | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page Vercel Edge Config The Vercel Edge Config integration allows you to automatically sync DevCycle flagging configurations to Vercel Edge Config , a lightning fast data storage system optimized for deployments running on Vercel. With this integration, DevCycle SDKs running in Vercel can obtain flag configurations faster than ever, reducing latency and improving your user experience. Prerequisites The Edge Config integration is currently available for the following SDKs: Node.js Next.js Using the integration requires a Vercel account, an application running in that account which is using one of the above SDKs, and an Edge Config connected to a project in Vercel . info Using this integration will count towards your usage of Edge Config in Vercel and is subject to billing in your Vercel account. There are also limits to the size of data that can be stored in Edge Config. You can see the limit your plan allows on the Limits and Pricing page. We recommend using a separate Edge Config specifically for storing DevCycle configurations in order to allocate as much storage as possible. We also recommend having a Pro or Enterprise plan, as a typical DevCycle configuration will not fit within the hobby plan size limit. The configuration size will grow with the number of flags in your project, and heavy users may require an enterprise plan to fit their configuration. If your Edge Config cannot store your project's configuration after the integration has been set up, we will reach out to you with more information. Setup Configure Integration To get started, visit the integration's page on Vercel and click "Add Integration". This will open a separate window which will load the setup form on the DevCycle dashboard. If you don't already have a DevCycle account, you will be taken through the signup process. Once you've created your account and Organization, you'll be brought back to the integration setup. Once logged in, you can select which projects in DevCycle you want to sync to Edge Config, and which Edge Config to sync them to. If you don't have an Edge Config already set up, one will be created for you with the name "devcycle" during the configuration flow. You can also create an Edge Config ahead of time by following Vercel's documentation . After you have made your selections, hit "Submit" to finish configuring the integration. Connect a Vercel Project To use the integration in your application, you must connect the desired Edge Config in Vercel to the Vercel project where you will be running DevCycle. To do so, navigate to the "Storage" tab in the Vercel Dashboard, click on the Edge Config you want to connect, and visit the "Projects" section in the sidebar. See the Vercel docs for more information Once a project has been connected, an environment variable called EDGE_CONFIG will be set in that project which can be used in the following steps to connect to your Edge Config. In order to set this variable locally for testing, make sure to pull down the latest environment variables using the Vercel CLI by executing the following in your project's directory: vercel env pull If you do not have the Vercel CLI set up for this project, follow their steps to link the CLI to your Vercel Project Setup SDK If you haven't already installed the DevCycle Node.js or Next.js SDK you can follow the installation and usage guides for those SDKs in our documentation here . You can also find helpful setup information like where to find DevCycle SDK keys in our Quickstart Tutorial . In order to use the integration in a DevCycle SDK, you must install the @devcycle/vercel-edge-config package and provide it during SDK initialization. Using that package requires the @vercel/edge-config package to be installed as well: npm install @devcycle/vercel-edge-config @vercel/edge-config or yarn add @devcycle/vercel-edge-config @vercel/edge-config Follow the SDK-specific instructions below: Node.js For more information on Node SDK usage, see the docs import { createClient } from '@vercel/edge-config' import { EdgeConfigSource } from '@devcycle/vercel-edge-config' import { initializeDevCycle } from '@devcycle/nodejs-server-sdk' // the EDGE_CONFIG environment variable contains a connection string for a particular edge config. It is set automatically // when you connect an edge config to a project in Vercel. const edgeClient = createClient ( process . env . EDGE_CONFIG ) const edgeConfigSource = new EdgeConfigSource ( edgeClient ) const devcycleClient = initializeDevCycle ( process . env . DEVCYCLE_SERVER_SDK_KEY , // pass the edgeConfigSource as the "configSource" option during SDK intialization to tell the SDK to use Edge Config // for retrieving its configuration { configSource : edgeConfigSource } ) Now the SDK will retrieve its configuration from Edge Config. That's it! Next.js For more information on Next.js SDK usage, see the docs App Router import { createClient } from '@vercel/edge-config' import { EdgeConfigSource } from '@devcycle/vercel-edge-config' import { setupDevCycle } from '@devcycle/nextjs-sdk/server' const edgeClient = createClient ( process . env . EDGE_CONFIG ) const edgeConfigSource = new EdgeConfigSource ( edgeClient ) export const { getVariableValue , getClientContext } = setupDevCycle ( { serverSDKKey : process . env . DEVCYCLE_SERVER_SDK_KEY ?? '' , clientSDKKey : process . env . NEXT_PUBLIC_DEVCYCLE_CLIENT_SDK_KEY ?? '' , userGetter : ( ) => ( { user_id : 'test_user' } ) , options : { // pass the configSource option with the instance of EdgeConfigSource configSource : edgeConfigSource } } ) Pages Router import { createClient } from '@vercel/edge-config' import { EdgeConfigSource } from '@devcycle/vercel-edge-config' import { getServerSideDevCycle } from '@devcycle/nextjs-sdk/pages' const edgeClient = createClient ( process . env . EDGE_CONFIG ) const edgeConfigSource = new EdgeConfigSource ( edgeClient ) export const getServerSideProps : GetServerSideProps = async ( context ) => { const user = { user_id : 'server-user' , } return { props : { ... ( await getServerSideDevCycle ( { serverSDKKey : process . env . DEVCYCLE_SERVER_SDK_KEY || '' , clientSDKKey : process . env . NEXT_PUBLIC_DEVCYCLE_CLIENT_SDK_KEY || '' , user , context , options : { // pass the configSource option with the instance of EdgeConfigSource configSource : edgeConfigSource } } ) ) , } , } } Edit this page Last updated on Jan 9, 2026 Prerequisites Setup Configure Integration Connect a Vercel Project Setup SDK DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://www.python.org/downloads/release/python-3142 | Python Release Python 3.14.2 | Python.org Notice: While JavaScript is not essential for this website, your interaction with the content will be limited. Please turn JavaScript on for the full experience. Skip to content ▼ Close Python PSF Docs PyPI Jobs Community ▲ The Python Network Donate ≡ Menu Search This Site GO A A Smaller Larger Reset Socialize LinkedIn Mastodon Chat on IRC Twitter About Applications Quotes Getting Started Help Python Brochure Downloads All releases Source code Windows macOS Android Other Platforms License Alternative Implementations Documentation Docs Audio/Visual Talks Beginner's Guide FAQ Non-English Docs PEP Index Python Books Python Essays Community Diversity Mailing Lists IRC Forums PSF Annual Impact Report Python Conferences Special Interest Groups Python Logo Python Wiki Code of Conduct Community Awards Get Involved Shared Stories Success Stories Arts Business Education Engineering Government Scientific Software Development News Python News PSF Newsletter PSF News PyCon US News News from the Community Events Python Events User Group Events Python Events Archive User Group Events Archive Submit an Event Python 3.14.2 Release date: Dec. 5, 2025 This is the second maintenance release of Python 3.14 Python 3.14.2 is the second maintenance release of 3.14, containing 18 bugfixes, build improvements and documentation changes since 3.14.1. This is an expedited release to fix the following regressions: gh-142206 : Exceptions in multiprocessing in running programs while upgrading Python. gh-142214 : Exceptions in dataclasses without __init__ method. gh-142218 : Segmentation faults and assertion failures in insertdict. gh-140797 : Crash when using multiple capturing groups in re.Scanner And these security fixes: gh-142145 : Remove quadratic behavior in node ID cache clearing ( CVE-2025-12084 ) gh-119452 : Fix a potential virtual memory allocation denial of service in http.server Major new features of the 3.14 series, compared to 3.13 Some of the major new features and changes in Python 3.14 are: New features PEP 779 : Free-threaded Python is officially supported PEP 649 : The evaluation of annotations is now deferred, improving the semantics of using annotations. PEP 750 : Template string literals (t-strings) for custom string processing, using the familiar syntax of f-strings. PEP 734 : Multiple interpreters in the stdlib. PEP 784 : A new module compression.zstd providing support for the Zstandard compression algorithm. PEP 758 : except and except* expressions may now omit the brackets. Syntax highlighting in PyREPL , and support for color in unittest , argparse , json and calendar CLIs. PEP 768 : A zero-overhead external debugger interface for CPython. UUID versions 6-8 are now supported by the uuid module, and generation of versions 3-5 are up to 40% faster. PEP 765 : Disallow return / break / continue that exit a finally block. PEP 741 : An improved C API for configuring Python. A new type of interpreter . For certain newer compilers, this interpreter provides significantly better performance. Opt-in for now, requires building from source. Improved error messages. Builtin implementation of HMAC with formally verified code from the HACL* project. A new command-line interface to inspect running Python processes using asynchronous tasks. The pdb module now supports remote attaching to a running Python process . For more details on the changes to Python 3.14, see What’s new in Python 3.14 . Build changes PEP 761 : Python 3.14 and onwards no longer provides PGP signatures for release artifacts. Instead, Sigstore is recommended for verifiers. Official macOS and Windows release binaries include an experimental JIT compiler . Official Android binary releases are now available. Incompatible changes, removals and new deprecations Incompatible changes Python removals and deprecations C API removals and deprecations Overview of all pending deprecations Python install manager The installer we offer for Windows is being replaced by our new install manager, which can be installed from the Windows Store or from its download page . See our documentation for more information. The JSON file available for download contains the list of all the installable packages available as part of this release, including file URLs and hashes, but is not required to install the latest release. The traditional installer will remain available throughout the 3.14 and 3.15 releases. More resources Online documentation PEP 745 , 3.14 Release Schedule Report bugs at github.com/python/cpython/issues Help fund Python directly (or via GitHub Sponsors ) and support the Python community Enjoy the new release Thanks to all of the many volunteers who help make Python Development and these releases possible! Please consider supporting our efforts by volunteering yourself or through organisation contributions to the Python Software Foundation . 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close # analysis Follow Hide Scene and theme analyses Create Post Older #analysis posts 1 2 3 4 5 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . 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Ayanda Follow May 19 '23 Bash Script: SonarQube and Postgres database Setup for Code Analysis. # sonarqube # postgres # devops # analysis 5 reactions Comments 1 comment 3 min read Basic Text Analysis with Python and MongoDB CertosinoLab CertosinoLab CertosinoLab Follow May 7 '23 Basic Text Analysis with Python and MongoDB # python # mongodb # analysis # matplotlib 1 reaction Comments Add Comment 2 min read Getting started with Sentiment Analysis and Implementation Njeri Gitome Njeri Gitome Njeri Gitome Follow Mar 25 '23 Getting started with Sentiment Analysis and Implementation # datascience # analysis 3 reactions Comments 1 comment 5 min read Compliance Monitoring for Call Centers Tonya Sims Tonya Sims Tonya Sims Follow for Deepgram Dec 1 '22 Compliance Monitoring for Call Centers # python # speechtotext # callcenter # analysis 6 reactions Comments Add Comment 5 min read Stack Trace / Thread Dump Analysis Darius Juodokas Darius Juodokas Darius Juodokas Follow Sep 2 '22 Stack Trace / Thread Dump Analysis # performance # analysis # stack # threaddump 17 reactions Comments Add Comment 26 min read Getting started with Sylver - Part1 : Building a JSON parser in 49 lines of code geoffreycopin geoffreycopin geoffreycopin Follow Aug 10 '22 Getting started with Sylver - Part1 : Building a JSON parser in 49 lines of code # json # parser # analysis 13 reactions Comments Add Comment 5 min read Common Risk Analysis and Management Strategies and Why It Should be Done arnabroychowdhury arnabroychowdhury arnabroychowdhury Follow for LambdaTest May 25 '22 Common Risk Analysis and Management Strategies and Why It Should be Done # management # analysis # testing 13 reactions Comments Add Comment 5 min read Why a Pnpm and Nx monorepo? 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https://docs.devcycle.com/integrations/snowflake/ | Snowflake | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up On this page Snowflake DevCycle's Snowflake Data Sharing integration allows you to gain access to all data that are collected by the DevCycle SDKs for your organization. Data Sharing provides a secure and straightforward approach to sharing data between companies. For further information, you may visit Snowflake's dedicated page on Data Sharing . Requirements The integration is only available to Organizations that are on Business or Enterprise plans at this time. Your Organization's Snowflake Account Region must be AWS - US East 1. Setup To begin the setup process for data sharing, you'll need to provide DevCycle with a few details from your Snowflake instance. Please reach out to your contact or to [email protected] and include the following information: Name/ID of DevCycle Organization Snowflake Account Name Snowflake Organization DevCycle will create the request to share data to your Snowflake instance. Once it's ready, please visit your Snowflake portal to accept the sharing request. Schema DevCycle's Snowflake Schema containing definitions of each field. Field Type Description _ID string DVC unique event identifier TYPE string Type of the event TARGET string Location of tracked event CUSTOMTYPE string Custom defined type for the event _PROJECT string Unique Project ID _ENVIRONMENT string Unique Environment ID USER_ID string User ID DATE timestamp_ntz Date on which the event was saved to DVC servers CLIENTDATE timestamp_ntz Date on which the event was tracked on the device VALUE float Any value associated with the event FEATUREVARS JSON Set of Variation IDs mapped to Feature IDs USER JSON User and Device information METADATA JSON Any metadata associated with the event A0_ORGANIZATION string Unique Organization ID Contributing to DevCycle or creating a new Integration: If you would like to contribute to an existing integration or tool, all of DevCycle's tools and integrations are open source on the DevCycle github repository. Further, if you'd like to create a new tool or integration, a great starting point is DevCycle's Management API which allows you to modify and interact with features and more within a devcycle project, as well as the DevCycle Bucketing API which is used to give users features and variables (as used within the DevCycle SDKs!) Edit this page Last updated on Jan 9, 2026 Requirements Setup Schema DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:45 |
https://www.npopov.com/2023/10/03/LLVM-Scalar-evolution.html | LLVM: Scalar evolution Blog by nikic . Find me on GitHub , StackOverflow , Twitter and Mastodon . Learn more about me . « Back to article overview. LLVM: Scalar evolution 03. October 2023 Scalar evolution (SE or SCEV) models how values change inside loops. It can answer questions like “how many iterations does this loop perform?” and “What value does this variable have on the Nth iteration?” As such, it is the main building block for loop optimizations. Scalar evolution models values using non-recursive expressions, which are called SCEVs. Yes, we use the same term for the whole analysis and individual expressions. The most important expression type is the add recurrence (addrec). Add recurrences Add recurrences model induction variables (IVs) which have a start value on the first iteration, and then increment by a fixed amount on each loop iteration. In LLVM IR, such an IV might look like this: loop: %iv = phi i32 [ 0 , %preheader ], [ %iv.next , %loop ] call void @do_something ( i32 %iv ) %iv.next = add i32 %iv , 1 %cmp = icmp ne i32 %iv.next , 100 br i1 %cmp , label %loop , label %exit The value %iv corresponds to the addrec {0,+,1}<%loop> , which says it is 0 on the first iteration, and then increments by one on each following iteration. We call this the pre-inc addrec. The value %iv.next corresponds to the addrec {1,+,1}<%loop> , which is 1 on the first iteration, and also increments by one. This is the post-inc addrec. This example uses the simplest possible canonical induction variable, but it generalizes to arbitrary start and step values: loop: %iv = phi i32 [ %start , %preheader ], [ %iv.next , %loop ] call void @do_something ( i32 %iv ) %iv.next = add i32 %iv , %step %cmp = icmp ne i32 %iv.next , 100 br i1 %cmp , label %loop , label %exit Here %iv is {%start,+,%step}<%loop> and %iv.next is {(%start + %step),+,%step}<%loop> . However, %step still needs to be a loop-invariant value, which is the same for all iterations. SCEV cannot model recurrences with loop-varying steps, such as in this example: loop: %iv = phi i32 [ %start , %preheader ], [ %iv.next , %loop ] call void @do_something ( i32 %iv ) %step = call i32 @get_step () %iv.next = add i32 %iv , %step %cmp = icmp ne i32 %iv.next , 100 br i1 %cmp , label %loop , label %exit The exception to this is that the step might be an addrec itself, resulting in something like {0,+,1,+,1} , which should be interpreted as {0,+,{1,+,1}} and takes values 0, 1, 2, 4, 7, 11, 16, etc. These are called non-affine addrecs and have little practical relevance, so we can mostly ignore them. Expressions While add recurrences are what we primarily want to reason about, SCEV expressions also represent a number of other basic operations: Addition: (%a + %b + %c) Multiplication: (%a * %b * %c) Min/max: (%a umin %b umin %c) (same for umax , smin and smax ) Sequential umin: (%a umin_seq %b umin_seq %c) (a non-commutative form of umin that has different poison-propagation semantics) Unsigned division: (%a /u %b) Zero-extend: (zext i32 %a to i64) Sign-extend: (sext i32 %a to i64) Truncate: (trunc i64 %a to i32) Pointer-to-int cast: (ptrtoint ptr %a to i64) SCEV expressions can be nested inside each other, while the leafs will always be either constants like 0 or “unknown” nodes that map to an IR value that cannot be analyzed further (like %a etc in these examples). Unlike IR instructions, SCEV nodes for add/mul/min/max accept multiple operands. Rather than (%a + (%b + %c)) or ((%a + %b) + %c) we have (%a + %b + %c) . These operations are associative and commutative, so grouping or ordering of operands does not matter. This nicely ties into the next property: SCEV expressions are canonicalized on construction. While something like (%a + (%b + %c)) can technically be represented, SCEV will automatically fold it into (%a + %b + %c) . It also performs many other canonicalizations. One of the more important ones is complexity sorting. As these operations are commutative, SCEV will bring them into a canonical order that is based on the type of SCEV expression first and domination relationships second. For example, constant operands will always come first, so that (%a + 1) always becomes (1 + %a) instead. A notable absence from the expression list are subtractions: (%a - %b) is instead represented as ((-1 * %b) + %a) . This ties in with the idea of having a single canonical form. SCEV expressions are interned, which means that there is only one expression with a given set of operands/types. To determine whether two SCEV expressions are the same, you can just check if the pointers are equal. Pointer expressions SCEV expressions have a type, which must be either an integer or pointer (the so called “SCEVable types”). However, only a subset of expressions support pointers: addrecs can have a pointer start, while the step is always an integer. add can have a single pointer operand, while the rest must be integers. min/max are either all-integer or all-pointer. For adds/addrecs the type of the integer operands must match the “index type” of the pointer. Typically, this will be the integer type with the same size as the pointer, but it can also be smaller for some exotic architectures like CHERI. This integer type is also known as the “effective type” of the SCEV. An important consideration for pointer SCEVs is what happens if you try to subtract them: ((-1 * %b) + %a is not a well-defined expression if %a and %b are pointers, because SCEV does not support pointer multiplication. Instead, subtracting two pointer SCEVs requires that they have a common “pointer base”: If you recursively follow the pointer operand of adds/addrecs, you must arrive at the same value. In that case, the common pointer base can be subtracted from both sides, and you are left with a well-defined integer subtraction. This is the reason why, unlike most other SCEV construction methods, getMinusSCEV() is fallible. Nowrap flags Add, mul and addrec nodes support the nuw (no unsigned wrap) and nsw (no signed wrap) flags familiar from IR. These specify that addition/multiplication cannot wrap in an unsigned/signed sense. For addrecs, this property is limited to the number of iterations made by the loop. If the loop has 100 iterations, we don’t care if the addrec would wrap on iteration 101. Additionally, addrecs have a no-self-wrap nw flag, which determines whether the addrec may cross its initial value. This is a weaker property than either nsw or nuw. We write the flags with the syntax (%a + %b + %c)<nuw> or {0,+,1}<nsw> . If the expression has more than two operands, then the nowrap flags must apply regardless of the order in which the additions/multiplications are performed. While the nowrap flags in SCEV and IR look similar on the surface, they are actually fundamentally different. In IR, violating a nowrap flag returns poison, and it’s possible for an add with and without nowrap flags to coexist. This is not the case for SCEV expressions. SCEV expressions are interned without taking nowrap flags into account, which means that (%a + %b) and (%a + %b)<nuw> aren’t considered to be distinct expressions. Once the nuw flag on (%a + %b) has been set, it must hold for all uses of (%a + %b) . A corollary of this is that nowrap flags cannot be removed. More precisely, the nowrap property must hold within the defining scope of the SCEV expression, otherwise the behavior is undefined. The defining scope for addrecs would be their loop header, while for “unknown” nodes it is their defining IR instruction. This is not a problem for nowrap flags that are inferred (e.g. from range information). However, it severely limits transfer of nowrap flags from IR instructions to SCEV expressions: Such transfer is only possible if we can show that violating the nowrap flags is guaranteed to result in undefined behavior once the defining scope has been entered. To give a silly example, we can’t transfer the nuw flag to SCEV in the first function, but can in the second: define i32 @cant_transfer ( i32 %a , i32 %b ) { %add = add nuw i32 %a , %b ret i32 %add } define noundef i32 @can_transfer ( i32 %a , i32 %b ) { %add = add nuw i32 %a , %b ret i32 %add } The defining scope here is the entire function. If nuw is violated, the first function will just return poison. The second function will cause undefined behavior due to the noundef attribute. It is guaranteed to do so whenever the function is entered, so we can transfer the flag to SCEV. Exit count, backedge-taken count, trip count Next to modeling the evolution of values, SCEV can also determine how many iterations a loop performs. There are two different notions of “iterations” commonly used in this context: The backedge-taken count (BE count) is how often the loop backedge is followed before the loop is exited. The trip count is how often the loop header is executed, which is probably what we’d usually call the “number of iterations”. The trip count is the BE count plus one. A loop that exits on the first iteration has a BE count of 0 and a trip count of 1. Part of the reason why we often work with BE counts is that (if it exists) it can always be represented in the same bit-width as the IV controlling the loop. The “plus one” addition of the trip count can overflow, so the trip count might require extending into a larger bit width. Next, we need to distinguish between normal and abnormal exits. A loop can have multiple normal exits, in the form of conditional branches that either stay in the loop or leave it. Additionally, it can have abnormal exits in the form of potentially throwing operations. These are not explicitly modeled in the control-flow graph, but still exit the loop. It is also possible for calls to diverge (i.e. never return), which is not technically an exit, but for most practical purposes behaves like one. For each normal exit, SCEV provides an exact, constant max and symbolic max exit count, which is a BE count in all cases. The exit count says that if this exit is taken, then it will be taken after this many iterations. It does not make any statements about whether it will actually be taken – a different normal or abnormal exit could be taken instead. The exact exit count is symbolic and specifies an exact number of backedges taken, such as (-1 + %n) . If we take this exit, it will be after this many iterations, not more, not less. The symbolic max exit count only guarantees that the exit will be taken after at most that many iterations. For example, if the exit condition is %iv uge %n or %iv uge %m the symbolic max exit count will be (-1 + %n) umin (-1 + %m) , and no exact exit count will be known. The constant max exit count is a constant upper bound on the symbolic max. A common value for it is -1 (or UINT_MAX in an unsigned interpretation). This means we don’t know anything about the range of the exit count, but we do know that it is finite. The same three options (exact, constant max, symbolic max) exist when we move from considering a single exit to considering the entire loop. These are once again under the assumption that the loop exits normally. The exact BE count will usually only be available for single exit loops. SCEV cannot always determine an exit count (not even a constant/symbolic max). If the condition is not based on an induction variable (e.g. on a loop-varying load) there’s no way to determine the exit count. Even if it’s based on an IV, SCEV may not be able to exclude the possibility of an infinite loop. If we have a known backedge taken count, we can determine which value an addrec (or expression based on one) will take once the loop has been exited. This is known as the “exit value”, or the “SCEV at scope”, where the “scope” is outside the loop of the addrec. For example, if we have addrec {2,+,3} with BE count (-1 + %n) , then the exit value is (2 + (3 * (-1 + %n))) or (-1 + (3 * %n)) . Dispositions SCEV expressions have a lazily computed “disposition” towards any basic block and loop in the function. The possible block dispositions are “properly dominates”, “dominates” and “does not dominate”. The loop dispositions are “loop invariant”, “loop computable” and “loop variant”. Most of these terms should be familiar from the IR layer, and have essentially the same meaning here. The only addition is “loop computable”, which is the disposition of an addrec in its own loop: It’s not invariant, but we can compute its value on any iteration. However, there is one very important difference to IR-level queries: Because these are working on SCEV expressions, dominance and loop invariance is independent of the position of the original instruction in the IR, and only depends on the SCEV operands. Consider the following example: loop: %iv = phi i32 [ 0 , %preheader ], [ %iv.next , %loop.latch ] %iv.next = add i32 %iv , 1 %cmp = icmp ne i32 %iv.next , %end br i1 %cmp , label %loop.latch , label %exit loop.latch: %div = udiv i32 %a , %b ; operands defined outside of loop br label %loop Here, udiv i32 %a, %b cannot be hoisted out of the loop by LICM, because it could be that the loop exits on the first iteration, and the udiv is never reached. If %b were zero, hoisting the division would cause division by zero, which is undefined behavior. If you ask LoopInfo whether %div is loop invariant with respect to %loop the answer will be “no”, as the instruction is inside the loop. However, if we instead look at the SCEV expression (%a /u %b) , then it is loop invariant with respect to %loop . As such, domination / invariance on the SCEV level is stronger than on the IR level, as it behaves as-is expressions were fully hoisted, even for non-speculatable operations. SCEV expansion Sometimes, we want to go back from SCEV expressions to IR instructions, for example to materialize a backedge taken count or exit value in IR. This is handled by a separate SCEVExpander component, which will generate the necessary instruction sequence to compute the value of a SCEV expression at a given insertion point. Before performing expansion, there are two things one usually wants to check: The first is whether the expression is “safe to expand”. The most common reason for an expansion to be unsafe is that it contains a division where the denominator cannot be proven to be non-zero. This ties in directly with the previous section: Because SCEV expressions are effectively hoisted, expanding a division may move it outside a conditional branch. The second is whether it is a “high cost expansion”. SCEV expressions can become very large, and performing a transform that requires 50 instructions to compute a backedge taken count is likely not worthwhile. The expander tries to generate more efficient code in two ways: By hoisting instructions as much as possible, and by reusing existing instructions. For the latter purpose (as well as invalidation), SCEV keeps track of which instructions map to a given SCEV expression. The expander can then use an existing instruction that dominates the insertion point. However, there is a big caveat here: An instruction may be more poisonous than the corresponding SCEV expression. For example, consider two instructions add i32 %a, %b and add nuw i32 %a, %b . Both of these map to the same SCEV (%a + %b) , but if we want to expand it, reusing the instruction with the nuw flag may be incorrect, because it is more poisonous if the addition overflows. For that reason, the expander has to prove that that the reused instruction either cannot be more poisonous, or it’s possible to make it so by dropping poison-generating flags like nuw . The last thing to be aware of, is that the expander has multiple modes of operation, which affect how add recurrences are expanded. By default, it uses “canonical mode”, in which case addrecs will get expressed based on a canonical {0,+,1} induction variable. If you expand multiple different addrecs, there will only be a single canonical IV, and the other addrecs will be expressed as a multiply-add of that IV. In non-canonical mode, the recurrence is instead expanded literally. Finally, there is also a special LSR mode, which is used by the loop strength reduction pass and allows precise control of how add recurrences are expanded. The details of how this works are outside the scope of this article. Ranges, multiples and predicates SCEV supports calculating the possible value range of expressions: This is provided as a separate unsigned and signed range. Both ranges are always correct, but one of them may be more precise in an unsigned/signed context. Ranges are heavily used to infer nowrap flags and answer predicates like “is this SCEV known non-zero?” Additionally, SCEV can determine whether an expression is always a multiple of some constant. For example {0,+,3} is always a multiple of three. Unsurprisingly, this is primarily used to determine loop trip multiples. Finally, SCEV provides various functionality around answering predicates, such as “is A unsigned lower than B?” Answering this can involve range information, but also general symbolic reasoning. These queries come in a number of different flavors: Does a predicate always hold? (isKnownPredicate) Does one predicate imply another predicate? (isImpliedCond) Does a predicate hold at a specific program point? (isKnownPredicateAt, isLoopEntryGuardedByCond, etc) The last one is heavily based on the second one: It checks whether any dominating conditions imply the predicate. However, there is an alternative mechanism to achieve this: It is possible to “apply loop guards” to a SCEV expression, which will rewrite the expression itself based on information derived from dominating conditions. For example, if the loop is guarded by %x != 0 , then the expression %x would be rewritten to %x umin 1 . As a result, range (and known multiple) queries on the rewritten expression become more precise. For certain types of predicates (like “is this expression non-zero?”) this tends to both provide more accurate results and be more efficient. Predicated scalar evolution Sometimes, the incoming IR simply doesn’t satisfy some pre-condition that is necessary for a transform. For example, to perform a vectorization we might need to know that an addrec does not wrap, but we don’t actually know it. In this case, “predicated” scalar evolution allows us to assume that a certain predicate holds, and compute SCEV expressions under that assumption. To actually perform the transform, the loop needs to be versioned: Two copies of the loop are generated, one of them guarded by the assumed predicates. The guarded loop can then be transformed based on these predicates. Problems Scalar evolution is an important and powerful analysis for loop optimizations, but it has quite a few and fairly fundamental problems. A few that come to mind are: The fact that nowrap flags are global causes two main issues: First, it is easy for non-experts to get wrong, because it differs from IR semantics. This has resulted in many miscompiles over the years. Second, SCEV can’t represent cases where nowrap flags are only known to hold at a specific use, leading to missed optimizations. SCEV does not support subtraction and instead represents sub i32 %a, %b as ((-1 * %b) + %a) . This means that nowrap flags from the subtraction cannot be preserved straightforwardly. It is generally impossible to represent nowrap for unsigned subtraction. This is also a problem in IR because sub by constant gets converted to add by constant. It’s worse in SCEV because even non-constant cases are represented by adds. This is why loops that count down will usually get optimized worse than loops that count up. SCEV results are query-order dependent. If you fetch the SCEV for %a and then for %b , you might get different results than for %b then %a . SCEV queries can also affect nowrap flags on existing expressions. For example, computing a zext of an expression will often infer additional nowrap flags on it. SCEV has dozens of caches, making invalidation perilous and very expensive. Invalidation tends to scan large value graphs to make sure that all indirect dependencies are accounted for. We don’t have a good understanding of what precisely needs to be invalidated and tend to go for the big hammer. SCEV in general, but especially anything involving context-sensitive reasoning, is very slow. Trying to use context-sensitive queries in a new place is pretty much a guaranteed major compile-time regression. None of these are easy to resolve. If you liked this article, you may want to browse my other articles or follow me on Twitter or Mastodon . | 2026-01-13T08:49:45 |
https://www.npopov.com/2024/01/01/This-year-in-LLVM-2023.html | This year in LLVM (2023) Blog by nikic . Find me on GitHub , StackOverflow , Twitter and Mastodon . Learn more about me . « Back to article overview. This year in LLVM (2023) 01. January 2024 This is my second year working on LLVM at Red Hat. Similar to last year’s blog post , I want to summarize some of the things I’ve worked on this year. Opaque pointers The opaque pointer migration is the main task I worked on last year, and it carried substantially over into this year as well. The goal of this change was to replace typed pointers like i32* , void(i32)** or %struct.T* with a single opaque pointer type ptr . I’m not going to reiterate the full motivation behind the change here, but it basically comes down to simplifying the IR design, by removing a pervasive element that does not actually carry any semantic meaning. Opaque pointers were enabled by default in LLVM 15, but typed pointer support was only removed one year later, in LLVM 17. In the meantime, we had to support both modes. The reason for this is simple: Tests. We have tens of thousands of regression tests, and a large fraction of them were affected by the opaque pointer migration. Over the course of a year, I have ported well over fifteen thousand tests to opaque pointers. This was supported by a healthy dose of automation , but unfortunately also required a significant amount of manual intervention, which is why it took so much time to complete this step. The -no-opaque-pointers option was removed in July, which essentially marked the completion of the migration. The only remaining work (that is still ongoing) are code cleanups to remove no longer needed code, such as unnecessary pointer bitcasts. I’m very happy that this migration is finally finished! But this just means that it’s time to start the next project… ptradd The natural followup to opaque pointers is to migrate away from out current type-based representation for pointer arithmetic. There are many redundant, but semantically equivalent, ways to encode the same operation: %struct = type { i32 , { i32 , { [ 2 x i32 ] } } } %ptr = getelementptr %struct , ptr %base , i64 1 , i32 1 , i32 1 , i32 0 , i64 1 %ptr = getelementptr i32 , ptr %base , i64 7 %ptr = getelementptr i8 , ptr %base , i64 28 This causes a lot of implementation complexity, missed optimization opportunities and compile-time overhead. The plan is to replace the type-based representation with an offset-based one, which matches the actual semantics of the operation: %ptr = ptradd ptr %base , i64 28 While I have written an RFC on the topic, I’ve not made much implementation progress this year. Based on feedback from the RFC, we will not be introducing a new instruction right away, and instead start by performing more canonicalization of getelementptr instructions. In particular, it should be noted that getelementptr i8 and ptradd are equivalent, so we can start by canonicalizing GEPs to use i8 as the element type. The first change in this direction, which is canonicalization of constant offset GEPs , is ready and will land soon. This is a very important first step, and will already fix many optimization issues by itself. Next steps after that will be to change the inrange representation, canonicalize constant expression GEPs, canonicalize variable index GEPs, and finally to disallow non-i8 GEPs (at which point we can just spell them ptradd ). I hope to make significant progress on this next year. Constant expression removal Another project that carried over from last year is the removal of most constant expressions from LLVM. The goal of this project is to remove constant expressions that cannot be represented as relocations in common object formats, and represent them as ordinary instructions instead. I’ve made good progress this year, removing the select, and, or, lshr, ashr, zext, sext, fptrunc, fpext, fptoui, fptosi, uitofp, and sitofp expressions. The only expressions still scheduled for removal are icmp, fcmp, mul, xor, extractelement, insertelement and shufflevector. Of the remaining ones, getting rid of icmp is probably the most important, because it will at least make it technically possible to fix certain miscompiles in -fno-delete-null-pointer-checks mode (which depends on function attributes, while constant expressions are independent of the function, and even the module). Metadata semantics changes Undefined behavior in LLVM comes in two flavors: One of those is immediate undefined behavior, which basically works the same as in C, and poison values, which get propagated by most operations and only turn into immediate undefined behavior in certain contexts, for example when you try to branch on them. We generally prefer using poison values over immediate undefined behavior whenever possible, because the former can be speculatively executed, while the latter can not. The !nonnull , !range , and !align metadata for loads and calls used to be specified in terms of undefined behavior, which means that it had to be dropped during speculation. I have changed the semantics to make them return poison on violation instead. In this case we get the best of both worlds, because poison-generating metadata like !range can be combined with !noundef to turn the resulting poison value into immediate undefined behavior. This is the representation that frontends would typically generate. Then, during speculation, we can drop the !noundef metadata, while keeping the !range metadata. For example, in the following snippet we can retain the fact that %res is actually a boolean: start: br i1 %c , label %if , label %join if: %load = load i8 , ptr %p , !range !{ i8 0 , i8 2 }, !noundef !{} br label %join join: %res = phi i8 [ %load , %if ], [ 0 , %start ] ; Can fold to (assuming the load is speculatable): start: %load = load i8 , ptr %p , !range !{ i8 0 , i8 2 } %res = select i1 %c , i8 %load , i8 0 Keeping this information around is especially relevant for languages like Rust, which tend to have a lot more information about value constraints than languages like C. For this semantics change, the main tricky part was identifying and updating various transforms to correctly drop or combine metadata under the new semantics. In particular, many CSE/GVN style transforms were affected. getelementptr inbounds The inbounds flag on the getelementptr instruction indicates (if we gloss over a lot of detail) that the pointer arithmetic does not go outside an allocated object (where the pointer to the end of an object is still considered “inbounds”). Historically, there was a special case that the offset 0 is considered inbounds of the null pointer, even though no allocated object may exist at that address (by default). This is necessary to satisfy C++ language semantics. Now we generalize this by saying that offset 0 is always inbounds, regardless of what the base pointer is. This was a long-standing request from the Rust community, to ensure that operations on zero-sized objects at arbitrary addresses are always well-defined. An additional benefit from this change is that we can now preserve inbounds during certain transforms that materialize zero-offset GEPs, such as the following: %gep = getelementptr inbounds i8 , ptr %p , i64 %offset %sel = select i1 %c , ptr %gep , ptr %p ; Can fold to: %sel = select i1 %c , i64 %offset , i64 0 %gep = getelementptr inbounds i8 , ptr %p , i64 %sel Previously we had to drop inbounds , leading to significant optimization regressions in some cases. writable and dead_on_unwind Consider the following Rust snippet: pub fn new_from_uninit () -> Foo { let mut x = std :: mem :: MaybeUninit :: uninit (); unsafe { init ( x .as_mut_ptr ()); x .assume_init () } } This will currently create a stack allocation for x , call init on it, and then perform a memcpy to move the memory into the return slot. It would be much more efficient if we could call init() directly on the return slot and save the temporary stack allocation, as well as the copy. LLVM’s “call slot optimization” is responsible for optimizing cases like this. However, in this case it fails, because the call to init() may unwind (panic). In that case, as far as LLVM knows, there would be an observable difference in behavior: The return slot would not be modified on unwind before the optimization, and would potentially be modified after. Of course, we know that the return value will not actually be used if an unwind (panic) occurs, but we had no way to tell LLVM that this is the case. To enable this optimization, I’ve added two new parameter attributes. The first one is writable , which indicates that it’s legal to introduce stores to the parameter. This both excludes read-only memory, as well as memory for which introducing a store would result in a data race. The transform was previously incorrectly applied in cases where this could not be guaranteed. The second one is dead_on_unwind , which indicates that the parameter will not be read after an unwind occurs. Both attributes together (plus a long list of other preconditions) enable the desired transform, once the frontend starts emitting them. zext nneg LLVM generally prefers unsigned operations over signed ones, as they tend to optimize better. For that reason, we perform a number of canonicalizations from signed to unsigned if the operations are actually equivalent. For example, we replace sign extension (sext) with zero extension (zext) if the operand is non-negative. We also do the same for arithmetic shift right (ashr) to logical shift right (lshr), and for integer comparisons (signed predicate to unsigned predicate). However, this is not always beneficial. For example, if a value is used in a signed context (like a signed comparison), then a sign extend may optimize better. There are also some weird architectures like RISC-V that have a preference for sign extensions. It is sometimes possible to undo the canonicalization if necessary, but this is unreliable, because information may have been lost, or there is an impedance mismatch in analysis capabilities. The zext nneg flag addresses this, by retaining the fact the operand is non-negative. This makes it easy to treat the zext nneg as a sext when profitable. or disjoint The or disjoint flagaddresses another, very similar problem: We canonicalize add instructions a + b to a | b , if a and b have no common bits (in which case all of add, or and xor are equivalent). Once again, this is not universally profitable. While it allows various bitwise optimizations to apply, we also lose optimizations that operate on additions. For example, many targets have addressing modes that incorporate an offset, while a bitwise or contributing to an address generally needs to be materialized as a separate instruction. The or disjoint flag retains the information that the bits are disjoint, and makes it trivial to convert it back into an add , if it is found to be beneficial. I didn’t add this flag myself (Craig Topper worked on the implementation), but did a lot of the related bringup work (fixing miscompilations it introduces and using it in various transforms). Future work: More flags While zext nneg and or disjoint are a good start, I think this is an area that would benefit from further work. A number of additional cases where we could preserve information during canonicalization are: lshr nneg when canonicalizing ashr with non-negative LHS. This has come up as an issue a few times, but possibly not often enough to justify the flag yet. icmp signed to unsigned predicate canonicalization. This one can be applied when either both operands are non-negative, or both are negative, so an nneg flag isn’t quite the right model here. A possible way to handle this would be support sign-agnostic predicates like icmp lt , which produce a poison value if the ult and slt result differs. I think that solving this problem would definitely be worthwhile, as not retaining this information can cause severe regressions (e.g. due to failure to eliminate conditional branches). trunc nuw and trunc nsw to indicate that the truncated bits are all zero or all signs. For example, when induction variable (IV) widening is performed, we know that all truncated users will be one of these, but the information is easily lost, and we end up inserting unnecessary masking operations. Compile-time improvements Unlike the last, this year has been quite good for compile-times. The compile-time changes since 2023-01-22 are plotted in the following graph. It shows the percentual improvement in the “instructions retired” metric for the geomean of all CTMark benchmarks. I have cut off the start of January due to a change in server configuration, which would make the results misleading. Overall, we see improvements in the 8-12% range for optimized builds, while unoptimized builds only improve marginally. In the following, I’ll go through some of the more significant improvements I have made. The largest improvement of 4-5% comes from making InstCombine use a single-iteration , instead of trying to reach a fix-point. See that blog post for a detailed discussion of this change. I have made a series of improvements to semi-NCA (part of dominator tree construction), starting with reducing hash table lookups . These changes add up to more than 1% improvement. I think there is still some more room for improvement here, especially around DFS walks. We get a 0.7% improvement from removing a fold from CorrelatedValuePropagation, which other existing transforms perfom much more efficiently. Not calculating BlockFrequencyInfo (BFI) in various passes for non-PGO builds gives a 0.7% improvement . The calculated BFI is not actually used in this case. Switching loop-invariant code motion (LICM) to not require optimized MemorySSA gives a 0.9% improvement. Instead, we lazily optimize the small subset of memory uses we’re interested in. Arthur Eubanks later won another 0.5% by doing the same for DSE . One of my GSoC students this year also worked on compile-time improvements . Read the report for all the details, but here are a couple of highlights from their work: Moving alignment inference from InstCombine into a dedicated pass gives a 0.6% improvement . This is mainly because the pass is run less often than InstCombine. Optimizing pruning worklist population in DAGCombine gives a 0.5% improvement . Just adding things to a worklist can be quite expensive! Optimizing the SetVector data structure gives a 0.4% improvement . This uses a linear scan instead of a hashtable lookup when the SetVector is in “small” mode, similar to what we already did for SmallPtrSet. Finally, I have also made a number of improvements to the compile-time tracking infrastructure. I’ve migrated the runner from an AX41 to an AX52 server, which is a good bit faster. This new time budget is used to perform a two-stage clang build. The new procedure looks like this: Build stage1 clang using host gcc and ccache. This will be fast for most commits thanks to ccache. Build stage2 clang using stage1 clang and ThinLTO. Collect wall-time timings from ninja log and instrument each compiler/linker invocation to get instructions:u data. Also collect file sizes. Build CTMark in O3, ReleaseThinLTO, ReleaseLTO-g and O0-g configuration using stage1 clang. Build CTMark in O3 and O0-g configuration using stage2 clang. This gives us a number of new data points: We get compile-time coverage for a project (clang) that is much, much larger than anything in CTMark. Though from the data I have to date, it seems like CTMark is actually more representative than I would have guessed. We can catch regressions to clang build time unrelated to optimizer changes. I’ve caught a few instances of significant build time regressions due to includes being (unnecessarily) added to core LLVM headers. We can catch optimization regressions that affect clang itself (which manifest by regressing the stage2 CTMark builds only). This doesn’t seem to be common. We can see the different effect of changes on a gcc-built and a clang-built compiler. It happens sometimes that a change gives a large improvement for the gcc-built stage1 compiler, but makes little difference for the clang-built stage2 compiler, or vice versa. The following graph shows the compile-time of stage2 clang (in percent since start using the instructions:u metric) since I started collecting data: If you’re wondering what’s up with that 10% spike, this was a huge compile-time regression to a single file, caused by some large generated functions being added . Optimization improvements I don’t do much work on optimization improvements, if they don’t require changes to IR design or substantial infrastructure changes. We have a lot of people who want to contribute new optimizations, and our bottleneck is in getting them reviewed, so that’s what I tend to focus on. I think all of the more interesting optimizations I worked on this year were related to conditions in some way. Some examples: Performing optimizations based on dominating KnownBits conditions . For example, if we have a condition like if (x & 1 == 0) then we can optimize based on the fact that the low bit of x must be zero. This is based on a new DomConditionCache, and comes at some compile-time cost. Performing range-based optimization based on conditions known at the use-site . In particular, this enables range-based optimization of instructions used only inside a select based on the select condition. This is a partial solution to a general problem that still requires further work. Perform optimizations of ands/ors using equality conditions by trying to perform simplification with replaced operands. This is interesting in that this generic fold ended up subsuming a large number of existing hand-written folds. I always love it when that happens. Zero-length operations on null Together with Aaron Ballman, I have been working on a proposal for the C standard, to make “zero length” operations on null pointers well defined. In particular: NULL + 0 becomes well-defined and returns NULL . NULL - NULL becomes well-defined and returns 0 . memcpy(NULL, NULL, 0) becomes well-defined and does nothing. Similar for other library calls. The original proposal was targeted at library functions only, and additionally made memcpy(p, p, n) well-defined, which is something that all major C compilers already rely on. Based on feedback, I ended up switching the proposal to focus on various NULL + zero length cases instead. This will bring C semantics more in line with C++ semantics, and put use of libc as a builtin provider for languages like Rust on a more stable footing. Rust As usual, I carried out the upgrades to LLVM 16 and later LLVM 17 in Rust. The LLVM 16 upgrade was more painful than usual. Early on, we hit a bolt instrumentation miscompile , which manifested in our most complex build configuration (a sequence of multiple rebuilds of libLLVM and rustc with various PGO settings) and thus took quite a while to track down. After the upgrade landed, it completely broke CI due to a bad interaction with the download-ci-llvm feature. The problem was that LLVM 16 started using C++ 17, but did not require a GCC version that has a stable C++ 17 ABI, and the std::optional ABI did indeed change between libstdc++ 7 and 8. We “fixed” this by making sure all our builders use at least GCC 8, but this is probably something we should take into account the next time LLVM raises its host compiler requirements. We should not consider a compiler version “supported” if it still uses an experimental ABI for the used C++ version. One of my GSoC students this year worked on Rust-related optimizations in LLVM . Read the report for all the details, but I think that most interesting/important optimizations are related to removal of memcpy’s in additional situations. One of these is about removing memcpy’s before function calls where the argument is known immutable, so there is no need to copy it into a temporary stack slot. The second is the so-called “stack move” optimization, where a memcpy between two stack slots can be removed by merging the slots into one. A number of the topics covered above (metadata semantics change, getelementptr inbounds, dead_on_unwind) are at least partially motivated by Rust needs. Some other things I worked on are: Bringing the change to allow inlining of drop calls in landing pads over the finishing line. This was blocked on a stack coloring bug affecting Windows exception handling. This has firmly reinforced that Windows exception handling is “batshit insane” in my mind. Do preparatory work to allow us to change the alignment of i128 on x86 to 16 bytes, to match the SystemV ABI. The alignment of i128 was changed in LLVM 18, but we also need to get this working on older LLVM versions, by making sure we don’t rely on LLVM’s own layout calculations. RHEL This is the first year I actually did some direct work on the product we’re selling, namely Red Hat Enterprise Linux . We ship LLVM via the LLVM toolset , which gets updated to the next LLVM version with each minor RHEL release. Due to the way the release cycles of LLVM and RHEL align, it tends to be one version behind the latest one. LLVM updates happen in Fedora first. Then, we mostly copy over the changes to CentOS Stream , which then get merged into RHEL by automation and go through QE there. Relative to Fedora, there are mostly some workarounds due to a different build environment (e.g. packages not being available or old), as well as integration with the GCC toolset, which provides up-to-date versions of GCC, binutils, etc. This year, I performed the update to LLVM 16 for RHEL 9.3 and LLVM 17 for RHEL 8.10 (not released yet). As part of the RHEL 9 work, I also switched our LLVM packages to be built with clang and ThinLTO, just like we do on Fedora. This turned out to be more complex than anticipated, due to various issues related to too old system binutils/gdb/etc versions. Other I attended my first EuroLLVM conference this year, and gave a talk there: A whirlwind tour of the LLVM optimizer . It provides an overview of LLVM’s middle-end optimizer. The conference was great and quite productive as far as discussions were concerned. The presentations were unfortunately heavily skewed towards MLIR content, which I don’t particularly care for. I have spent a lot of time this year on code review. Especially after Sanjay Patel stopped working on LLVM, it feels like I’m the primary reviewer for most of the LLVM middle-end. I don’t have good statistics on this (especially with Phabricator being decomissioned), but based on a back of the envelope calculation, I probably reviewed something like 1500 patches this year. Despite that, the review backlog is always increasing, so some patches invariably end up in review limbo… If you liked this article, you may want to browse my other articles or follow me on Twitter or Mastodon . | 2026-01-13T08:49:45 |
https://dev.to/terceranexus6/freedom-of-security-with-paula-de-la-hoz-51fk | Freedom of Security with Paula de la Hoz - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Paula Posted on Jul 23, 2020 Freedom of Security with Paula de la Hoz # codeland # security I'm 24 years old, working as a Redteam in Telefonica, based in Madrid. Conding since I was 15, my favorite languages are Python, Bash and C. I love hardware hacking and electronics, and I love building electronics for security, I also enjoy teaching and mentoring. At the beginning of the talk I will explain the security issues I've faced in my job, mostly when talking to app developers and users, as well as from research. Towards the middle of the talk I want to introduce how freedom of software has provided many tools to cover those issues and then discuss the importance of open cybersecurity in society. At this point, I'll show off some tools and scripts. Finally, I want to conclude by providing some security tips for developers, along with guidance on how to help security professionals when auditing their companies. Here is a download link to the talk slides (PDF) This talk will be presented as part of CodeLand:Distributed on July 23 . After the talk is streamed as part of the conference, it will be added to this post as a recorded video. Top comments (30) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Collapse Expand Paul Caoile Paul Caoile Paul Caoile Follow Father, Coffee drinker and a Life long learner passionate about Front-end Development Work Administrator and an Analyst Joined Jan 8, 2019 • Jul 23 '20 Dropdown menu Copy link Hide @terceranexus6 , how did you get into this career and what is required to get into this career? Like comment: Like comment: 7 likes Like Comment button Reply Collapse Expand Paula Paula Paula Follow 29 years old. Cyber. I really like bash and simple scripts. Solarpunk and free software advocate! Location Many places Education Computer science, fine arts Pronouns she/her Work Cybersecurity Joined Apr 3, 2017 • Jul 23 '20 Dropdown menu Copy link Hide I started studying every single day online and actually I posted all my development here in dev.to. I went to all the security events in my country I could afford to go and started to send papers to conferences until they got me, same happened while looking for a job! I'm just passionate about this so I'm happy about studying everyday. Like comment: Like comment: 7 likes Like Comment button Reply Collapse Expand Miriam Tocino Miriam Tocino Miriam Tocino Follow Sparking children’s curiosity for the world of technology through the power of stories with Zerus & Ona, a picture book series designed to make it fun and motivating for young and old alike. Location Amsterdam Work CEO, Author at Zerus & Ona Joined Jan 13, 2020 • Jul 27 '20 Dropdown menu Copy link Hide That's truly inspiring. 💙 Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Paul Caoile Paul Caoile Paul Caoile Follow Father, Coffee drinker and a Life long learner passionate about Front-end Development Work Administrator and an Analyst Joined Jan 8, 2019 • Jul 23 '20 Dropdown menu Copy link Hide Thank you, Paula. I will look into your posts. Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Marlyn Suarez Marlyn Suarez Marlyn Suarez Follow Community Organizer| Aspiring Data Scientist| Python, SQL, & Javascript| Obsessed with the non-profit industrial complex Location New York, NY Work Community Organizer at CASA Joined Mar 31, 2020 • Jul 23 '20 • Edited on Jul 23 • Edited Dropdown menu Copy link Hide Honestly, I think this is the coolest talk of the day! 💛 Like comment: Like comment: 7 likes Like Comment button Reply Collapse Expand Paula Paula Paula Follow 29 years old. Cyber. I really like bash and simple scripts. Solarpunk and free software advocate! Location Many places Education Computer science, fine arts Pronouns she/her Work Cybersecurity Joined Apr 3, 2017 • Jul 23 '20 Dropdown menu Copy link Hide Oh wow thank you! Although I loved all the previous talks as well. I'm glad you enjoyed thank you! Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Omar Omar Omar Follow Self-taught from 12 years old and going on, pragmatic software engineer who is eager to learn more and more about this amazing Universe. Location Lebanon Education Computer Science, Lebanese University - Faculty of science Joined Jun 6, 2020 • Jul 23 '20 Dropdown menu Copy link Hide my love for Computer science was since childhood because of the Anonymous news back then it was a trend. I think a lot of SE/CS also have they inspire from them , also from games. BTW thanks <3. Like comment: Like comment: 7 likes Like Comment button Reply Collapse Expand Paula Paula Paula Follow 29 years old. Cyber. I really like bash and simple scripts. Solarpunk and free software advocate! Location Many places Education Computer science, fine arts Pronouns she/her Work Cybersecurity Joined Apr 3, 2017 • Jul 23 '20 Dropdown menu Copy link Hide Thank you!! yes those media resources are a truly inspiration. I'm a lover of "Hacker files" comic series for example. Thank you for attending :) Like comment: Like comment: 6 likes Like Comment button Reply Collapse Expand Bryant Richards Bryant Richards Bryant Richards Follow Experienced in Ruby, Ruby on Rails, JavaScript and React based programming and a background in the service industry. Pos Location Dickson, TN Work Software Engineer at Self-Employed Joined Jul 15, 2020 • Jul 23 '20 Dropdown menu Copy link Hide Is there some kind of sandbox environment to practice these in without messing around with a real website or business? Like comment: Like comment: 5 likes Like Comment button Reply Collapse Expand Paula Paula Paula Follow 29 years old. Cyber. I really like bash and simple scripts. Solarpunk and free software advocate! Location Many places Education Computer science, fine arts Pronouns she/her Work Cybersecurity Joined Apr 3, 2017 • Jul 23 '20 Dropdown menu Copy link Hide Sure! try using hackthissite, portswigger academy and hack the box! you should end up being a pro if you get used to those! ;) Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Bryant Richards Bryant Richards Bryant Richards Follow Experienced in Ruby, Ruby on Rails, JavaScript and React based programming and a background in the service industry. Pos Location Dickson, TN Work Software Engineer at Self-Employed Joined Jul 15, 2020 • Jul 23 '20 Dropdown menu Copy link Hide Thank you so much! Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Sam Sam Sam Follow Work Not a developer Joined Aug 21, 2019 • Jul 23 '20 Dropdown menu Copy link Hide Great intro to cybersec, thanks, Paula! Like comment: Like comment: 5 likes Like Comment button Reply Collapse Expand Paula Paula Paula Follow 29 years old. Cyber. I really like bash and simple scripts. Solarpunk and free software advocate! Location Many places Education Computer science, fine arts Pronouns she/her Work Cybersecurity Joined Apr 3, 2017 • Jul 23 '20 Dropdown menu Copy link Hide Thank you! It's been difficult for me, I could spend hours talking about it hahaha Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Sam Sam Sam Follow Work Not a developer Joined Aug 21, 2019 • Jul 23 '20 Dropdown menu Copy link Hide Hahah I can imagine that! I am very new to the industry, so I'm very excited to check out your publications :) I was also curious if you are asked to social engineer the employees as part of the pentest? :D Like comment: Like comment: 5 likes Like Thread Thread Paula Paula Paula Follow 29 years old. Cyber. I really like bash and simple scripts. Solarpunk and free software advocate! Location Many places Education Computer science, fine arts Pronouns she/her Work Cybersecurity Joined Apr 3, 2017 • Jul 24 '20 Dropdown menu Copy link Hide It depends on the project but I've done it, yes. I speak about it widely in this post . I love social engineering. Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand mumbledenoise mumbledenoise mumbledenoise Follow Joined Jul 23, 2020 • Jul 23 '20 Dropdown menu Copy link Hide Loving this security demo! Like comment: Like comment: 6 likes Like Comment button Reply Collapse Expand Paula Paula Paula Follow 29 years old. Cyber. I really like bash and simple scripts. Solarpunk and free software advocate! Location Many places Education Computer science, fine arts Pronouns she/her Work Cybersecurity Joined Apr 3, 2017 • Jul 23 '20 Dropdown menu Copy link Hide Thanks!! Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Gidget Gidget Gidget Follow Java/Kotlin/Gradle! Finishing my diploma in computer programming and hoping to start job seeking in 2021 Location Ottawa, Canada Work Student at N/A Joined Jul 21, 2020 • Jul 23 '20 Dropdown menu Copy link Hide Super interested this talk, as I don't know too much about cybersecurity! What are some good outside resources for people who would like to move into this field from more standard programming? Books, websites, etc. Like comment: Like comment: 8 likes Like Comment button Reply Collapse Expand Paula Paula Paula Follow 29 years old. Cyber. I really like bash and simple scripts. Solarpunk and free software advocate! Location Many places Education Computer science, fine arts Pronouns she/her Work Cybersecurity Joined Apr 3, 2017 • Jul 23 '20 Dropdown menu Copy link Hide Oh well if you are already a programmer thats a big step there you already took. Portswigger academy, hackthissite and Hack the box are really good labs to get into it, and the best way to learn about sec is to go to events from my point of view, because you can have a beer with speakers and discuss their expertise! (with security distance of course ;)) and books, there are some nice books such as the hacker playbook which updates every year, but it kind of depends on what you want to focus on cybersecurity. DM for a more focused advice if you want :) Like comment: Like comment: 5 likes Like Comment button Reply Collapse Expand Miriam Tocino Miriam Tocino Miriam Tocino Follow Sparking children’s curiosity for the world of technology through the power of stories with Zerus & Ona, a picture book series designed to make it fun and motivating for young and old alike. Location Amsterdam Work CEO, Author at Zerus & Ona Joined Jan 13, 2020 • Jul 27 '20 Dropdown menu Copy link Hide Really nice presentation, Paula. I loved hearing more about your back story and I just learned about red, blue, and purple teams!! You mentioned you talk on a radio show in Madrid, right? Where is that? I'd love to check it out. :D Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand mumbledenoise mumbledenoise mumbledenoise Follow Joined Jul 23, 2020 • Jul 23 '20 Dropdown menu Copy link Hide Awesome security talk Paula! What's your favorite security tool? Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Paula Paula Paula Follow 29 years old. Cyber. I really like bash and simple scripts. Solarpunk and free software advocate! Location Many places Education Computer science, fine arts Pronouns she/her Work Cybersecurity Joined Apr 3, 2017 • Jul 23 '20 Dropdown menu Copy link Hide I love the lower level the best, so openssl and nmap are the absolute winners because I can use them in bash scripting so smoothly! Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Ruanna Ruanna Ruanna Follow Aficionado of words, espresso, and house music. Passionate about education, diversity in tech, & tech for good. Location San Francisco Bay Area, CA, USA Work Content Marketing | Community Management Joined Mar 23, 2020 • Jul 23 '20 Dropdown menu Copy link Hide Loved your talk & panel! Also, me encanta España y no puedo esperar hasta que es posible a viajar allí de nuevo! 😊 Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Paula Paula Paula Follow 29 years old. Cyber. I really like bash and simple scripts. Solarpunk and free software advocate! Location Many places Education Computer science, fine arts Pronouns she/her Work Cybersecurity Joined Apr 3, 2017 • Jul 24 '20 Dropdown menu Copy link Hide Let's hope that's soon! Like comment: Like comment: 1 like Like Comment button Reply View full discussion (30 comments) Some comments may only be visible to logged-in visitors. Sign in to view all comments. Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Paula Follow 29 years old. Cyber. I really like bash and simple scripts. Solarpunk and free software advocate! Location Many places Education Computer science, fine arts Pronouns she/her Work Cybersecurity Joined Apr 3, 2017 More from Paula Linux Exfiltration # noai # security # linux Analyzing a Linux malware binary # security # malware # linux The broken spaceship Honeypot updates # security # linux 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . 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https://dev.to/t/reinvent | Reinvent - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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Right menu DEV Track Spotlight: Breakout Sessions & Code Talks from AWS re:Invent 2025 Gunnar Grosch Gunnar Grosch Gunnar Grosch Follow for AWS Jan 5 DEV Track Spotlight: Breakout Sessions & Code Talks from AWS re:Invent 2025 # aws # ai # serverless # reinvent Comments Add Comment 11 min read AWS 2025: A Year of Agentic AI, Custom Chips, and Multicloud Bridges Damien Gallagher Damien Gallagher Damien Gallagher Follow for AWS Community Builders Jan 2 AWS 2025: A Year of Agentic AI, Custom Chips, and Multicloud Bridges # aws # reinvent # cloud # 2025 Comments Add Comment 8 min read Key Takeaways from Werner Vogels' Final re:Invent Keynote (2025) Ravi Patel Ravi Patel Ravi Patel Follow Dec 9 '25 Key Takeaways from Werner Vogels' Final re:Invent Keynote (2025) # aws # reinvent # ai # engineering Comments Add Comment 2 min read re:Invent25, jour 1 : « boom » Paul SANTUS Paul SANTUS Paul SANTUS Follow for AWS Community Builders Dec 3 '25 re:Invent25, jour 1 : « boom » # aws # reinvent # ai 60 reactions Comments Add Comment 5 min read re:Invent25, jour 2 : le chemin vers l'IA Paul SANTUS Paul SANTUS Paul SANTUS Follow for AWS Community Builders Dec 4 '25 re:Invent25, jour 2 : le chemin vers l'IA # aws # reinvent # data Comments Add Comment 4 min read re:Invent2025ライブストリーミングを楽しもう Yasuhiro Matsuda Yasuhiro Matsuda Yasuhiro Matsuda Follow for AWS Community Builders Dec 2 '25 re:Invent2025ライブストリーミングを楽しもう # aws # reinvent # japanese 41 reactions Comments Add Comment 1 min read How to re:Invent, episode 1 - 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https://www.fine.dev/blog/ai-coding-guide#best-large-language-models-llms-for-coding | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
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https://www.fine.dev/blog/coded-by-ai-backend-webhook#pricing | Using Fine to Set Up a Retry Mechanism for Failed Webhooks Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back Using Fine to Set Up a Retry Mechanism for Failed Webhooks Setting up retry mechanisms for failed webhooks used to be a tedious task for developers, but with AI like Fine, it's now a breeze. Let's see how Fine can automate this process, saving you time and headaches. Scenario: Webhooks Need a Reliable Retry Mechanism Let's say you have an application that sends webhooks to various third-party services. Occasionally, these webhooks fail — whether it’s due to momentary downtime on the receiver’s side or a blip in network connectivity. To ensure reliability, we need a retry mechanism that handles these failures automatically. Fine can take the task of building this functionality, freeing developers to focus on higher-level logic. The objective here is to implement a retry mechanism for failed webhooks using RabbitMQ. This includes setting up exponential backoff to progressively delay retries and configuring a dead-letter queue for requests that fail after multiple attempts. Finally, we’ll simulate failures to validate the entire process. The Prompt for Fine Using Fine’s intelligent AI agents, we can set up the retry mechanism efficiently. Here’s the prompt that we’ll provide to Fine: "Implement a retry mechanism for failed outbound webhooks using RabbitMQ. Add the retry logic to @services/webhook_service.js , configure exponential backoff in @config/rabbitmq.js , and set up a dead-letter queue. Write tests in @tests/integration/webhook_retries.test.js to simulate failures and validate the retry mechanism." How Fine Executes This Task Upon receiving the prompt, Fine acts as a powerful coding assistant, utilizing its context and capabilities to implement each part of the solution: Adding Retry Logic to @services/webhook_service.js retry mechanism in the webhook_service.js file. It integrates RabbitMQ to re-queue failed webhooks, ensuring they’re attempted multiple times in case of failure. Configuring Exponential Backoff in @config/rabbitmq.js Exponential backoff is key to avoiding overwhelming a temporarily down service. Fine configures exponential delays in @config/rabbitmq.js , progressively increasing the wait time between retry attempts to give third-party services enough time to recover. Setting Up a Dead-Letter Queue To handle webhook requests that keep failing even after retries, Fine sets up a dead-letter queue. This is essential for maintaining system stability and identifying consistent issues — webhooks that can’t be processed are moved to this queue for manual review or alerts. Testing the Retry Mechanism in @tests/integration/webhook_retries.test.js . Fine creates integration tests to simulate webhook failures and validate the retry mechanism’s behavior. The tests ensure that failed webhooks are retried with exponential backoff, and eventually moved to the dead-letter queue if they continue to fail. Results: Reliable Webhooks, Efficient Development With Fine, the implementation of a retry mechanism becomes a manageable microtask, rather than an overwhelming project. Developers don’t have to start from scratch or worry about getting the nuances of RabbitMQ configuration just right. Instead, they can trust Fine to handle these repetitive and detail-heavy parts of development. The end result is a robust system where webhooks are reliable, minimizing the risk of losing important events due to transient issues. Fine’s contribution doesn’t just save time — it provides peace of mind, knowing that every step from retry logic to exponential backoff and dead-letter handling is well taken care of. Ready to Automate Your Dev Tasks? Retry mechanisms are just one of the many workflows that Fine can automate, allowing you to focus on innovation rather than boilerplate. Whether it’s setting up robust event-driven systems or managing other critical workflows, Fine’s AI agents are here to help. Give it a try and see how much development you can automate with Fine! Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
https://dev.to/sameer_saleem | Sameer Saleem - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. DUMB DEV Community Follow Memes and software development shitposting Design Community Follow Web design, graphic design and everything in-between Security Forem Follow Your central hub for all things security. From ethical hacking and CTFs to GRC and career development, for beginners and pros alike Golf Forem Follow A community of golfers and golfing enthusiasts Crypto Forem Follow A collaborative community for all things Crypto—from Bitcoin to protocol development and DeFi to NFTs and market analysis. Parenting Follow A place for parents to the share the joys, challenges, and wisdom that come from raising kids. We're here for them and for each other. Forem Core Follow Discussing the core forem open source software project — features, bugs, performance, self-hosting. Maker Forem Follow A community for makers, hobbyists, and professionals to discuss Arduino, Raspberry Pi, 3D printing, and much more. HMPL.js Forem Follow For developers using HMPL.js to build fast, lightweight web apps. A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Follow User actions Sameer Saleem Software Developer | JavaScript Specialist Joined Joined on Sep 16, 2025 Email address sameersaleemq75@gmail.com github website Work Software Developer at IBA More info about @sameer_saleem Badges Writing Debut Awarded for writing and sharing your first DEV post! Continue sharing your work to earn the 4 Week Writing Streak Badge. Got it Close Skills/Languages React, Next, Node, Express, TailwindCSS, PostgreSQL, MongoDB, Prisma, Neon, Sequelize, GeneXus Available for Open to any dev talk, conference or any dev discussion. Post 5 posts published Comment 0 comments written Tag 0 tags followed The Ultimate Guide to Drizzle ORM + PostgreSQL (2025 Edition) Sameer Saleem Sameer Saleem Sameer Saleem Follow Jan 13 The Ultimate Guide to Drizzle ORM + PostgreSQL (2025 Edition) # webdev # drizzle # postgres # typescript Comments Add Comment 3 min read Mocking Your Postgres DB Doesn't Have to Suck Sameer Saleem Sameer Saleem Sameer Saleem Follow Jan 6 Mocking Your Postgres DB Doesn't Have to Suck # prisma # webdev # fakerjs # postgres Comments Add Comment 2 min read The Great ORM Pivot: Why Teams are Moving to Drizzle in 2025 Sameer Saleem Sameer Saleem Sameer Saleem Follow Dec 30 '25 The Great ORM Pivot: Why Teams are Moving to Drizzle in 2025 # webdev # drizzle Comments Add Comment 2 min read Beyond the Autocomplete: Mastering Agentic Workflows in 2025 Sameer Saleem Sameer Saleem Sameer Saleem Follow Dec 24 '25 Beyond the Autocomplete: Mastering Agentic Workflows in 2025 # ai # productivity # programming # webdev 1 reaction Comments Add Comment 2 min read The Ultimate Guide to Prisma ORM + PostgreSQL (2025 Edition) Sameer Saleem Sameer Saleem Sameer Saleem Follow Dec 22 '25 The Ultimate Guide to Prisma ORM + PostgreSQL (2025 Edition) # webdev # prisma # postgres # programming 2 reactions Comments Add Comment 2 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . DEV Community © 2016 - 2026. We're a place where coders share, stay up-to-date and grow their careers. Log in Create account | 2026-01-13T08:49:45 |
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Right menu 2025 Wrapup: Articles, Talks, Papers, and Software I Loved Peter Mbanugo Peter Mbanugo Peter Mbanugo Follow Jan 10 2025 Wrapup: Articles, Talks, Papers, and Software I Loved # webdev # 2025 # general # programming Comments Add Comment 5 min read AWS 2025: A Year of Agentic AI, Custom Chips, and Multicloud Bridges Damien Gallagher Damien Gallagher Damien Gallagher Follow for AWS Community Builders Jan 2 AWS 2025: A Year of Agentic AI, Custom Chips, and Multicloud Bridges # aws # reinvent # cloud # 2025 Comments Add Comment 8 min read 2025 in retrospective Nicolas Fränkel Nicolas Fränkel Nicolas Fränkel Follow Jan 8 2025 in retrospective # 2025 # retrospective 6 reactions Comments Add Comment 4 min read Advent of Code 2025 - December 10th Rob van der Leek Rob van der Leek Rob van der Leek Follow Dec 16 '25 Advent of Code 2025 - December 10th # advent # adventofcode # cpp # 2025 Comments Add Comment 3 min read 1.Git Install and Create Repository Thu Kha Kyawe Thu Kha Kyawe Thu Kha Kyawe Follow Dec 16 '25 1.Git Install and Create Repository # git # gitlevel2 # kodekloud # 2025 Comments Add Comment 2 min read Day 20.Create IAM Role for EC2 with Policy Attachment Thu Kha Kyawe Thu Kha Kyawe Thu Kha Kyawe Follow Dec 15 '25 Day 20.Create IAM Role for EC2 with Policy Attachment # aws # 100daysofcloudaws # 2025 Comments Add Comment 2 min read Day 19.Attach IAM Policy to IAM User Thu Kha Kyawe Thu Kha Kyawe Thu Kha Kyawe Follow Dec 14 '25 Day 19.Attach IAM Policy to IAM User # aws # 100daysofcloudaws # 2025 Comments Add Comment 1 min read Advent of Code 2025 - December 11th Rob van der Leek Rob van der Leek Rob van der Leek Follow Dec 13 '25 Advent of Code 2025 - December 11th # advent # adventofcode # cpp # 2025 Comments Add Comment 2 min read 5.Create VPC with IPv6 Using Terraform Thu Kha Kyawe Thu Kha Kyawe Thu Kha Kyawe Follow Dec 13 '25 5.Create VPC with IPv6 Using Terraform # terraform # terraformlevel1 # kodekloud # 2025 Comments Add Comment 2 min read 6.Create Elastic IP Using Terraform Thu Kha Kyawe Thu Kha Kyawe Thu Kha Kyawe Follow Dec 13 '25 6.Create Elastic IP Using Terraform # terraform # terraformlevel1 # kodekloud # 2025 Comments Add Comment 2 min read Advent of Code 2025 - December 8th Rob van der Leek Rob van der Leek Rob van der Leek Follow Dec 9 '25 Advent of Code 2025 - December 8th # advent # adventofcode # cpp # 2025 1 reaction Comments Add Comment 3 min read How to Prevent IP Spoofing in Firewall gges GRP gges GRP gges GRP Follow Dec 8 '25 How to Prevent IP Spoofing in Firewall # vpn # internet # networking # 2025 Comments Add Comment 2 min read Advent of Code 2025 - 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https://www.fine.dev/blog/ai-coding-guide#3-large-codebase-refactoring | AI Coding – A Simple Guide for Developers Home Docs Changelog Pricing Sign in Get started -> Menu Home Docs Changelog Pricing <- Go Back AI Coding – A Simple Guide for Developers Table of Contents Introduction: What is AI Coding The Importance of Context in AI Coding Tips for Providing Better Context Practical Instructions for Providing Context to AI Coding Tools 1. Creating a Knowledge Graph 2. Implementing Retrieval-Augmented Generation (RAG) 3. Copy-Pasting Relevant Code into ChatGPT and Claude 4. Understanding Potential Mistakes Without Proper Context Using AI to Generate Code Incorporating AI Tools into Your Workflow Advice for Front-End Developers Practical Tips Advice for Back-End Developers Practical Tips Use Cases for AI in Coding 1. Automated Bug Fixes 2. Predicting Performance Bottlenecks 3. Large Codebase Refactoring Industry-Specific Benefits Best Large Language Models (LLMs) for Coding 1. OpenAI 2. Anthropic 3. Google Gemini 4. Other Notable Models Choosing the Right LLM for Your Needs Popular AI Coding Tools 1. Fine 2. ChatGPT 3. Replit 4. Devin 5. Cursor Conclusion Introduction: What is AI Coding In today's rapidly evolving tech landscape, AI coding has emerged as a game-changer for developers. But what exactly is AI coding? Simply put, it's the use of artificial intelligence to assist in writing, optimizing, and managing code. AI coding tools help developers write better, faster, and more efficient code by automating repetitive tasks, providing intelligent code suggestions, and even debugging. This blog will delve into the importance of context in AI coding, how to use AI for generating code, offer practical advice for both front-end and back-end developers, explore various use cases, introduce some of the top AI coding tools available today, and discuss the best large language models (LLMs) for coding. The Importance of Context in AI Coding The first key to success in AI coding is understanding context . AI tools analyze the surrounding code to generate relevant and accurate suggestions. Without proper context, AI-generated code can be irrelevant or even introduce errors. Here's why context matters: Code Quality: In complex systems, context helps maintain consistency and functionality across different modules. Relevance: AI tools can provide more precise code snippets when they understand the broader scope of the project. Efficiency: Proper context reduces the time developers spend correcting AI-generated code. Imagine asking a lawyer off the street to represent you in court, without knowing anything about you, the case, or the evidence. The best lawyer in the world would struggle! The same goes for AI in coding - only if you provide the relevant information will you get relevant results. Tips for Providing Better Context: Descriptive Comments: Write clear and detailed comments to guide the AI tool. Structured Code: Organize your code logically to help AI understand the flow and dependencies. Consistent Naming Conventions: Use meaningful and consistent names for variables, functions, and classes. Integrate Platforms: The more of your tech stack that can be integrated, the more data the AI will be able to access and the better the output will be. Fine offers GitHub, Linear, and Sentry integrations with more on the way. Practical Instructions for Providing Context to AI Coding Tools To maximize the effectiveness of AI coding tools, providing comprehensive and well-structured context is essential. Here are some practical methods to enhance context for AI tools: 1. Creating a Knowledge Graph A knowledge graph is a structured representation of information that outlines the relationships between different components of your codebase. By creating a knowledge graph, you can provide AI tools with a holistic view of your project, enabling them to make more informed suggestions. How to Create a Knowledge Graph: Identify Key Components: List out all the modules, classes, functions, and their interactions within your project. Define Relationships: Establish how these components interact, depend on each other, and contribute to the overall functionality. Use Visualization Tools: Utilize tools like Neo4j or Graphviz to visualize the knowledge graph, making it easier to understand and update. Benefits: Enhances AI's understanding of the project structure. Facilitates better code suggestions and optimizations. Helps in identifying dependencies and potential areas for improvement. Fine creates a knowledge graph called Atlas, which includes your codebase from GitHub and issues from Sentry and Linear. This way, it prepares the AI to handle any task you give it. You don’t need to work hard creating your own knowledge graph when we’ve done it for you. 2. Implementing Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) combines traditional information retrieval techniques with generative AI models to provide more accurate and contextually relevant responses. How to Use RAG: Integrate Data Sources: Connect your AI coding tool to relevant data sources such as documentation, code repositories, and knowledge bases. Contextual Retrieval: Ensure that the AI can retrieve pertinent information from these sources before generating code suggestions. Continuous Learning: Update the data sources regularly to keep the AI informed about the latest changes and best practices in your project. Benefits: Improves the relevance and accuracy of AI-generated code. Enables AI to leverage existing knowledge and documentation. Enhances the tool's ability to handle complex queries and tasks. 3. Copy-Pasting Relevant Code into ChatGPT and Claude When using conversational AI tools like ChatGPT for coding assistance, providing snippets of relevant code can significantly improve the quality of the responses. How to Provide Relevant Code: Select Key Sections: Identify and copy the sections of code that are directly related to your query or the task at hand. Provide Contextual Information: Along with the code, include comments or explanations that describe the functionality and purpose of the code segments. Ask Specific Questions: Clearly state what you need help with, such as debugging a particular function or optimizing a code block. Example: # Function to calculate the factorial of a number def factorial(n): if n == 0: return 1 else: return n * factorial(n-1) # I need to optimize this recursive factorial function to handle larger numbers without hitting the recursion limit. Question: How can I optimize the above factorial function to handle larger inputs efficiently? Benefits: Provides AI with the necessary context to generate accurate solutions. Reduces ambiguity, leading to more precise and helpful responses. Saves time by directly addressing specific issues within the code. This is similar to GitHub Copilot and some other tools where you can highlight the relevant context to direct the AI. 4. Understanding Potential Mistakes Without Proper Context AI coding tools, while powerful, can make mistakes if not provided with adequate context. Common errors include: Irrelevant Code Suggestions: Without understanding the project structure, AI might suggest code that doesn't fit the existing framework. Syntax Errors: Lack of context can lead to syntax mistakes, especially in languages with strict syntax rules. Logical Flaws: AI might introduce logical errors if it doesn't fully grasp the intended functionality. Security Vulnerabilities: Inadequate context can result in code that exposes security loopholes or fails to follow best practices. Backend Errors In languages commonly used for backend such as Python, AI may make more mistakes if it doesn’t have context, such as NameErrors and IndentationErrors - mistakes that you wouldn’t have made coding manually. You can read more about common Python errors and how different AI applications handle them here. Fine is less likely to make such errors, as it has full knowledge of your codebase. Mitigation Strategies: Always Review AI-Generated Code: Never blindly trust the AI's suggestions; always verify and test the code. Provide Comprehensive Context: The more information you provide, the better the AI can assist accurately. Use Multiple Sources: Cross-reference AI suggestions with official documentation and best practices. Continuous Feedback: Provide feedback to the AI tool to help it learn and improve over time. Using AI to Generate Code AI coding tools are revolutionizing the way developers write code by automating mundane tasks and enhancing creativity. Here's how AI is being used to generate code: Code Snippets: AI can suggest entire lines or blocks of code based on the current context. Automating Repetitive Tasks: Tasks like boilerplate code generation, formatting, and refactoring can be handled by AI, freeing up developers to focus on more complex problems. Bug Detection: AI can identify potential bugs and vulnerabilities in real-time, ensuring higher code quality. Incorporating AI Tools into Your Workflow: Choose the Right Tool: Select an AI coding tool that integrates seamlessly with your development workflow. Customize Settings: Tailor the tool’s settings to match your coding style and project requirements. Regularly Review Suggestions: While AI can assist, always review and test AI-generated code to ensure it meets your standards. Advice for Front-End Developers Front-end development focuses on the user interface and user experience. AI coding tools can significantly enhance this process: UI/UX Enhancement: AI can suggest design improvements and optimize user interfaces for better engagement. Streamlining CSS/HTML/JS: Automate the generation of responsive designs and ensure cross-browser compatibility. Automated Testing: AI tools can perform repetitive testing tasks, ensuring your front-end code is robust and error-free. Practical Tips: Use AI for Responsive Design: Let AI suggest layout adjustments for different screen sizes. Optimize Performance: AI can analyze and optimize front-end performance, reducing load times and improving user experience. Leverage AI for Accessibility: Ensure your applications are accessible by using AI to identify and fix accessibility issues. Advice for Back-End Developers Back-end development involves server-side logic, database management, and ensuring the smooth operation of applications. AI coding tools can streamline these processes: Automating Server-Side Logic: AI can generate efficient server-side code, handling complex operations with ease. Security Vulnerability Detection: Identify and fix security issues before they become problematic. Database Query Optimization: AI can analyze and optimize database queries for better performance. Practical Tips: API Generation: Use AI to create and manage APIs, ensuring they are secure and efficient. Automate Testing: Implement AI-driven testing to validate back-end processes and ensure reliability. Optimize Code Performance: Leverage AI to analyze and enhance the performance of your server-side code. Use Cases for AI in Coding AI coding has a wide range of applications across various industries. Here are some real-world use cases: 1. Automated Bug Fixes Fine’s AI can identify and fix bugs in your codebase, reducing the time spent on debugging and improving overall code quality. 2. Predicting Performance Bottlenecks By analyzing code patterns, AI can predict potential performance issues, allowing developers to address them proactively. 3. Large Codebase Refactoring Managing and refactoring large codebases can be daunting. AI tools can assist with this process, ensuring consistency and reducing errors. Industry-Specific Benefits: E-Commerce: Enhance platform performance and security with AI-driven optimizations. Add features to improve user experience and conversion rates rapidly. Fintech: Ensure the reliability and security of financial applications through AI-assisted coding. SaaS Platforms: Improve scalability and performance with AI-generated and optimized code. Healthcare: Streamline data processing and ensure compliance with regulatory standards through AI-assisted code generation. Education Technology: Enhance learning platforms by personalizing features and improving code quality with AI-driven development. Gaming: Optimize game performance and identify bugs faster with AI-generated suggestions and automated testing. Best Large Language Models (LLMs) for Coding Large Language Models (LLMs) are at the heart of modern AI coding tools. They power the intelligent features that assist developers in writing and managing code. Here are some of the best LLMs for coding: 1. OpenAI OpenAI's models, including GPT-4 , are renowned for their versatility and capability in understanding and generating human-like text. In coding, GPT-4 excels at code generation, debugging, and providing intelligent suggestions across multiple programming languages. OpenAI also offers Codex , specifically fine-tuned for programming tasks, making it a popular choice for developers seeking advanced AI assistance. OpenAI also recently released preview and mini versions of their latest model, o1, which is outperforming competitors on many benchmarks. 2. Anthropic Anthropic's Claude models focus on safety and reliability, ensuring that AI-generated code adheres to best practices and minimizes errors. These models are designed to understand complex coding contexts and provide suggestions that align with developers' intent. Anthropic emphasizes ethical AI use, making their models a trustworthy option for sensitive and critical development environments. Claude Sonnet 3.5 was widely regarded as the most powerful LLM for coding, until o1’s release, and many developers still prefer it. 3. Google Gemini Google's Gemini models leverage Google's extensive research in natural language processing and machine learning. Gemini is designed to integrate seamlessly with Google's ecosystem, offering robust support for various programming languages and frameworks. With a focus on scalability and performance, Gemini models are ideal for large-scale projects requiring consistent and efficient code generation. 4. Other Notable Models: Cohere : Known for their fast and efficient language models, Cohere offers solutions tailored for real-time coding assistance and integration into development workflows. Grok: A versatile AI model designed to assist developers in writing, debugging, and optimizing code effectively. IBM Watson: IBM's AI offerings include models that specialize in enterprise-level coding assistance, focusing on security, compliance, and integration with existing IT infrastructures. Choosing the Right LLM for Your Needs: When selecting an LLM for coding, consider the following factors: Language Support: Ensure the model supports the programming languages you use. Integration: Look for models that integrate smoothly with your development environment and tools. Customization: Some models offer more flexibility for customization and fine-tuning based on specific project requirements. Safety and Reliability: Prioritize models that emphasize code accuracy and security to minimize the risk of introducing vulnerabilities. Click here to learn about the leading LLMs for coding and how they compare. o1-preview and Claude 3.5 Sonnet are considered to be the prominent AI models for coding. Popular AI Coding Tools There are several AI coding tools available, each with unique features tailored to different needs. Here are some of the leading options: 1. Fine Features: Fine offers advanced code generation, intelligent suggestions, automations and a full-context knowledge graph. It leverages state-of-the-art LLMs including o1 and Claude Sonnet to provide accurate and context-aware code assistance. Best For: Professional developers seeking a comprehensive AI assistant that enhances productivity across multiple programming languages, working on existing codebases. Integration: Integrates with GitHub, Linear, Sentry and Slack - with further integrations such as Jira, Monday Dev, Clickup, Data Dog, Jam.dev and posthog coming soon. 2. ChatGPT Features: ChatGPT provides conversational AI assistance, allowing developers to ask questions, seek code examples, and receive real-time support. It excels in understanding natural language queries and providing detailed explanations. Best For: Asking short questions about coding in general - such as explaining functions you’re not familiar with. Integration: Accessible via web interface, API, and can be integrated into various development tools through plugins and extensions. 3. Replit Features: Replit offers an online coding platform with integrated AI assistance. It supports collaborative coding, real-time code suggestions, and automated debugging. Best For: Teams and individual developers looking for a cloud-based development environment with built-in AI support. Integration: Fully web-based, allowing seamless collaboration and access from any device with internet connectivity. 4. Devin Features: Devin focuses on optimizing backend development with AI-driven code generation, API creation, and database management. It offers robust security features and performance optimization tools. Best For: Back-end developers seeking specialized AI tools to streamline server-side development and database interactions. Integration: Compatible with major backend frameworks and integrates with popular cloud services for deployment and management. Devin isn’t currently publicly available, but you can apply for Beta access via their website. 5. Cursor Features: Cursor provides AI-powered code generation and real-time collaboration features. It emphasizes building large blocks of code and reducing development time. Best For: Developers who prioritize code quality and seek tools that can begin a project from scratch and take it to MVP. Integration: Cursor is built on VSCode making it familiar for many developers. Equally as time-consuming as writing code is reviewing code. Here's a comparison of how different AI Coding tools handle code reviews. Conclusion AI coding boosts productivity, improves code quality, and lets developers focus on creative tasks. Providing context, using AI for code generation, and choosing the right tools can greatly benefit developers. Pick the best large language models for your needs to optimize your workflow. Automate tasks, optimize performance, and enhance security with AI coding tools. Embrace AI to unlock new efficiency and innovation. Try Fine for free at ai.fine.dev and elevate your coding workflow today. Start building today Try out the smoothest way to build, launch and manage an app Try for Free -> © Fine.dev - All rights reserved. Product Overview AI Workflows Pricing & Plans Changelog Blog Docs Company Press Terms & Conditions Privacy policy | 2026-01-13T08:49:45 |
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