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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 Future 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 Ribhav Posted on Jan 6 • Originally published at Medium Solidity Basics (Part 2) — Arrays, Mappings & Structs (Upgrading the Web3 Journey Logger) # crypto # blockchain # solidity # beginners 60 DAY WEB3 JOURNEY (29 Part Series) 1 Blockchain for Non-Technical People: Breaking Down the Basics 2 Bitcoin for Non-Technical People: Why the First Cryptocurrency Matters ... 25 more parts... 3 Bitcoin vs Traditional Money for Non-Technical People 4 Ethereum for Non-Technical People: The Programmable Blockchain 5 Smart Contracts and dApps on Ethereum (for Non‑Technical People) 6 Ethereum Wallets and Gas (for Non‑Technical People) 7 Why Ethereum Needs Layer 2s (for Curious Builders and Beginners) 8 Your First Ethereum Smart Contract, Step by Step 9 DeFi 101: Decentralized Finance 10 NFTs Explained Simply – What’s Actually Happening in 2025? 11 Understanding Tokenomics – Why Token Design Matters 12 Consensus Mechanisms Explained: How Blockchain Networks Agree Without a Boss 13 Layer 2 Solutions Deep-Dive: Optimistic vs ZK Rollups Explained 14 Ethereum vs Solana: Consensus in Action 15 DAOs Explained: How Decentralized Organizations Actually Work 16 Stablecoins – The Bridges Between Volatility and Value 17 DAOs in Practice – From Multi-Sig to Voting (And Why Ownership Tokens exist) 18 Blockchain Oracles: How Smart Contracts See the Real World (Featuring Chainlink) 19 Cross-Chain Bridges: How Assets Travel Between Blockchains (Without Getting Robbed) 20 MEV (Maximal Extractable Value): The Invisible Tax on Every Blockchain Transaction 21 Layer 0 & Layer 3 — How Blockchains Become an Internet, Not Islands 22 Web3 Infrastructure: RPCs, Nodes, Infura/Alchemy (The Invisible Plumbing) 23 On-Chain Identity — ENS, Soulbound Tokens & Your Web3 Resume 24 Crypto Regulation 101 — SEC, MiCA & What Builders Should Actually Care About 25 Solidity Basics (Part 1) — Variables, Functions & Your First Real Contract 26 Solidity Basics (Part 2) — Arrays, Mappings & Structs (Upgrading the Web3 Journey Logger) 27 Vibecoding On‑Chain — Using AI to Prototype Solidity Contracts (Safely) 28 How to Review AI‑Generated Solidity Like an Auditor (For Beginners) 29 Smart Contract Security 101 — Reentrancy & Common AI‑Generated Mistakes Yesterday we took our first real steps into Solidity: variables, functions, and a simple Web3JourneyLogger contract that stored a single note on-chain. Today we’re going to upgrade that tiny contract into something closer to how real dApps manage data. This is Day 27 of the 60‑Day Web3 journey, still in Phase 3: Development . The goal for today is to understand how Solidity handles collections of data : arrays, mappings, and structs and then use them together to build a multi‑entry, multi‑user on-chain journal. 1. Why we need more than simple variables Storing a single dayNumber and note is cute, but it doesn’t scale. Real smart contracts usually need to: Track many pieces of data , not just one (multiple entries, multiple users). Group related data into records (like “user profile”, “order”, “position”). Look up data quickly by a key like an address or an ID. In normal programming, you’d reach for arrays, dictionaries, and objects. In Solidity, you get very similar tools: Arrays → ordered lists of items. Mappings → key → value lookups (like hash maps). Structs → custom data types that bundle fields together. We’ll see each one separately, then combine them into a better Web3 Journey Logger. 2. Arrays: storing ordered lists An array in Solidity is an ordered list of elements of the same type. 2.1 Fixed vs dynamic arrays There are two main kinds of arrays: Fixed-size array uint256 public fixedNumbers; Enter fullscreen mode Exit fullscreen mode This always has exactly 5 elements. You cannot grow or shrink it. Dynamic array uint256[] public numbers; Enter fullscreen mode Exit fullscreen mode This can grow as you push new elements to it. For most dApp use cases, you’ll use dynamic arrays . 2.2 Basic operations on dynamic arrays Here’s a tiny example: uint256[] public daysLearned; function addDay(uint256 _day) public { daysLearned.push(_day); // add element at the end } function getDayAtIndex(uint256 index) public view returns (uint256) { return daysLearned[index]; } function getTotalDays() public view returns (uint256) { return daysLearned.length; } Enter fullscreen mode Exit fullscreen mode Key points: push appends a new element. You access elements with array[index] . You can check array.length to know how many items are stored. Arrays are great for ordered lists, but they aren’t efficient for lookups like “give me the entry for this address”. For that, we use mappings. 3. Mappings: key–value storage on-chain A mapping is like a dictionary or hash map: you give it a key, and it gives you a value. 3.1 Basic mapping syntax The general form: mapping(KeyType => ValueType) public myMapping; Enter fullscreen mode Exit fullscreen mode Example: mapping(address => uint256) public entryCountByUser; Enter fullscreen mode Exit fullscreen mode This mapping says: “for each address, store a uint256 count”. If you do: entryCountByUser[msg.sender] = 5; Enter fullscreen mode Exit fullscreen mode then entryCountByUser[msg.sender] will later return 5 . 3.2 Important mapping quirks Mappings have some important properties: They behave like infinite default dictionaries . Any key you haven’t set yet returns the default value for that type ( 0 , false , address 0x0 , etc.). They are not iterable. You can’t “loop over all keys” from inside the contract. If you need iteration, you must keep a separate array of keys or a counter. Because of these quirks, mappings are best used for “given a key, fetch the value” patterns, like: address → user profile token ID → owner order ID → order struct 4. Structs: custom data types A struct lets you define your own data shape by grouping fields together. 4.1 Defining and using a struct Example struct: struct Entry { uint256 day; string note; } Enter fullscreen mode Exit fullscreen mode You can use it like this: Entry public latestEntry; function setLatestEntry(uint256 _day, string calldata _note) public { latestEntry = Entry({day: _day, note: _note}); } Enter fullscreen mode Exit fullscreen mode Structs are especially powerful when combined with arrays and mappings. 5. Upgrading the Web3 Journey Logger Let’s upgrade yesterday’s contract to support multiple entries per user and multiple users. 5.1 New design We want: A struct Entry representing (day, note) . For each user address, an array of Entry structs. Helper functions to: Add a new entry. Read a single entry by index. Get how many entries a user has. This leads to a pattern like: mapping(address => Entry[]) public entriesByUser; Enter fullscreen mode Exit fullscreen mode Which you can read as: “for each address, store an array of Entry structs”. 5.2 Full upgraded contract Here’s a full version of an upgraded Web3JourneyLoggerV2 : // SPDX-License-Identifier: MIT pragma solidity ^0.8.20; contract Web3JourneyLoggerV2 { // --- Structs --- struct Entry { uint256 day; string note; } // --- State variables --- // Address of the person who deployed the contract address public owner; // Name or handle of the learner (optional global name) string public name; // For each user address, store an array of their entries mapping(address => Entry[]) private entriesByUser; // --- Events --- event EntryAdded(address indexed user, uint256 indexed day, uint256 index, string note); // --- Constructor --- constructor(string memory _name) { owner = msg.sender; name = _name; } // --- Core functions --- /// @notice Add a new journal entry for the caller function addEntry(uint256 _day, string calldata _note) public { Entry memory newEntry = Entry({day: _day, note: _note}); entriesByUser[msg.sender].push(newEntry); uint256 index = entriesByUser[msg.sender].length - 1; emit EntryAdded(msg.sender, _day, index, _note); } /// @notice Get a specific entry for a user by index function getEntry(address _user, uint256 _index) public view returns (uint256 day, string memory note) { require(_index < entriesByUser[_user].length, "Index out of bounds"); Entry storage entry = entriesByUser[_user][_index]; return (entry.day, entry.note); } /// @notice Get how many entries a user has function getEntryCount(address _user) public view returns (uint256) { return entriesByUser[_user].length; } } Enter fullscreen mode Exit fullscreen mode A few things to notice: Entry is a struct containing day and note . mapping(address => Entry[]) private entriesByUser; creates a mapping from user to an array of their entries. addEntry builds a new Entry in memory, pushes it into the caller’s array, and emits an EntryAdded event. getEntry lets you read a specific entry by user and index. getEntryCount tells you how many entries a user has so front-ends can loop over them off-chain. This contract is no longer just a single note; it’s a tiny, multi‑user journaling dApp backend. 6. Deploying V2 and testing it You can deploy Web3JourneyLoggerV2 using the same Remix + Sepolia flow from yesterday: Open Remix and create Web3JourneyLoggerV2.sol . Paste the full contract above. Compile with a 0.8.x compiler. In “Deploy & Run Transactions”, choose Injected Provider – MetaMask and the Sepolia network. Deploy with a name (e.g., "Web3ForHumans" ). Once deployed: Call addEntry(27, "Learned arrays, mappings, and structs today") from your wallet. Call getEntryCount(yourAddress) — you should see 1 . Call getEntry(yourAddress, 0) to read back the first entry. Ask a friend to connect their wallet and call addEntry too. Now your contract is tracking multiple learners and their progress on-chain. 7. Why arrays + mappings + structs matter These three tools — arrays, mappings, and structs — are the backbone of almost every serious Solidity contract: A DEX might use mapping(address => mapping(address => uint256)) to track token balances. A DAO might use structs + arrays for proposals and votes. An NFT contract uses mappings from token IDs to owners and metadata. By understanding how to combine them, you’re no longer just “deploying example contracts” — you’re modeling real-world data structures on-chain. Tomorrow, we can build on this by adding more features like: Editing or deleting entries. Restricting certain actions to the contract owner. Or exposing your journal data to a simple frontend. For now, if you deploy Web3JourneyLoggerV2 on Sepolia, share your contract address — let’s see how many on-chain learning journals we can spin up. Further reading Solidity official docs – Arrays, Structs, and Mappings Structs, Mappings and Arrays in Solidity Understanding mappings in Solidity How the EVM stores mappings, arrays, and structs Follow the series on Medium | Twitter | Future Jump into Web3ForHumans on Telegram and we’ll brainstorm Web3 together. 60 DAY WEB3 JOURNEY (29 Part Series) 1 Blockchain for Non-Technical People: Breaking Down the Basics 2 Bitcoin for Non-Technical People: Why the First Cryptocurrency Matters ... 25 more parts... 3 Bitcoin vs Traditional Money for Non-Technical People 4 Ethereum for Non-Technical People: The Programmable Blockchain 5 Smart Contracts and dApps on Ethereum (for Non‑Technical People) 6 Ethereum Wallets and Gas (for Non‑Technical People) 7 Why Ethereum Needs Layer 2s (for Curious Builders and Beginners) 8 Your First Ethereum Smart Contract, Step by Step 9 DeFi 101: Decentralized Finance 10 NFTs Explained Simply – What’s Actually Happening in 2025? 11 Understanding Tokenomics – Why Token Design Matters 12 Consensus Mechanisms Explained: How Blockchain Networks Agree Without a Boss 13 Layer 2 Solutions Deep-Dive: Optimistic vs ZK Rollups Explained 14 Ethereum vs Solana: Consensus in Action 15 DAOs Explained: How Decentralized Organizations Actually Work 16 Stablecoins – The Bridges Between Volatility and Value 17 DAOs in Practice – From Multi-Sig to Voting (And Why Ownership Tokens exist) 18 Blockchain Oracles: How Smart Contracts See the Real World (Featuring Chainlink) 19 Cross-Chain Bridges: How Assets Travel Between Blockchains (Without Getting Robbed) 20 MEV (Maximal Extractable Value): The Invisible Tax on Every Blockchain Transaction 21 Layer 0 & Layer 3 — How Blockchains Become an Internet, Not Islands 22 Web3 Infrastructure: RPCs, Nodes, Infura/Alchemy (The Invisible Plumbing) 23 On-Chain Identity — ENS, Soulbound Tokens & Your Web3 Resume 24 Crypto Regulation 101 — SEC, MiCA & What Builders Should Actually Care About 25 Solidity Basics (Part 1) — Variables, Functions & Your First Real Contract 26 Solidity Basics (Part 2) — Arrays, Mappings & Structs (Upgrading the Web3 Journey Logger) 27 Vibecoding On‑Chain — Using AI to Prototype Solidity Contracts (Safely) 28 How to Review AI‑Generated Solidity Like an Auditor (For Beginners) 29 Smart Contract Security 101 — Reentrancy & Common AI‑Generated Mistakes 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 Ribhav Follow Web3 Learner | Community Builder | Technical Writer | Learning DevRel in Public New to Web3 ? Join: https://t.me/Web3ForHumans Location Ludhiana Education MIT Manipal Joined Feb 1, 2022 More from Ribhav Smart Contract Security 101 — Reentrancy & Common AI‑Generated Mistakes # security # crypto # blockchain # beginners How to Review AI‑Generated Solidity Like an Auditor (For Beginners) # crypto # blockchain # ai # beginners Vibecoding On‑Chain — Using AI to Prototype Solidity Contracts (Safely) # crypto # blockchain # vibecoding # beginners 💎 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 Future — News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. 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. 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https://www.youtube.com/watch?v=JBWgvUrb-q8 | Nikita Popov – PHP Performance Trivia - 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://opensource.org/ai#study | Open Source AI – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Source AI Open Source AI OSAID 1.0 Process Timeline Open Weights FAQ Endorsements Open Main Menu THE OPEN SOURCE AI DEFINITION 1.0 We have released the first stable version of the Definition. Read version 1.0 What’s Open Source AI? Following the same idea behind Open Source Software, an Open Source AI is a system made available under terms that grant users the freedoms to: Use Study Modify Share Use the system for any purpose and without having to ask for permission. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Study how the system works and understand how its results were created. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Modify the system for any purpose, including to change its output. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Share the system for others to use with or without modifications, for any purpose. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Benefits of Open Source AI Transparency & Safety Open Source AI provides information essential for auditing systems and to mitigate bias, ensures accountability and transparency of data sources, and accelerates AI safety research. Competition & Polyculture Open Source AI makes more models available, spurs innovation and quality due to increased competition and tackles AI monoculture by providing more stakeholders access to foundational technology. Diverse Applications Open Source AI gives developers access to resources crucial for developing context-specific, localized applications that are representative of cultural and linguistic diversity and allow for model aligned with different value systems. Read the white paper The Open Source Initiative and Open Future have taken a significant step toward addressing this challenge by releasing this white paper. The document is the culmination of a global co-design process, enriched by insights from a vibrant two-day workshop held in Paris in October 2024. Read the white paper Why Open Source AI needs a definition? Open Source Frontier The traditional view of Open Source code and licenses when applied to AI components are not sufficient to guarantee the freedoms to use, study, share and modify the systems. Informing Regulators Government regulations have begun in Europe, the United States, and elsewhere. Communities need a common understanding to educate policy makers. Combat Openwashing Companies are calling AI systems “Open Source” even though their licenses contain restrictions that go against the accepted principles and freedoms of Open Source. Who’s behind the Open Source AIDefinition View all Endorsers Overall process 0 Supporting Organizations 0 Supporting Individuals 0 Co-designers 0 Systems reviewed Representation in the co-design process 0 Nationalities 0 People Of Color 0 Global South 0 Femme, Trans, & Nonbinary Co-design 2023 – 2024 In 2023, we started the co-design process hosting several online and in-person activities around the world. Research 2022 – 2023 Alongside AI experts from various fields we produced a podcast , panels and webinars . Endorsements 2024 – 2025 Late 2024 into 2025, the OSI is gathering endorsements from various individuals and organizations, including Mozilla, Suse, Eleuther AI, Ai2, Eclipse Foundation, and the OpenInfra Foundation, among many others. Which AI systems comply with the OSAID 1.0? As part of our validation and testing of the OSAID, the volunteers checked whether the Definition could be used to evaluate if AI systems provided the freedoms expected. The list of models that passed the Validation phase are: Pythia (Eleuther AI), OLMo (AI2), Amber and CrystalCoder (LLM360), and T5 (Google). There are a couple of others that were analyzed and would probably pass if they changed their licenses/legal terms: BLOOM (BigScience), Starcoder2 (BigCode), Falcon (TII). Those that have been analyzed and don’t pass because they lack required components and/or their legal agreements are incompatible with the Open Source principles: Llama2 (Meta), Grok (X/Twitter), Phi-2 (Microsoft), Mixtral (Mistral). These results should be seen as part of the definitional process, a learning moment; they’re not certifications of any kind. OSI will continue to validate only legal documents, and will not validate or review individual AI systems, just as it does not validate or review software projects. If you are wondering about Open Weights models , please refer to our dedicated page . The OSAID co-design process was open to everyone interested in collaborating . How to participate There are many ways to get involved: Endorse the Open Source AI Definition : have your organization appended to the list of supporters of version 1.0. Join the forum : support and comment on the releases, record your approval or concerns to new and existing threads. Subscribe to our newsletter and read our blog to be kept up-to-date. Watch the town hall recordings to learn more about the process. Join the workshops and scheduled conferences : meet the OSI and other participants at in-person events around the world. Open Source AI Definition Governance Governance for the Open Source AI Definition is provided by the OSI Board of Directors . The OSI board members have expertise in business, legal, and open source software development, as well as experience across a range of commercial, public sector, and non-profit organizations. Formal progress reports including achievements, budget updates, and next steps are provided monthly by the Program Lead for advice and guidance as part of regular Board business. Additionally, informal updates on the outcomes of key meetings and milestones are provided via email to the Board as required. Supported by OSI’s efforts wouldn’t be possible without the support of our sponsors and thousands of individual members. 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https://www.fine.dev/blog/ai-coding-guide#1-creating-a-knowledge-graph | 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 | 2026-01-13T08:49:43 |
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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 ChatGPT Follow Hide GPT models are capable of natural language processing tasks such as text generation, summarization, and analysis. ChatGPT interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. Create Post submission guidelines We welcome all posts around the topic of ChatGPT - technical articles, personal stories, discussions, and more! Older #chatgpt 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 Writing with AI Assistance Burve (Burve Story Lab) Burve (Burve Story Lab) Burve (Burve Story Lab) Follow Jan 5 Writing with AI Assistance # writing # ai # productivity # chatgpt Comments Add Comment 7 min read Cerberus-GPT Mxfalconmx1 Mxfalconmx1 Mxfalconmx1 Follow Jan 8 Cerberus-GPT # chatgpt # cybersecurity Comments Add Comment 1 min read When long chats change the code: context drift and hidden errors James M James M James M Follow Dec 30 '25 When long chats change the code: context drift and hidden errors # codequality # chatgpt # llm # python Comments Add Comment 3 min read How to Hack ChatGPT - Security Vulnerabilities in GPT Store Apps dss99911 dss99911 dss99911 Follow Dec 30 '25 How to Hack ChatGPT - Security Vulnerabilities in GPT Store Apps # knowledge # ai # chatgpt # gptstore 5 reactions Comments Add Comment 2 min read Why I Chose Custom Code Over CMS — As a Freelance Dev Starting from Scratch Hugo Calmels Hugo Calmels Hugo Calmels Follow Jan 3 Why I Chose Custom Code Over CMS — As a Freelance Dev Starting from Scratch # career # chatgpt # react # webdev 1 reaction Comments Add Comment 2 min read Keep extending ADV game engine with Antigravity tomokat tomokat tomokat Follow Dec 29 '25 Keep extending ADV game engine with Antigravity # antigravity # chatgpt # gamedev # phaser Comments Add Comment 2 min read Perplexity AI and Jay Collaborate on Peace With AI Jay Dorval Jay Dorval Jay Dorval Follow Dec 30 '25 Perplexity AI and Jay Collaborate on Peace With AI # perplexity # gemini # chatgpt # programming Comments Add Comment 9 min read 📌 Day 21: 21 Days of Building a Small Language Model: Complete Journey Recap: Book Giveaway📌 Prashant Lakhera Prashant Lakhera Prashant Lakhera Follow Dec 29 '25 📌 Day 21: 21 Days of Building a Small Language Model: Complete Journey Recap: Book Giveaway📌 # llm # ai # chatgpt # programming Comments Add Comment 1 min read I solved one of my major pain point during my game development with AI (Chat GPT + Antigravity) tomokat tomokat tomokat Follow Jan 5 I solved one of my major pain point during my game development with AI (Chat GPT + Antigravity) # gamedev # antigravity # chatgpt # gemini 4 reactions Comments Add Comment 2 min read Real-World LLM AI Examples I've Used in My Projects i Ash i Ash i Ash Follow Dec 26 '25 Real-World LLM AI Examples I've Used in My Projects # chatgpt # ai # programming # llm Comments Add Comment 6 min read Vercel AI SDK Complete Guide: Building Production-Ready AI Chat Apps with Next.js HK Lee HK Lee HK Lee Follow Jan 7 Vercel AI SDK Complete Guide: Building Production-Ready AI Chat Apps with Next.js # vercelaisdk # nextjs # ai # chatgpt Comments 2 comments 13 min read MCP Code Mode: How We Can Cut Token Costs By Writing Less Prompts and More TypeScript Debby McKinney Debby McKinney Debby McKinney Follow Jan 7 MCP Code Mode: How We Can Cut Token Costs By Writing Less Prompts and More TypeScript # mcp # programming # ai # chatgpt 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 How to Rank on ChatGPT: LLM Visibility Strategies for B2B SaaS Infrasity: Devrel for B2B SaaS Infrasity: Devrel for B2B SaaS Infrasity: Devrel for B2B SaaS Follow Dec 21 '25 How to Rank on ChatGPT: LLM Visibility Strategies for B2B SaaS # chatgpt # saas # llm # marketing Comments Add Comment 3 min read 🎉 Announcing mkarchi v0.1.6 - Now Ready to Use! 🚀 Soufyan Soufyan Soufyan Follow Dec 21 '25 🎉 Announcing mkarchi v0.1.6 - Now Ready to Use! 🚀 # chatgpt # python # mkarchi # opensource 5 reactions Comments Add Comment 2 min read The lost logos of code Tasos Tsournos Tasos Tsournos Tasos Tsournos Follow Dec 20 '25 The lost logos of code # discuss # claudecode # chatgpt Comments Add Comment 3 min read I built a tool that converts plain text song lists (like from ChatGPT) into playable music links instantly. Smit Patil Smit Patil Smit Patil Follow Dec 20 '25 I built a tool that converts plain text song lists (like from ChatGPT) into playable music links instantly. # webdev # chatgpt # music # ai 4 reactions Comments 1 comment 1 min read ChatGPT Now Understands mkarchi - Generate Project Structures with AI published Soufyan Soufyan Soufyan Follow Dec 20 '25 ChatGPT Now Understands mkarchi - Generate Project Structures with AI published # chatgpt # tooling # architecture # python Comments Add Comment 1 min read Build something vs. add new features and maintaining it tomokat tomokat tomokat Follow Dec 23 '25 Build something vs. add new features and maintaining it # antigravity # chatgpt # gamedev # phaser 1 reaction Comments Add Comment 3 min read AI Roadmap 2026 for AIML Students: Which Tools and Skills to Learn Month by Month Keerthana Keerthana Keerthana Follow Dec 19 '25 AI Roadmap 2026 for AIML Students: Which Tools and Skills to Learn Month by Month # chatgpt # ai # students # skills 1 reaction Comments Add Comment 4 min read 📌 Most models use Grouped Query Attention. That doesn’t mean yours should.📌 Prashant Lakhera Prashant Lakhera Prashant Lakhera Follow Dec 19 '25 📌 Most models use Grouped Query Attention. That doesn’t mean yours should.📌 # llm # chatgpt # ai # deepseek 1 reaction Comments Add Comment 1 min read Building a Simple MCP Server That Just Returns the Current Time kiyo-e kiyo-e kiyo-e Follow Dec 19 '25 Building a Simple MCP Server That Just Returns the Current Time # mcp # cloudflare # chatgpt # claude 1 reaction Comments Add Comment 3 min read Delete Your ChatGPT History. Right Now. 🚨 Naved Shaikh Naved Shaikh Naved Shaikh Follow Dec 31 '25 Delete Your ChatGPT History. Right Now. 🚨 # ai # security # privacy # chatgpt 7 reactions Comments 2 comments 2 min read This Open-Source LLM Gateway is 54x Faster Than LiteLLM (Here's Why) Debby McKinney Debby McKinney Debby McKinney Follow Jan 8 This Open-Source LLM Gateway is 54x Faster Than LiteLLM (Here's Why) # opensource # ai # programming # chatgpt 11 reactions Comments 1 comment 5 min read Using a Single GPT Client as a Language Runtime (No API, No Agents) yuer yuer yuer Follow Dec 21 '25 Using a Single GPT Client as a Language Runtime (No API, No Agents) # chatgpt # llm # architecture # ai 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://opensource.org/ai/open-weights#content | Open Weights: not quite what you’ve been told – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Source AI Open Source AI OSAID 1.0 Process Timeline Open Weights FAQ Endorsements Open Main Menu In the rapidly evolving landscape of artificial intelligence, Open Weights have emerged as a buzzword indicating incremental progress in AI transparency. By sharing the final parameters of a trained model, developers offer some insight into how a neural network operates. However, these weights reveal only a fraction of the information required for full accountability. While Open Weights represent a milestone in opening up AI systems, they still stop short of delivering the level of transparency many researchers and regulators deem essential. What are Open Weights? Open Weights refer to the final weights and biases of a trained neural network. These values, once locked in, determine how the model interprets input data and generates outputs. When AI developers share these parameters under an OSI Approved License , they empower others to fine-tune, adapt, or deploy the model for their own projects. However, Open Weights differ significantly from Open Source AI because they do not include: Training code – The scripts or frameworks used to create and curate the training dataset. Training dataset – The full dataset used for training, when legally possible. As an alternative, when distribution of the training dataset is not legally possible, Comprehensive data transparency – Full details about dataset composition, such as source domains, cleaning methods, or balancing techniques. By withholding these critical elements, developers only provide a glimpse into the final state of the model, making it difficult for others to replicate, audit, or deeply understand the training process. Is Open Weights a new concept? Far from it. Over the past decade, AI practitioners have experimented with different ways of sharing or withholding information, often balancing trade secrets with mounting calls for AI transparency . The renewed interest in Open Weights arose in response to regulatory scrutiny and growing awareness that completely opaque systems can embed biases and discriminatory behaviors. In 2023, Heather Meeker , a recognized expert in Open Source licensing, published an Open Weights Definition that formalized many of these conversations. Her work clarifies permissible usage and distribution of final model parameters while highlighting a gap: the full process behind model creation still remains undisclosed. As discussions on Open Source AI intensify, so does the debate around whether Open Weights alone can deliver the transparency needed for ethical and responsible AI. The limitations of Open Weights While Open Weights stand out as more transparent than purely proprietary AI , they still lack several key elements of Open Source AI . 1. Lack of reproducibility Reproducibility is critical in scientific and technological progress. Without training code or intermediate checkpoints , researchers and auditors cannot replicate the model’s development process. This gap hinders efforts to identify when and where biases might have been introduced, making it nearly impossible to rectify errors or vulnerabilities. 2. Data opacity The phrase “garbage in, garbage out” applies strongly to AI. If the training data is not representative or ethically sourced, the model’s outputs can exhibit harmful biases. However, Open Weights often do not clarify how the dataset was constructed or cleaned. This oversight leaves a significant blind spot, preventing anyone outside the original development team from fully assessing the dataset’s quality or diversity. 3. Regulatory hurdles Governments worldwide are formulating policies that mandate higher standards of transparency in AI, especially for systems deployed in sensitive areas such as finance, healthcare, and public administration. Disclosing only the final weights may not meet these emerging regulations, as the lack of training code or dataset details could violate requirements for fairness, privacy, or explainability. 4. Limited community collaboration One of the core strengths of Open Source AI lies in the collaborative potential it unlocks. When the entire pipeline—training scripts, dataset composition, and intermediate checkpoints—is openly available, a global community can work together to improve the model, fix bugs, or address ethical concerns. By contrast, Open Weights significantly reduce these possibilities, limiting meaningful contributions to superficial fine-tuning rather than in-depth improvements. Open Weights vs. Open Source AI Open Source AI’s four freedoms Following the same idea behind open source software, an Open Source AI is made available under terms that grant users the following freedoms: Use – The freedom to use the system for any purpose without seeking additional permission. Study – The freedom to study how the system works and understand how its results are generated. Modify – The freedom to modify the system for any purpose, including changing its outputs. Share – The freedom to share the system with others, with or without modifications, for any purpose. A fundamental precondition to exercise these freedoms is having access to the preferred form needed to make modifications, and the practical means to use it. Open Weights alone fall short of this because they do not provide the underlying training process, code, or comprehensive data details required for full-fledged use, study, modification, and sharing. To better understand why Open Weights and Open Source AI differ so drastically, consider the following comparison: Feature Open Weights Open Source AI Weights & Biases Released Released Training Code Not Shared Fully Shared Intermediate Checkpoints Withheld Nice to have Training dataset Not Shared/Not disclosed Released* Training Data Composition Partially/Not Disclosed Fully Disclosed Clearly, Open Weights mark a notable advancement over fully proprietary solutions by offering the final model parameters. However, Open Source AI goes further by unlocking the entire development process. This holistic openness enables complete reproducibility, thorough bias audits, and robust community-driven improvements. * When legally allowed. See Open Source AI Definition FAQ . Why transparency in AI matters Ethical AI development A model’s fairness depends heavily on data quality and balanced training procedures. Open source AI allows reviewers to spot and address potential biases early, while Open Weights alone can’t provide enough context to guarantee ethical performance. Regulatory compliance Policymakers need concrete proof that AI models comply with laws on privacy, discrimination, and consumer protection. With Open Weights , regulators see only the end result, not the steps taken to reach it. Full openness eases the burden of proving a model’s compliance across various jurisdictions. Innovation and collaboration When experts worldwide can inspect training code , dataset details , and, ideally, intermediate checkpoints , they can collectively refine algorithms, fix bugs, and broaden the model’s applicability. This communal effort drives forward innovation in a way that Open Weights alone cannot match. Trust and public perception In an era of data breaches and algorithmic controversies, public trust in AI remains fragile. Models that offer complete transparency—which is the hallmark of Open Source AI —are more likely to gain acceptance from stakeholders who worry about issues such as hidden biases or unaccountable decision-making. The role of Open Weights: a lesser evil? Many see Open Weights as a compromise—a lesser evil than completely proprietary AI . By at least making the final parameters accessible, developers provide some degree of insight into the model’s decision logic. This can be enough for certain low-stakes applications where minimal accountability suffices. However, for industries like healthcare, autonomous vehicles, or financial underwriting—where AI decisions carry significant consequences—the partial transparency of Open Weights is insufficient. Full accountability demands understanding not just the final model, but also how it was built, the data it relied on, and the points at which it might have diverged from ethical best practices. The bottom line Open Weights might seem revolutionary at first glance, but they’re merely a starting point. While they do move the needle closer to transparency than strictly closed, proprietary models, they lack the detailed insights found in Open Source AI . For AI to be both accountable and scalable , every part of the pipeline—from the initial dataset to the final set of parameters—needs to be open to scrutiny, validation, and collective improvement. If you care about AI systems that are trustworthy, fair, and compliant with upcoming regulations, look beyond Open Weights. Learn more about Open Source AI , where full reproducibility and transparency foster a healthier, more innovative ecosystem. By championing this evolution, we can move closer to AI solutions that benefit everyone, not just a select few. Stay informed, stay involved Join us in our discussion forum to connect with researchers, developers, and policymakers who are shaping the future of Open Source AI. This is where you can share insights, learn from others’ experiences, and stay updated on the latest breakthroughs and regulatory changes in the AI space. 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https://opensource.org/deepdive/ | Open Source AI – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Source AI Open Source AI OSAID 1.0 Process Timeline Open Weights FAQ Endorsements Open Main Menu THE OPEN SOURCE AI DEFINITION 1.0 We have released the first stable version of the Definition. Read version 1.0 What’s Open Source AI? Following the same idea behind Open Source Software, an Open Source AI is a system made available under terms that grant users the freedoms to: Use Study Modify Share Use the system for any purpose and without having to ask for permission. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Study how the system works and understand how its results were created. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Modify the system for any purpose, including to change its output. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Share the system for others to use with or without modifications, for any purpose. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Benefits of Open Source AI Transparency & Safety Open Source AI provides information essential for auditing systems and to mitigate bias, ensures accountability and transparency of data sources, and accelerates AI safety research. Competition & Polyculture Open Source AI makes more models available, spurs innovation and quality due to increased competition and tackles AI monoculture by providing more stakeholders access to foundational technology. Diverse Applications Open Source AI gives developers access to resources crucial for developing context-specific, localized applications that are representative of cultural and linguistic diversity and allow for model aligned with different value systems. Read the white paper The Open Source Initiative and Open Future have taken a significant step toward addressing this challenge by releasing this white paper. The document is the culmination of a global co-design process, enriched by insights from a vibrant two-day workshop held in Paris in October 2024. Read the white paper Why Open Source AI needs a definition? Open Source Frontier The traditional view of Open Source code and licenses when applied to AI components are not sufficient to guarantee the freedoms to use, study, share and modify the systems. Informing Regulators Government regulations have begun in Europe, the United States, and elsewhere. Communities need a common understanding to educate policy makers. Combat Openwashing Companies are calling AI systems “Open Source” even though their licenses contain restrictions that go against the accepted principles and freedoms of Open Source. Who’s behind the Open Source AIDefinition View all Endorsers Overall process 0 Supporting Organizations 0 Supporting Individuals 0 Co-designers 0 Systems reviewed Representation in the co-design process 0 Nationalities 0 People Of Color 0 Global South 0 Femme, Trans, & Nonbinary Co-design 2023 – 2024 In 2023, we started the co-design process hosting several online and in-person activities around the world. Research 2022 – 2023 Alongside AI experts from various fields we produced a podcast , panels and webinars . Endorsements 2024 – 2025 Late 2024 into 2025, the OSI is gathering endorsements from various individuals and organizations, including Mozilla, Suse, Eleuther AI, Ai2, Eclipse Foundation, and the OpenInfra Foundation, among many others. Which AI systems comply with the OSAID 1.0? As part of our validation and testing of the OSAID, the volunteers checked whether the Definition could be used to evaluate if AI systems provided the freedoms expected. The list of models that passed the Validation phase are: Pythia (Eleuther AI), OLMo (AI2), Amber and CrystalCoder (LLM360), and T5 (Google). There are a couple of others that were analyzed and would probably pass if they changed their licenses/legal terms: BLOOM (BigScience), Starcoder2 (BigCode), Falcon (TII). Those that have been analyzed and don’t pass because they lack required components and/or their legal agreements are incompatible with the Open Source principles: Llama2 (Meta), Grok (X/Twitter), Phi-2 (Microsoft), Mixtral (Mistral). These results should be seen as part of the definitional process, a learning moment; they’re not certifications of any kind. OSI will continue to validate only legal documents, and will not validate or review individual AI systems, just as it does not validate or review software projects. If you are wondering about Open Weights models , please refer to our dedicated page . The OSAID co-design process was open to everyone interested in collaborating . How to participate There are many ways to get involved: Endorse the Open Source AI Definition : have your organization appended to the list of supporters of version 1.0. Join the forum : support and comment on the releases, record your approval or concerns to new and existing threads. Subscribe to our newsletter and read our blog to be kept up-to-date. Watch the town hall recordings to learn more about the process. Join the workshops and scheduled conferences : meet the OSI and other participants at in-person events around the world. Open Source AI Definition Governance Governance for the Open Source AI Definition is provided by the OSI Board of Directors . The OSI board members have expertise in business, legal, and open source software development, as well as experience across a range of commercial, public sector, and non-profit organizations. Formal progress reports including achievements, budget updates, and next steps are provided monthly by the Program Lead for advice and guidance as part of regular Board business. Additionally, informal updates on the outcomes of key meetings and milestones are provided via email to the Board as required. Supported by OSI’s efforts wouldn’t be possible without the support of our sponsors and thousands of individual members. 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https://dev.to/pockit_tools/pnpm-vs-npm-vs-yarn-vs-bun-the-2026-package-manager-showdown-51dc#npm-11x | 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://wiki.php.net/rfc/foreach-non-scalar-keys | PHP: rfc:foreach-non-scalar-keys Login Register You are here: start › rfc › foreach-non-scalar-keys rfc:foreach-non-scalar-keys Allow non-scalar keys in ''foreach'' version 1.0 Date: 2013-01-28 Authors: Levi Morrison levim@php.net , Nikita Popov nikic@php.net Status: Implemented in PHP 5.5 ( commit ) Current situation The Iterator::key function can currently return a value of any type, but the handling code in foreach and several other places only allows integer and string keys to be used. This limitation makes some use-cases unnecessarily complicated. From the SPL two examples are MultipleIterator and SplObjectStorage . The MutlipleIterator allows you to traverse several Iterator s at the same time. It's ::current method returns an array of values and the ::key method returns an array of keys. But due to the foreach key type limitation the keys can not be directly fetched: $it = new MultipleIterator ; $it -> attachIterator ( $it1 ) ; $it -> attachIterator ( $it2 ) ; // This is NOT possible foreach ( $it as $keys => $values ) { // ... } // Instead you have to use this foreach ( $it as $values ) { $keys = $it -> keys ( ) ; // ... } SplObjectStorage is a map/set implementation for object keys. Here the issue is circumvented by returning the keys as values and requiring a manual lookup on the values: // NOT possible foreach ( $objectStore as $key => $value ) { // ... } // Instead you have to use foreach ( $objectStore as $key ) { $value = $objectStore [ $key ] ; // ... } These are just two examples from core classes, but it obviously also applies in many other cases (and now that we have generators, it will probably become an even larger issue). Another key issue is that you can't really work around this generically. If you want to write code that is also compatible with Iterator s that return array/object keys, you can no longer use the foreach ($it as $k => $v) syntax. You are forced to use foreach ($it as $v) { $k = $it->key(); ... } , but this will obviously only with with Iterator s and not with aggregates, Traversable s or normal arrays. In order to properly support all use cases you'd have to wrap everything in iterators (i.e. make extensive use of IteratorIterator and ArrayIterator ), which obviously is an option, but cumbersome to a degree that nobody does it. What this means is that iterators like MultipleIterator are to a large part excluded from use in iterator chaining/pipelines (which is probably the most important thing about using iterators). Suggested fix This RFC proposes to lift the restriction and allow values of arbitrary types to be used as keys (in particularly allowing also arrays and objects) in iterators. (Note: This proposal does not suggest allowing those key types in arrays. This is only about Iterator s.) In order to remove this restriction the internal ''zend_object_iterator_funcs'' API has to be changed: // This entry: int ( * get_current_key ) ( zend_object_iterator * iter , char ** str_key , uint * str_key_len , ulong * int_key TSRMLS_DC ) ; // Is replaced with this entry: void ( * get_current_key ) ( zend_object_iterator * iter , zval * key TSRMLS_DC ) ; The handler has to write into the passed zval* using one of the ZVAL_* macros. iterator_to_array() When using non-string/int keys iterator_to_array with the $preserve_keys option will behave in the same way as PHP would when it does normal array key assignments, i.e. its behavior would be the same as the following PHP snippet: function iterator_to_array ( $iter ) { foreach ( $iter as $k => $v ) { $array [ $k ] = $v ; } return $array ; } For array and object keys this would give an Illegal offset type warning. For NULL the "" key is used, doubles are truncated to the integral part, resources use their resource ID and issue a warning, booleans are cast to integers. In order to support this a new function is added in Zend/zend_ API .h (which more or less reimplements the internal inline function zend_fetch_dimension_address_inner ): /* The refcount of value is incremented by the function itself */ ZEND_API int array_set_zval_key ( HashTable * ht , zval * key , zval * value ) ; Patch The patch for this change can be found here: https://github.com/php/php-src/commit/fcc6611de9054327441786e52444b5f8eecdd525 The change itself is rather small, but there are quite a few extensions that require minor adjustments to use the new API . Vote Voting ends on March 6th. A 50% + 1 majority is required. This RFC targets PHP 5.5. Remove type-restrictions on foreach keys? Real name Yes No ab arpad ashnazg colder datibbaw dm frozenfire gwynne indeyets ircmaxell jpauli juliens jwage kalle levim nikic pajoye patrickallaert peehaa treffynnon willfitch Final result: 21 0 This poll has been closed. rfc/foreach-non-scalar-keys.txt · Last modified: 2025/04/03 13:08 by 127.0.0.1 Page Tools Show pagesource Old revisions Backlinks Back to top Table of Contents Allow non-scalar keys in ''foreach'' Current situation Suggested fix iterator_to_array() Patch Vote Copyright © 2001-2026 The PHP Group Other PHP.net sites Privacy policy | 2026-01-13T08:49:43 |
https://opensource.org/ai/faq#content | OSAID FAQs – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Source AI Open Source AI OSAID 1.0 Process Timeline Open Weights FAQ Endorsements Open Main Menu THE open source ai definition 1.0 We have released the first stable version of the Definition. Read version 1.0 Answers to Frequently Asked Questions The Open Source AI Definition (OSAID) represents an important first step in defining Open Source in an AI context. Because AI systems differ fundamentally from traditional software, the OSAID seeks to establish the first set of clear, practical guidelines for development, use and modification of AI systems in keeping with the Open Source ethos. Unfortunately, misconceptions about the definition persist, often stemming from a lack of understanding of the nature of AI. This post aims to clarify key points and provide a forward-looking perspective on the importance of the OSAID. THE open source ai definition 1.0 FAQs What Is an AI System? What is an Open Source AI? Why did you write the Open Source AI Definition? What’s the difference between the Open Source Definition and the Open Source AI Definition? Isn’t training data required to program the AI system? What is the role of training data in the Open Source AI Definition? Why do you allow the exclusion of some training data? How did you arrive at this conclusion? Is it compromising Open Source ideals? What kind of data should be required in the Open Source AI Definition? How do you fix a buggy AI system? What is a skilled person? Is the Open Source AI Definition covering models and weights and parameters? Why do you require training code while OSD #2 doesn’t require compilers? Why is there no mention of safety and risk limitations in the Open Source AI Definition? Are model parameters copyrightable? Why will parameters be available under “OSI-approved terms” but the code will be under “OSI-approved licenses”? Are you going to allow restrictions on the terms for models? Why is the “Preferred form to make modifications” limited to machine learning? Which AI systems comply with the Open Source AI Definition? What’s the next step? FAQs What Is an AI System? According to the OSAID, an AI system aligns with the definition provided by the Organisation for Economic Co-operation and Development (OECD): An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment. In simple terms, an AI system is “the thing” that processes input to produce output, whether that’s a prediction, recommendation, or another result. Anchoring discussions in such common definitions is essential because AI systems differ radically from traditional software. What is an Open Source AI? An Open Source AI is an AI system made freely available with all necessary code, data and parameters under legal terms approved by the Open Source Initiative. For more details, read below. Why did you write the Open Source AI Definition? Point #2 of the Open Source Definition (OSD) says “The program must include source code […] The source code must be the preferred form in which a programmer would modify the program […]” . When we embarked on this initiative, nobody had a clear answer to the question, “What is the preferred form to modify an AI system?” So OSI offered to find the answer along with the broader community by engaging in a co-design process . What’s the difference between the Open Source Definition and the Open Source AI Definition? The Open Source Definition (OSD) refers to software programs. AI and specifically machine learning systems are not simply software programs; they blend boundaries with data, configuration options, documentation and new artifacts, like weights and biases. The Open Source AI Definition describes the preferred form to modify an AI system, providing clarity on interpreting the principles of the OSD in the domain of AI. Isn’t training data required to program the AI system? A frequent misunderstanding about AI systems is equating training data with source code, suggesting that “training data is how the model gets programmed.” Unlike traditional software, AI systems are not programmed in the conventional sense. Instead, they acquire capabilities autonomously during the training process — a phenomenon distinguishing them from software like the Linux kernel. For example, the Linux kernel is: Programmed by humans. Composed of source code that developers can read, study, and modify. Reproducible, meaning its binary form can be reliably rebuilt from its source code. In contrast, modern AI systems such as large language models develop their behavior in ways that are often unpredictable and inexplicable. Training processes are challenging to replicate reliably, even by the system’s creators. These differences necessitated the establishment of a unique definition beyond the Open Source Definition for software. What is the role of training data in the Open Source AI Definition? Open Source means giving anyone the ability to meaningfully fork (study and modify) your system without requiring additional permissions to make it more useful for themselves and everyone else. This is why point #2 of the OSD requires that the source code be provided in the preferred form for making modifications. This way, everyone has the same rights and ability to fork as the original developers, starting a virtuous innovation cycle. However, training data does not equate to a software source code. Training data is important for studying modern machine learning systems. However, it is not what AI researchers and practitioners necessarily use as part of the preferred form for modifying a trained model. The Data Information and Code requirements of the OSAID allow Open Source AI systems to be forked by third-party AI builders downstream using the same information as the original developers. These forks could include removing nonpublic or non-open data from the training dataset in order to train a new Open Source AI system on fully public or open data. Why do you allow the exclusion of some training data? We want Open Source AI to exist in fields where data cannot be legally shared, such as medical AI. Laws that permit training on data often limit the resharing of that same data to protect copyright or other interests. Privacy rules also give people the right to control their most sensitive information, like decisions about their health. Similarly, much of the world’s Indigenous knowledge is protected through mechanisms that are not compatible with later-developed frameworks for rights exclusivity and sharing. There are also many cases where terms of use of publicly available data may give entity A the confidence that they may use it freely and call it “open data,” but not give entity A the confidence they can, in turn, give entity B the same guarantees for use in a different jurisdiction. Meanwhile, entity B may not feel confident using that data in their own jurisdiction. An example is so-called public domain data, where the definition of public domain varies from country to country. Another example is fair-use or private data, where the finding of fair use or privacy laws may require a good knowledge of the law of a given jurisdiction. This resharing is not so much limited as lacking legal certainty . How did you arrive at this conclusion? Is it compromising Open Source ideals? During our co-design process, relationships between the weights and the data drove the highest community engagement. In the “System analysis” phase , the volunteer groups suggested that training and data processing codes were more important for modifying the AI system than accessing the training and testing data. That result was validated in the “Validation phase” and suggested a path that allows Open Source AI to exist on equal grounds with proprietary systems: both can train on the same kind of data. Some people believe that full, unfettered access to all training data (with no distinction of its kind) is paramount, arguing that anything less would compromise the full reproducibility of AI systems, transparency and security. This approach would relegate Open Source AI to a niche of AI trainable only on open data. That niche would be tiny, even relative to the niche occupied by Open Source in the traditional software ecosystem. The requirements of Data Information keep the same approach present in the Open Source Definition; it doesn’t mandate full reproducibility and transparency but enables them (i.e., reproducible builds ). At the same time, setting a baseline requiring Data Information doesn’t preclude others from formulating and demanding more requirements, like the Digital Public Goods Standard or the Free Systems Distribution Guidelines , which add requirements to the Open Source Definition. One of the key aspects of OSI’s mission is to drive and promote Open Source innovation. The approach OSI takes here enables complete user choice with Open Source AI. Users can keep the insights derived from training+data preprocessing code and a description of unshareable training data and build upon those with their own unshareable data and give the insights derived from further training to everyone, allowing for Open Source AI in areas like health care. Alternatively, users can obtain the available and public data from the Data Information and retrain their model without any unshareable data, resulting in more data transparency in the AI system. Like copyleft and permissive licensing, this approach leaves the choice with the user. What kind of data should be required in the Open Source AI Definition? A significant challenge in AI is the role of data. Unlike traditional software, where source code is the primary artifact, AI systems depend heavily on data — not just any data, but data processed and curated into training datasets. The OSAID recognizes the legal and ethical complexities of data sharing and uses precise legal terms to outline expectations. While not all raw data can be freely distributed, the Definition ensures that the essential elements for modifying an AI system are accessible. There are four classes of data, based on their legal constraints, all of which can be used to train Open Source AI systems: Open training data: data that can be copied, preserved, modified and reshared. It provides the best way to enable users to study the system. This must be shared. Public training data: data that others can inspect as long as it remains available. This also enables users to study the work. However, this data can degrade as links or references are lost or removed from network availability. To obviate this, different communities will have to work together to define standards, procedures, tools and governance models to overcome this risk, and Data Information is required in case the data becomes later unavailable. This must be disclosed with full details on where to obtain it. Obtainable training data is data that can be obtained, including for a fee. This information provides transparency and is similar to a purchasable component in an open hardware system. The Data Information provides a means of understanding this data other than obtaining or purchasing it. This area is likely to change rapidly and will need careful monitoring to protect Open Source AI developers. It must be disclosed with full details on where to obtain it. Unshareable nonpublic training data: data that cannot be shared for explainable reasons, like Personally Identifiable Information (PII). For this class of data, the ability to study some of the system’s biases demands a detailed description of the data – what it is, how it was collected, its characteristics, and so on – so that users can understand the biases and categorization underlying the system. This must be revealed in detail so that, for example, a hospital can create a dataset with identical structure using its own patient data. OSI believes that all these classes of data can be part of the preferred form of making modifications to the AI system. This approach both advances openness in all the components of the AI system and drives more Open Source AI, including private-first areas such as health care. How do you fix a buggy AI system? A core question the OSAID addresses is: How do you fix a buggy AI system? For traditional software, the Open Source Definition provides a clear answer: The program must include source code, and must allow distribution in source code as well as compiled form. The source code must be the preferred form in which a programmer would modify the program. However, modifying an AI system requires more than just source code. After extensive consultation with AI developers, researchers and practitioners, the community, through the OSAID co-design process, concluded that the preferred form for modifying an AI system includes: The software used to create the dataset (i.e., to transform raw data into tokens). The software used to train the system. The results of the training (i.e., the parameters). All legally shareable data used in the training process. These components collectively enable the study, use, modification and sharing of AI systems in a manner consistent with Open Source principles. What is a skilled person? In legal circles, Skilled Person means any person having the current knowledge, experience and competence to perform a certain duty. This Wikipedia entry provides more details. Is the Open Source AI Definition covering models and weights and parameters? Yes. The Open Source AI Definition makes no distinction between what might be called AI system, model, or weights and parameters. Whether the offering is characterized as an AI system, a model, or weights and parameters, to be called Open Source AI, the requirements for providing the preferred form for making modifications will be the same. If you are interested in learning more about Open Weights, please read this article. Why do you require training code while OSD #2 doesn’t require compilers? AI and software are radically different domains, and drawing comparisons between them is rarely productive. OSD point #2 doesn’t mandate that Open Source software uses only compilers released with an OSI-Approved License because compilers are standardized, de jure (like ANSI C) or de facto like TurboPascal or Python. It was generally accepted that to develop more Open Source software one could use a proprietary development environment. For machine learning, the training code is not standardized, and therefore it must be part of the preferred form of making modifications to preserve the right to fork an AI system. Why is there no mention of safety and risk limitations in the Open Source AI Definition? The Open Source AI Definition does not specifically guide or enforce ethical, trustworthy, or responsible AI development practices. However, it does not put up any barriers that would prevent developers from adhering to such principles if they chose to. The efforts to discuss the responsible development, deployment and use of AI systems, including through appropriate government regulation, are a separate conversation. A good starting point is OECD’s Recommendation of the Council on Artificial Intelligence, Section 1: Principles for responsible stewardship of trustworthy AI . Are model parameters copyrightable? The Open Source AI Definition does not take a stance on the legal nature of Parameters. They may be free by nature, or a license or other legal instrument may be required to ensure their freedom. Whether model parameters are copyrightable will become clearer over time as the legal system has more opportunities to address this issue. In any case, we require an explicit assertion accompanying the distribution of Parameters that assures that they are freely available to all. Why will parameters be available under “OSI-approved terms” but the code will be under “OSI-approved licenses”? Are you going to allow restrictions on the terms for models? We used the word “terms” instead of “license” for models because, as mentioned above, we do not yet know what the legal mechanism will be to assure that the models are available to use, study, modify and share. We used “terms” to avoid suggesting that a “license” is the only legal mechanism that could be used. That said, to be approved by the OSI, the terms for parameters must assure the freedom to use, study, modify and share. Why is the “Preferred form to make modifications” limited to machine learning? The principles stated in the Open Source AI Definition are generally applicable to any kind of AI, but machine learning challenges the Open Source Definition. For machine learning, a set of artifacts (components) is required to study and modify the system, thus requiring a new explanation of what is necessary to study and modify the system. Which AI systems comply with the Open Source AI Definition? As part of our validation and testing of the OSAID, the volunteers checked whether the Definition could be used to evaluate if AI systems provided the freedoms expected. The list of models that passed the Validation phase are: Pythia (Eleuther AI), OLMo (AI2), Amber and CrystalCoder (LLM360), and T5 (Google). A few others that were analyzed would probably pass if they changed their licenses/legal terms, for example, BLOOM (BigScience), Starcoder2 (BigCode), and Falcon (TII). Those that have been analyzed and don’t pass because they lack required components and/or their legal agreements are incompatible with the Open Source principles include Llama2 (Meta), Grok (X/Twitter), Phi-2 (Microsoft), and Mixtral (Mistral). These results should be seen as part of the definitional process, a learning moment; they are not certifications of any kind. OSI will continue to validate only legal documents and will not validate or review individual AI systems, just as it does not validate or review software projects. What’s the next step? The Open Source AI Definition reflects a thoughtful and inclusive process endorsed by leading AI developers, researchers and practitioners. It culminates in a first step represented in version 1.0 of the OSAID. The definition acknowledges that AI fundamentally differs from software and requires a tailored approach. Misunderstandings about the OSAID often arise from attempts to apply software engineering paradigms to AI, leading to confusion. By embracing the unique characteristics of AI systems, the OSAID offers a robust framework for fostering transparency, innovation and collaboration in AI development. As we navigate AI’s evolving landscape, it is crucial to engage thoughtfully and constructively with these definitions. By doing so, we can ensure that AI systems remain open, accessible and aligned with the principles of the broader Open Source movement. Our work is only possible thanks to our members. Join us today! 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https://opensource.org/?p=330 | Bylaws – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Main Menu Home Bylaws Bylaws Page created on July 24, 2006 | Last modified on August 21, 2023 AMENDED AND RESTATED BYLAWS OF OPEN SOURCE INITIATIVE A California Nonprofit Public Benefit Corporation ARTICLE I NAME Section 1. NAME. The name of this corporation is Open Source Initiative. ARTICLE II OFFICES Section 1. PRINCIPAL OFFICE. The principal office for the transaction of the activities and affairs of the corporation (principal office) is located at 702 Marshall Street, Suite 301, Redwood City, California 94063 in San Mateo County. The board of directors may change the principal office from one location to another. Any change of location of the principal office shall be noted by the secretary on these bylaws opposite this Section, or this Section may be amended to state the new location. Section 2. OTHER OFFICES. The board may at any time establish branch or subordinate offices at any place or places where the corporation is qualified to conduct its activities. ARTICLE III PURPOSES AND LIMITATIONS Section 1. GENERAL PURPOSES. This corporation is a nonprofit public benefit corporation and is not organized for the private gain of any person. It is organized under the Nonprofit Public Benefit Corporation Law for public educational purposes. Section 2. SPECIFIC PURPOSES. Within the context of the general purposes stated above, this corporation shall: (1) educate the public about the advantages of open source software; (2) encourage the software community to participate in open source software development; (3) identify how software users’ objectives are best served through open source software; (4) persuade organizations and software authors to distribute source software freely they otherwise would not distribute; (5) provide resources for sharing information about open source software and licenses; (6) assist attorneys to craft open source licenses; (7) manage a program to allow use of one or more marks in association with open source software licenses; and (8) advocate for open source principles. Section 3. LIMITATIONS. This corporation shall not, except to an insubstantial degree, engage in any activities or exercise any powers that are not in furtherance of the purposes of this corporation, and the corporation shall not carry on any other activities not permitted to be carried on (a) by a corporation exempt from federal income tax under Section 501(c)(3) of the Internal Revenue Code of 1954 or the corresponding provision of any future Unites States internal revenue law, or (b) by a corporation, contributions to which are deductible under Section 170(c)(2) of the Internal Revenue Code of 1954 or the corresponding provision of any future United States internal revenue law. No substantial part of the activities of this corporation shall consist of carrying on propaganda, or otherwise attempting to influence legislation, and this corporation shall not participate in or intervene in (including publishing or distributing statements) any political campaign on behalf of any candidate for public office. No part of the net earnings of this corporation shall inure to the benefit of any of its directors, trustees, officers, private shareholders or members, or to individuals. On the winding up and dissolution of this corporation, after paying or adequately providing for the debts, obligations, and liabilities of the corporation, the remaining assets of this corporation shall be distributed to such organization (or organizations) organized and operated exclusively for educational purposes which has established its tax-exempt status under Section 501(c)(3) of the Internal Revenue Code of 1954 (or the corresponding provision of any future Unites States internal revenue law) and which has established its tax-exempt status under Section 23701d of the California Revenue and Taxation Code (or the corresponding provision of any future California revenue and tax law). The corporation will distribute its income for each tax year at such time and in such manner as not to become subject to the tax on undistributed income imposed by Section 4942 of the Internal Revenue Code of 1954 or corresponding provisions of any later federal tax laws. The corporation will not engage in any act of self-dealing as defined in Section 4941(d) of the Internal Revenue Code of 1954, or corresponding provisions of any later federal tax laws. The corporation will not retain any excess business holdings as defined in Section 4943(c) of the Internal Revenue Code of 1954, or corresponding provisions of any later federal tax laws. The corporation will not make any investments in such manner as to subject it to tax under Section 4944 of the Internal Revenue Code of 1954, or corresponding provisions of any later federal tax laws. The corporation will not make any taxable expenditures as defined in Section 4945(d) of the Internal Revenue Code of 1954, or corresponding provisions of any later federal tax laws. ARTICLE IV MEMBERS Section 1. MEMBERSHIP. This corporation shall have no members. ARTICLE V DIRECTORS Section 1. GENERAL CORPORATE POWERS. Subject to the provisions and limitations of the California Nonprofit Public Benefit Corporation law and any other applicable laws, the corporation’s activities and affairs shall be managed, and all corporate powers shall be exercised, by or under the direction of the board. Section 2. SPECIFIC POWERS. Without prejudice to the general powers set forth in Section 1 of this Article, but subject to the same limitations, the directors shall have the power to: Appoint and remove, at the pleasure of the board, all the corporation’s officers, agents, and employees; prescribe powers and duties for them that are consistent with law, with the articles of incorporation, and with these bylaws; and fix their compensation and require from them security for faithful performance of their duties; Change the principal office or the principal business office in California from one location to another; and cause the corporation to be qualified to conduct its activities in any other state, territory, dependency, or country and conduct its activities within or outside California; Adopt and use a corporate seal; and alter the forms of the seal and certificates; Borrow money and incur indebtedness on behalf of the corporation and cause to be executed and delivered for the corporation’s purposes, in the corporate name, promissory notes, bonds, debentures, deeds of trust, mortgages, pledges, hypothecations, and other evidences of debt and securities. Section 3. AUTHORIZED NUMBER AND QUALIFICATIONS. The board of directors shall consist of at least 5 but no more than 21 directors until changed by amendment to these bylaws. The exact number of directors shall be fixed, within those limits, by a resolution adopted by the board of directors. Any member of the board, except an employee who is serving as a member of the board, who has served six consecutive years on the board will not be eligible for election to the board until one year has passed from the last day of such member’s term. Section 4. RESTRICTION ON INTERESTED PERSONS AS DIRECTORS. No more than forty-nine percent (49%) of the persons serving on the board may be interested persons. An interested person is (a) any person compensated by the corporation for services rendered to it within the previous 12 months, whether as a full-time or part-time employee, independent contractor, or otherwise, excluding any reasonable compensation paid to a director as a director; and (b) any brother, sister, ancestor, descendant, spouse, brother-in-law, sister-in-law, son-in-law, daughter-in-law, mother-in-law, or father-in-law of such person. However, any violation of the provisions of this paragraph shall not affect the validity or enforceability of any transaction entered into by the corporation. Section 5. ELECTION, DESIGNATION, AND TERM OF OFFICE. (a) Commencing with the term starting April 1, 2015, each director shall be designated by a resolution of the board as serving either a three year term or a two year term. Except as provided below, the director shall serve until a successor has been elected by the board of directors. Thereafter, each successor director shall be elected by a majority of the board of directors. Each director, including a director elected or appointed to fill a vacancy, shall hold office until expiration of the term for which elected or appointed, and until a successor has been elected and qualified. (b) In the event of a director’s elected term having expired and two or more meetings of the Board or ninety (90) calendar days (whichever is less) having passed since the expiry of the director’s term without a new director having been elected, the term of such director shall be terminated. (c) A director may be removed from the Board at any time prior to the expiry of such director’s term for any reason by a vote of two thirds of the authorized members of the Board or if less than all of the authorized members of the Board have been elected, then a quorum of the elected Board members at two meetings of the Board, the second of which shall be more than forty five (45) days after the first Board meeting and for which a vote of a majority of the authorized members of the Board if less than all of the authorized members of the Board have been elected, then a quorum of the elected Board members at such second meeting of the Board . A director may be removed from the Board at any time prior to the expiry of such director’s term for cause as defined in a resolution of the Board by a vote of two thirds of the authorized members of the Board or if less than all of the authorized members of the Board have been elected, then a quorum of the elected Board members then elected at a single meeting of the Board. Section 6. EVENTS CAUSING VACANCY. A vacancy or vacancies on the board shall exist on the occurrence of the following: (a) the death or resignation of any director, (b) the declaration by resolution of the board of a vacancy in the office of a director who has been declared of unsound mind by an order of court or convicted of a felony, or, if the corporation holds assets in charitable trust, has been found by a final order or judgment of any court to have breached a duty arising under Section 7238 of the California Corporations Code; (c) the increase of the authorized number of directors, or (d) a removal or resignation as provided in this Article. Section 7. RESIGNATIONS. Except as provided below, any director may resign by giving written notice to the chairman of the board, if any, or to the president or the secretary of the board. The resignation shall be effective when the notice is given unless it specifies a later time for the resignation to become effective. If a director’s resignation is effective at a later time, the board may elect a successor to take office when the resignation becomes effective. Section 8. FILLING VACANCIES. Vacancies on the board may be filled by a majority of the directors then in office, whether or not less than a quorum, or by a sole remaining director. Section 9. NO VACANCY ON REDUCTION OF NUMBER OF DIRECTORS. No reduction of the authorized number of directors shall have the effect of removing any director before that director’s term of office expires. Section 10. PLACE OF DIRECTORS’ MEETINGS. Meetings of the board shall be held at any place within or outside California that has been designated by resolution of the board or in the notice of the meeting or, if not so designated, at the principal office of the corporation. Section 11. DIRECTORS’ MEETINGS BY TELEPHONE OR OTHER ELECTRONIC MEANS OF COMMUNICATION. Any meeting may be held by conference telephone or by other electronic means of communication, as long as all directors participating in the meeting can hear one another or read what each other is saying. All such directors shall be deemed to be present in person at such a meeting. Section 12. INITIAL DIRECTORS’ MEETING. The board shall hold a regular meeting for purposes of organization, election of officers, and the transaction of other business. Notice of this meeting is not required. Section 13. OTHER REGULAR MEETINGS. Other regular meetings of the board may be held without notice at such time and place as the board may fix from time to time. Section 14. AUTHORITY TO CALL SPECIAL MEETINGS. Special meetings of the board for any purpose may be called at any time by the chairman of the board, if any, the president or any vice president, or the secretary or any two directors. Section 15. MANNER OF GIVING NOTICE OF SPECIAL MEETINGS. Notice of the time and place of special meetings shall be given to each director by one of the following methods: (a) by personal delivery of written notice; (b) by first-class mail, postage prepaid; (c) by telephone, either directly to the director or to a person at the director’s office who would reasonably be expected to communicate that notice promptly to the director; (d) by telegram, charges prepaid; or (e) by electronic mail. All such notices shall be given or sent to the director’s address, telephone number, or electronic mail address as shown on the records of the corporation. Section 16. TIME REQUIREMENTS FOR NOTICES OF SPECIAL MEETINGS. Notices of special meetings sent by first-class mail shall be deposited in the United States mails at least four days before the time set for the meeting. Notices given by personal delivery, telephone, telegraph or electronic mail shall be delivered, telephoned, given to the telegraph company, or transmitted by electronic mail at least 48 hours before the time set for the meeting. Section 17. CONTENTS OF NOTICES OF SPECIAL MEETINGS. The notice of a special meeting shall state the time of the meeting, and the place if the place is other than the principal office of the corporation. It need not specify the purpose of the meeting. Section 18. QUORUM FOR DIRECTORS’ MEETINGS. Four directors shall constitute a quorum for the transaction of business, except to adjourn; provided however if less than all of the authorized directors have been elected, no less than one fifth of the authorized number of directors or two (2) whichever is greater. Every action taken or decision made by a majority of the directors present at a duly held meeting at which a quorum is present shall be the act of the board, subject to the more stringent provisions of the California Nonprofit Public Benefit Corporation Law, including, without limitation, those provisions relating to (a) approval of contracts or transactions between the corporation and one or more directors or between the corporation and any entity in which a director has a material financial interest, (b) creation of and appointments to committees of the board, and (c) indemnification of directors. A meeting at which a quorum is initially present may continue to transact business, despite the withdrawal of directors, if any action taken or decision made is approved by at least a majority of the required quorum for that meeting. Section 19. WAIVER OF NOTICE OF DIRECTORS’ MEETING. Notice of a meeting need not be given to any director who, either before or after the meeting, signs a waiver of notice, a written consent to the holding of the meeting, or an approval of the minutes of the meeting. The waiver of notice or consent need not specify the purpose of the meeting. All such waivers, consents, and approvals shall be filed with the corporate records or made a part of the minutes of the meetings. Notice of a meeting need not be given to any director who attends the meeting and does not protest, before or at the commencement of the meeting, the lack of notice to him or her. Section 20. ADJOURNMENT OF DIRECTORS’ MEETING. A majority of the directors present, whether or not a quorum is present, may adjourn any meeting to another time and place. Section 21. NOTICE OF ADJOURNED DIRECTORS’ MEETING. Notice of the time and place of holding an adjourned meeting need not be given unless the original meeting is adjourned for more than 24 hours. If the original meeting is adjourned for more than 24 hours, notice of any adjournment to another time and place shall be given, before the time of the adjourned meeting, to the directors who were not present at the time of the adjournment. Section 22. ACTION WITHOUT A DIRECTORS’ MEETING. Any action that the board is required or permitted to take may be taken without a meeting if all members of the board consent in writing to that action. Such action by written consent shall have the same force and effect as any other validly approved action of the board. All such consents shall be filed with the minutes of the proceedings of the board. Section 23. COMPENSATION AND REIMBURSEMENT OF DIRECTORS. Directors may receive such compensation, if any, for their services, and such reimbursement of expenses, as may be determined by board resolution to be just and reasonable as to the corporation at the time the resolution is adopted. Section 24. COMMITTEES OF THE BOARD. The board, by resolution adopted by a majority of the directors then in office, provided a quorum is present, may create one or more committees, each consisting of two or more directors, and no persons who are not directors, to serve at the pleasure of the board which shall be the voting members of the committee. The committee may have one or more members who are not directors; such committee members shall be either be (i) “advisory members” who shall not have any voting rights on the committee or (ii) voting members in which case the actions of the committee shall be advisory and need to be approved by the Board to be effective. Any such committee shall limit its activities to the accomplishment of the tasks for which it was appointed and shall have no power to act except as specifically conferred by action of the Board. Upon completion of the tasks for which created, a committee shall be discharged. Appointments to committees of the board shall be by majority vote of the authorized number of directors. The board may appoint one or more directors as alternate members of any such committee, who may replace any absent member at any meeting. Any such committee, to the extent provided in the board resolution, shall have all the authority of the board except that no committee, regardless of board resolution, may: Fill vacancies on the board or on any committee that has the authority of the board; Fix compensation of the directors for serving on the board or on any committee; Amend or repeal bylaws or adopt new bylaws; Amend or repeal any board resolution that by its express terms is not so amendable or repealable; Create any other committees of the board or appoint the members of committees of the board; Expend corporate funds to support a nominee for director after more people have been nominated for director than can be elected; or With respect to any assets held in charitable trust, approve any contract or transaction between the corporation and one or more of its directors or between the corporation and an entity in which one or more of its directors have a material financial interest, subject to the special approval provisions of Section 5233(d)(3) of the California Corporations Code. Section 25. MEETINGS AND ACTION OF COMMITTEES OF THE BOARD. Meetings and actions of committees of the board shall be governed by, held, and taken in accordance with, the provisions of these bylaws concerning meetings and other board actions except that the time for regular meetings of such committees and calling of special meetings of such committees may be determined either by board resolution, or if there is none, by resolution of the committee. Minutes of each meting of any committee shall be kept and shall be filed with the corporate records. The board may adopt rules for the government of any committee that are consistent with these bylaws or, in the absence of rules adopted by the board, the committee may adopt such rules. ARTICLE VI OFFICERS Section 1. OFFICERS OF THE CORPORATION. The officers of the corporation shall be a president, a secretary, and a chief financial officer. The corporation may also have, at the board’s discretion, a chairman of the board, one or more vice presidents, one or more assistant secretaries, one or more assistant treasurers, and such other officers as may be appointed in accordance with Section 3 of this Article. Any number of offices may be held by the same person. Section 2. ELECTION OF OFFICERS. The officers of the corporation, except those appointed under Section 3 of this Article, shall be chosen annually by the board and shall serve at the pleasure of the board, subject to the rights, if any, of any officer under any contract of employment. Section 3. OTHER OFFICERS. The board may appoint and may authorize the chairman of the board, the president, or other officer to appoint any other officers that the corporation may require. Each officer so appointed shall have the title, hold office for the period, have the authority, and perform the duties specified in the bylaws or determined by the board. Section 4. REMOVAL OF OFFICERS. Without prejudice to any rights of an officer under any contract of employment, an officer may be removed with or without cause by the board, and also, if the board did not choose the officer, by any officer on whom the board may confer that power of removal. Section 5. RESIGNATION OF OFFICERS. Any officer may resign at any time by giving written notice to the corporation. The resignation shall take effect as of the date the notice is received or at any later time specified in the notice and, unless otherwise specified in the notice, the resignation need not be accepted to be effective. Any resignation shall be without prejudice to the rights, if any, of the corporation under any contract to which the officer is a party. Section 6. VACANCIES IN OFFICE. A vacancy in any office because of death, resignation, removal, disqualification, or any other cause shall be filled in the manner prescribed in these bylaws for regular appointments to that office, provided, however, that vacancies need not be filled on an annual basis. Section 7. RESPONSIBILITIES OF THE CHAIRMAN OF THE BOARD. If a chairman of the board is elected, he or she shall preside at board meetings and shall exercise and perform such other powers and duties as the board may assign from time to time. If there is no president, the chairman of the board shall also be the chief executive officer and shall have the powers and duties prescribed by these bylaws for the president of the corporation. Section 8. RESPONSIBILITIES OF THE PRESIDENT. Subject to such supervisory powers as the board may give to the chairman of the board, if any, and subject to the control of the board, the president shall be the general manager of the corporation and shall supervise, direct, and control the corporation’s activities, affairs, and officers. In the absence of the chairman of the board, or if there is none, the president shall preside at all board meetings. The president shall have such other powers and duties as the board or bylaws may prescribe. Section 9. RESPONSIBILITIES OF VICE PRESIDENTS. In the absence or disability of the president, the vice presidents, if any, in order of their rank as fixed by the board or, if not ranked, a vice president designated by the board, shall perform all duties of the president. When so acting, a vice president shall have all powers of and be subject to all restrictions on the president. The vice presidents shall have such other powers and perform such other duties as the board or the bylaws may prescribe. Section 10. RESPONSIBILITIES OF THE SECRETARY; BOOK OF MINUTES. The secretary shall keep or cause to be kept, at the corporation’s principal office or such other place as the board may direct, a book of minutes of all meetings, proceedings, and actions of the board and of committees of the board. The minutes of meetings shall include the time and place of holding, whether the meeting was annual, regular, or special and, if special, how authorized, the notice given, and the names of those present at board and committee meetings. The secretary shall keep or cause to be kept, at the principal office in California, a copy of the articles of incorporation and bylaws, as amended to date. Section 11. RESPONSIBILITIES OF THE SECRETARY; NOTICES, SEAL, AND OTHER DUTIES. The secretary shall give, or cause to be given, notice of all meetings of members, of the board, and of committees of the board required by these bylaws to be given. The secretary shall have such other powers and perform such other duties as the board or the bylaws may prescribe. Section 12. RESPONSIBILITIES OF THE CHIEF FINANCIAL OFFICER; BOOKS OF ACCOUNT. The chief financial officer shall keep and maintain, or cause to be kept and maintained, adequate and correct books and accounts of the corporation’s properties and transactions. The chief financial officer shall send or cause to be given to the directors such financial statements and reports as are required by law, by these bylaws, or by the board to be given. The books of account shall be open to inspection by any director at all reasonable times. Section 13. RESPONSIBILITIES OF THE CHIEF FINANCIAL OFFICE; DEPOSIT AND DISBURSEMENT OF MONEY AND VALUABLES. The chief financial officer shall deposit, or cause to be deposited, all money and other valuables in the name and to the credit of the corporation with such depositories as the board may designate, shall disburse the corporation’s funds as the board may order, shall render to the president, chairman of the board, if any, and the board, when requested, an account of all transactions as chief financial officer and of the financial condition of the corporation, and shall have such other powers and perform such other duties as the board or the bylaws may prescribe. Section 14. RESPONSIBILITIES OF THE CHIEF FINANCIAL OFFICER; BOND. If required by the board, the chief financial officer shall give the corporation a bond in the amount and with the surety or sureties specified by the board for faithful performance of the duties of the office and for restoration the corporation of all its books, papers, vouchers, money, and other property of any kind in the possession or under the control of the chief financial officer on his or her death, resignation, retirement, or removal from office. Section 15. PROJECT MANAGEMENT COMMITTEES . In addition to the officers of the corporation, the Board of Directors may, by resolution, establish one or more Project Management Committees consisting of at least one officer of the corporation, who shall be designated chairman of such committee, and may include one or more other individuals as the Board or the chairman of the committee deems appropriate. Unless elected or appointed as an officer in accordance with Section 6.3 of these Bylaws, a member of a Project Management Committee shall not be deemed an officer of the corporation. All Project Management Committees shall be advisory in nature. Each Project Management Committee shall be responsible for the active management of one or more projects identified by resolution of the Board of Directors which may include, without limitation, activities furthering the purposes of the Corporation as defined in Section 3.2 of these Bylaws. Subject to the direction of the Board of Directors, the chairman of each Project Management Committee shall be primarily responsible for project(s) managed by such committee, and he or she shall establish rules and procedures for the day to day management of project(s) for which the committee is responsible. The Board of Directors of the corporation may, by resolution, terminate a Project Management Committee at any time. ARTICLE VII INDEMNIFICATION Section 1. RIGHT OF INDEMNITY. To the fullest extent permitted by law, this corporation shall indemnify its directors, officers, employees, and other persons described in Section 7237(a) of the California Corporations Code, including persons formerly occupying any such position, against all expenses, judgments, fines, settlements, and other amounts actually and reasonably incurred by them in connection with any “proceeding,” as that term is used in that Section, and including any action by or in the right of the corporation, by reason of the fact that the person is or was a person described in that Section. “Expenses,” as used in this bylaw, shall have the same meaning as in Section 7237(a) of the California Corporations Code. Section 2. APPROVAL OF INDEMNITY. On written request to the board by any person seeking indemnification under Section 7237(b) or Section 7237(c) of the California Corporations Code, the board shall promptly determine under Section 7327(e) of the California Corporations Code whether the applicable standard of conduct set forth in Section 7237(b) or Section 7237(c) has been met and, if so, the board shall authorize indemnification. Section 3. ADVANCEMENT OF EXPENSES. To the fullest extent permitted by law and except as otherwise determined by the board in a specific instance, expenses incurred by a person seeking indemnification under Sections 17 and 18 of this Article in defending any proceeding covered by those Sections shall be advanced by the corporation before final disposition of the proceeding, on receipt by the corporation of an undertaking by or on behalf of that person that the advance will be repaid unless it is ultimately determined that the person is entitled to be indemnified by the corporation for those expenses. ARTICLE VIII INSURANCE Section 1. INSURANCE. The corporation shall have the right to purchase and maintain insurance to the full extent permitted by law on behalf of its officers, directors, employees, and other agents, against any liability asserted against or incurred by any officer, director, employee, or agent in such capacity or arising out of the officer’s, director’s, employee’s, or agent’s status as such. ARTICLE IX RECORDS AND REPORTS Section 1. MAINTENANCE OF CORPORATE RECORDS. The corporation shall keep: (1) adequate and correct books and records of account; and (2) written minutes of the proceedings of its board and committees of the board. Section 2. MAINTENANCE AND INSPECTION OF ARTICLES AND BYLAWS. The corporation shall keep at its principal office, or if its principal office is not in California, at its principal business office in this state, the original or a copy of the articles of incorporation and bylaws, as amended to date, which shall be open to inspection by the directors at all reasonable times during office hours. Section 3. INSPECTION BY DIRECTORS. Every director shall have the absolute right at any reasonable time to inspect the corporation’s books, records, documents of every kind, physical properties, and the records of each of its subsidiaries. The inspection may be made in person or by the director’s agent or attorney. The right of inspection includes the right to copy and make extracts of documents. Section 4. ANNUAL REPORT. An annual report shall be prepared within 120 days after the end of the corporation’s fiscal year. That report shall contain the following information in appropriate detail: A balance sheet as of the end of the fiscal year, and an income statement and statement of changes in financial position for the fiscal year, accompanied by any report on them by independent accounts, or, if there is no such report, by the certificate of an authorized officer of the corporation that they were prepared without audit from the books and records of the corporation. Any information that is required by Section 7 of this Article. This Section shall not apply if the corporation receives less than $10,000 in gross revenues or receipts during the fiscal year. Section 5. ANNUAL STATEMENT OF CERTAIN TRANSACTIONS AND INDEMNIFICATIONS. As part of the annual report, or as a separate document if no annual report is issued, the corporation shall annually prepare and furnish to each director a statement of any transaction or indemnification of the following kind within 120 days after the end of the corporation’s fiscal year: Any transaction (i) in which the corporation, its parent, or its subsidiary was a party, (ii) in which an “interested person” had a direct or indirect material financial interest, and (iii) which involved more than $50,000, or was one of a number of transactions with the same interested person involving, in the aggregate, more than $50,000. For this purpose, an “interested person” is either of the following: Any director or officer of the corporation, its parent, or subsidiary (but mere common directorship shall not be considered such an interest); or Any holder of more than 10 percent of the voting power of the corporation, its parent, or its subsidiary. The statement shall include a brief description of the transaction, the names of interested persons involved, their relationship to the corporation, the nature of their interest in the transaction and, if practicable, the amount of that interest, provided that if the transaction was with a partnership in which the interested person is a partner, only the interest of the partnership need be stated. A brief description of the amounts and circumstances of any loans, guaranties, indemnifications, or advances aggregating more than $10,000 paid during the fiscal year to any officer or director of the corporation under Article 8 of these bylaws, unless the loan, guaranty, indemnification, or advance is not subject to the provisions of subdivision (a) of Section 7235(a) of that Code. ARTICLE X CONSTRUCTION AND DEFINITIONS Section 1. CONSTRUCTION AND DEFINITIONS. Unless the context requires otherwise, the general provisions, rules of construction, and definitions in the California Nonprofit Public Benefit Corporation Law shall govern the construction of these bylaws. Without limiting the generality of the preceding sentence, the masculine gender includes the feminine and neuter, the singular includes the plural and the plural includes the singular, and the term “person” includes both a legal entity and a natural person. ARTICLE XI AMENDMENTS Section 1. LIMITATION ON AMENDMENT BY BOARD. Subject to the limitations set forth below, the board may adopt, amend, or repeal bylaws. The board may not extend the term of a director beyond that for which the director was elected. Section 2. HIGH VOTE REQUIREMENT. If any provision of these bylaws requires the vote of a larger proportion of the board than is otherwise required by law, that provision may not be altered, amended, or repealed except by that greater vote. CERTIFICATE OF SECRETARY I certify that I, Patrick Masson, am the duly elected and acting Secretary of Open Source Initiative, a California nonprofit public benefit corporation, that the above bylaws, consisting of 12 pages, are the bylaws of this corporation as adopted by the board of directors on November 6th, 2011, and that they have not been amended or modified since that date. Executed on December 4th, 2013 at San Francisco, California, Patrick Masson Secretary Get involved Mastodon Twitter LinkedIn Reddit About About Our team Board of directors Sponsors Programs Blog Press mentions Trademark Bylaws Licenses Open Source Definition Licenses License Review Process Open Standards Requirement for Software Open Source AI Open Source AI OSAI Definition Process Timeline Open Weights FAQ Checklist Forum Community Become an Individual Member Become an OSI Affiliate Affiliate Organizations Maintainers Events Forum OpenSource.net The content on this website, of which Opensource.org is the author, is licensed under a Creative Commons Attribution 4.0 International License . Opensource.org is not the author of any of the licenses reproduced on this site. Questions about the copyright in a license should be directed to the license steward. Read our Privacy Policy Proudly powered by WordPress. Hosted by Pressable. 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https://wiki.php.net/rfc/variable_syntax_tweaks | PHP: rfc:variable_syntax_tweaks Login Register You are here: start › rfc › variable_syntax_tweaks rfc:variable_syntax_tweaks PHP RFC: Variable Syntax Tweaks Date: 2020-01-07 Author: Nikita Popov nikic@php.net Target Version: PHP 8.0 Status: Implemented Implementation: https://github.com/php/php-src/pull/5061 Introduction The Uniform Variable Syntax RFC resolved a number of inconsistencies in PHP's variable syntax. This RFC intends to address a small handful of cases that were overlooked. Proposal Terminology PHP supports four principal types of “dereferencing” operations: Array: $foo[$bar] , $foo{$bar} Object: $foo->bar , $foo->bar() Static: Foo::$bar , Foo::bar() , Foo::BAR Call: foo() A fully dereferencable construction is dereferencable by all four operations. Interpolated and non-interpolated strings Non-interpolated strings "foo" are currently considered fully dereferencable, i.e. constructions such as "foo"[0] or "foo"->bar() are considered legal (syntactically at least). However, interpolated strings "foo$bar" are non-dereferencable. This RFC proposed to treat both types of strings consistently, i.e. "foo$bar"[0] , "foo$bar"->baz() etc become legal. This inconsistency was originally reported in the context of scalar objects . Constants and magic constants Constants are currently array-dereferencable, that is FOO[0] is legal. However, magic constants are non-dereferencable. This RFC proposes to treat magic constants the same way as constants, that is, writing __FUNCTION__[0] etc becomes legal. Constant dereferencability Constants (and class constants) are currently special cased and only dereferencable under the [] operator (and do not even support the nominally equivalent {} operator). This RFC proposes to make constants (and class constants) fully array and object dereferencable, i.e. to allow both FOO{0} and FOO->length() (the latter only being relevant in conjunction with scalar objects ). This makes the set of array-dereferencable and object-dereferencable operations strictly identical. Class constant dereferencability Currently static property accesses are array, object and static dereferencable. However, even with the change from the previous section, class constant accesses are only array and object dereferencable. This means that while Foo::$bar::$baz is legal, Foo::BAR::$baz is not. This RFC proposes to make class constant accesses static derefencable as well, so that Foo::BAR::$baz and Foo::BAR::BAZ become legal. It should be noted that the same is not possible for plain constants, as FOO::$bar already interprets FOO as a class name, not a constant name. Similarly, both types of constants cannot be call dereferenced, as FOO() is interpreted as a function name and Foo::BAR() as a static method call. This inconsistency was originally reported on Twitter . Arbitrary expression support for new and instanceof Most syntactic constructions that normally require an identifier/name also accept a syntax variation that allows an arbitrary expression. Depending on the case, this uses either {expr} or (expr) syntax. Places that accept a simple (non-namespaced) identifier use curly braces, such as ${expr} , $foo->{expr} , and FOO::{expr}() . Places that accept a namespaced name use parentheses, such as (expr)() and (expr)::FOO . These cases also accept more than just strings, in particular (expr)() additionally also allows other callables, and (expr)::FOO also allows objects. One place where arbitrary expressions are currently not supported are class names in new and instanceof (which are currently treated as syntactically the same). In line with the considerations above, this RFC proposes to introduce the syntax new (expr) and $x instanceof (expr) respectively. Backward Incompatible Changes There are not backwards incompatible changes, this RFC allows strictly more syntax than before. Future Scope It is in principle possible to relax the limitations on the right-hand-side of instanceof entirely: It could be treated as a normal expression, with plain constant accesses being reinterpreted as class name references instead (as this is the only ambiguity between them). While I think that this is the best option in principle, I am concerned that it will cause issues in the future if we introduce generic types, in which case a class name will no longer necessarily coincide with an ordinary expression. Vote Voting started 2020-01-28 and ends 2020-02-11. Implement syntax changes outlined above in PHP 8? Real name Yes No alcaeus alec asgrim ashnazg bishop bwoebi carusogabriel cmb colinodell derick dmitry duncan3dc galvao gasolwu girgias googleguy guilhermeblanco kalle kguest kinncj kocsismate kriscraig levim marandall mariano mbeccati mcmic mike nicolasgrekas nikic pierrick pmjones pollita ramsey reywob ruudboon sammyk santiagolizardo sebastian sergey sirsnyder sorin stas trowski villfa yunosh zeev Final result: 47 0 This poll has been closed. rfc/variable_syntax_tweaks.txt · Last modified: 2025/04/03 13:08 by 127.0.0.1 Page Tools Show pagesource Old revisions Backlinks Back to top Table of Contents PHP RFC: Variable Syntax Tweaks Introduction Proposal Terminology Interpolated and non-interpolated strings Constants and magic constants Constant dereferencability Class constant dereferencability Arbitrary expression support for new and instanceof Backward Incompatible Changes Future Scope Vote Copyright © 2001-2026 The PHP Group Other PHP.net sites Privacy policy | 2026-01-13T08:49:43 |
https://www.youtube.com/watch?v=5H77TnoLLcU | PHPKonf 2019 - Nikita Popov: Looking Forward to PHP 8 - 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://opensource.org/events | Events – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Main Menu Home Events Upcoming events The OSI is involved in a variety of events around the globe. You can see upcoming events where the OSI will participate, as well as events and activities of the OSI Affiliate organizations and our sponsors . Software Heritage Symposium and Summit January 28, 2026 12:00 am Paris, France To mark the 10th anniversary of Software Heritage, UNESCO and Software Heritage will co-host the 2026 Symposium on January 28, 2026. This event will recognize [...] More Info Code & Compliance January 29, 2026 12:00 am Brussels, Belgium Join us in Brussels for the next edition of Code & Compliance, the community event where open source developers, project stewards, manufacturers, and industry experts [...] More Info EU Open Source Policy Summit 2026 January 30, 2026 12:00 am Brussels, Belgium Europe is confronting fundamental questions about its digital sovereignty — a concept that now encapsulates the full range of its strategic challenges: competitiveness, market concentration [...] More Info FOSDEM 2026 January 31, 2026 - February 1, 2026 12:00 am Brussels, Belgium FOSDEM is a free event for software developers to meet, share ideas and collaborate. Every year, thousands of developers of free and open source software [...] More Info SCALE 23x March 5, 2026 - March 8, 2026 12:00 am Pasadena, California SCALE is North America's largest community-run open source conference. We will return to the Pasadena Convention Center March 5 - 8, 2026. Join us for [...] More Info All Things AI 2026 March 23, 2026 - March 24, 2026 12:00 am Durham, NC All Things AI is both a community and a conference. It brings together practitioners, business leaders, and innovators to focus on the technologies, processes, and [...] More Info Open Community Experience April 21, 2026 - April 23, 2026 12:00 am Brussels, Belgium The Open Community Experience brings together developers, researchers, industry leaders, and open -source communities for three days of collaboration and innovation. In 2026, OCX will [...] More Info Page created on October 21, 2015 | Last modified on December 16, 2025 Get involved Mastodon Twitter LinkedIn Reddit About About Our team Board of directors Sponsors Programs Blog Press mentions Trademark Bylaws Licenses Open Source Definition Licenses License Review Process Open Standards Requirement for Software Open Source AI Open Source AI OSAI Definition Process Timeline Open Weights FAQ Checklist Forum Community Become an Individual Member Become an OSI Affiliate Affiliate Organizations Maintainers Events Forum OpenSource.net The content on this website, of which Opensource.org is the author, is licensed under a Creative Commons Attribution 4.0 International License . Opensource.org is not the author of any of the licenses reproduced on this site. Questions about the copyright in a license should be directed to the license steward. Read our Privacy Policy Proudly powered by WordPress. Hosted by Pressable. Manage Cookie Consent To provide the best experiences, we use technologies like cookies to store and/or access device information. Consenting to these technologies will allow us to process data such as browsing behavior or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions. Functional Functional Always active The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network. Preferences Preferences The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user. Statistics Statistics The technical storage or access that is used exclusively for statistical purposes. The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you. Marketing Marketing The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes. Manage options Manage services Manage {vendor_count} vendors Read more about these purposes Accept Deny View preferences Save preferences View preferences {title} {title} {title} Manage consent | 2026-01-13T08:49:43 |
https://opensource.org/programs#content | Programs – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Main Menu Home Programs We define Open Source For developers, lawyers and users To simplify software development and procurement Explore our activities Our programs License and legal Prevents any corporation or trade association from controlling and directing Open Source. We provide an anchor for open community consensus on what constitutes Open Source. As a charity, we protect the Open Source principles, enforcing the marks “Certified Open Source” and “Open Source Approved License”. Policy and standards Support the community, monitoring policy and standards setting organizations. 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https://wiki.php.net/rfc/forbid_dynamic_scope_introspection | PHP: rfc:forbid_dynamic_scope_introspection Login Register You are here: start › rfc › forbid_dynamic_scope_introspection rfc:forbid_dynamic_scope_introspection PHP RFC: Forbid dynamic calls to scope introspection functions Version: 1.0 Date: 2016-05-01 Author: Nikita Popov nikic@php.net Status: Implemented (in PHP 7.1) Introduction Dynamic calls to functions like extract() , which either inspect or modify the parent scope or stack frame, have unclear behavior (with disagreements between PHP versions and runtimes and dependence on surrounding code), may result in highly unexpected scope modifications (e.g. when used as an autoloader) and for this reason also pose a significant problem for the optimization of PHP. This RFC aims to forbid dynamic calls to such functions. Proposal For the functions extract() compact() get_defined_vars() parse_str() with one arg mb_parse_str() with one arg assert() with string argument (eval) func_get_args() func_get_arg() func_num_args() dynamic calls of the form $fn() call_user_func($fn) array_map($fn, $array) etc. will be forbidden. Such calls will result in a warning being thrown and an error-indicating return value being returned, that is consistent with other error-indicating return values of the respective functions. Rationale There are two fundamental reasons for making this change: Firstly, it is not clear how these functions should behave in this situation. In the following, examples will be shown where behavior depends on inconsequential changes (e.g. whether code is namespaced or not) or differs between PHP versions and runtimes. Furthermore cases will be illustrated where such dynamic calls may lead to highly unexpected scope modifications. Secondly, these dynamic calls pose a significant problem for optimization, because they may lead to unexpected scope modifications which are hard to account for. Even without optimization, such calls have been known to cause crashes, because their implementation does not account for such edge-cases. Unclear behavior The primary issue is that dynamic calls to scope introspection functions have behavior ranging between unclear to downright evil. They all fundamentally work by inspecting higher stack frames, but don't agree on which frame they should operate on. namespace { function test ( $a , $b , $c ) { var_dump ( call_user_func ( 'func_num_args' ) ) ; } test ( 1 , 2 , 3 ) ; } namespace Foo { function test ( $a , $b , $c ) { var_dump ( call_user_func ( 'func_num_args' ) ) ; } test ( 1 , 2 , 3 ) ; } This code will print int(3) int(1) on PHP 7 and HHVM (and two times int(1) on PHP 5). The reason is that in the non-namespaced case the number of arguments of the test() frame is returned, while in the namespaced case the number of arguments of the call_user_func() frame is returned, because of internal differences in stack frame management. function test ( ) { array_map ( 'extract' , [ [ 'a' => 42 ] ] ) ; var_dump ( $a ) ; } test ( ) ; This will print int(42) on PHP 5 and PHP 7, but result in an undefined variable on HHVM. The reason is that HHVM will extract ['a' => 42] into the scope of the array_map() frame, rather than the test() frame. It does this because HHVM implements array_map as a userland (HHAS) function, rather than an internal function. One might write this off as a bug in the HHVM implementation, but really this illustrates a dichotomy between internal and userland functions with regard to dynamic calls of these functions. Namely, if you were to reimplement the array_map function in userland function array_map ( $fn , $array ) { $result = [ ] ; foreach ( $array as $k => $v ) { $result [ $k ] = $fn ( $v ) ; } return $result ; } and then try the same array_map call, it would indeed extract the array into the scope of array_map() and not the calling test() function. So maybe HHVM is correct and PHP is wrong? This example further illustrates why calling these functions dynamically is a problem: They will generally be executed in a different scope than the one where the callback is defined. This means you can actually arbitrarily modify the scope of functions that accept callbacks, even though they were certainly not designed for this use. E.g. you can switch the $fn callback in the middle of the array_map execution using something like: array_map ( 'extract' , [ [ 'fn' => ... ] ] ) ; But this is only where it starts. PHP has a number of magic callbacks that may be implicitly executed in all kinds of contexts. For example, what happens if one of these is used in spl_autoload_register? spl_autoload_register ( 'parse_str' ) ; function test ( ) { $FooBar = 1 ; class_exists ( 'FooBar' ) ; var_dump ( $FooBar ) ; // string(0) "" } test ( ) ; Now any invocation of the autoloader (here using class_exists, but can be generalized to new or anything else) will create a variable for the class name in the local scope (with value "" ). Of course there are many more possibilities in this area, e.g. using tick functions. Stability and Optimization As might be expected, nobody has bothered testing edge-cases of dynamic calls to these functions previously. Recently two segfaults relating to this were found, see bug #71220 and bug #72102. However, these are “just bugs”. The more important issue is that these dynamic calls to scope modifying functions go against assumptions in the current optimizer. For example the following very simple script currently crashes if opcache is enabled, because $i is incorrectly determined to be an integer: function test ( ) { $i = 1 ; array_map ( 'extract' , [ [ 'i' => new stdClass ] ] ) ; $i += 1 ; var_dump ( $i ) ; } test ( ) ; This is, of course, a bug in the optimizer and not in PHP. However, if we try to catch this kind of situation in the optimizer we will have to do very pessimistic assumptions (especially if you consider things like the spl_autoload_register example), for a “feature” nobody needs and that doesn't work particularly well anyway (see previous point). Backward Incompatible Changes Dynamic calls to the listed functions will no longer be possible. The practical impact of this backwards compatibility break is assumed to be minimal. Vote The vote requires a 2/3 majority. Voting closed on 2016-05-24. Forbid dynamic calls to scope introspection functions? Real name Yes No ajf bishop cmb colinodell daverandom davey dm dmitry francois galvao guilhermeblanco hywan jpauli jwage kalle kguest klaussilveira krakjoe lcobucci levim lstrojny magnus malukenho marcio mariano mattwil mbeccati mike nikic ocramius pauloelr pollita ramsey sammyk sebastian svpernova09 trowski weierophinney yohgaki zimt Final result: 39 1 This poll has been closed. Patches and Tests Pull request: https://github.com/php/php-src/pull/1886 The patch works by setting an additional call flag for dynamic calls and subsequently checking it in the respective functions. rfc/forbid_dynamic_scope_introspection.txt · Last modified: 2025/04/03 13:08 by 127.0.0.1 Page Tools Show pagesource Old revisions Backlinks Back to top Table of Contents PHP RFC: Forbid dynamic calls to scope introspection functions Introduction Proposal Rationale Unclear behavior Stability and Optimization Backward Incompatible Changes Vote Patches and Tests Copyright © 2001-2026 The PHP Group Other PHP.net sites Privacy policy | 2026-01-13T08:49:43 |
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https://www.fine.dev/blog/ai-coding-guide#4-understanding-potential-mistakes-without-proper-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. 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https://opensource.org/programs#programs | Programs – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Main Menu Home Programs We define Open Source For developers, lawyers and users To simplify software development and procurement Explore our activities Our programs License and legal Prevents any corporation or trade association from controlling and directing Open Source. We provide an anchor for open community consensus on what constitutes Open Source. As a charity, we protect the Open Source principles, enforcing the marks “Certified Open Source” and “Open Source Approved License”. Policy and standards Support the community, monitoring policy and standards setting organizations. As the importance of Open Source grows, so has our support for legislators and policy makers educating them about the Open Source ecosystem, its role in innovation and its value for an open future. Advocacy and research Convene global conversations with stakeholders, non-profits, corporations and individuals. Aware that software technologies change, OSI investigates the impacts with its wide network of Affiliate organizations providing a well-informed voice to the ongoing debates around Open Source, from artificial intelligence to security. Enable permission-less development Keeping the list of Open Source Approved License® Developers and lawyers can assume that software released licenses approved by the OSI conform to the Open Source Definition and is indeed Open Source software. Learn more Support compliance managers Maintaining a crowdsourced database of licensing metadata for every software component A global community contributes, corrects and curates licensing information found in public repositories for the common good. Learn more Educate communities about policies Studying legislation as it’s developed and providing public commentary Our team collaborates with the network of Affiliates to study the impact of upcoming regulations on the Open Source ecosystem. Learn more Join the Alliance to educate and inform US public policy decisions Learn more Latest from the Policy team January 8, 2026 Open Policy Alliance Welcomes the Open Source Technology Improvement Fund as New Member December 17, 2025 Top Open Source licenses in 2025 December 12, 2025 Celebrating Generosity and Growth in the OSI Community Spreading the word about Open Source Providing a neutral place to discuss and share best practices We’re hosting meetings, and joining conversations where they happen. Deep Dive Deep Dive is a new series where OSI dives deep into the topics shaping the future of open source business, ethics and practice. The first Deep Dive focused on AI to help OSI stakeholders frame a conversation to discover what’s acceptable for AI systems to be “Open Source.” View recordings State of the source The State of the source encourages a global conversation on the current state of open source software: non-technical issues that foster development and community, the licenses that enable collaboration, the practices that promote contribution, and the issues confronting cooperation. View recordings Support Open Source As a public charity organization, OSI is supported by donations of corporations and individuals. Thanks to our sponsors that support our vision to be the leading voice on policies and principles of Open Source. Book time with us Get involved Mastodon Twitter LinkedIn Reddit About About Our team Board of directors Sponsors Programs Blog Press mentions Trademark Bylaws Licenses Open Source Definition Licenses License Review Process Open Standards Requirement for Software Open Source AI Open Source AI OSAI Definition Process Timeline Open Weights FAQ Checklist Forum Community Become an Individual Member Become an OSI Affiliate Affiliate Organizations Maintainers Events Forum OpenSource.net The content on this website, of which Opensource.org is the author, is licensed under a Creative Commons Attribution 4.0 International License . Opensource.org is not the author of any of the licenses reproduced on this site. Questions about the copyright in a license should be directed to the license steward. Read our Privacy Policy Proudly powered by WordPress. Hosted by Pressable. 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https://dev.to/drownie/odoo-101-create-a-module-1fd2#comments | Odoo 101: Create a Module - 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 Abraham Posted on Jun 1, 2025 Odoo 101: Create a Module # odoo # erp # programming # tutorial Introduction Hello everyone today I want to share about Odoo Development. In this tutorial I will cover about creating module, and install it on your local Odoo. Creating a new module 1. Create module dir In this steps we must create a dir file in the addons directory. Requirement: Lowercase only , Example: sale_order, not SaleOrder or Sale-Order. Underscores for separation , Use underscores (_) to separate words instead of dashes or camelCase. Example: helpdesk_repair, product_warranty. Avoid special characters Only use letters, numbers, and underscores. Avoid spaces, dashes, dots, or other symbols. Unique across the Odoo instance ps: addons directory can be seen on odoo.conf tips: dir name is very important because it will be used when a module is being depend on. 2. Create Manifest file In this steps we will add __manifest__.py file in the root of the module directory. Example: sale_dashboard | └── __manifest__.py Enter fullscreen mode Exit fullscreen mode Below is manifest example { ' name ' : < Application Name ( str ) > , ' version ' : < Application Version ( str ) > , ' summary ' : < Application Summary ( str ) > , ' description ' : < Application Description ( str ) > , ' author ' : < Author ( str ) > , ' depends ' : < Dependency ( list ( str )) > , ' category ' : < Application Category ( str ) > , ' data ' : < Data File ( list ( str )) > , ' assets ' : < Static asset ( dict ( str )) > , ' license ' : < Application License ( str ) > , ' application ' : < Is this application or technical app ( Bool ) > , } Enter fullscreen mode Exit fullscreen mode depends: Specify dependency modules of the current module. When the module is installed, the depend module will automatically installed. license: Specify license for the module. Supported license: AGPL-3, GPL-3, LGPL-3, OPL-1, Other OSI approved licence, Other proprietary, OEEL-1. More information here . 3. Install module into Odoo Firstly, we must open the Odoo in your web browser. In my case I use http://localhost:10017/web Secondly, we activate the debug mode. Debug mode can be activated by adding ?debug=1 in the url. After activating debug mode, we can now see Update App List button. After clicking Update App List , a window will be opened and we can update the app list. Updating app list could be used to insert new added app into Odoo app. Finally, we can search the app by using the search bar. When searching odoo module we can use the name from module directory or the name from manifest file. Final Words This is the end of tutorial, there are still a lot of things to covers for example creating a view, adding icons, creating models, wizard, depending on other module, Qweb, uploading module to Odoo store, etc. I will continue about Odoo tutorial in the next article. 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Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Abraham Follow Eternal Learner Location Indonesia Education International University Liaison Indonesia Pronouns He Work Software Engineer Joined Aug 27, 2023 More from Abraham Odoo Docker fix print bug # odoo # bugfix # docker Odoo Developer 101: OOP # odoo # oop # programming # webdev Odoo 101: View # odoo # erp # programming # 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 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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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 # llm Follow Hide Create Post Older #llm posts 5 6 7 8 9 10 11 12 13 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu When long chats change the model (and your build breaks) Gabriel Gabriel Gabriel Follow Dec 30 '25 When long chats change the model (and your build breaks) # discuss # codequality # llm # promptengineering Comments Add Comment 3 min read I Benchmarked LLM APIs on Live BGP Streams. Here’s What Actually Matters. Manas Mudbari Manas Mudbari Manas Mudbari Follow Dec 28 '25 I Benchmarked LLM APIs on Live BGP Streams. Here’s What Actually Matters. # llm # ai # analytics Comments Add Comment 3 min read RAG y búsqueda semántica Joaquin Jose del Cerro Murciano Joaquin Jose del Cerro Murciano Joaquin Jose del Cerro Murciano Follow Dec 29 '25 RAG y búsqueda semántica # spanish # ai # nlp # llm Comments Add Comment 2 min read "가시성은 중앙에서, 제어는 서비스에서" : Azure 환경의 실전 LLM 토큰 거버넌스 설계 GyeongSeon GyeongSeon GyeongSeon Follow Dec 28 '25 "가시성은 중앙에서, 제어는 서비스에서" : Azure 환경의 실전 LLM 토큰 거버넌스 설계 # architecture # azure # llm # devops Comments Add Comment 1 min read Reemplacé Docker por Podman para ejecutar LLMs locales (Ollama + DeepSeek) Afu Tse (Chainiz) Afu Tse (Chainiz) Afu Tse (Chainiz) Follow Dec 28 '25 Reemplacé Docker por Podman para ejecutar LLMs locales (Ollama + DeepSeek) # llm # deepseek # spanish # podman Comments Add Comment 3 min read Detecting AI Slop: Techniques & Red Flags Rost Rost Rost Follow Dec 28 '25 Detecting AI Slop: Techniques & Red Flags # ai # llm # nlp # python Comments Add Comment 11 min read BAML vs Instructor: Structured LLM Outputs Rost Rost Rost Follow Dec 28 '25 BAML vs Instructor: Structured LLM Outputs # llm # ai # python # api Comments Add Comment 9 min read Re-Prompting: The Loop That Turns “Meh” LLM Output Into Production-Ready Results Dechun Wang Dechun Wang Dechun Wang Follow Dec 29 '25 Re-Prompting: The Loop That Turns “Meh” LLM Output Into Production-Ready Results # llm # promptengineering # genai # ai Comments Add Comment 6 min read Production AI: Monitoring, Cost Optimization, and Operations Vinicius Fagundes Vinicius Fagundes Vinicius Fagundes Follow Dec 28 '25 Production AI: Monitoring, Cost Optimization, and Operations # llm # dataengineering # mlops # machinelearning Comments Add Comment 9 min read Testing MCP Servers Made Easy with agent-benchmark Dmytro Mykhaliev Dmytro Mykhaliev Dmytro Mykhaliev Follow Dec 28 '25 Testing MCP Servers Made Easy with agent-benchmark # mcp # llm # testing # ai Comments Add Comment 5 min read 📌 Day 21: 21 Days of Building a Small Language Model: Complete Journey Recap: Book Giveaway📌 Prashant Lakhera Prashant Lakhera Prashant Lakhera Follow Dec 29 '25 📌 Day 21: 21 Days of Building a Small Language Model: Complete Journey Recap: Book Giveaway📌 # llm # ai # chatgpt # programming Comments Add Comment 1 min read 📚 RAG Explained Like You're 5 Sreekar Reddy Sreekar Reddy Sreekar Reddy Follow Dec 28 '25 📚 RAG Explained Like You're 5 # eli5 # ai # llm # programming Comments Add Comment 1 min read 10 Best Practices to Manage Unstructured Data for Enterprises EzInsights AI EzInsights AI EzInsights AI Follow Dec 29 '25 10 Best Practices to Manage Unstructured Data for Enterprises # unstructured # llm # rag # data Comments Add Comment 8 min read How a Solo Dev at a Dealership Runs the Entire IT Department with Claude Code aj aj aj Follow Dec 27 '25 How a Solo Dev at a Dealership Runs the Entire IT Department with Claude Code # agents # llm # productivity Comments Add Comment 2 min read Self-Hosting Cognee: LLM Performance Tests Rost Rost Rost Follow Dec 28 '25 Self-Hosting Cognee: LLM Performance Tests # selfhosting # llm # ai # rag Comments Add Comment 9 min read The Brain of the Future Agent: Why VL-JEPA Matters for Real-World AI Himanjan Himanjan Himanjan Follow Jan 11 The Brain of the Future Agent: Why VL-JEPA Matters for Real-World AI # ai # genai # llm 1 reaction Comments 1 comment 5 min read 🚀 Introducing LoongFlow — A Cognitive Evolutionary AI Framework (Open Source) Freshman Freshman Freshman Follow Jan 9 🚀 Introducing LoongFlow — A Cognitive Evolutionary AI Framework (Open Source) # llm # ai # agents # opensource 1 reaction Comments Add Comment 2 min read Clone Your CTO: The Architecture of an 'AI Twin' (DSPy + Unsloth) Aparna Pradhan Aparna Pradhan Aparna Pradhan Follow Dec 26 '25 Clone Your CTO: The Architecture of an 'AI Twin' (DSPy + Unsloth) # ai # llm # rag # agenticai Comments Add Comment 3 min read I Told the AI to “Continue and Redeploy” — Then It Got Stuck Waiting for Itself Akshay Joshi Akshay Joshi Akshay Joshi Follow Dec 31 '25 I Told the AI to “Continue and Redeploy” — Then It Got Stuck Waiting for Itself # ai # programming # software # llm 1 reaction Comments Add Comment 17 min read How I Improved RAG Accuracy from 73% to 100% - A Chunking Strategy Comparison Yu-Chen, Lin Yu-Chen, Lin Yu-Chen, Lin Follow Dec 30 '25 How I Improved RAG Accuracy from 73% to 100% - A Chunking Strategy Comparison # rag # llm # performance # ai Comments Add Comment 7 min read When long chats drift: context windows and hidden errors in AI-assisted coding Himanshu Jetani Himanshu Jetani Himanshu Jetani Follow Dec 26 '25 When long chats drift: context windows and hidden errors in AI-assisted coding # python # programming # llm # promptengineering Comments Add Comment 3 min read Understanding LLM vs AI: What Every Engineer Needs to Know | My Site i Ash i Ash i Ash Follow Dec 26 '25 Understanding LLM vs AI: What Every Engineer Needs to Know | My Site # machinelearning # ai # beginners # llm Comments Add Comment 5 min read SLMs vs. LLMs : Why Bigger Isn’t Always Better for Logistics & Supply Chain Intelligence Arijit Ghosh Arijit Ghosh Arijit Ghosh Follow Dec 31 '25 SLMs vs. LLMs : Why Bigger Isn’t Always Better for Logistics & Supply Chain Intelligence # ai # machinelearning # llm # datascience Comments 1 comment 5 min read One Year of Model Context Protocol: From Experiment to Industry Standard Ajeet Singh Raina Ajeet Singh Raina Ajeet Singh Raina Follow Dec 26 '25 One Year of Model Context Protocol: From Experiment to Industry Standard # mcp # ai # llm # rag Comments Add Comment 3 min read TOON vs JSON en RAG (Java): el Grinch de los formatos cuando cada token cuenta 🎁 Aylen Tejas Aylen Tejas Aylen Tejas Follow Dec 25 '25 TOON vs JSON en RAG (Java): el Grinch de los formatos cuando cada token cuenta 🎁 # java # rag # llm # performance Comments Add Comment 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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http://young-programmers.blogspot.com/search/label/python | Young Programmers Podcast: python skip to main | skip to sidebar Young Programmers Podcast A video podcast for computer programmers in grades 3 and up. We learn about Scratch, Tynker, Alice, Python, Pygame, and Scala, and interview interesting programmers. From professional software developer and teacher Dave Briccetti, and many special guests. Viewing the Videos or Subscribing to the Podcast Some of the entries have a picture, which you can click to access the video. Otherwise, to see the videos, use this icon to subscribe to or view the feed: Or, subscribe in iTunes Showing posts with label python . Show all posts Showing posts with label python . Show all posts Thursday, July 18, 2013 Introduction to Python Functions A look at how to, and why you should, use functions in your Python programs. at 10:06 PM Labels: functions , python , random Wednesday, June 26, 2013 A Simple Python Quiz Program, Part 3 We enhance the quiz program by reading the questions and answers from a file. Source code at 11:49 PM Labels: python Sunday, June 2, 2013 Simple Python Calculator using a Dictionary and Functions for Operators This lesson creates a simple calculator using a dictionary to look up functions for operators. The point is to practice more with dictionaries, and to learn about higher-order functions. Source code at 10:10 PM Labels: dictionary , higher-order functions , python Saturday, January 14, 2012 Using Python's Input Function with Numbers We show how to use Python's input function to get numbers, without the program breaking if something other than a number is entered. at 1:26 PM Labels: python Saturday, November 12, 2011 A Simple Python Quiz Program, Part 2 We respond to a viewer question and add features to limit the number of questions displayed, and to display the questions that were answered wrong. at 1:02 PM Labels: python Tuesday, July 5, 2011 Carter Sande Teaches Python Functions Carter Sande teaches how to use functions to a class at Diablo Valley College College for Kids. A couple of notes on this excellent presentation: At one point Carter says “brackets” when he means parentheses. And for style, I prefer spaces around operators, and whitespace around functions. at 11:49 AM Labels: python Older Posts Home Subscribe to: Comments (Atom) About Me Dave Briccetti View my complete profile Where to Get Software Kojo Python Alice Scratch Other Blogs Dave Briccetti’s Blog One of My Best Classes Ever 10 years ago Tags alice (3) Android (1) arduino (1) art (1) audacity (2) dictionary (2) Flickr (1) functions (2) gamedev (1) garageband (1) GIMP (2) Google (2) guest (4) hacker (1) higher-order functions (1) inkscape (1) interview (9) Java (2) JavaFX (2) Jython (3) Kojo (2) lift (1) music (2) physics (1) platform (1) programmer (4) pygame (6) python (31) PythonCard (1) random (6) Sande (2) Scala (5) scratch (10) shdh (2) shdh34 (2) sound (3) sprite (2) Swing (3) teaching (3) twitter (2) Tynker (1) Web Services (1) xturtle (1) Followers Blog Archive ▼  2015 (1) ▼  February (1) This Podcast Moves to YouTube ►  2013 (4) ►  July (1) ►  June (3) ►  2012 (2) ►  February (1) ►  January (1) ►  2011 (8) ►  November (1) ►  July (3) ►  May (1) ►  February (2) ►  January (1) ►  2010 (6) ►  October (2) ►  June (2) ►  February (2) ►  2009 (37) ►  December (4) ►  November (1) ►  September (7) ►  August (11) ►  July (14)   | 2026-01-13T08:49:43 |
https://opensource.org/data-governance-open-source-ai | Data Governance in Open Source AI – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Main Menu February 3, 2025 February 7, 2025 Alek Tarkowski Data Governance in Open Source AI Data Governance in Open Source AI Artificial intelligence (AI) is changing the world at a remarkable pace, with Open Source AI playing a pivotal role in shaping its trajectory. Yet, as AI advances, a fundamental challenge emerges: How do we create a data ecosystem that is not only robust but also equitable and sustainable? The Open Source Initiative (OSI) and Open Future have taken a significant step toward addressing this challenge by releasing a white paper: “Data Governance in Open Source AI: Enabling Responsible and Systematic Access.” This document is the culmination of a global co-design process, enriched by insights from a vibrant two-day workshop held in Paris in October 2024. Download white paper ABOUT Dr. Alek Tarkowski is the Strategy Director at Open Future. He holds a PhD in sociology from the Polish Academy of Science. He has over 15 years of experience with public interest advocacy, movement building, and research into the intersection of society, culture, and digital technologies. The OSI is the authority that defines Open Source, recognized globally by individuals, companies, and by public institutions. Open Future is a European think tank that develops new approaches to an open internet that maximize societal benefits of shared data, knowledge and culture. ACKNOWLEDGEMENTS The publication of this white paper was possible thanks to grant number 2024-22486 from the Alfred P. Sloan Foundation . LICENSE @ 2025 Alek Tarkowski, Open Future and Open Source Initiative This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International license. Get involved Mastodon Twitter LinkedIn Reddit About About Our team Board of directors Sponsors Programs Blog Press mentions Trademark Bylaws Licenses Open Source Definition Licenses License Review Process Open Standards Requirement for Software Open Source AI Open Source AI OSAI Definition Process Timeline Open Weights FAQ Checklist Forum Community Become an Individual Member Become an OSI Affiliate Affiliate Organizations Maintainers Events Forum OpenSource.net The content on this website, of which Opensource.org is the author, is licensed under a Creative Commons Attribution 4.0 International License . Opensource.org is not the author of any of the licenses reproduced on this site. Questions about the copyright in a license should be directed to the license steward. Read our Privacy Policy Proudly powered by WordPress. Hosted by Pressable. Manage Cookie Consent To provide the best experiences, we use technologies like cookies to store and/or access device information. Consenting to these technologies will allow us to process data such as browsing behavior or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions. Functional Functional Always active The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network. Preferences Preferences The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user. Statistics Statistics The technical storage or access that is used exclusively for statistical purposes. The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you. Marketing Marketing The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes. Manage options Manage services Manage {vendor_count} vendors Read more about these purposes Accept Deny View preferences Save preferences View preferences {title} {title} {title} Manage consent | 2026-01-13T08:49:43 |
https://opensource.org/blog/exploring-the-business-side-of-ai | Exploring the business side of AI – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Main Menu Home Open Source AI Panels Exploring the business side of AI Exploring the business side of AI Transcript from October 11th Deep Dive: AI Business panel Stefano Maffulli: Then welcome everyone officially. Okay. Here we are. Thanks everyone, and welcome to Deep Dive AI. This is an event series from the Open Source Initiative that started as a podcast series. First, exploring how artificial intelligence impacts open source software from developers to businesses and to the rest of us. Today we start the second phase of this exploration with a panel focusing on the challenges and opportunities of AI as seen from the perspective of corporations businesses. There will be more three more panel discussions. One on Thursday, the 13th and then the 18th and the 20th. And the objectives, the objective of the panels is to better understand the similarities and differences between AI and what I would call probably classic software and particularly open source software. And so I’m, Stefano Maffulli and I’m the executive director of the Open Source Initiative. Stefano Maffulli: And today I’m joined by – in random order – David Kanter. He’s the founder and executive director of MLCommons, which is an open engineering organization dedicated to making machine learning better for everyone. And the members of MLCommons are basically the who’s who of AI from Google to Baidu to hardware manufacturers like Super Mink, Micro, Dells. There’s startups in it, like really an impressive list of sponsors and members. David also co-leads the development of MLPerf, which is a set of industry standard suites for measuring machine learning performance at every scale. Lots of experience in AI and development is also the MLCommons also maintains the list I mean, maintains two large open data sets in speech domain. And welcome. Thank you, David, for taking the time. David Kanter: Thank you so much for the opportunity to be here in the fantastic introduction. Everyone should know he did a great job of taking what was probably too long of an introduction and compacting it. So excellent work. Thank you so much. Thank you. Stefano Maffulli: Stella. Stella Biderman is next. She’s a leading natural language processing researcher and open source AI advocate. She runs ElutherAI, which is one of my favorite grassroot research group. Best known for pioneering open source large language models alternative to the ones that have been released in the proprietary fashion. She worked also on dataset such as the Pile, the similar Lara Aesthetic captions, and developing the v key gun clip methodology for generating images from text. She’s also a member of the big science research workshop where she worked on developing open multitask and multi multilingual language bottles, and she co leads the evaluation working group. Thanks, Stella, for being here. Astor Nummelin Carlberg: A pleasure. Stefano Maffulli: Next we have Astor Nummelin Carlberg, is, he’s the executive director of Open Forum Europe which is an independent non-profit think tank based in Brussels. Astor is responsible for the overall vision activities of the organization and policy development. He has an extensive experience on open or European policy making , communications and network building, and he leads the conversations on Europe’s digital challenges and role of open technology in in achieving its full potential. Thanks Astor for being here. Astor Nummelin Carlberg: Or as you say, least some, maybe some conversations. There are many conversations ongoing. Stefano Maffulli: Oh, there are so many, in fact, going on, so many Sal Kimmich, thank you. She’s an, they are an engineer passionate about helping peers, ethical actors, and digital enthusiasm to fill the cracks in the open source supply, open source software supply chain. They work primarily with the open web application security project and the open source security foundation to build systemic solutions to security issues. They also lead the efforts on rewards and incentives mechanisms for cybersecurity in the US federal cybersecurity Mobility mobilization plan. Thank you, Sal. Sal Kimmich: Thank you very much. I also am a machine learning engineer by training. I work player primarily in like scaling Kubernetes, and I used to work with supercomputers to do realtime brain image processing. So I’m excited to have this chat today. Thank you all. Stefano Maffulli: Wonderful. And finally, Alek Tarkowski. He’s the director of strategy at Open Future, another European think tank that develops new approaches to an open internet maximize societal benefits of shared data, knowledge and culture. He has an extensive experience with public interest, advocacy, movement, building and research into the intersection of society, cultural and digital technologies. Is also a sociologist by training and holds a PhD in sociology from the Polish Academy of Science. He’s worked with the Prime Minister of Poland as a strategic advisor and other experience very relevant to this field. Thank you Alek Tarkowski: Know, well that I’m on the board of Creative Comments, which I think is quite relevant. Stefano Maffulli: Very relevant, very relevant, super relevant, <laugh>, super relevant. So there are three main points that I’d like to cover with, with you all today. And one is how AI is different or similar to other technologies that we’ve seen before. The other is what lessons we can learn from open source that can enable the collaboration and speed up the progress of AI. And what are responsibilities that you see businesses that should have to keep the society safe from the abuse of AI or from abusive AI. And so let’s start with the first topic question for all of you. And you, you can pick who wants to answer. One of the comments I heard often is that AI is somewhat different from any other technology we’ve seen before, and therefore it poses really unique problems regarding ethics and responsible use. But you know, throughout history we’ve seen technologies emerge with great promise and potential peril all the time with debates about whether it would be too dangerous to be given to the public. Likefrom firearms to social media, genetic engineering. We’ve seen a lot of technology where public or, you know, corporations wanted to retain full control. What’s your take? How is AI different from the rest of other technology and competing industry specifically? Maybe David? David Kanter: We should go back to the original technology and mythology, right? If we, we think about fire, right? You know, in, in the Greek myth, right? The the Titan Prometheus, I believe who brought fire to humanity was not supposed to do that, got chained up to a rock and had a very unpleasant future. So it’s not just, you know, modern technology, right? I I think this is a trope that actually goes back, you know, thousands of years in some ways. Stefano Maffulli: Indeed. David Kanter: Or you could even say Adam and Eve you know, for those, the Christian. Stefano Maffulli: Apples. David Kanter: Abrahamist traditions. But yeah. Stefano Maffulli: So yeah. So how about AI and fire? David Kanter: Yeah, I, I’m someone else want to take this? I’m, I’m always happy to talk, but I wanna – Sal Kimmich: I can jump onto that metaphor for a second, right? Because I think it is pretty crucial to consider that fire is only dangerous if it’s not containable. And in this case, the discussion, I, it’s, there’s one context in this, which is the runaway algorithm, which always gets brought up a lot. But when we’re talking about artificial intelligence and the way that we need to be viewing it, kind of one of the things that I think is most important is like, are we starting wildfires? So the question here, a lot of times where we’re at really in this stage of our maturity and understanding AI as a sector is understanding really the computational costs and how to do large scale computing efficiently at a global scale. I think that for me would be containing the fire, letting us have humanity, and also getting to learn new stuff about the world with these beautiful machines. Stefano Maffulli: So do you think there is a difference? So did you think this is really brand new all we’ve seen it before? Astor Nummelin Carlberg: Well, maybe I can add just one thing and here, of course, take taking a step back because it, it’s sometimes more comfortable than, than saying something specific about AI and trying to predict the future. But it is interesting, like you mentioned in your intro that we, from a policy perspective, in the kind of political discussions we have heard very, it’s kind of the same reactions to a new technology. We can go back all the way back to fire, but also to encryption. But we also can look at other sectors that are not AI. There are also questions around, let’s say, open sourcing or increasing the availability of knowledge around check designs, which is considered something very strategic for governments. And it’s, we see a lot of analogs in other technological technology areas, and especially my angle into this through open source. And it of course, brings not only a lot of access, but could, it also brings a lot of speed to the development itself. And that I think are kind of the two elements in the open source AI policy discussion that really stand out to me. Stella Biderman: I think. So I’m gonna, I’m strike out against what, what seems to be the, the dominant life on. I don’t think AI is particularly different or particularly special. I think that it has, I think that the way AI works is not particularly well understood by policy and legal experts, and that there are, it often doesn’t fit the mold of currently existing laws and regulations. And I’m sure we’ll, we’ll talk about that soon when we talk about, you know, what does it mean to have an open source AI? But on a philosophical level, I don’t think it’s special. I think that, you know, oftentimes when there are new types of technology, be it AI, be it the internet, be it whatever, you need to adapt legal in societal frameworks to have this more nuanced and inclusive view of technology and how to regulate it. Stella Biderman: But I don’t think that there’s something fundamentally special about artificial intelligence. I see people you know, I see people on Twitter all the time saying, talking about open the importance of opensourcing AI on the internet, and a lot of people respond to me like, well, you wouldn’t want to give everyone plan to build a nuclear weapon. And I think it’s really important to push back into like, that is not the scale. That that is not what anyone is talking about doing. And I think that there’s a, a tendency to assume that the latest and greatest technology must be, you know, world cataclysmic scaled, dangerous. And I don’t think that, you know, you know, if Open AI had had open source the GPT-3 model, for example, as soon as they made it, I don’t think that would’ve, I don’t think that’s in the same conversation as you know, giving everyone in the world their own personal nuke and – Stella Biderman: Policy makers use language like that. Academics use language like that. But I think it’s really dangerous and irresponsible, honestly. David Kanter: I would say that my views are actually, you know, fairly close to Stella’s. Like I think there’s a lot of ways in which, you know, AI is not that different than technologies that came before. I think there are some important ways where it is different, some, some idiosyncrasies, right? Like I think to pick one in particular AI explainability sort explainability for ML and neural networks is sort of, to me, that’s a reaction to the fact that, you know, neural networks are currently somewhat inscrutable. And, you know, I’m not old enough to like, understand was steam power inscrutable given the, the, the understanding of physics at that point in time. But, you know, I sort of think like, let’s go look at this technology and find the ways that it is, you know, sort of fundamentally different. And I think there may be, not fundamentally, but I think there are sort of second order characteristics of ML that are a little bit different, and that requires maybe a little bit different tooling to think about. David Kanter: But I think one of the things that is a first order top level difference getting into sort of the legal side is, and particularly with respect to open source, is there is a lot of intuition that we collectively have built up around sort of open source that focuses on code. And so the big thing today, and you know, this is not an original David Kanter thought, right? You know, and Andrej Karpathy, you know, sort of said like, look, in the context of ML data is essentially the new code, right? And so what that means is all of a sudden we now have a regime where data was something that, you know, was not gone over with a fine tooth comb and sort of trying to understand licensing and different combinations and things like that. And so now that’s something that we will need to do. David Kanter: And then there’s both data taking first stage along with code, and then there’s the interactions of that and then all the different parties. So I think it exposes a lot of complexity, which is I think what Astor sort of you were saying, right? There’s like policy areas that we haven’t touched. But yeah, it doesn’t seem to me that AI at the top level is so fundamentally different. I think there’s also a lot of confusion as with any new technology. Like one of an experience I had was going to a conference where there was a lot of people talking about AGI and – Stefano Maffulli: AGI stands for? David Kanter: Artificial General Intelligence. And this is sort of the idea. I, so here’s the issue. I’m not actually sure what it means. Like so I had emailed Stella and both of us studied math at U Chicago, which only comes in one flavor, which is theoretical. And so you start with a very crisp, clear definition of anything. And one of the challenges I find is like getting a definition of AGI is sort of like, you know, holding a greased watermelon in the ocean, it just, it like keeps on slipping away and that’s fine, right? Like, it sort of reflects some societal fears, right? Is my job gonna be displaced? And I think those are interesting things to take account to. But like every time, like I, I think there’s a, a lot of education and understanding that needs to happen because I think when many people who pose these fears, it’s because they aren’t intimately familiar with it. David Kanter: And it’s sort of like any technology, it’s going to start out as sort of arcane black magic until at one point it’s prosaic. And like flight is a great example of that. Like, I don’t know anyone who takes a flight on a commercial airliner and says, I need to study what plane I’m on to figure out if I’m gonna die cause it’s gonna crash. Now that was a reasonable thing to do, potentially like the 1910s. But like, you know, today, like flight is just this like amazingly magical and beautiful thing that’s in the background for almost everyone. And one day I hope AI will get there. Stella Biderman: I think that’s actually a really interesting analogy because flight is exceptionally dangerous. David Kanter: Mm-Hmm. Stella Biderman: Nowadays nobody ever dies on airplanes. Like the, the odds of dying in a crash of an airplane is exceptionally small. It is arguably the greatest safety accomplishment of the technological sector is developing safe commercial air flights. Just full stop. But the way that we got there was basically most of the governments of the world getting together and telling the overwhelming majority of people they weren’t allowed to build airplanes and creating a, a very small monopoly. You know, you people, people think about you know, there, there’s a handful of companies in the world that produce basically all of the aircraft we’re talking now, commercial aircraft. There’s like six or seven companies that produce basically every commercial airliner in existence. It doesn’t matter if you’re flying an American Airline, if you’re flying a British Airline or flying in the UAE or China. There’s just a very small number of companies that produce all of them, and they have extremely careful regulations both internally and externally from government about, you know, quality control and manufacturing processes and all of this stuff. And ultimately like that is how come we have these extraordinarily complex systems that can take tens of thousands of people, hundreds of thousands of people into the air every day and not kill them. Alek Tarkowski: If I could could add one more analogy. It’s housing. There’s this amazing episode of the 99% Invisible podcast, which is a podcast about design, which talks about how houses didn’t have fire safety systems for a very long time and were basically very dangerous technologies in which everyone had to live. The state of our system was the basket on which you could try to lower the baby from the top floor hoping that at least the baby will survive, right? And, and then a major breakthrough happened, which had to do both with technology and, and standards and safety codes. And I, I always like that. I also like Stella, your example of GPT-3, I always thought that moment where they said we have this system, but the responsible thing for us to do will be not to release it is a very interesting one. Alek Tarkowski: And of course I like it that we’re talking about technology and the public discourse on technology. And I think there are very two different things than I’m more an expert on the latter. So I will not comment on the technology, but, but for me, that GPT-3 move was quite symbolic and it’s repeated a lot both in policy debate among policy makers, but in industry debates, basically things like responsible and ethical. I’m not saying these haven’t been discussed before with regard to technologies. Of course they were, but if we think about the like, traditional mode of like, like ship it while breaking things of many of the web techno, maybe not technologies but products, right? Or business models, I think there is something new, right? And we can discuss whether the for instance, policy proposals around safe AI or are good or miss misjudged. But I think in, if we look at the policy debate, there is something you hear, and maybe we can come back to it for me, that becomes very practically visible with the new rail licenses, which suddenly say, let’s take the open source stock and attach to it a responsible module. Right? and, and that for me feels very interesting. I guess we’ll talk about it. David Kanter: Do you think that the statement of we built this thing and it’s so powerful that we’re not gonna release it for ethical reasons. I mean, do you think, like, to me, I sort of read that as partially being marketing, to be perfectly honest. Alek Tarkowski: Very much so I think, but it nevertheless built the debate. Stefano Maffulli: That’s it. Yeah. I got that same impression. It built the foundation of oh my god, or freaked more people out by giving that, that by stating that from a powerful organization. Alek Tarkowski: And, and about two weeks ago, again I read on Twitter someone says, Table diffusion is reckless open AI is being responsible. Stefano Maffulli: Mm-hmm. Alek Tarkowski: Which I find very interesting in terms of the debate. Stefano Maffulli: In fact, it is very interesting. So if I can summarize, I, I think that what I’ve heard from you is saying that one of you mentioned the, the speed of which AI has been moving as a differentiator somewhat but not very important. The most important thing that you highlighted David, is the importance of data and how the data has changed a little bit. It’s level of importance and explainability or other technical issues that are still not clear inside the researcher community. Or at least, you know, it still has lots of jumps to go through to evolve. But overall, basically you’re all agreeing it sounds, sounds to me like this is nothing too special. Sal Kimmich: I’m not sure if I do, I really do want us to make a clarification here because if what I’ve heard discussed so far, still very much does sound like large scale, but machine learning. It is absolutely different when we are looking at building something which is true real artificial generative intelligence, right? It’s very different in that case because I’m not then going from collecting and curating a data set to then be able to have a machine that’s very efficient in making known outcomes. What I’m then doing is having a machine sometimes make its own choices about exploring that data set to then go on to build a data set. Now for me, we don’t have anything existing in policy and we cannot be doing this by a case by case basis. If this is going to do a policy enactment, it has to be categorical. Categorically, right now I do not think we have anything in place in order to be able to identify for whom the intent belongs to for negative secondary effects of a data set produced by or an algorithm produced by an artificial agent. Sal Kimmich: Right now in the US alone, if that’s done by a developer, that intent goes back to the developer. If it’s built by a developer who uses generative code, does that go back to that developer or a percentage of that policy can help us with that. And I think we need to distinguish right now the ethics in open source and the ethics in artificial intelligence, cuz they’re super different. The premise for what we’re doing for ethics in open source is all around intellectual property. If we are actually having a discussion here about artificial intelligence and not just large scale ML, AI is actually a discussion much more around the policy of intent. So that is what I really want to understand. Are we, are we able to parse that apart in two separate discussions? Cause I think they’re two very different ethics. Stella Biderman: Can you give a couple examples of what would be artificial intelligence in this lingo that, so like, so some examples of things that come to mind were, like OpenAI’s GPT-3, someone mentioned stable diffusion, you have reinforcement learning algorithms like alpha zero. Which of those, which like category do each of those fall under? Sal Kimmich: So I think for, I think for these Stell, if we are producing something which very fundamentally has data set first, which all of these models run by, right? This is how we’re getting our, our stacks to pull from. If it’s a data set first that we’re then learning from, and I think this contextualization that you’re working with is good. I think these stacks that we’re able to pull from make a lot of sense, but that’s very different from some of the things that I’ve built where I was simply telling a computer, here’s a massive global data set of real time sensors. I want you then to go out into that state’s place, explore it and decide yourself how you wanna do the feature representation. I think really the way that that feels as a developer is that the intent and the production for those features, I don’t even always tell it what initially it should optimize around that does feel different to developing a deterministic pipeline. Astor Nummelin Carlberg: Could I, this becomes a meta comment again, but bear with me. It’s very interesting of course to listen to you as experts talking about this, but from my point of view, I’m bringing you in here also, Alek we are both working a lot in the, in the Brussel space. There’s an interesting question around this general space where of course I made the point about speed of technological development on the one hand, but there’s also the limited number of people who actually have deep understanding of how the system worked and what the potential effect in society could be. At the same time, there is this instinct or reaction that we need to regulate this space. Then it bring, it creates this question in my mind that is like, who then holds the responsibility of education and learning and like teaching or explaining. If it is a very limited number of people that can actually, let’s say, bring that education. Because I think that’s quite different now, isn’t it? Like compared to fire, compared to steam engines. How fast ideas of a limited number of people can have effects very broadly in society, like the time to market so to say is a lot quicker and policy making is not always super comfortable with that kind of speed. So how do we square this circle with, for example, two experts as yourselves here where, who does the responsibility fall onto? Stella Biderman: I think that’s a good point, but I’m, I don’t think that that is necessarily like core to artificial intelligence. It’s more core to the way that most modern advanced artificial intelligences are developed in specifically who they are developed by. The overwhelming, you know, whether you’re interested in text generation or text image modeling or or reinforcement learning for playing games. The overwhelming majority of the research in this field is controlled by very large tech companies and a very small number of them globally. And, you know, they have a lot of money and resources and influence to be able to first of all pump out this research very quickly. You know, a lot of what’s currently en vogue in AI is roughly speaking, if you have twice as much money, you can finish the problem twice as quickly. Stella Biderman: They’re extraordinarily paralyzable. Your ability to actually purchase GPUs or purchase data assets is the primary model map. And so for, to make this a little personal, like I’ve trained a 20 million perimeter language model called GTP New X, and that took me about three months. Which is, you know, if if I had enough money, I could have done that in three weeks instead of three months. The difference there is solely about the number of GPUs I can afford to pay for. And so, you know, and also we know that some of the, these AI so the DALL-E for example, that DALL-E 2 that OpenAI developed was, was streamed in less than a month. And that’s really a statement about resources is not something about the, the AI itself, it’s a statement about the fact that OpenAI actually had the resources to go do that. Someone else could have turned the same model in a year. But yeah, Stefano Maffulli: Yeah, in fact, I think this is an important topic because in my mind it’s one of the largest difference between AI machine learning, the way we’re talking about, and classic software, right? Classic software as today can be developed with less than a hundred bucks and a text editor and, and lots of open source software to write application. But when it comes to AI models, you know, we’re talking about half a million dollars and up to get the data set. So the, but one thing that happened, I mean, this was not always the case. Like when software started to appear, machines were, were expensive, hardware was expensive, the availability of of basic software was almost nonexistent. And so it was, it was hackers who came together and, and started to democratize and, and share the means of production if you want to use the sort of names. So what do you think we need to do in order to achieve the same, to accelerate and create this basic set of commons that open source has enabled? Sal Kimmich: Well, I mean, I, I mean at least this answer I don’t think is so much on a theoretical scale. I think this is something we’re working on on a corporate level right now. So it’s really interesting in the federal mandate that’s come down for cyber security, we have to put an SBOM, right? We have to put in software bill of materials, we’ve gotta get a discrete outline of exactly what was utilized. Now they put that out with the intent and thinking really just around cloud architectures. They didn’t really think about some of the more complex architectures that we’ve been looking at. Now, this has brought up a really fundamental question now that we have to be able to label the data sets in those SBOMs and state their true providence. And when we’re speaking to these big data sets that people have been pulling from these really problematic, centralized, closed off data sets, even when we’re able to get into those, to clarify the providence of those, they’re just literally trying to go through the top six data sets that corporations use right now and see if they’re valid, see if the images that they took were valid even to be used legally in the first place. Sal Kimmich: And they’re finding problems in every single data set. So it’s not even as if leaning on the corporate model has worked for us, we’re running into problems that’ll look a lot like open source just because of the size of the data that we need and the way that they’ve been scraped so far. Stella Biderman: This is huge cultural issue in machine learning. Historically speaking, machine learning researchers tend to think that if they go out and collect or reprocess or repackage data it’s its own thing and they can license it however they want and there is no providence before that. And not to put, you know, that’s false, that is just simply not true. But it is historically the way the overwhelming majority of ML researchers have conducted themselves. And now we’re in a really awkward spot where there are a lot of very widely used data that explicitly have falsified providence that are, are used by thousands or even more researchers all the time that have, like I said, falsified providence and no real ability to either prevent that from happening in the future or kind of undo that, like building the correct documentation of even a relatively modest by modern standpoint, status set is an exceptionally large amount of work, and it’s not something that really the organizations, the companies have the resources to do it really care about doing. Stefano Maffulli: Right? How do we fix that? Any thoughts? Stella Biderman: We could make it financially viable for companies to do this in the future, but the, the US government’s track record with actually loving penalties like that is basically nonexistent. So I wouldn’t hold out too much hope in that regards. Sal Kimmich: Yeah. But on the positive side, one of the people that’s doing some of the best work around this for NLP right now is on the call, so, right there’s two sides to the coin, but it really does take having that understanding of I think, I think we are perhaps that level of education needs to come in is, you know, yeah, we could teach it in schools, but right now the people that need to be aware of it are probably the legal entities of large corporations because this is a massive lawsuit that we are all aware could be coming down the line. If you’re using two or three of these data sets, which several of these corporations are using the providence is known to be invalid. And so they would have to then resource, and this is, this is where it gets interesting. Sal Kimmich: This is where open source does come back into it because in order to effectively resource the data sets and the computation for this, it does make sense to share both that compute and the outcomes and to efficiently store those outcomes in a way that’s feature engineered so that people can query from it and pull what they need to. But so that we’re not storing unnecessary data if we don’t need to. And that again, is a bit of its own question. I think there’s computational answers to what that is, but not everyone agrees with me as to what should be kept in a data set. So yeah, lots of, so much work to be done. Stella Biderman: I feel like I have to make at this point, which is thatI was one of the people who created one of the largest currently widely used data sets for training these models, most of the data, in it is above board, some of it is not. We were trying to put together approximately 1.5 terabytes of text. We basically went with the standard of it’s okay to use something if it’s widely used in machine learning already, because we’re a bunch of people off, like in a discord channel hacking away and trying to train our own AI. Obviously people aren’t gonna change their decision making based on, on what we choose and that we were okay with doing, you know, if it’s already widely used by people who have a lot more resources and companies, then fine. And, but anything new that we’re gonna pull in needs to, needs to actually have a real license on it. Of course, that’s paper that we put out describing that data set is now my most commonly cited paper and is widely used across the world. So I feel kind of bad about that for sure. David Kanter: I mean, one of the things I would say, and I have a lot, I’ve spent a lot of time dealing with licensing and data sets because of ML perf. And we have been extremely, in the two data sets we built, we were extremely particular about making sure that they had licenses that were compatible with the intended use. And so to step back our intended use of our speech data sets, one is keyword spotting. One is for full on automatic speech recognition. We wanted to support both commercial and research applications. And so we were only using data that was CC BY or friendlier, essentially. I think we actually, in one of the papers, we may have had CC BY share alike, but that has its own problems just from the commercial standpoint potentially. One of the things that I’ve found surprising, and you know, I think that a lot of folks are gonna run headlong into is that, you know, when you’re doing research, you can kind of get away with whatever you want. David Kanter: Like ImageNet, for example, is a super classic machine learning data set. The licensing around that is to put it politely, a quagmire. Some of our benchmarks are built around ImageNet. I would love to fix that in the near term, but like, one of the challenges is just everyone historically uses that. And the other thing that is complicated is there’s not a uniform, any sort of real uniform agreement on commercial use. Right? And so to some extent, one of these aspects, so first of all, you know, for all of us technologists in the room, right? One of the bizarre characteristics of the legal system is that, you know, many things are not considered settled until they have been fully litigated in the courts. And that’s like, nor, you know, if you think about that as a programmer, you’re like, litigating things in the court should be an exception handling process. David Kanter: Not the like inner loop. It’s kind of weird, but, you know, it is what it is. And then the other thing is like, even things that seem very unambiguous, like, oh, commercial use is granted, some legal departments will be more conservative and they might say, for instance, hey, you have a license that allows any use. Now, in David’s definition, any use includes training and ML model on that data. But some people might say, and as a matter of policy, it might be a good thing. I don’t wanna weigh in on the policy aspects, but you could take the interpretation well, that license was granted before, you know, people knew about AI, so they didn’t really, when they said all, they didn’t really know what they were. Right. And, and so there’s a lot of issues around licensing and making sure that things can be clean. And I think this is one of the areas where there’s a lot of opportunity to make the ML space lower friction. Stefano Maffulli: Yeah, for sure. Yeah. AleK, I I just wanted to hear from you about your, cause you, you studied this space a lot. Alek Tarkowski: Yeah. Oh, I’ve been looking at a very specific case because it’s interested thing from the perspective of content licensing with Creative Commons licenses and that’s the case of the user photographs for face recognition training data sets that do that. And this goes back by now a decade. It’s, for me a fascinating case because in 2014 when the YFCC100M dataset was built with 100 million images taken mainly from Flicker, maybe with media commons, it was really, it seems around the quarter of openly licensed photographs. It’s really huge. I know today these numbers are not so huge and the data sets are much bigger and by the way, I don’t think has a lot of openly licensed content. But, so that was the big case and basically exploring these issues and great work was largely done by Adam Harvey. Alek Tarkowski: He’s a Berlin based American sort of activist researcher and artist who, for instance, created MegaFace a search engine where you can search whether you’re in, in the dataset, which currently seems to be becoming a, which I find very interesting a tool that’s being also running online on by Andy Baio. And apparently other people are doing it, which makes me a bit hopeful. And, sorry, I’m jumping around a bit the topic, but when you ask what are the solutions, maybe these little steps, they of course don’t solve everything. But if I see that in a few years, something shifts from being an art project, basically, you know, a critical art project to something that starts to feel like maybe a standard, that’s a good step. But basically these cases show that all over the place just the license compliance, as you say, the quagmire. Alek Tarkowski: Maybe that’s the easiest way to describe it, which is really confusing. You have really big research projects usually you have companies involved in this research, which take a very less as fair approach to how they understand the license. Admittedly, there will be people who will immediately tell you, in the end, it’s not even clear why they wanted to use openly license content because it’s very probable that especially in the US this is all fair use. And indeed, especially when your data set is actually not the photographs, for instance, the case we’re studying with basically a list of URLs. You know, so from a purely copyright point of view, there might not be an issue. I think it’s not a purely copyright point of issue, by the way, and I hope we can apply some of these broader issues we discuss in the previous session to, to frame this issue, not just as am I being compliant with copyright law, I think that’s too narrow. What I would see as an approach. I would like to have one data set, and I think it’s slowly happening that will admit that in the past, this has not been state of the art and define a really high standard and run that data set in is governance against a high standard. Because I think this needs to be self-regulated. There will be probably policy debates on dataset on like legislative. I don’t think it should go that way. So yeah, my 2 cents. David Kanter: I was actually, can I inject a like more poignant example that will tie together like some interesting AI questions as well as sort of licensing conundra? I think conundra should be the plural of conundrum, by the way. Conundrum sounds okay, but conundra sounds cooler to me. So I mean, everyone here has probably heard about copilot, right by Github, which was done by ingesting a very large amount of code. Now I will, and some, a lot of that code has interesting licenses, varied licenses. And so it’s not really clear what the output actually is in terms of licensing. But one of the things that I would say anecdotally and Stella and Sal, you should, you know, please correct me if I’m wrong, but it’s not fully understood the extent to which you know, deep neural networks, there is both a memorization function they perform where they can emit some of the input that they received in some scenarios. David Kanter: And then there is also a transformative aspect, and you more commonly see the transformative expression, right? It’s relatively rare, but it can happen and right. And I believe there are instances where, you know, you have things that are just emitting memorized inputs. And so then the interesting question is, suppose you have a GPL input and you have the potential of emitting a copy of that, right? Like how does that work? Like sort of my intuition is, well, if you’re emitting what was previously GPL code, it definitely is GPL on the output, right? Stefano Maffulli: It’s a huge legal conversation. I don’t think that, the jury is still out from, from what I understand. And, and there is definitely lots of thinking that is going on from the legal that we have a bunch of legal experts coming up in the next few days. And that definitely, this is one of the questions for them for sure. But it is an interesting an interesting thing. I mean, it’s the same if you, if you remove the code from the picture and you start talking about art, you know, so many artists are so confused by now because they look at, you know, graphic designs and stuff that are produced that look exactly like if they made them. So what’s, what’s happening there? Well, how do we deal with it? Sal Kimmich: I mean, it does, it does come down. So again, I’m just constricting this to the US core precedence, but it does come down to the change in the look and feel that sort of defines an intent of change. So you can take websites that are literally, you know, like a e-scooter website and turn it into an e-bike website with a different color, and that is legally sufficient because the argument that was used as the precedent behind that in the US which I think is ridiculous, is the idea that you can have an entirely new song by doing like a Weird Al Yankovich cover, right? By taking the exact same style, the exact same intonation, and replacing quite literally in this case, different text snippets, right? In order to produce something which is a new consumable object. Now that’s the way that we would be thinking about this if we’re thinking that it’s taking larger snippets of code. But I think it’s actually a little bit more interesting, and I think this is a nuance that we’re missing with copilot, is that they’re genuinely short snippets. And so that begins to look and feel a lot more like, at least for me, like what is the minimally viable unit of intellectual property on the web? Is it that one line of code that I wrote is that that four line code? Stefano Maffulli: And it’s not an easy answer. I mean, there is, from what I hear from lawyers, and there is no, there is no easy answer to that. Cause it depends on case by case scenarios and depends on how good your lawyers are in defending that position in court too. Alek Tarkowski: But I think the interesting thing is that it’s sort of, it is a legal issue and also isn’t because when you look at it, it also shows that for now at least law is not working. I mean, the amount of basically clearly copyrighted images of Mickey Mouse, you can find on Lexica, you know, produced by stable diffusion under hypothetically a CC-0, you know, it’s not license, but the CC-0 sort of tool or mechanism is, is just staggering. Okay? It doesn’t go into millions, but it’s quite big. And and what does it tell us? For me, it’s about enforcement. And I know lawyers will be interested in asking, So how do we get that enforced? Is it enforceable? I think when we have a broader conversation about business or society there are also interesting questions. Does it need to be enforced? Stella, you shared that search engine. Alek Tarkowski: It’s done by artists, Mat Dryhurst and Holly Herndon who say it’s a post copyright project. Yes, they want to enforce some form of protection of their basic creativity, but they’re not interested in repeating the copyright debates, which I find very interesting. And maybe also suggest that vice versa, when we look at tools like open source licenses or open content licenses that build information commons I think there are lessons to be learned from the last 20 years, but maybe also good moment to ask, do we really want to repeat you know, exactly the same moves. I find that certainly sort of fresh and exciting that people openly share our values are the same. It’s about some balance between sharing but also protection of what I hold dear, but want to do it differently. Stefano Maffulli: Yeah. And as long as we’re talking about copyright, you’re basically pulling me into this are we, I started to wonder whether this is the right moment in history to think about some something else, like to ditch copyright. And especially when we think about open source and imagine something new, and let me be a little bit more clear. So if you go back into the, the sixties, the seventies and the early eighties, software started to emerge and it was a, there was a conscious decision policy decision at that point from hardware manufacturerIBM to separate, to unbundle the software from the hardware because they had this fear of being sued for creating a trust or having a monopoly. And so this entangled the two pieces of a computer system that they were used to sell. Stefano Maffulli: And they made a decision, they made a call and they said, well, we’re gonna use copyright. And it wasn’t until the eighties that the courts actually said yes, okay, believes copyright for source code and binary code. So today, you know, we talked about it, there is a lot, there are new things, there are new artifacts like these data sets that create a model. And we are deciding what seems like everybody’s thinking in terms of copyright. But is that the right thing to think of? Is that the right framework? David Kanter: Can I actually ask a very basic question? Is it a matter of settled case law what a trained model versus an untrained model is? Like what legal rubric it falls under? Stella Biderman: By an untrained model, do you mean like randomly initialized weights? So usually the way these models are trained is that you define an architecture and then you fill in a billion numbers with random numbers sample from zero to one or something close to that, and then you train it, and those numbers change from random values to non random values, David Kanter: Right? Precisely. And so you have sort of like this notion of model architecture, right? And then to extend this a little bit more, right? So there’s this notion of you start with random stuff and then you train it, but there may be multiple steps in training. So for example, and maybe using different data sets, right? The classic thing is, you know Stella, your work was right on large language models, which you might train up to a certain point and then someone downstream could fine tune using potentially, right? But I mean, so is copyright in fact – you know, maybe the answer is there isn’t a legal rubric for a trained model at the current point, right? Because you can think of it in some senses code and in some senses data. Stefano Maffulli: Right, Exactly. And it’s the output of a machine, from what I understand and traditionally the output of a machine is non-copyrightable, at least in the United States. Again, I’m not a lawyer, but this is what they told me. And so we, we, we we’re somewhat assuming like, eh, let’s slap a license that is deeply rooted on copyright and assume that that’s the right thing to do, but maybe we need to invent something new. Maybe we have an opportunity. What do you guys think? Stella Biderman: So the US patent trademark office actually very recently granted a copyright over a AI generated image to, there are actually two cases of this that I think are really illustrative of what current legal standards are. So in both cases, someone took an AI that takes texts and input and it generates images and output. And one person submitted a patent, a sorry, a copyright application regarding their ownership of the image that came out of the AI and that was granted. Stefano Maffulli: Yeah, I discussed this with one of our lawyers and we probably have to write an article about this because it’s fascinating. It’s not clear whether the US PTO knew about, or the US copyright office knew that the creation was actually generated by an AI. Stella Biderman: The application explicitly stated that I’ve read the application so that may now have been taken into account in their decision making properly or so, certainly disclosed. And then the other example I wanted to bring up is that someone else submitted basically the same application, but they wanted to have the AI own the copyright. Stefano Maffulli: Yeah, that’s, I mean, there are comments also on the – that seems like it’s absolutely – there are cases where the copyrightoffice did not accept the registration because of that. But without going too much into the weeds of the legal conversation because we don’t have lawyer, I mean Alek are you a lawyer? No, you’re a sociologist, but we’ll talk about the legal details more in depth in the future. But what is interesting to me is that at one point the European Commission created new rights. They have created a right to data mining. They have created an ad-hoc right to database structures. So, you know, is it completely out of the picture to invent something that is more useful, specific for AI to create that commons that has powered open source and open culture, open k | 2026-01-13T08:49:43 |
https://dev.to/logrocket/developing-cross-platform-apps-with-bun-v11-2pjc | Developing cross-platform apps with Bun v1.1 - 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 Megan Lee for LogRocket Posted on Jul 16, 2024 • Originally published at blog.logrocket.com Developing cross-platform apps with Bun v1.1 # bunjs # deno # node Written by Clara Ekekenta ✏️ For some years now, developers have faced many performance challenges while using traditional JavaScript runtimes like Node.js and Deno. Even though these frameworks are used by giant tech companies like Google, Amazon, and Facebook, they still have some drawbacks. Bun was developed to address these challenges. In this tutorial, you’ll learn how to develop cross-platform apps with Bun v1.1. To follow along with this tutorial, ensure you have Node and npm installed on your computer. What is Bun? If you’re not familiar with Bun, here’s a quick recap . Bun is a relatively new JavaScript runtime that uses the JavaScriptCore engine for running, building, testing, and debugging JavaScript and TypeScript code. While Node.js and Deno run on the JavaScript V8 engine, Bun runs directly on the JavaScriptCore engine. This solves the performance issues seen in Node.js and makes setting up, building, and bundling JavaScript code faster. It also makes Bun compatible with all npm packages, and even makes installation two times faster than with the npm i command. Bun's startup is up to four times faster than Node, which can boost your development experience. What’s new in Bun v1.1? Bun v1.1 was released with a lot of new features that complement the previous releases. First, it made Bun’s installation much simpler, as well as more similar to the way you'd do it in a Node.js project. Let's discuss other attributes that were brought with this version 1.1 release in detail. Windows support Bun was only running on Unix-based operating systems before now, but the Bun v1.1 release adds support for Windows 10. It’s been tested in Windows and passed 98 percent of the tests that were also done for MacOS and Linux, showing that the Bun's runtime, test runner, package manager, and bundler can run on Windows machines. To run Bun on your Windows computer, open PowerShell and run this command: powershell -c irm bun.sh/install.ps1 | iex Enter fullscreen mode Exit fullscreen mode Improved speed Bun was built with high speed in mind, and it’s relatively faster than other JavaScript runtimes like Node.js. This makes it all the more impressive that the v1.1 release has doubled the speed by integrating JavaScript, TypeScript, and JSX into Bun's custom-built transpiler. Also, the file caching mechanism allowed Bun to cache files that exceeded 50KB (which was introduced earlier in v1.0), contributing to the improved performance of Bun v1.1 and its command-line utilities like tsc . Bun shell The Bun shell has been upgraded to work as a multi-platform shell, working on both Unix-base operating systems and Windows operation systems. This upgrade implied the process of writing Javascript code by removing all the unnecessary ambiguous complexities associated with it. New file format To maintain the speed of Bun and its ability to work well across platforms, the team introduced a .Bunx file format in the v1.1 release. The .Bunx file serves as a cross-platform symlink that runs scripts with either Bun or Node.js, depending on the file system. This new feature solves several problems, such as: The uncertainty of traditional symlinks on Windows The absence of shebangs recognition Removing the need for separate executable files Handling error messages in the form of terminate batch job? (Y/n) , which can halt the development process Creating your first cross-platform Bun project Let’s get some hands-on experience with cross-platform development in Bun by building a to-do application. This tutorial should help you better understand Bun's strengths and how to use it in your projects. We’ll create a Bun server and cover how to integrate Bun with React-Native to build a cross-platform application. Installing Bun v1.1 Before we start building an application using Bun v1.1, you need to install it on Mac, Linux, and Windows operating systems. Run the command below to install Bun globally on your computer: npm install -g bun Enter fullscreen mode Exit fullscreen mode Note that, for Windows users, Bun works only on Windows 10 v1809 as of the time of creating this tutorial. The above command will install the latest version (v1.1.x) of Bun on your computer. Verify the installation with the command below: bun -- version Enter fullscreen mode Exit fullscreen mode You should see the installed version appear on a new line in your terminal: Creating a new Bun project With Bun installed on your macOS, Linux, or Windows computer, create a directory for your project and change the directory into the folder: mkdir bunapp cd bunapp Enter fullscreen mode Exit fullscreen mode Then, create a new project with this command: bun init Enter fullscreen mode Exit fullscreen mode This will create a new project.json file with with an entry index.ts file in your project directory. Once the init command is completed, install the dependencies by running npm install : Update the code in the index.ts file to create a web server using Bun’s built-in HTTP module: const server = Bun . serve ({ port : 3000 , fetch () { return new Response ( " Bun! " ); }, }); console . log ( `Listening on http://localhost: ${ server . port } ` ); Enter fullscreen mode Exit fullscreen mode Now start the server with this command: bun run index . ts Enter fullscreen mode Exit fullscreen mode You will see Listening on http://localhost:3000 on your terminal. Integrating Bun with React Native Currently, Bun runs server-side JavaScript and TypeScript projects. However, you could also use Bun in your React Native projects to run Node.js dependencies and scripts. To integrate Bun into your React Native project, first create the project with Bun: bun create expo my - bun - app Enter fullscreen mode Exit fullscreen mode Then, update your package.json file to replace the npm or Yarn scripts with Bun commands: "scripts" : { "start" : "bun expo start" , "android" : "bun expo run:android" , "ios" : "bun expo run:ios" , "web" : "bun expo run:web" } , Enter fullscreen mode Exit fullscreen mode Now, update your App.js file to build a to-do application with the code snippets below: import React , { useState } from ' react ' ; import { Text , View , TextInput , Button , FlatList , TouchableOpacity } from ' react-native ' ; export default function App () { return ( < View style = { styles . container } > < Text style = { styles . title } > To - Do App < /Text > < TextInput style = { styles . input } placeholder = " Enter a task " value = { task } onChangeText = { setTask } / > < Button title = " Add Task " onPress = { addTask } / > < FlatList data = { tasks } renderItem = {({ item }) => ( < TouchableOpacity onPress = {() => removeTask ( item . key )} > < Text style = { styles . task } > { item . text } < /Text > < /TouchableOpacity > )} / > < /View > ); } Enter fullscreen mode Exit fullscreen mode The above code creates a to-do app that will allow users to create a list of tasks. The users can add, remove, and view the list of tasks added. We’ve added event listeners and attached the functions required for the app to function to the component. Now add the addTask and removeTask functions above the return keyword: const [ task , setTask ] = useState ( '' ); const [ tasks , setTasks ] = useState ([]); const addTask = () => { if ( task . length > 0 ) { setTasks ([... tasks , { key : Date . now (). toString (), text : task }]); setTask ( '' ); } }; const removeTask = ( key ) => { setTasks ( tasks . filter ( task => task . key !== key )); }; Enter fullscreen mode Exit fullscreen mode In this code, we created a tasks state to store the list of to-dos, along with a task to manage the current input value for a new task , hold the value entered from the keyboard, and use setTask to update it. Then we created the addTask function to add a new task to the list of tasks and the removeTask to remove a to-do by its ID. The removeTask will filter through the arrays of tasks to remove the one that matches the selected task's ID. Lastly, add the code snippet below to style the application: const styles = StyleSheet .create ( { container : { flex : 1 , backgroundColor : '#fff' , alignItems : 'center' , justifyContent : 'center' , padding : 20 } , title : { fontSize : 24 , marginBottom : 20 } , input : { height : 40 , borderColor : 'gray' , borderWidth : 1 , marginBottom : 10 , paddingHorizontal : 10 , width : '100%' } , task : { padding : 10 , fontSize : 18 , backgroundColor : '#f9c2ff' , marginBottom : 5 , textAlign : 'center' } } ); Enter fullscreen mode Exit fullscreen mode Now start the application by running the command below: npm run start Enter fullscreen mode Exit fullscreen mode Then select the device you wish to run the application on. The app will look like the screenshot below on iPhone: On a web browser, it will look like the screenshot below: It will look the screenshot below on Android devices: You can see that all of our components are present and functional, although they render slightly differently on each device type. Our cross-platform app works correctly! If you want more consistent styling across devices, you may need to do some extra work at this stage, but now you know you can build an app with Bun that works on different platforms. Node.js vs. Bun vs. Deno Node.js, Bun, and Deno are all well-known JavaScript runtimes that were developed for building scalable and performant applications. Some years after Ryan Dahl developed Node.js, he co-created Deno to fix some of the challenges he identified in Node. As technology evolved over the years, developers discovered other challenges that neither Node nor Deno solved, such as performance, simplified JavaScript, and TypeScript development. While Deno and Bun offer better alternatives to Node.js, they haven’t been able to beat Node‘s massive adoption by big tech companies and the community of developers it has. Let's compare these three frameworks across various aspects. JavaScript runtime Node relies on the V8 engine and is the most mature and popular JavaScript runtime. Deno runs on the V8 JavaScript engine — exactly like Node, but with additional sandboxing security. Bun is another runtime that is available for Deno or Node — not as a runtime itself, but as an integration, depending on the project's requirements. Transpiler capabilities Deno supports TypeScript by default, which eliminates the need to add a separate transpiling tool. Node.js requires a separate transpiling tool such as Babel to transpile TypeScript or new JavaScript features. Similar to Deno, Bun has a default support for transpiling. Compatibility with ESM and CommonJS modules Deno supports ECMAScript modules and also uses CommonJS modules. Node.js was first developed for npm with the CommonJS build targets, but it now supports ESM as well. Bun also supports ESM and is compatible with CommonJS modules. Web API support The Deno runtime contains a standard library with inbuilt APIs for HTTP, file systems, and networking. Node.js has a larger ecosystem of third-party libraries and modules used for implementing different kinds of functionalities. Bun depends on the Deno or Node.js runtimes for web API support. Hot reloading functionality Deno is developed to support hot reloading by default. To enable hot reloading in Node, you'd have to install a third-party package like Nodemon . Bun works with hot reload capabilities from either Deno or Node.js. Comparison table Below is a table summarizing the aspects we discussed for Node.js, Bun, and Deno: Features Node.js Bun Deno JavaScript runtime V8 engine; most mature and popular Integration with Deno or Node.js V8 engine with additional sandboxing security Transpiler capabilities Requires separate tools like Babel Default support for transpiling Default support for TypeScript ESM and CommonJS compatibility Supports both (initially CommonJS) Supports both Supports both Web API support Large ecosystem of third-party libraries Depends on Deno or Node.js Standard library with inbuilt APIs Hot reloading functionality Requires third-party packages like Nodemon Works with capabilities from Deno or Node.js Supported by default Cross-platform functionality Supported by default Supported as of v1.1, although additional styling may be needed Supported by default Conclusion Throughout this tutorial, we’ve learned how to build a cross-platform application with Bun v1.1. We started by reviewing what Bun is and exploring the new features released in Bun v1.1. Then, we saw how to build a cross-platform to-do app using the Bun runtime, as well as how to integrate Bun with React Native. Lastly, we compared Bun with other JavaScript runtimes like Node.js and Deno. I hope you find this guide helpful. If you have any further questions or run into any issues, feel free to comment below. 200s only✓ Monitor failed and slow network requests in production Deploying a Node-based web app or website is the easy part. Making sure your Node instance continues to serve resources to your app is where things get tougher. If you’re interested in ensuring requests to the backend or third-party services are successful, try LogRocket . LogRocket is like a DVR for web and mobile apps, recording literally everything that happens while a user interacts with your app. Instead of guessing why problems happen, you can aggregate and report on problematic network requests to quickly understand the root cause. LogRocket instruments your app to record baseline performance timings such as page load time, time to first byte, slow network requests, and also logs Redux, NgRx, and Vuex actions/state. Start monitoring for free . Top comments (2) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Collapse Expand Fyodor Fyodor Fyodor Follow Why'd you (software engineers) have to go and make things (software development) so complicated... Location Backwoods Education MSc, Royal Holloway University of London Work Product Engineer Joined Feb 10, 2018 • Jul 16 '24 Dropdown menu Copy link Hide Nodemon is not necessary in Node anymore, watch mode is supported natively. The first row of the table looks weird in terms of the Bun info. Were other points of this article checked the same disparaging way or they can be trusted to some extent? Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Antonio Antonio Antonio Follow Currently building Litlyx, the simplest way to track website analytics without cookies. AI, FOSS & and Privacy-first. Find out more at Litlyx.com/philosophy. I build for many, not just one. Email Location Rome, Italy Pronouns He/Him Work CEO & Founder at Litlyx Joined May 30, 2024 • Jul 17 '24 Dropdown menu Copy link Hide I love bun! Great content! Like comment: Like comment: 1 like Like Comment button Reply Code of Conduct • Report abuse Are you sure you want to hide this comment? 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https://wiki.php.net/rfc/uniform_variable_syntax | PHP: rfc:uniform_variable_syntax Login Register You are here: start › rfc › uniform_variable_syntax rfc:uniform_variable_syntax PHP RFC: Uniform Variable Syntax Date: 2014-05-31 Author: Nikita Popov nikic@php.net Status: Implemented (in PHP 7) Discussion: http://markmail.org/message/mr4ihbubfbdxygci Introduction This RFC proposes the introduction of an internally consistent and complete variable syntax. To achieve this goal the semantics of some rarely used variable-variable constructions need to be changed. Examples of expressions that were previously invalid, but will be valid with the uniform variable syntax: // support missing combinations of operations $foo ( ) [ 'bar' ] ( ) [ $obj1 , $obj2 ] [ 0 ] -> prop getStr ( ) { 0 } // support nested :: $foo [ 'bar' ] :: $baz $foo :: $bar :: $baz $foo -> bar ( ) :: baz ( ) // support nested () foo ( ) ( ) $foo -> bar ( ) ( ) Foo :: bar ( ) ( ) $foo ( ) ( ) // support operations on arbitrary (...) expressions ( ... ) [ 'foo' ] ( ... ) -> foo ( ... ) -> foo ( ) ( ... ) :: $foo ( ... ) :: foo ( ) ( ... ) ( ) // two more practical examples for the last point ( function ( ) { ... } ) ( ) ( $obj -> closure ) ( ) // support all operations on dereferencable scalars (not very useful) "string" -> toLower ( ) [ $obj , 'method' ] ( ) 'Foo' :: $bar Example of expressions those meaning changes: // old meaning // new meaning $$foo [ 'bar' ] [ 'baz' ] $ { $foo [ 'bar' ] [ 'baz' ] } ( $$foo ) [ 'bar' ] [ 'baz' ] $foo -> $bar [ 'baz' ] $foo -> { $bar [ 'baz' ] } ( $foo -> $bar ) [ 'baz' ] $foo -> $bar [ 'baz' ] ( ) $foo -> { $bar [ 'baz' ] } ( ) ( $foo -> $bar ) [ 'baz' ] ( ) Foo :: $bar [ 'baz' ] ( ) Foo :: { $bar [ 'baz' ] } ( ) ( Foo :: $bar ) [ 'baz' ] ( ) Examples of statements which are no longer supported: global $$foo -> bar ; // instead use: global $ { $foo -> bar } ; Issues with the current syntax Root cause The root cause for most issues in PHP's current variable syntax are the semantics of the variable-variable syntax $$foo['bar'] . Namely this expression is interpreted as ${$foo['bar']} (lookup the variable with the name $foo['bar'] ) rather than ${$foo}['bar'] (take the 'bar' offset of the $$foo variable). Why this choice of semantics is problematic to our parser design is explained in the next section. Before getting to that, I will discuss why this choice is also bad from a language design perspective: Normally variable accesses are interpreted from left to right. $foo['bar']->baz will first fetch a variable named $foo , then will take the 'bar ' offset of the result and finally access the baz property. The $$foo['baz'] syntax goes against that basic principle. Rather than fetching $$foo and then taking its 'baz' offset, it will first fetch $foo , fetch its 'baz' offset and then look up a variable with the name of the result. This combination of an indirect reference and an offset is the only case where the interpretation is inverted. For example the very similar $$foo->bar will be interpreted as ${$foo}->bar and not as ${$foo->bar} . It follows normal left-to-right semantics. Similarly $$foo::$bar is also interpreted as ${$foo}::$bar and not as ${$foo::$bar} . To ensure maximum possible inconsistency there exists an exception to this rule. Namely global $$foo->bar will be interpreted as global ${$foo->bar} , even though this is not the case for normal variable accesses. This issue applies not only to simple indirect references, but also to indirected property and method names. For example Foo::$bar[1][2][3] is interpreted as an access to the static property Foo::$bar , followed by fetches of the 1, 2 and 3 offsets. On the other hand Foo::$bar[1][2][3]() (notice the parentheses at the end) has an entirely different interpretation: This does not call the function stored at Foo::$bar[1][2][3] . Instead it calls the static method of class Foo with name $bar[1][2][3] . The last issue implies that PHP's variable syntax is non-local. It is not possible to parse a PHP variable access with a fixed finite lookahead, without transplanting the generated syntax tree or instructions after the fact. Impact on parser definition In addition to the problems described above the semantics for indirect references also have far-reaching consequences on how the variable syntax is defined in our parser. In the following I will outline the kind of issues it causes, for readers not familiar with parser construction: The “standard” approach to defining a variable syntax for a LALR parser is to create a left-recursive variable rule, which could look roughly as follows (somewhat simplified): variable: T_VARIABLE /* $foo */ | variable '[' expr ']' /* variable['bar'] */ | variable '->' T_STRING /* variable->baz */ | variable '->' T_STRING '(' params ')' /* variable->oof() */ | variable '::' T_VARIABLE /* variable::$rab */ | ... ; This approach ensures that we can arbitrarily nest different access types (in the following called “dereferencing”). For example the above definition allows you to write $foo['bar']->baz->oof()::$rab . This expression is grouped from left-to-right, i.e. it is interpreted as (((($foo)['bar'])->baz)->oof())::$rab . What happens to this scheme if we add a (right-associative) $$foo['bar'] syntax? One might think that it could be defined as follows: reference_variable: T_VARIABLE | reference_variable '[' expr ']' ; variable: T_VARIABLE | '$' reference_variable | variable '[' expr ']' | variable '->' T_STRING | ... ; However, this is not possible because it makes the grammar ambiguous. When the parser encounters $$foo['bar'] it could either interpret it using the '$' reference_variable rule (i.e. ${$foo['bar']} semantics) or using the variable '[' expr ']' rule (i.e. ${$foo}['bar'] semantics). This kind of issue is called a “shift/reduce conflict”. How can this issue be resolved? By removing the variable '[' expr ']' rule. However, if this rule is removed, you can no longer write $foo->bar['baz'] etc either. As such offset access needs to be implemented anew for all other dereferencing types: You need to implement it for $foo->bar , for $foo->bar() and for $foo::$bar . Furthermore you need to ensure that you can continue to nest arbitrary types of dereferences after that. This is both extremely complicated and fragile. This is the reason why PHP only introduced the foo()['bar'] syntax in PHP 5.4 and even then the support is not perfect. Incomplete dereferencing support Because of the implementational hurdles described in the previous section, we do not support all combinations of dereferencing operations to an arbitrary depth. While PHP 5.4 fixed the most glaring issue (support for $foo->bar()['baz'] ), other problems still exist. Basically, there are two classes of issues. The first one is that we do not always properly support nesting of different dereferencing types. For example, while it is possible to write both $foo()['bar'] and $foo['bar']() , the combination $foo()['bar']() results in a parse error. Another example is that the constant dereferencing syntax implemented in PHP 5.5 allows you to write [$obj1, $obj2][0] , but [$obj1, $obj2][0]->prop is not possible. Yet another example is that the alternative array syntax $str{0} is not supported on function calls, i.e. getStr(){0} is not valid. The second class of issues is that some nesting types aren't supported altogether. For example :: only accepts simple reference variables on the left hand side. Writing something like $info['class']::${$info['property']} is not possible. Writing getFunction()() is not possible either. The (function() { ... })() pattern that is familiar from JavaScript is not allowed as well. Lack of support for dereferencing parenthesis-expressions also prevents you from properly disambiguating some expressions. For example, it is a common problem that $foo->bar() will always try to call the bar() method, rather than calling the closure stored in $foo->bar . However writing ($foo->bar)() is not possible and you need to use a temporary variable. Another example is the case of Foo::$bar[1][2][3]() from above (which is interpreted as Foo::{$bar[1][2][3]}() ). It is currently not possible to force the alternative behavior (Foo::$bar[1][2][3])() . Miscellaneous other issues Behavior in non-read context The new (new Foo)['bar'] and [...]['bar'] syntaxes introduced in PHP 5.4 and PHP 5.5 were implemented as “non-variable expressions”. This means that they are always compiled in “read context”, even when they are used in a different context. For example empty(['foo' => 42]['bar']) will generate an “Undefined index” notice, even though empty() usually suppresses such warnings. The reason behind this is that proper behavior in isset/empty requires compilation using BP_VAR_IS rather than BP_VAR_R . This also means that assignments to dereferences of parenthesis-expressions are never allowed, even when they would be technically possible. E.g. it's not possible to write (new Foo)['bar'] = 42 . Whether this would be particularly useful (the assignment would only be visible through offsetSet ) is another question, but it's not much different than writing something like foo()['bar'] = 42 , which is allowed. Superfluous CVs on static property access Upon encountering a static property access Foo::$bar PHP will currently emit a compiled variable (CV) for $bar , even though it is not necessary and never used. This is once again related to the way static member access needs to be implemented to support our weird indirect reference semantics. Proposal Formal definition A formal definition of the new variable syntax is provided in Bison syntax. This is a slightly simplified version of the grammer used in the actual implementation. Furthermore definitions for function_name , class_name , expr , function_call_parameter_list and array_pair_list have been omitted. variable: callable_variable | class_name_or_dereferencable T_PAAMAYIM_NEKUDOTAYIM simple_variable | dereferencable T_OBJECT_OPERATOR member_name ; callable_variable: simple_variable | dereferencable '[' dim_offset ']' | dereferencable '{' expr '}' | function_name function_call_parameter_list | dereferencable T_OBJECT_OPERATOR member_name function_call_parameter_list | class_name_or_dereferencable T_PAAMAYIM_NEKUDOTAYIM member_name function_call_parameter_list | callable_expr function_call_parameter_list ; simple_variable: T_VARIABLE | '$' '{' expr '}' | '$' simple_variable ; dereferencable: variable | '(' expr ')' | dereferencable_scalar ; dereferencable_scalar: T_ARRAY '(' array_pair_list ')' | '[' array_pair_list ']' | T_CONSTANT_ENCAPSED_STRING ; class_name_or_dereferencable: class_name | dereferencable ; member_name: T_STRING | '{' expr '}' | simple_variable ; dim_offset: /* empty */ | expr ; callable_expr: callable_variable | '(' expr ')' | dereferencable_scalar ; Semantic differences in existing syntax The main difference to the existing variable syntax, is that indirect variable, property and method references are now interpreted with left-to-right semantics. Examples: $$foo['bar']['baz'] interpreted as ($$foo)['bar']['baz'] $foo->$bar['baz'] interpreted as ($foo->$bar)['baz'] $foo->$bar['baz']() interpreted as ($foo->$bar)['baz']() Foo::$bar['baz']() interpreted as (Foo::$bar)['baz']() This change is backwards incompatible (with low practical impact), which is the reason why this RFC targets PHP 7. However it is always possible to recreate the old behavior by explicitly using braces: $ { $foo [ 'bar' ] [ 'baz' ] } Foo :: { $bar [ 'baz' ] } ( ) $foo -> { $bar [ 'baz' ] } ( ) This syntax will have guaranteed same behavior in both PHP 5 and PHP 7. Newly added and generalized syntax There are no longer any restrictions on nesting of dereferencing operations. In particular the examples $foo()['bar']() , [$obj1, $obj2][0]->prop and getStr(){0} from the previous sections are all supported now. Static property fetches and method calls can now be applied to any dereferencable expression. E.g. $foo['bar']::$baz , $foo::$bar::$baz and $foo->bar()::baz() are all valid now. The result of a call can now be directly called again, i.e. all of foo()() , $foo->bar()() , Foo::bar()() and $foo()() are valid now. All dereferencing operations can now be applied to arbitrary parenthesis-expressions. I.e. all of (...)['foo'] , (...)->foo , (...)->foo() , (...)::$foo , (...)::foo() and (...)() are supported now. In particular this also includes (function() { ... })() . All dereferencing operations can now be applied to dereferencable scalars (array and string literals as of PHP 5.5). E.g. it is possible to write "string"->toLower() , [$obj, 'method']() and 'Foo'::$bar . Note that these don't necessarily make sense by themselves, but the syntax is supported nonetheless. Extensions can then use it to implement the actual behavior for something like "string"->toLower() . Global keyword takes only simple variables Previously the global keyword accepted variables of the form global $$foo->bar , where an arbitrary variable could follow after the $ character. This is inconsistent with the usual variable syntax. The global keyword now only accepts simple variables. This means that you need to write global ${$foo->bar} instead. global $$foo->bar will result in a parse error. Behavior in write context Expressions of type (...)[42] and [...][42] (and other cases where some dereferencing operation is applied to a non-variable) are now parsed as a variable (rather than expr_without_variable , as it was previously). This means that the expression will behave correctly in empty() . E.g. empty([]['a']) will no longer throw an undefined offset notice. Furthermore it is now possible to assign to expressions of this kind, for example (new Foo)->bar = 'baz' is now possible (thought doesn't make much sense unless Foo implements __set ). However assignment is not allowed if the left hand expression yields an IS_CONST or IS_TMP_VAR operand. For example the expression 'foo'[0] = 'b' will result in a “Cannot use temporary expression in write context” compile error. For BP_VAR_FUNC_ARG fetches a runtime fatal error is thrown instead. Class name variable for new expression It has always been possible to create classes using a dynamically specified class name by writing new $className() or similar. However the supported variables are more limited in this case: They may not include calls anywhere, as this would cause ambiguity with the constructor parameter list. New variables are now defined as follows: new_variable: simple_variable | new_variable '[' dim_offset ']' | new_variable '{' expr '}' | new_variable T_OBJECT_OPERATOR member_name | class_name T_PAAMAYIM_NEKUDOTAYIM simple_variable | new_variable T_PAAMAYIM_NEKUDOTAYIM simple_variable ; This matches the previously allowed variable expressions, with the minor extension of allowing chaining of :: . For example new $foo['bar']::$baz would now be possible. Backward Incompatible Changes The changes described in Semantic differences in existing syntax and Global keyword takes only simple variables are backwards compatibility breaks. The former is a change in the behavior of currently existing syntax. Examples: // old meaning // new meaning $$foo [ 'bar' ] [ 'baz' ] $ { $foo [ 'bar' ] [ 'baz' ] } ( $$foo ) [ 'bar' ] [ 'baz' ] $foo -> $bar [ 'baz' ] $foo -> { $bar [ 'baz' ] } ( $foo -> $bar ) [ 'baz' ] $foo -> $bar [ 'baz' ] ( ) $foo -> { $bar [ 'baz' ] } ( ) ( $foo -> $bar ) [ 'baz' ] ( ) Foo :: $bar [ 'baz' ] ( ) Foo :: { $bar [ 'baz' ] } ( ) ( Foo :: $bar ) [ 'baz' ] ( ) An analysis of the Zend Framework and Symfony projects (including standard dependencies) showed that only a single occurrence of $loader[0]::$loader[1]($className) in the Doctrine class loader will be affected by this change. This occurrence must be replaced with $loader[0]::{$loader[1]}($className) to achieve compatibility with both PHP 5 and PHP 7. The latter change turns currently valid syntax into a parse error. Expressions like global $$foo->bar are no longer valid and global ${$foo->bar} must be used instead. As these changes only apply to some very rarely used syntax, the breakage seems acceptable for PHP 7. Open issues The current patch introduces a new “write context” issue. Namely ($foo)['bar'] = 'baz' will not behave this same was as $foo['bar'] = 'baz' . In the former case an undefined variable notice will be thrown if $foo does not exist, whereas the latter does not throw a notice. The reason for this is that (...) is always interpreted as an expression, and not a variable. This means that the part in parentheses will always be compiled in “read context”, even though write context is desired. This issue already exists currently, in a different context: The expression byRef(func()) will throw a strict standards notice, if byRef() accepts the first argument by-reference, but func() does not return by reference. On the other hand byRef((func())) will throw no such notice, because (func()) is recognized as an expression instead of a variable. Patch An implementation of this proposal against the phpng branch is available at https://github.com/php/php-src/pull/686 . The main changes are limited to the language parser and compiler. Furthermore some opcode handlers had to be modified to support CONST and TMP operands. Vote As this is a language change, a 2/3 majority is required for acceptance. The vote started on 2014-07-07 and ended on 2014-07-14. Implement Uniform Variable Syntax in PHP 6? Real name Yes No aharvey ajf ashnazg bwoebi datibbaw davey derick dm dragoonis guilhermeblanco gwynne indeyets hywan jpauli jwage kalle kassner kriscraig laruence levim lstrojny mbeccati mike nikic pajoye philstu ralphschindler rdlowrey stas tyrael yohgaki Final result: 30 1 This poll has been closed. rfc/uniform_variable_syntax.txt · Last modified: 2025/04/03 13:08 by 127.0.0.1 Page Tools Show pagesource Old revisions Backlinks Back to top Table of Contents PHP RFC: Uniform Variable Syntax Introduction Issues with the current syntax Root cause Impact on parser definition Incomplete dereferencing support Miscellaneous other issues Proposal Formal definition Semantic differences in existing syntax Newly added and generalized syntax Global keyword takes only simple variables Behavior in write context Class name variable for new expression Backward Incompatible Changes Open issues Patch Vote Copyright © 2001-2026 The PHP Group Other PHP.net sites Privacy policy | 2026-01-13T08:49:43 |
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https://opensource.org/ai#content | Open Source AI – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Source AI Open Source AI OSAID 1.0 Process Timeline Open Weights FAQ Endorsements Open Main Menu THE OPEN SOURCE AI DEFINITION 1.0 We have released the first stable version of the Definition. Read version 1.0 What’s Open Source AI? Following the same idea behind Open Source Software, an Open Source AI is a system made available under terms that grant users the freedoms to: Use Study Modify Share Use the system for any purpose and without having to ask for permission. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Study how the system works and understand how its results were created. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Modify the system for any purpose, including to change its output. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Share the system for others to use with or without modifications, for any purpose. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Benefits of Open Source AI Transparency & Safety Open Source AI provides information essential for auditing systems and to mitigate bias, ensures accountability and transparency of data sources, and accelerates AI safety research. Competition & Polyculture Open Source AI makes more models available, spurs innovation and quality due to increased competition and tackles AI monoculture by providing more stakeholders access to foundational technology. Diverse Applications Open Source AI gives developers access to resources crucial for developing context-specific, localized applications that are representative of cultural and linguistic diversity and allow for model aligned with different value systems. Read the white paper The Open Source Initiative and Open Future have taken a significant step toward addressing this challenge by releasing this white paper. The document is the culmination of a global co-design process, enriched by insights from a vibrant two-day workshop held in Paris in October 2024. Read the white paper Why Open Source AI needs a definition? Open Source Frontier The traditional view of Open Source code and licenses when applied to AI components are not sufficient to guarantee the freedoms to use, study, share and modify the systems. Informing Regulators Government regulations have begun in Europe, the United States, and elsewhere. Communities need a common understanding to educate policy makers. Combat Openwashing Companies are calling AI systems “Open Source” even though their licenses contain restrictions that go against the accepted principles and freedoms of Open Source. Who’s behind the Open Source AIDefinition View all Endorsers Overall process 0 Supporting Organizations 0 Supporting Individuals 0 Co-designers 0 Systems reviewed Representation in the co-design process 0 Nationalities 0 People Of Color 0 Global South 0 Femme, Trans, & Nonbinary Co-design 2023 – 2024 In 2023, we started the co-design process hosting several online and in-person activities around the world. Research 2022 – 2023 Alongside AI experts from various fields we produced a podcast , panels and webinars . Endorsements 2024 – 2025 Late 2024 into 2025, the OSI is gathering endorsements from various individuals and organizations, including Mozilla, Suse, Eleuther AI, Ai2, Eclipse Foundation, and the OpenInfra Foundation, among many others. Which AI systems comply with the OSAID 1.0? As part of our validation and testing of the OSAID, the volunteers checked whether the Definition could be used to evaluate if AI systems provided the freedoms expected. The list of models that passed the Validation phase are: Pythia (Eleuther AI), OLMo (AI2), Amber and CrystalCoder (LLM360), and T5 (Google). There are a couple of others that were analyzed and would probably pass if they changed their licenses/legal terms: BLOOM (BigScience), Starcoder2 (BigCode), Falcon (TII). Those that have been analyzed and don’t pass because they lack required components and/or their legal agreements are incompatible with the Open Source principles: Llama2 (Meta), Grok (X/Twitter), Phi-2 (Microsoft), Mixtral (Mistral). These results should be seen as part of the definitional process, a learning moment; they’re not certifications of any kind. OSI will continue to validate only legal documents, and will not validate or review individual AI systems, just as it does not validate or review software projects. If you are wondering about Open Weights models , please refer to our dedicated page . The OSAID co-design process was open to everyone interested in collaborating . How to participate There are many ways to get involved: Endorse the Open Source AI Definition : have your organization appended to the list of supporters of version 1.0. Join the forum : support and comment on the releases, record your approval or concerns to new and existing threads. 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Got it Close Show all 15 badges More info about @dhanushnehru Organizations AWS Community Builders GitHub Repositories 100DaysOfCode A repository to track my journey on 100DaysOfCode Challenge JavaScript • 5 stars Skills/Languages Java Javascript Python Html Css MongoDB Currently learning Cybersecurity, PostgreSQL Available for Collabs, Interview, Tech Talks, Tech discussions Post 93 posts published Comment 106 comments written Tag 13 tags followed Pin Pinned Rarely known Javascript Tips Dhanush N Dhanush N Dhanush N Follow Oct 3 '20 Rarely known Javascript Tips # showdev # javascript # node # tutorial 39 reactions Comments 4 comments 1 min read Rarely known CSS Tips Dhanush N Dhanush N Dhanush N Follow Sep 26 '20 Rarely known CSS Tips # css # html # webdev # design 101 reactions Comments 9 comments 2 min read Dev Retro 2025: Journey in review Dhanush N Dhanush N Dhanush N Follow Jan 10 Dev Retro 2025: Journey in review # devchallenge # newyearchallenge # career # beginners 5 reactions Comments Add Comment 1 min read Want to connect with Dhanush N? 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Sign in Building Markdown Scribe Dhanush N Dhanush N Dhanush N Follow Dec 27 '25 Building Markdown Scribe # rust # beginners # sideprojects # programming 5 reactions Comments Add Comment 1 min read Everything You Need to Know About AI — In One Repository Dhanush N Dhanush N Dhanush N Follow Nov 2 '25 Everything You Need to Know About AI — In One Repository # ai # resources # github # beginners 7 reactions Comments 1 comment 2 min read My Tech Journey: From Developer to DevOps and Cybersecurity Enthusiast Dhanush N Dhanush N Dhanush N Follow Sep 22 '25 My Tech Journey: From Developer to DevOps and Cybersecurity Enthusiast # webdev # programming # devops # cybersecurity 5 reactions Comments Add Comment 1 min read This Node.js Package Changes Everything About Running Shell Commands Dhanush N Dhanush N Dhanush N Follow Jul 25 '25 This Node.js Package Changes Everything About Running Shell Commands # opensource # npm # node # javascript 13 reactions Comments 2 comments 2 min read JavaScript vs Python: What to Choose? Dhanush N Dhanush N Dhanush N Follow Apr 19 '25 JavaScript vs Python: What to Choose? # showdev # javascript # python # programming 10 reactions Comments Add Comment 1 min read Hello, World! in 50 Programming Languages — How Many Can You Recognize? Dhanush N Dhanush N Dhanush N Follow Feb 22 '25 Hello, World! in 50 Programming Languages — How Many Can You Recognize? # discuss # programming # beginners # learning 14 reactions Comments 9 comments 6 min read Dev Retro 2024: Journey in review Dhanush N Dhanush N Dhanush N Follow Jan 4 '25 Dev Retro 2024: Journey in review # devchallenge # newyearchallenge # career # devresolutions2024 28 reactions Comments 2 comments 3 min read Must Known Top Hacker Tools for 2025 | Kali Linux | Cybersecurity Dhanush N Dhanush N Dhanush N Follow Dec 30 '24 Must Known Top Hacker Tools for 2025 | Kali Linux | Cybersecurity # showdev # security # cybersecurity # linux 18 reactions Comments Add Comment 1 min read Should you STOP using VPN for Privacy ? Dhanush N Dhanush N Dhanush N Follow Dec 15 '24 Should you STOP using VPN for Privacy ? # showdev # security # cybersecurity # discuss 17 reactions Comments Add Comment 1 min read Why you should not mistype a website name ? Dhanush N Dhanush N Dhanush N Follow Nov 6 '24 Why you should not mistype a website name ? # webdev # security # website # cybersecurity 12 reactions Comments Add Comment 1 min read Why Everyone Should Contribute to Open Source ( Even If You Can’t Code! ) Dhanush N Dhanush N Dhanush N Follow Oct 17 '24 Why Everyone Should Contribute to Open Source ( Even If You Can’t Code! ) # opensource # webdev # beginners # programming 11 reactions Comments Add Comment 1 min read How to contribute to opensource that can change your life ! Dhanush N Dhanush N Dhanush N Follow Oct 14 '24 How to contribute to opensource that can change your life ! # opensource # webdev # beginners # tutorial 7 reactions Comments Add Comment 1 min read How Much HTML, CSS & JavaScript You Need to Know to Get Hired as a Developer Dhanush N Dhanush N Dhanush N Follow Oct 12 '24 How Much HTML, CSS & JavaScript You Need to Know to Get Hired as a Developer # showdev # webdev # beginners # javascript 11 reactions Comments Add Comment 1 min read Kickstart Hacktoberfest with These Exciting Open Source Projects to Contribute To! 🚀 Dhanush N Dhanush N Dhanush N Follow Sep 30 '24 Kickstart Hacktoberfest with These Exciting Open Source Projects to Contribute To! 🚀 # hacktoberfest # hacktoberfest24 # webdev # beginners 17 reactions Comments 1 comment 2 min read WebRTC - One Byte Explainer Dhanush N Dhanush N Dhanush N Follow Sep 29 '24 WebRTC - One Byte Explainer # devchallenge # gamechallenge # gamedev # webdev 30 reactions Comments 2 comments 2 min read Alien Defender Game Dhanush N Dhanush N Dhanush N Follow Sep 29 '24 Alien Defender Game # devchallenge # gamechallenge # gamedev # webdev 20 reactions Comments 6 comments 3 min read Insights and Tips from a Lead Engineer Dhanush N Dhanush N Dhanush N Follow Aug 26 '24 Insights and Tips from a Lead Engineer # programming # career # softwaredevelopment # beginners 17 reactions Comments 1 comment 4 min read Introducing the TryHackMe Badge Action Workflow: Automate Your Security Achievements Display Dhanush N Dhanush N Dhanush N Follow Aug 10 '24 Introducing the TryHackMe Badge Action Workflow: Automate Your Security Achievements Display # showdev # security # github # githubactions 10 reactions Comments Add Comment 3 min read Super useful console.log tricks Dhanush N Dhanush N Dhanush N Follow Jun 13 '24 Super useful console.log tricks # productivity # tooling # javascript # beginners 12 reactions Comments 2 comments 3 min read Announcing runtime-environment: A Rust Crate for Detecting Operating Systems at Runtime Dhanush N Dhanush N Dhanush N Follow Jun 7 '24 Announcing runtime-environment: A Rust Crate for Detecting Operating Systems at Runtime # showdev # rust # codenewbie # beginners 13 reactions Comments 1 comment 1 min read Introducing sudoku-puzzle: Simplify your Sudoku experience with this new NPM Package Dhanush N Dhanush N Dhanush N Follow Jun 5 '24 Introducing sudoku-puzzle: Simplify your Sudoku experience with this new NPM Package # showdev # npm # opensource # codenewbie 12 reactions Comments Add Comment 2 min read I created an Open Source Custom Code Editor Dhanush N Dhanush N Dhanush N Follow May 18 '24 I created an Open Source Custom Code Editor # showdev # opensource # github # codenewbie 19 reactions Comments 7 comments 2 min read Most Important Linux Commands You Need To Know !!! Dhanush N Dhanush N Dhanush N Follow May 13 '24 Most Important Linux Commands You Need To Know !!! # linux # beginners # tutorial # productivity 16 reactions Comments 4 comments 2 min read What happens when you type a URL into your browser? Dhanush N Dhanush N Dhanush N Follow Apr 15 '24 What happens when you type a URL into your browser? # showdev # webdev # beginners # programming 17 reactions Comments Add Comment 2 min read Devil or Devin : The World’s First AI Software Engineer Dhanush N Dhanush N Dhanush N Follow Mar 25 '24 Devil or Devin : The World’s First AI Software Engineer # ai # programming # career # job 12 reactions Comments Add Comment 4 min read Dev Retro 2023: Journey in review Dhanush N Dhanush N Dhanush N Follow Jan 17 '24 Dev Retro 2023: Journey in review # career # programming # productivity # devimpact2023 11 reactions Comments 4 comments 3 min read A Year of Consistent Leetcode 🏆 Dhanush N Dhanush N Dhanush N Follow Jan 16 '24 A Year of Consistent Leetcode 🏆 # leetcode # programming # productivity # interview 18 reactions Comments Add Comment 4 min read How to write code fast and efficiently ❓ Dhanush N Dhanush N Dhanush N Follow Jan 7 '24 How to write code fast and efficiently ❓ # coding # tips # productivity # beginners 24 reactions Comments Add Comment 1 min read 10 Programming Mistakes to Avoid ❎ 👨🏻💻 Dhanush N Dhanush N Dhanush N Follow Dec 12 '23 10 Programming Mistakes to Avoid ❎ 👨🏻💻 # programming # development # beginners # coding 18 reactions Comments 2 comments 3 min read Significant Guidelines All Programmers Should Adhere To Dhanush N Dhanush N Dhanush N Follow Dec 4 '23 Significant Guidelines All Programmers Should Adhere To # programming # beginners # productivity # tutorial 14 reactions Comments Add Comment 3 min read Hacktoberfest: A Journey of Continuous Learning Dhanush N Dhanush N Dhanush N Follow Oct 30 '23 Hacktoberfest: A Journey of Continuous Learning # hack23contributor 8 reactions Comments Add Comment 2 min read Javascript Deobfuscation Dhanush N Dhanush N Dhanush N Follow Oct 6 '23 Javascript Deobfuscation # javascript # security # hacking # coding 9 reactions Comments Add Comment 3 min read Celebrate Hacktoberfest with these Exciting Open Source Repositories to contribute to Hacktoberfest: Maintainer Spotlight Dhanush N Dhanush N Dhanush N Follow Oct 4 '23 Celebrate Hacktoberfest with these Exciting Open Source Repositories to contribute to # opensource # resources # hacktoberfest # hacktoberfest23 7 reactions Comments 2 comments 2 min read A Comprehensive Guide to JSON Web Tokens Dhanush N Dhanush N Dhanush N Follow Sep 29 '23 A Comprehensive Guide to JSON Web Tokens # javascript # devops # programming # tutorial 5 reactions Comments Add Comment 3 min read Potential of Data Analytics : AWS Glue and AWS Athena Dhanush N Dhanush N Dhanush N Follow Jul 23 '23 Potential of Data Analytics : AWS Glue and AWS Athena # aws # cloud # microservices # devops 5 reactions Comments Add Comment 3 min read 10 Common Mistakes to Avoid While Writing JavaScript Code Dhanush N Dhanush N Dhanush N Follow Mar 18 '23 10 Common Mistakes to Avoid While Writing JavaScript Code # javascript # beginners # webdev # programming 24 reactions Comments 4 comments 4 min read Debugging the bug free code Dhanush N Dhanush N Dhanush N Follow Feb 25 '23 Debugging the bug free code # watercooler 7 reactions Comments 2 comments 5 min read Dev Retro 2022: Journey in review Dhanush N Dhanush N Dhanush N Follow Jan 3 '23 Dev Retro 2022: Journey in review # postgres # sql # data # learning 5 reactions Comments Add Comment 5 min read Handling negative or no response in AWS EventBridge Dhanush N Dhanush N Dhanush N Follow Dec 5 '22 Handling negative or no response in AWS EventBridge 6 reactions Comments 1 comment 1 min read Pro Googling Tips Dhanush N Dhanush N Dhanush N Follow Nov 27 '22 Pro Googling Tips # codenewbie # tips # tricks # productivity 5 reactions Comments Add Comment 2 min read Difference between exports and module.exports Dhanush N Dhanush N Dhanush N Follow Nov 13 '22 Difference between exports and module.exports # javascript # node # angular # codenewbie 12 reactions Comments Add Comment 2 min read List of repositories to contribute to Hacktoberfest Dhanush N Dhanush N Dhanush N Follow Oct 3 '22 List of repositories to contribute to Hacktoberfest # hacktoberfest # opensource # contributorswanted # github 7 reactions Comments Add Comment 2 min read Build a breakout game Dhanush N Dhanush N Dhanush N Follow Sep 24 '22 Build a breakout game # gamedev # beginners # tutorial # webdev 5 reactions Comments Add Comment 4 min read ADOBE acquires FIGMA !!! Dhanush N Dhanush N Dhanush N Follow Sep 18 '22 ADOBE acquires FIGMA !!! # news # webdev # design 7 reactions Comments Add Comment 2 min read Whatsapp Portfolio Website Dhanush N Dhanush N Dhanush N Follow Sep 17 '22 Whatsapp Portfolio Website # showdev # webdev # javascript # programming 52 reactions Comments 14 comments 2 min read Javascript Console Methods Dhanush N Dhanush N Dhanush N Follow Sep 12 '22 Javascript Console Methods # javascript # webdev # programming # beginners 6 reactions Comments Add Comment 2 min read Looping through objects in javascript Dhanush N Dhanush N Dhanush N Follow Sep 8 '22 Looping through objects in javascript # javascript # react # node # angular 27 reactions Comments 4 comments 2 min read Multiprocessing in NodeJs Dhanush N Dhanush N Dhanush N Follow Aug 28 '22 Multiprocessing in NodeJs # node # javascript # webdev # tutorial 25 reactions Comments Add Comment 2 min read Url in Javascript Dhanush N Dhanush N Dhanush N Follow Jul 25 '22 Url in Javascript # javascript # programming # webdev # tutorial 7 reactions Comments Add Comment 1 min read Handling different node versions in Windows Dhanush N Dhanush N Dhanush N Follow Jul 19 '22 Handling different node versions in Windows # javascript # typescript # webdev # node 10 reactions Comments 2 comments 1 min read Execute inside a docker container Dhanush N Dhanush N Dhanush N Follow Jun 11 '22 Execute inside a docker container # discuss # docker # commands # shortcutkeys 8 reactions Comments 1 comment 1 min read Do you monetize by writing blogs ? Dhanush N Dhanush N Dhanush N Follow May 1 '22 Do you monetize by writing blogs ? # discuss # programming # career 21 reactions Comments 15 comments 1 min read Javascript ML Libraries Dhanush N Dhanush N Dhanush N Follow Apr 17 '22 Javascript ML Libraries # javascript # machinelearning # opensource # programming 17 reactions Comments Add Comment 3 min read 50 Github Repositories for a developer Dhanush N Dhanush N Dhanush N Follow Apr 3 '22 50 Github Repositories for a developer # github # programming # webdev # beginners 547 reactions Comments 15 comments 6 min read Learn coding by playing games 🎮 Dhanush N Dhanush N Dhanush N Follow Mar 26 '22 Learn coding by playing games 🎮 # codenewbie # beginners # tutorial # javascript 30 reactions Comments 1 comment 2 min read 13 Steps for building a website Dhanush N Dhanush N Dhanush N Follow Mar 17 '22 13 Steps for building a website # webdev # programming # tutorial # startup 70 reactions Comments 12 comments 5 min read ES2022 Features Dhanush N Dhanush N Dhanush N Follow Mar 6 '22 ES2022 Features # news # javascript # webdev # node 5 reactions Comments Add Comment 1 min read Google Ranking Algorithm 🤔 Dhanush N Dhanush N Dhanush N Follow Feb 20 '22 Google Ranking Algorithm 🤔 # google # algorithms # tutorial # datascience 6 reactions Comments 2 comments 1 min read Npm packages hacking Dhanush N Dhanush N Dhanush N Follow Feb 6 '22 Npm packages hacking # javascript # npm # node # hacking 6 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 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://docs.devcycle.com/sdk/server-side-sdks/node/node-openfeature | Node.js OpenFeature Provider | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up SDK Overview SDK Lifecycle SDK Features Client-side SDKS Server-side SDKS Node.js SDK Installation Getting Started Usage OpenFeature Typescript Bootstrapping / SSR Example App NestJS SDK PHP SDK Go SDK Ruby SDK Python SDK Java SDK .NET SDK SDK Proxy Server-side SDKS Node.js SDK OpenFeature On this page OpenFeature Provider AI-Powered Install MCP Install Follow the MCP Getting Started guide to quickly set up the DevCycle MCP server and connect your AI tool. Run this prompt: "Install DevCycle into this app" 📦 Install in Cursor 📦 Install in VS Code claude mcp add --transport http devcycle https://mcp.devcycle.com/mcp AI Prompt Copy Prompt OpenFeature is an open standard that provides a vendor-agnostic, community-driven API for feature flagging that works with DevCycle. DevCycle provides a NodeJS implementation of the OpenFeature Provider interface directly from the SDK using the DevCycleProvider class. Usage Installation Install the DevCycle NodeJS Server SDK which includes the OpenFeature Server SDK as a dependency NPM npm install --save @devcycle/nodejs-server-sdk Yarn yarn add @devcycle/nodejs-server-sdk Getting Started Create the DevCycleProvider and set it as the provider for OpenFeature: import { OpenFeature , Client } from '@openfeature/server-sdk' import { DevCycleProvider } from '@devcycle/nodejs-server-sdk' const { DEVCYCLE_SERVER_SDK_KEY } = process . env ... // Create the DevCycleProvider const devcycleProvider = new DevCycleProvider ( DEVCYCLE_SERVER_SDK_KEY ) // Set the provider for OpenFeature await OpenFeature . setProviderAndWait ( devcycleProvider ) // Create the OpenFeature client openFeatureClient = OpenFeature . getClient ( ) Evaluate a Variable Use a Variable value by creating the EvaluationContext, then passing the Variable key, default value, and EvaluationContext to one of the OpenFeature flag evaluation methods. // Set the context for the OpenFeature client, you can use 'targetingKey' or 'user_id' const context = { targetingKey : 'node_sdk_test' } // Retrieve a boolean flag from the OpenFeature client const boolFlag = await openFeatureClient . getBooleanValue ( 'boolean-flag' , false , context , ) Tracking Events You can use the OpenFeature track method to track events which will be sent to DevCycle as custom events. Calling track will queue the event, which will be sent in batches to the DevCycle servers. const context = { targetingKey : 'node_sdk_test' } openFeatureClient . track ( 'custom-event' , context , { target : 'event-target' , value : 100 , metaDataField : 'value' , } ) To track custom events with OpenFeature you are required to set the first argument as the event name, and pass the EvaluationContext as the second argument. The event name will be used as the event's type in DevCycle, and you can optionally set a value / target / date as defined in the DevCycleEvent Typescript Schema . Any additional properties will be added to the event as metaData fields. Passing DevCycleOptions to the DevCycleProvider Ensure that you pass any custom DevCycleOptions set on the DevCycleClient instance to the DevCycleProvider constructor const options = { logger : dvcDefaultLogger ( { level : 'debug' } ) } const devcycleProvider = new DevCycleProvider ( DEVCYCLE_SERVER_SDK_KEY , options ) await OpenFeature . setProviderAndWait ( devcycleProvider ) Accessing the DevCycleClient If you need to access the underlying DevCycleClient from the provider, it is exposed using provider.devcycleClient : const devcycleProvider = new DevCycleProvider ( DEVCYCLE_SERVER_SDK_KEY ) await OpenFeature . setProviderAndWait ( devcycleProvider ) ... const allFeatures = devcycleProvider . devcycleClient . allFeatures ( dvcUser ) Required TargetingKey For DevCycle SDK to work we require either a targetingKey or user_id to be set on the OpenFeature context. This is used to identify the user as the user_id for a DevCycleUser in DevCycle. Context properties to DevCycleUser The provider will automatically translate known DevCycleUser properties from the OpenFeature context to the DevCycleUser object. DevCycleUser TypeScript Interface For example all these properties will be set on the DevCycleUser : openFeatureClient . setContext ( { user_id : 'user_id' , email : ' [email protected] ' , name : 'name' , language : 'en' , country : 'CA' , appVersion : '1.0.11' , appBuild : 1000 , customData : { custom : 'data' } , privateCustomData : { private : 'data' } , } ) Context properties that are not known DevCycleUser properties will be automatically added to the customData property of the DevCycleUser . Context Limitations DevCycle only supports flat JSON Object properties used in the Context. Non-flat properties will be ignored. For example obj will be ignored: openFeatureClient . setContext ( { user_id : 'user_id' , obj : { key : 'value' } , } ) JSON Flag Limitations The OpenFeature spec for JSON flags allows for any type of valid JSON value to be set as the flag value. For example the following are all valid default value types to use with OpenFeature: // Invalid JSON values for the DevCycle SDK, will return defaults openFeatureClient . getObjectValue ( 'json-flag' , [ 'arry' ] ) openFeatureClient . getObjectValue ( 'json-flag' , 610 ) openFeatureClient . getObjectValue ( 'json-flag' , false ) openFeatureClient . getObjectValue ( 'json-flag' , 'string' ) openFeatureClient . getObjectValue ( 'json-flag' , null ) However, these are not valid types for the DevCycle SDK, the DevCycle SDK only supports JSON Objects: // Valid JSON Object as the default value, will be evaluated by the DevCycle SDK openFeatureClient . getObjectValue ( 'json-flag' , { default : 'value' } ) Edit this page Last updated on Jan 9, 2026 Previous Usage Next Typescript AI-Powered Install Usage Installation Getting Started Evaluate a Variable Tracking Events Passing DevCycleOptions to the DevCycleProvider Accessing the DevCycleClient Required TargetingKey Context properties to DevCycleUser Context Limitations JSON Flag Limitations DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. 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https://opensource.org/ai#modify | Open Source AI – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Source AI Open Source AI OSAID 1.0 Process Timeline Open Weights FAQ Endorsements Open Main Menu THE OPEN SOURCE AI DEFINITION 1.0 We have released the first stable version of the Definition. Read version 1.0 What’s Open Source AI? Following the same idea behind Open Source Software, an Open Source AI is a system made available under terms that grant users the freedoms to: Use Study Modify Share Use the system for any purpose and without having to ask for permission. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Study how the system works and understand how its results were created. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Modify the system for any purpose, including to change its output. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Share the system for others to use with or without modifications, for any purpose. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Benefits of Open Source AI Transparency & Safety Open Source AI provides information essential for auditing systems and to mitigate bias, ensures accountability and transparency of data sources, and accelerates AI safety research. Competition & Polyculture Open Source AI makes more models available, spurs innovation and quality due to increased competition and tackles AI monoculture by providing more stakeholders access to foundational technology. Diverse Applications Open Source AI gives developers access to resources crucial for developing context-specific, localized applications that are representative of cultural and linguistic diversity and allow for model aligned with different value systems. Read the white paper The Open Source Initiative and Open Future have taken a significant step toward addressing this challenge by releasing this white paper. The document is the culmination of a global co-design process, enriched by insights from a vibrant two-day workshop held in Paris in October 2024. Read the white paper Why Open Source AI needs a definition? Open Source Frontier The traditional view of Open Source code and licenses when applied to AI components are not sufficient to guarantee the freedoms to use, study, share and modify the systems. Informing Regulators Government regulations have begun in Europe, the United States, and elsewhere. Communities need a common understanding to educate policy makers. Combat Openwashing Companies are calling AI systems “Open Source” even though their licenses contain restrictions that go against the accepted principles and freedoms of Open Source. Who’s behind the Open Source AIDefinition View all Endorsers Overall process 0 Supporting Organizations 0 Supporting Individuals 0 Co-designers 0 Systems reviewed Representation in the co-design process 0 Nationalities 0 People Of Color 0 Global South 0 Femme, Trans, & Nonbinary Co-design 2023 – 2024 In 2023, we started the co-design process hosting several online and in-person activities around the world. Research 2022 – 2023 Alongside AI experts from various fields we produced a podcast , panels and webinars . Endorsements 2024 – 2025 Late 2024 into 2025, the OSI is gathering endorsements from various individuals and organizations, including Mozilla, Suse, Eleuther AI, Ai2, Eclipse Foundation, and the OpenInfra Foundation, among many others. Which AI systems comply with the OSAID 1.0? As part of our validation and testing of the OSAID, the volunteers checked whether the Definition could be used to evaluate if AI systems provided the freedoms expected. The list of models that passed the Validation phase are: Pythia (Eleuther AI), OLMo (AI2), Amber and CrystalCoder (LLM360), and T5 (Google). There are a couple of others that were analyzed and would probably pass if they changed their licenses/legal terms: BLOOM (BigScience), Starcoder2 (BigCode), Falcon (TII). Those that have been analyzed and don’t pass because they lack required components and/or their legal agreements are incompatible with the Open Source principles: Llama2 (Meta), Grok (X/Twitter), Phi-2 (Microsoft), Mixtral (Mistral). These results should be seen as part of the definitional process, a learning moment; they’re not certifications of any kind. OSI will continue to validate only legal documents, and will not validate or review individual AI systems, just as it does not validate or review software projects. If you are wondering about Open Weights models , please refer to our dedicated page . The OSAID co-design process was open to everyone interested in collaborating . How to participate There are many ways to get involved: Endorse the Open Source AI Definition : have your organization appended to the list of supporters of version 1.0. Join the forum : support and comment on the releases, record your approval or concerns to new and existing threads. Subscribe to our newsletter and read our blog to be kept up-to-date. Watch the town hall recordings to learn more about the process. Join the workshops and scheduled conferences : meet the OSI and other participants at in-person events around the world. Open Source AI Definition Governance Governance for the Open Source AI Definition is provided by the OSI Board of Directors . The OSI board members have expertise in business, legal, and open source software development, as well as experience across a range of commercial, public sector, and non-profit organizations. Formal progress reports including achievements, budget updates, and next steps are provided monthly by the Program Lead for advice and guidance as part of regular Board business. Additionally, informal updates on the outcomes of key meetings and milestones are provided via email to the Board as required. Supported by OSI’s efforts wouldn’t be possible without the support of our sponsors and thousands of individual members. Become a sponsor or join us today! Get involved Mastodon Twitter LinkedIn Reddit About About Our team Board of directors Sponsors Programs Blog Press mentions Trademark Bylaws Licenses Open Source Definition Licenses License Review Process Open Standards Requirement for Software Open Source AI Open Source AI OSAI Definition Process Timeline Open Weights FAQ Checklist Forum Community Become an Individual Member Become an OSI Affiliate Affiliate Organizations Maintainers Events Forum OpenSource.net The content on this website, of which Opensource.org is the author, is licensed under a Creative Commons Attribution 4.0 International License . Opensource.org is not the author of any of the licenses reproduced on this site. Questions about the copyright in a license should be directed to the license steward. Read our Privacy Policy Proudly powered by WordPress. Hosted by Pressable. 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https://www.fine.dev/blog/ai-coding-guide#practical-tips | 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 | 2026-01-13T08:49:44 |
https://dev.to/savagepixie/wielding-replace-in-all-its-power-1m1m | Wielding replace with all its power - 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 SavagePixie Posted on Sep 3, 2019 • Edited on Sep 5, 2019 Wielding replace with all its power # javascript # todayilearned # devtips Learning JavaScript (8 Part Series) 1 My New Friends filter() and map() 2 Destructuring Assignment in JavaScript ... 4 more parts... 3 Butchering Arrays (or not) with splice() and slice() 4 Lookaheads in Javascript 5 Wielding replace with all its power 6 Understanding reduce in JavaScript 7 An introduction to promises in JavaScript 8 Cool Object methods in JavaScript Regular expressions can be a great help when we need to analyse or transform user input or other strings the precise content of which we don't know until runtime, especially so if we need to work with complex expressions. String 's method .replace() is very versatile. We can use it to look for a pattern in the form of a string or a regular expression within a string and return a new one with the pattern replaced. Up until here, here, this sounds very good but very bland. The exciting part come when one realises that we can pass a function as a parameter to act on each match. How does that work? const newStr = str . replace ( pattern , replacement ) This is the method's basic syntax. The pattern will be either a string or a regular expression. The replacement will be a string or a function. If it is a function, it can take several arguments: match the first argument is the entire match in the string group next, there will be one argument for each captured group offset the offset of the match within the string (ie, the index in the string where the match starts) string the whole string What is it useful for? A while back, I wrote an article explaining how lookaheads helped me turn camelCase into kebab-case . Let us imagine that I want to do just the opposite this time, turn kebab-case into camelCase. One way to do it would be this. const toCamelCase = str => str . split ( " - " ) . map (( x , i ) => i == 0 ? x : x . slice ( 0 , 1 ). toUpperCase () + x . slice ( 1 )) . join ( '' ) toCamelCase ( " some-stuff-written-here " ) //returns someStuffWrittenHere If I wanted, instead of six methods and a callback function, I could just use two methods and a callback function. const toCamelCase = str => { const regEx = /- (\w) /g return str . replace ( regEx , ( _ , letter ) => letter . toUpperCase ()) } toCamelCase ( " some-stuff-written-here " ) //returns someStuffWrittenHere The result is the same, but the statement much shorter. I think it is important to note, however, that some not very conclusive tests seem to indicate that the first function is marginally faster for small amounts of text. Doing some more complex stuff Now let's imagine that we have a text that includes an expression for a dice roll. We don't know how many dice were rolled or how many sides those dice had until our website gets the data. But we know that the expression will be in the form of xdy where x indicates the amount of dice rolled and y how many sides those dice had. We want a function that returns a new string with the result of the dice roll in it. The first thing that we need is a function that simulates a dice roll. Let's make something simple. const rollDice = ( dice , sides ) => { let result = 0 for ( let i = 0 ; i < dice ; i ++ ) result += Math . ceil ( Math . random () * sides ) return result } With that out of the way, let's dive into the function that will add the result next to each dice roll: const addResult = ( string , roller ) => { const regEx = / (\d + ) d (\d + ) /g return string . replace ( regEx , ( roll , dice , sides ) => ` ${ roll } [ ${ roller ( dice , sides )} ]` ) } const string = " The first dice roll is 2d6 and the second one is 3d8. " addResult ( string , rollDice ) //Returns something like this: "The first dice roll is 2d6 [7] and the second one is 3d8 [17]." Here we've created a function that takes a string and another function to roll dice. Inside the function, we call .replace() to find dice roll expressions in the string and execute our dice roller for each match, so that the resulting new string includes the result of the roll after the expression. In this case, our regular expression has two captured groups, each of them consisting of one or more digits ( (\d+) ). When we call our callback function, we name the first group dice and the second sides . The whole matching expression will be captured in the argument roll . We don't need any of the other arguments that it can take, so we simply omit them. When it gets really fun But we can even use .replace() without actually replacing anything in the string. Consider the following example. const addResult = ( string , roller ) => { const regEx = / (\d + ) d (\d + ) /g const newArr = [] string . replace ( regEx , ( roll , dice , sides ) => newArr . push ({ roll : roll , result : roller ( dice , sides ) })) return newArr } This time, our addResult() returns an array of objects where it has stored all the dice roll expressions and their calculated result without actually doing anything to the original string. This technique is useful when we want to gather data from a string, but we aren't particularly interested in changing it. Conclusion Because it can take a function as a parameter, .replace() can be a very useful method when we need to manipulate a text in complex ways or we simply want to gather information from it. If you want to learn more about it, you can always check MDN web docs . Personally, I also found the chapter dedicated to regular expressions in Secrets of the JavaScript Ninja very enlightening. Learning JavaScript (8 Part Series) 1 My New Friends filter() and map() 2 Destructuring Assignment in JavaScript ... 4 more parts... 3 Butchering Arrays (or not) with splice() and slice() 4 Lookaheads in Javascript 5 Wielding replace with all its power 6 Understanding reduce in JavaScript 7 An introduction to promises in JavaScript 8 Cool Object methods in JavaScript 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 SavagePixie Follow Always learning new things. I love web development and coding in general. Joined Jul 23, 2019 More from SavagePixie Best practices to authenticate with Passport.js # javascript # node # help # discuss Mutability and reassignability in JavaScript # beginners # javascript # tips Asteroid App: Project Submission # twiliohackathon # javascript # node 💎 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. 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https://opensource.org/ai#use | Open Source AI – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Source AI Open Source AI OSAID 1.0 Process Timeline Open Weights FAQ Endorsements Open Main Menu THE OPEN SOURCE AI DEFINITION 1.0 We have released the first stable version of the Definition. Read version 1.0 What’s Open Source AI? Following the same idea behind Open Source Software, an Open Source AI is a system made available under terms that grant users the freedoms to: Use Study Modify Share Use the system for any purpose and without having to ask for permission. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Study how the system works and understand how its results were created. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Modify the system for any purpose, including to change its output. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Share the system for others to use with or without modifications, for any purpose. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Benefits of Open Source AI Transparency & Safety Open Source AI provides information essential for auditing systems and to mitigate bias, ensures accountability and transparency of data sources, and accelerates AI safety research. Competition & Polyculture Open Source AI makes more models available, spurs innovation and quality due to increased competition and tackles AI monoculture by providing more stakeholders access to foundational technology. Diverse Applications Open Source AI gives developers access to resources crucial for developing context-specific, localized applications that are representative of cultural and linguistic diversity and allow for model aligned with different value systems. Read the white paper The Open Source Initiative and Open Future have taken a significant step toward addressing this challenge by releasing this white paper. The document is the culmination of a global co-design process, enriched by insights from a vibrant two-day workshop held in Paris in October 2024. Read the white paper Why Open Source AI needs a definition? Open Source Frontier The traditional view of Open Source code and licenses when applied to AI components are not sufficient to guarantee the freedoms to use, study, share and modify the systems. Informing Regulators Government regulations have begun in Europe, the United States, and elsewhere. Communities need a common understanding to educate policy makers. Combat Openwashing Companies are calling AI systems “Open Source” even though their licenses contain restrictions that go against the accepted principles and freedoms of Open Source. Who’s behind the Open Source AIDefinition View all Endorsers Overall process 0 Supporting Organizations 0 Supporting Individuals 0 Co-designers 0 Systems reviewed Representation in the co-design process 0 Nationalities 0 People Of Color 0 Global South 0 Femme, Trans, & Nonbinary Co-design 2023 – 2024 In 2023, we started the co-design process hosting several online and in-person activities around the world. Research 2022 – 2023 Alongside AI experts from various fields we produced a podcast , panels and webinars . Endorsements 2024 – 2025 Late 2024 into 2025, the OSI is gathering endorsements from various individuals and organizations, including Mozilla, Suse, Eleuther AI, Ai2, Eclipse Foundation, and the OpenInfra Foundation, among many others. Which AI systems comply with the OSAID 1.0? As part of our validation and testing of the OSAID, the volunteers checked whether the Definition could be used to evaluate if AI systems provided the freedoms expected. The list of models that passed the Validation phase are: Pythia (Eleuther AI), OLMo (AI2), Amber and CrystalCoder (LLM360), and T5 (Google). There are a couple of others that were analyzed and would probably pass if they changed their licenses/legal terms: BLOOM (BigScience), Starcoder2 (BigCode), Falcon (TII). Those that have been analyzed and don’t pass because they lack required components and/or their legal agreements are incompatible with the Open Source principles: Llama2 (Meta), Grok (X/Twitter), Phi-2 (Microsoft), Mixtral (Mistral). These results should be seen as part of the definitional process, a learning moment; they’re not certifications of any kind. OSI will continue to validate only legal documents, and will not validate or review individual AI systems, just as it does not validate or review software projects. If you are wondering about Open Weights models , please refer to our dedicated page . The OSAID co-design process was open to everyone interested in collaborating . How to participate There are many ways to get involved: Endorse the Open Source AI Definition : have your organization appended to the list of supporters of version 1.0. Join the forum : support and comment on the releases, record your approval or concerns to new and existing threads. Subscribe to our newsletter and read our blog to be kept up-to-date. Watch the town hall recordings to learn more about the process. Join the workshops and scheduled conferences : meet the OSI and other participants at in-person events around the world. Open Source AI Definition Governance Governance for the Open Source AI Definition is provided by the OSI Board of Directors . The OSI board members have expertise in business, legal, and open source software development, as well as experience across a range of commercial, public sector, and non-profit organizations. Formal progress reports including achievements, budget updates, and next steps are provided monthly by the Program Lead for advice and guidance as part of regular Board business. Additionally, informal updates on the outcomes of key meetings and milestones are provided via email to the Board as required. Supported by OSI’s efforts wouldn’t be possible without the support of our sponsors and thousands of individual members. 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https://dev.to/mitul3737/devops-prerequisite-part-9-ssl-and-tls-53di#comments | 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. 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https://www.w3.org/WAI/standards-guidelines/wcag/ | WCAG 2 Overview | Web Accessibility Initiative (WAI) | W3C Skip to Content Change Text Size or Colors This page in: English čeština español français 日本語 polski All Translations Show Customization, Languages, Translations' data-hidetext='Hide Options'> Show Customization, Languages, Translations W3C homepage Web Accessibility Initiative (WAI) homepage Strategies, standards, resources to make the Web accessible to people with disabilities Menu Get Involved About W3C WAI Search: Accessibility Fundamentals Planning & Policies Design & Develop Test & Evaluate Teach & Advocate Standards/ Guidelines Home Standards/ Guidelines Web Content – WCAG 2 Standards/ Guidelines Web Content – WCAG 2 How to Meet WCAG 2 (Quick Reference) At a Glance The Documents Applying to Non-Web ICT New in 2.2 New in 2.1 Translations Commenting Conformance Logos FAQ WCAG 3 Draft Authoring Tools – ATAG At a Glance For LMS For No-Code Tools For Social Media Platforms User Agents – UAAG WAI-ARIA Evaluation – ACT & EARL Accessibility Conformance Testing – ACT Evaluation and Report Language – EARL WAI-Adapt Pronunciation Standards Harmonization is Essential W3C Process for Developing Standards Referencing and Linking to Standards Cognitive Accessibility at W3C Mobile Accessibility at W3C Languages/Translations English (original) čeština español français 日本語 polski All Translations Translating WAI Resources WCAG 2 Overview Summary This page introduces the Web Content Accessibility Guidelines (WCAG) international standard, including WCAG 2.0, WCAG 2.1, and WCAG 2.2. WCAG documents explain how to make web content more accessible to people with disabilities. A different page introduces WCAG 3 . WCAG is not an introduction to accessibility. For introductions, see Accessibility Fundamentals Overview . Quick links to resources: How to Meet WCAG 2 (Quick Reference) WCAG 2.2 Standard , What’s New in WCAG 2.2 WCAG 2.1 Standard Page Contents Introduction Who WCAG is for What is in WCAG 2 WCAG 2.0, 2.1, 2.2 Translations ISO/IEC 40500, EAA, EN 301 549 Who develops WCAG More about WCAG Introduction Web Content Accessibility Guidelines (WCAG) 2 is developed through the W3C process in cooperation with individuals and organizations around the world, with a goal of providing a single shared standard for web content accessibility that meets the needs of individuals, organizations, and governments internationally. The WCAG documents explain how to make web content more accessible to people with disabilities. Web “content” generally refers to the information in a web page or web application, including: natural information such as text, images, and sounds code or markup that defines structure, presentation, etc. Who WCAG is for WCAG is for those who want a technical standard. It is not an introduction to accessibility. For links to introductory material, see “Where should I start?” in the FAQ . WCAG is primarily intended for: Web content developers (page authors, site designers, etc.) Web authoring tool developers Web accessibility evaluation tool developers Others who want or need a standard for web accessibility, including for mobile accessibility To meet the needs of others — including policy makers, managers, and researchers — there are many different WAI Resources . What is in WCAG 2 The WCAG 2.2 has 13 guidelines. The guidelines are organized under 4 principles: perceivable, operable, understandable, and robust . For each guideline, there are testable success criteria . The success criteria are at three levels: A, AA, and AAA . The success criteria are what determine “conformance” to WCAG. That is, in order to meet WCAG, the content needs to meet the success criteria. Details are in the Conformance section of WCAG . For a short summary of the WCAG 2 guidelines, see WCAG 2 at a Glance . Supporting material and supplemental guidance The following resources help you understand and implement WCAG, and improve accessibility beyond WCAG: Quick Reference / How to Meet WCAG 2 / Checklist Understanding WCAG 2 Techniques for WCAG 2 Test Rules for WCAG 2 Supplemental Guidance Please read about these WCAG 2 resources from WCAG 2 Documents . WCAG 2.0, 2.1, 2.2 The Web Content Accessibility Guidelines (WCAG) standards are referenceable when they are published as a ‘W3C Recommendation’ web standard. WCAG 2.0 was published on 11 December 2008. WCAG 2.1 was published on 5 June 2018, and updates were published on 21 September 2023, 12 December 2024, and 6 May 2025. WCAG 2.2 was published on 5 October 2023, and an update was published on 12 December 2024. For information on the updates, see the WCAG 2 FAQ . WCAG 2.0, 2.1, and 2.2 are designed to be “backwards compatible”, which means content that conforms to WCAG 2.2 also conforms to WCAG 2.1 and WCAG 2.0. If you want to meet all the versions, you can use the WCAG 2.2 resources and you don’t need to bother looking at earlier versions. All the success criteria from 2.0 are included in 2.1, and all from 2.1 are in 2.2 (except 4.1.1, explained in the next paragraph). WCAG 2.0 has 12 guidelines. WCAG 2.1 adds 1 guideline and 17 success criteria. They are introduced in What’s New in WCAG 2.1 . WCAG 2.2 adds 9 success criteria. They are introduced in What’s New in WCAG 2.2 . A few things have changed, and we intend the updates in the related documents to support backwards compatibility in practice. The main change is that in WCAG 2.2, one success criteria (4.1.1 Parsing) is obsolete. Notes added to WCAG 2.1 and WCAG 2.0 errata address this, as explained in WCAG 2 FAQ, 4.1.1 Parsing . WCAG 2.2 also includes Notes about different languages; more information is in WCAG 2 FAQ, internationalization . WCAG 2.0, WCAG 2.1, and WCAG 2.2 are all existing standards. WCAG 2.2 does not deprecate or supersede WCAG 2.1, and WCAG 2.1 does not deprecate or supersede WCAG 2.0. W3C encourages you to use the latest version of WCAG. Translations Authorized Translations and unofficial translations of WCAG 2 are listed in WCAG 2 Translations . ISO/IEC 40500, EAA, EN 301 549 WCAG 2.2 is an approved International Organization for Standardization (ISO) standard: ISO/IEC 40500:2025 , and is available free from ISO. ISO/IEC 40500:2025 is exactly the same as the October 2023 version of WCAG 2.2. We expect the December 2024 version of WCAG 2.2 to be available as ISO/IEC 40500:2026 by late 2026. In addressing the European Accessibility Act (EAA), most organizations use WCAG and the European Standard EN 301 549: Accessibility requirements for ICT products and services. EN 301 549 currently uses WCAG 2.1. We expect the next version of EN 301 549 to use the latest version of WCAG 2.2. To find how laws around the world use WCAG, see Web Accessibility Laws & Policies . W3C encourages you to use the latest version of WCAG. Content that meets WCAG 2.2 also meets WCAG 2.1 and WCAG 2.0. Who develops WCAG The WCAG technical documents are developed by the Accessibility Guidelines Working Group ( AG WG ) (formerly the Web Content Accessibility Guidelines Working Group) , which is part of the World Wide Web Consortium ( W3C ) Web Accessibility Initiative ( WAI ). WAI updates Techniques for WCAG 2 and Understanding WCAG 2 periodically. We welcome comments and submission of new techniques . Opportunities for contributing to WCAG and other WAI work are introduced in Participating in WAI . More about WCAG WCAG is part of a series of accessibility guidelines, including the Authoring Tool Accessibility Guidelines (ATAG) and the User Agent Accessibility Guidelines (UAAG). Essential Components of Web Accessibility explains the relationship between the different guidelines. Frequently asked questions (FAQ) See the WCAG 2 FAQ for more information on: WCAG 2 coverage of mobile accessibility Applying WCAG 2 to documents and software and more… JSON machine-readable files The WCAG JSON (JavaScript Object Notation) files include the principles, guidelines, success criteria, and glossary terms from WCAG and the supporting Techniques. For more information, see JSON Serialization of WCAG 2 – GitHub . WCAG 3 For information on the early draft of W3C Accessibility Guidelines 3.0 (formerly known as “Silver”), see the WCAG 3 Introduction . Help improve this page Please share your ideas, suggestions, or comments via e-mail to the publicly-archived list wai@w3.org or via GitHub. E-mail Fork & Edit on GitHub New GitHub Issue Back to Top Updated: 20 October 2025. Latest changes . First published July 2005. Editor: Shawn Lawton Henry . Developed with input from the Education and Outreach Working Group ( EOWG ) and the Accessibility Guidelines Working Group ( AG WG ). W3C Web Accessibility Initiative (WAI) Strategies, standards, resources to make the Web accessible to people with disabilities Get News in Email LinkedIn Mastodon YouTube Home Contact Site map Support WAI News Accessibility statement All Translations Resources for roles Copyright © 2026 World Wide Web Consortium . W3C ® liability , trademark and permissive license rules apply unless otherwise noted. See Permission to Use WAI Material . | 2026-01-13T08:49:44 |
https://www.fine.dev/blog/ai-coding-guide#introduction-what-is-ai-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 | 2026-01-13T08:49:44 |
https://dev.to/pockit_tools/pnpm-vs-npm-vs-yarn-vs-bun-the-2026-package-manager-showdown-51dc#the-quick-verdict | 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://www.fine.dev/blog/ai-coding-guide#advice-for-back-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 | 2026-01-13T08:49:44 |
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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.suprsend.com/docs/embeddable-inbox#content-area | Integration - SuprSend, Notification infrastructure for Product teams Skip to main content SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Community Trust Center Platform Status Postman Collection GETTING STARTED What is SuprSend? Quick Start Guide Best Practices Plan Your Integration Go-live checklist CORE CONCEPTS Templates Users Events Workflow Notification Categories Preferences Tenants Lists Broadcast Objects Translations DLT Guidelines Whatsapp Template Guidelines WORKFLOW BUILDER Design Workflow Node List Workflow Settings Trigger Workflow Validate Trigger Payload Tenant Workflows Notification Inbox Overview Multi Tabs React Javascript (Angular, Vuejs etc) Integration Customization options React Native Flutter (Headless) PREFERENCE CENTRE Embedded Preference Centre Javascript Angular React VENDOR INTEGRATION GUIDE Overview Email Integrations SMS Integrations Android Push Whatsapp Integrations iOS Push Chat Integrations Vendor Fallback Tenant Vendor INTEGRATIONS Webhook Connectors MONITORING & DEBUGGING Logs Audit Logs Error Guides MANAGE YOUR ACCOUNT Authentication Methods Contact Us Get Started SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Ask AI Contact Us Get Started Get Started Search... Navigation Javascript (Angular, Vuejs etc) Integration Documentation API Reference Management API CLI Reference Developer Resources Changelog Documentation API Reference Management API CLI Reference Developer Resources Changelog Javascript (Angular, Vuejs etc) Integration OpenAI Open in ChatGPT How to integrate SuprSend inbox/feed components in Angular, Vue, VanillaJS, and other non-React frameworks. OpenAI Open in ChatGPT End of Support for @suprsend/web-inbox . Migrate to @suprsend/web-components We have upgraded authentication of inbox from HMAC to JWT as it is more secure. Please migrate to newer SDK if you are on old one. There are 2 ways in which you can implement inbox functionality: Drop-in components: Pre-built UI with many customizable options which require minimal effort to build. Headless implementation: For more advanced use cases where you want to build UI/UX from scratch. This guide help you integrate drop-in components in your non-react frameworks (angular, vuejs, vanillajs etc). If you want to build your own UI (headless) instead of using drop-in components please refer docs . Integration Integrate using script tag This integration is used in Vanillajs, Django, Laravel, ruby etc where npm is not used. Copy Ask AI <!-- for dropin inbox with bell --> < div id = "suprsend-inbox" ></ div > <!-- for feed without bell as a fullscreen notification etc --> < div id = "suprsend-feed" ></ div > < script > window . suprsendConfig = { distinctId: "YOUR_DISTINCT_ID" , publicApiKey: "YOUR_PUBLIC_API_KEY" , userAuthenticationHandler : ({ response }) => { console . log ( "User Authentication Response" , response ); }, }; let scriptElem = document . createElement ( "script" ); scriptElem . async = 1 ; scriptElem . src = "https://web-components.suprsend.com/v0.3.0/bundle.umd.js" ; scriptElem . onload = () => { console . log ( "SuprSend SDK loaded" , window . suprsend ); }; document . body . appendChild ( scriptElem ); </ script > Integrate as npm package This integration is used in framework based applications like angular, vuejs etc. Copy Ask AI npm install @suprsend/web-components@latest Copy Ask AI import { initSuprSend , clearSuprSend } from "@suprsend/web-components" ; // for dropin inbox with bell < div id = "suprsend-inbox" ></ div > // for feed without bell as a fullscreen notification etc < div id = "suprsend-feed" ></ div > const suprsendConfig = { distinctId: "YOUR_DISTINCT_ID" , publicApiKey: "YOUR_PUBLIC_API_KEY" , userAuthenticationHandler : ({ response }) => { console . log ( "User Authentication Response" , response ); }, }; initSuprSend ( suprsendConfig ) // for creating instance and rendering component console . log ( "Instance created but user authentication pending" , window . suprsend ) NOTE: If you are using suprsend-feed , specify height for the container for infinite scroll to work properly. Copy Ask AI const suprsendConfig = { distinctId: "YOUR_DISTINCT_ID" , publicApiKey: "YOUR_PUBLIC_API_KEY" , feed: { theme: { notificationsContainer: { container: { height: "100vh" } } }, // add this to specify height }, }; Removing instance Components will be removed automatically if you navigate away from the page (on unmounting). If you want to remove them manually, you can use below methods. Using script tag Using npm package Copy Ask AI window . suprsend . clearSuprSend (); // clears instance and remove all components window . suprsend . clearSuprSendInbox (); // unmount only inbox component window . suprsend . clearSuprSendFeed (); // unmount only feed component Updating configuration dynamically Copy Ask AI window . suprsend . updateSuprSendConfig ( config : IUpdateSuprSendConfigOptions ); // refresh userToken, change locale, translations dymanically window . suprsend . updateInboxConfig ( config : IInbox ); window . suprsend . updateFeedConfig ( config : IFeed ); window . suprsend . updateToastConfig ( config : IToastNotificationProps ); Accessing other instance methods SDK internally calls new SuprSend() when you call initSuprSend() then you can access instance using window.suprsend.client . This instance has methods like preferences , webpush , event and user updates . Copy Ask AI // example methods window . suprsend . client . isIdentified (); window . suprsend . client . user . addEmail ( email : string ); window . suprsend . client . track ( event : string , properties ?: Dictionary ) window . suprsend . client . webpush . registerPush (); window . suprsend . client . user . preferences . getPreferences ( args ?: {tenantId? : string }); Config options To customise SuprSend components you can pass config object. Config Options Inbox Config Options Feed Config Options Toast Config Options Copy Ask AI interface ConfigProps { publicApiKey : string ; distinctId ? : unknown ; userToken ? : string ; host ? : string ; initOnLoad ? : boolean ; // pass false if you don't want to initialise instance just after loading script refreshUserToken ? : ( oldUserToken : string , tokenPayload : Dictionary ) => Promise < string > ; vapidKey ? : string ; swFileName ? : string ; userAuthenticationHandler ? : ({ response : ApiResponse }) => void ; inbox ? : IInbox ; // inbox config options feed ? : IFeed ; // feed config options toast ? : IToastNotificationProps ; // toast config options shadowRoot ?: ShadowRoot ; //shadowRoot reference } Parameter Description publicApiKey Public API Key is mandatory field without which error will be thrown by SuprSendProvider. You can get this from SuprSend Dashboard . distinctId Unique identifier to identify a user across platform. If a value is passed SDK will create user and authenticate user. If null value is passed authenticated user’s instance data will be cleared in your application, kind of logout. userToken Mandatory when enhanced security mode is on. This is ES256 JWT token generated in your server-side. Refer docs to create userToken. refreshUserToken This function is called by SDK internally to get new userToken before existing token is expired. The returned JWT token string is used as the new userToken. userAuthenticationHandler This callback will be called after authenticating user internally when you pass distinctId field to give you back the response of user creation API call. host Customise the host url. vapidKey This key is needed only if you are implementing WebPush notifications. You can get it in SuprSend Dashboard —> Vendors —> WebPush swFileName This key is needed only if you are implementing WebPush notifications and want to customise default serviceworker.js file name with your own service worker file name. shadowRoot Shadow root reference to render components inside shadow dom For further component specific customisations please refer to the docs . Was this page helpful? Yes No Suggest edits Raise issue Previous Customization options How to customize the styling, CSS, and layout of the Inbox Feed to match your product’s design in non-React websites. Next ⌘ I x github linkedin youtube Powered by On this page Integration Integrate using script tag Integrate as npm package Removing instance Updating configuration dynamically Accessing other instance methods Config options | 2026-01-13T08:49:44 |
https://www.algolia.com/de/products/ai/generative-experiences | Generative Experiences | 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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We got you covered! Brand guidelines Download logo pack Generative Experiences Schaffen Sie dynamischere Kundenerlebnisse Ihre Grundlage für die Entwicklung kontextbezogener, inhaltsreicher, dynamischer und personalisierter Lösungen – mit Large Language Models (LLMs) und fortschrittlicher KI. Demo anfordern Kostenlos starten Einführung in Generative Experiences Die Generative Experiences von Algolia sind ein flexibles Framework, das Entwicklern ermöglicht, generative KI-Funktionen schnell und sicher zu entwerfen, zu testen und bereitzustellen. Von APIs über UX-Bibliotheken bis hin zu integrierten Merchandising-Tools – alles, was Sie brauchen, um von der Idee zur Umsetzung zu gelangen. Beschleunigen Sie die Entwicklung von KI-Lösungen Generative Experiences sind der ideale Ausgangspunkt für die schnelle Bereitstellung KI-gestützter Lösungen – sei es für die Erstellung eines Shopping-Assistenten, die Verbesserung der Produktsuche oder die Neugestaltung von Landingpages. Bringen Sie Ihr eigenes LLM mit Mit den APIs für Generative Experiences von Algolia können Sie reichhaltigere und relevantere digitale Erlebnisse schaffen, die sich intuitiv und persönlich anfühlen. So können Sie Ihrem Katalog und Suchindex neue Details oder Beschreibungen hinzufügen. Sie können unsere LLMs nutzen oder sie mit anderen Modellen Ihrer Wahl kombinieren – und gleichzeitig auf Algolias AI-first-Infrastruktur setzen. Ergebnisse, die Umsatz generieren Generative Experiences greifen auf die gesamte Algolia-Plattform zurück — Search, Recommendations und Personalization — und optimieren die Ergebnisse kontinuierlich. Jede Interaktion mit generativer KI liefert neue Erkenntnisse zurück in Ihr System. Entwickler können Tools wie Chatbots, virtuelle Assistenten oder interaktive Guides erstellen, die originelle Inhalte generieren, komplexe Fragen beantworten oder Entscheidungen in Echtzeit unterstützen. Algolia Guides Algolia Guides ist eine sofort einsatzbereite Lösung aus dem Bereich Generative Experiences. Sie ermöglicht es, aus Ihrem Produktkatalog im Handumdrehen Einkaufsführer zu erstellen, die Kunden mit personalisierten Tipps und Empfehlungen unterstützen. Mehr erfahren FAQ – Generative Experiences Was sind Algolia Generative Experiences? 0 Generative Experiences helfen Unternehmen dabei, KI-generative Features für jede Anwendung zu entwickeln und bereitzustellen – mithilfe von Algolias APIs, UX-Bibliotheken und Merchandising-Tools. Was brauche ich, um zu starten? 0 Alles, was Sie benötigen, sind Ihre Produktdaten und der Zugang zu den Algolia-APIs. Von dort aus können Sie sofort mit einsatzbereiten Komponenten experimentieren oder maßgeschneiderte generative Experiences für Ihre Marke entwickeln. Brauchen Sie Unterstützung? Kontaktieren Sie unser Team. Welche LLMs kann ich mit Algolias Generative Experiences verbinden? 0 Algolia Generative Experiences unterstützt OpenAI, AWS Bedrock, Vertex AI und viele weitere Modelle und Anbieter. Mehr erfahren in unserer Dokumentation. 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https://dev.to/pockit_tools/pnpm-vs-npm-vs-yarn-vs-bun-the-2026-package-manager-showdown-51dc#medium-project-200-dependencies | 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 . 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https://dev.to/wakeupmh | Marcos Henrique - 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 Follow User actions Marcos Henrique "Programming isn't about what you know; it's about what you can figure out.” Learning in Public 🧑🏻💻 Location São José dos Campos Joined Joined on May 21, 2019 Email address wakeupmh@gmail.com Personal website https://github.com/wakeupmh github website Work Cloud Engineer | AWS Community Builder Top 7 Awarded for having a post featured in the weekly "must-reads" list. 🙌 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 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 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 8 Week Writing Streak The streak continues! 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 13 badges More info about @wakeupmh Organizations AWS Community Builders Skills/Languages aws, serverless, ts, go, ai Currently learning aws, ts, ai Available for open source projects Post 83 posts published Comment 57 comments written Tag 39 tags followed Pin Pinned Can we implement asynchronous circuit breakers with serverless architecture? Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Dec 22 '23 Can we implement asynchronous circuit breakers with serverless architecture? # todayilearned # aws # serverless 24 reactions Comments 7 comments 3 min read Aurora Serverless + Backtrack: Unleash AWS CDK V2 with TypeScript! A Comedy of (Cloud) Errors Turned Epic Win Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Oct 13 '23 Aurora Serverless + Backtrack: Unleash AWS CDK V2 with TypeScript! A Comedy of (Cloud) Errors Turned Epic Win # todayilearned # aws # typescript 10 reactions Comments Add Comment 3 min read Autoscaling using Spot Instances with AWS CDK + TS Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Mar 15 '23 Autoscaling using Spot Instances with AWS CDK + TS # aws # typescript 16 reactions Comments 4 comments 3 min read Debugging Mocha tests in vscode 🧪 Marcos Henrique Marcos Henrique Marcos Henrique Follow Jun 2 '20 Debugging Mocha tests in vscode 🧪 # todayilearned # vscode # testing 38 reactions Comments 2 comments 2 min read OpenSkills, adding Claude Skills and Superpowers for any agent or IDE Marcos Henrique Marcos Henrique Marcos Henrique Follow Dec 20 '25 OpenSkills, adding Claude Skills and Superpowers for any agent or IDE # todayilearned 2 reactions Comments Add Comment 2 min read Want to connect with Marcos Henrique? Create an account to connect with Marcos Henrique. 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Sign in Real-Time ALB Log Analysis for Proactive Integration Recovery via Datadog Monitors, Workflows and AWS Lambda Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Dec 8 '25 Real-Time ALB Log Analysis for Proactive Integration Recovery via Datadog Monitors, Workflows and AWS Lambda # todayilearned # aws # datadog 3 reactions Comments Add Comment 7 min read Building an Agentic Medical Analysis System That Actually Thinks Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Sep 29 '25 Building an Agentic Medical Analysis System That Actually Thinks # ai # aws 38 reactions Comments 2 comments 8 min read Rebuilding Bloated cron jobs with Event Bridge Scheduler Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Jun 25 '25 Rebuilding Bloated cron jobs with Event Bridge Scheduler # todayilearned # aws 15 reactions Comments Add Comment 3 min read The Gift of Reasoning: Enhancing Amazon Nova Lite with VoltAgent for Character Quirks Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders May 6 '25 The Gift of Reasoning: Enhancing Amazon Nova Lite with VoltAgent for Character Quirks # aws # ai # bedrock 24 reactions Comments 3 comments 4 min read 📊 Validating Amazon Bedrock (Nova Lite) outputs w/ SFN + CDK Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Apr 16 '25 📊 Validating Amazon Bedrock (Nova Lite) outputs w/ SFN + CDK # ai # aws 4 reactions Comments Add Comment 6 min read Chain-of-Thought is (probably) a Lie, and that’s a Problem Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Apr 9 '25 Chain-of-Thought is (probably) a Lie, and that’s a Problem # ai # cot 6 reactions Comments Add Comment 3 min read DynamoDB Monitoring with cdk-monitoring-constructs Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Mar 24 '25 DynamoDB Monitoring with cdk-monitoring-constructs # cdk # aws # typescript 7 reactions Comments Add Comment 4 min read If you gaze long into an abyss, the abyss also gazes into you, avoiding the empty void in JS Marcos Henrique Marcos Henrique Marcos Henrique Follow Mar 21 '25 If you gaze long into an abyss, the abyss also gazes into you, avoiding the empty void in JS # javascript 6 reactions Comments Add Comment 2 min read Supercharging Retail Feedback Analysis with Serverless AI (Bedrock + SFN + MCP) Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Mar 4 '25 Supercharging Retail Feedback Analysis with Serverless AI (Bedrock + SFN + MCP) # aws # ai # mcp 5 reactions Comments Add Comment 4 min read You should use CAG instead RAG everywhere Marcos Henrique Marcos Henrique Marcos Henrique Follow Mar 3 '25 You should use CAG instead RAG everywhere # todayilearned # todayisearched # ai # rag Comments 1 comment 2 min read Bedrock Engineer: Your AI dev sidekick Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Feb 9 '25 Bedrock Engineer: Your AI dev sidekick # aws # bedrock # ai 9 reactions Comments Add Comment 3 min read Taming the Map State Beast in AWS Step Functions with CDK Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Jan 8 '25 Taming the Map State Beast in AWS Step Functions with CDK # aws # typescript 5 reactions Comments Add Comment 4 min read Haunted by EMFILE Issue and some ways to exorcize it Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Nov 19 '24 Haunted by EMFILE Issue and some ways to exorcize it # aws 13 reactions Comments Add Comment 3 min read Accelerating Your Generative AI Journey: Danilo Poccia’s Open-Source Toolbox Marcos Henrique Marcos Henrique Marcos Henrique Follow Oct 25 '24 Accelerating Your Generative AI Journey: Danilo Poccia’s Open-Source Toolbox # todayilearned # opensource # tooling # todayisearched 5 reactions Comments Add Comment 2 min read EXO: Run Beefy LLMs on Your Grandma's Flip Phone 📱🧠 Marcos Henrique Marcos Henrique Marcos Henrique Follow Oct 14 '24 EXO: Run Beefy LLMs on Your Grandma's Flip Phone 📱🧠 # todayilearned # tooling # ai # opensource 7 reactions Comments Add Comment 3 min read Chunking in AI - The Secret Sauce You're Missing Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Oct 10 '24 Chunking in AI - The Secret Sauce You're Missing # javascript # aws # ai # rag 6 reactions Comments Add Comment 6 min read Configuring Bedrock as your LLM with llamindex-TS Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Jul 24 '24 Configuring Bedrock as your LLM with llamindex-TS # todayilearned # bedrock # aws # ai 4 reactions Comments 1 comment 1 min read Process S3 Objects with Step Functions using CDK + TS Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders May 27 '24 Process S3 Objects with Step Functions using CDK + TS # aws # typescript # cdk 10 reactions Comments Add Comment 12 min read SES Observability with CDK + TS Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Jan 31 '24 SES Observability with CDK + TS # aws # cdk # typescript 7 reactions Comments Add Comment 3 min read AWS Shield: Your Digital Bodyguard in the Wild World of Cloud Computing Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Nov 9 '23 AWS Shield: Your Digital Bodyguard in the Wild World of Cloud Computing # todayilearned # aws 6 reactions Comments Add Comment 2 min read Cracking the AWS Migration Code with a Humorous Brush: Discovering the 7 Rs for Cloud Adventure Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Nov 2 '23 Cracking the AWS Migration Code with a Humorous Brush: Discovering the 7 Rs for Cloud Adventure # todayilearned # aws 6 reactions Comments Add Comment 2 min read Why You Should Dodge NAT Instances Like a Pro in the AWS Jungle Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Sep 30 '23 Why You Should Dodge NAT Instances Like a Pro in the AWS Jungle # todayilearned # aws 2 reactions Comments 1 comment 3 min read DynamoDB GSI vs LSI Unleashed! Explained like you're five! Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Jul 3 '23 DynamoDB GSI vs LSI Unleashed! Explained like you're five! # aws 5 reactions Comments Add Comment 1 min read Why AWS EFS Rocks and How to Create it with CDK + TS Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Jun 1 '23 Why AWS EFS Rocks and How to Create it with CDK + TS # aws # typescript 2 reactions Comments Add Comment 3 min read Workaround to Create an S3 glacier with CDK + TS Marcos Henrique Marcos Henrique Marcos Henrique Follow for AWS Community Builders Apr 20 '23 Workaround to Create an S3 glacier with CDK + TS # todayilearned # tutorial # aws # typescript 6 reactions Comments Add Comment 2 min read Using AppConfig Layer in your nodejs lambda with AWS CDK Marcos Henrique Marcos Henrique Marcos Henrique Follow Jan 24 '23 Using AppConfig Layer in your nodejs lambda with AWS CDK # discuss 3 reactions Comments Add Comment 3 min read Tips to improve AWS Lambda performance Marcos Henrique Marcos Henrique Marcos Henrique Follow Jan 23 '23 Tips to improve AWS Lambda performance # automation # manufacturing 3 reactions Comments Add Comment 3 min read Have you noticed that you can short-circuiting the spread operator? Marcos Henrique Marcos Henrique Marcos Henrique Follow Sep 23 '22 Have you noticed that you can short-circuiting the spread operator? # todayilearned # javascript 23 reactions Comments Add Comment 1 min read Create SQS + DLQ with aws-cdk v2 on typescript Marcos Henrique Marcos Henrique Marcos Henrique Follow Aug 3 '22 Create SQS + DLQ with aws-cdk v2 on typescript # typescript # aws # cdk 17 reactions Comments Add Comment 1 min read [SOLVED] glue view is stale; it must be re-created Marcos Henrique Marcos Henrique Marcos Henrique Follow Jun 17 '22 [SOLVED] glue view is stale; it must be re-created # aws # glue 9 reactions Comments Add Comment 1 min read Create Aurora MySQL on top of CDK with TS Marcos Henrique Marcos Henrique Marcos Henrique Follow May 18 '22 Create Aurora MySQL on top of CDK with TS # typescript # aws # tutorial # cloud 11 reactions Comments Add Comment 5 min read How to search a value on a JSON array column with MySQL? Marcos Henrique Marcos Henrique Marcos Henrique Follow Apr 20 '22 How to search a value on a JSON array column with MySQL? # mysql 18 reactions Comments 1 comment 1 min read Validate related schema attributes with Zod Marcos Henrique Marcos Henrique Marcos Henrique Follow Apr 12 '22 Validate related schema attributes with Zod # todayilearned # typescript # javascript 17 reactions Comments Add Comment 1 min read You should use Buildpacks instead Dockerfile and I'll tell you why Marcos Henrique Marcos Henrique Marcos Henrique Follow Oct 8 '21 You should use Buildpacks instead Dockerfile and I'll tell you why # todayilearned # docker # buildpack 15 reactions Comments 1 comment 2 min read How to ace zsh's villain on vscode Marcos Henrique Marcos Henrique Marcos Henrique Follow Jun 22 '21 How to ace zsh's villain on vscode # todayilearned # zsh # vscode 15 reactions Comments 3 comments 1 min read Optional handmade chaining 🤓 Marcos Henrique Marcos Henrique Marcos Henrique Follow Jan 4 '21 Optional handmade chaining 🤓 # node # javascript 11 reactions Comments 7 comments 1 min read Functor's flavor in JS Marcos Henrique Marcos Henrique Marcos Henrique Follow Dec 9 '20 Functor's flavor in JS # todayilearned # javascript # functional 11 reactions Comments 1 comment 1 min read Working with multiple terraform's versions like a charm Marcos Henrique Marcos Henrique Marcos Henrique Follow Nov 23 '20 Working with multiple terraform's versions like a charm # todayilearned # terraform 8 reactions Comments Add Comment 1 min read The fastest docker build in the old west 🤠 Marcos Henrique Marcos Henrique Marcos Henrique Follow Oct 24 '20 The fastest docker build in the old west 🤠 # todayilearned # docker 10 reactions Comments 1 comment 1 min read 👾 Monster kill on all containers from docker Marcos Henrique Marcos Henrique Marcos Henrique Follow Oct 23 '20 👾 Monster kill on all containers from docker # todayilearned # docker 6 reactions Comments Add Comment 1 min read Decouple your express applications using the amazing express-decorator-router Marcos Henrique Marcos Henrique Marcos Henrique Follow Sep 12 '20 Decouple your express applications using the amazing express-decorator-router # node # javascript # express 14 reactions Comments Add Comment 3 min read Batch processing flavor with Bluebird.map() 🔭🌌 Marcos Henrique Marcos Henrique Marcos Henrique Follow Aug 19 '20 Batch processing flavor with Bluebird.map() 🔭🌌 # node # javascript 7 reactions Comments Add Comment 1 min read Graceful Shutdown with NodeJS and Terminus Marcos Henrique Marcos Henrique Marcos Henrique Follow Jun 6 '20 Graceful Shutdown with NodeJS and Terminus # node # javascript # productivity 30 reactions Comments Add Comment 3 min read C8 a native V8 code-coverage Marcos Henrique Marcos Henrique Marcos Henrique Follow Jun 2 '20 C8 a native V8 code-coverage # node # testing # javascript 24 reactions Comments Add Comment 1 min read Usando Yapay fingerprint no Vue Marcos Henrique Marcos Henrique Marcos Henrique Follow Apr 19 '20 Usando Yapay fingerprint no Vue # todayilearned # vue # yapay # javascript 5 reactions Comments Add Comment 3 min read How to avoid Insecure Binder Configuration in JAVA Marcos Henrique Marcos Henrique Marcos Henrique Follow Apr 19 '20 How to avoid Insecure Binder Configuration in JAVA # todayilearned # security # java 10 reactions Comments 1 comment 2 min read Building 3rd party JARs in Spring (Maven project) Marcos Henrique Marcos Henrique Marcos Henrique Follow Mar 19 '20 Building 3rd party JARs in Spring (Maven project) # java # ttodayilearned 8 reactions Comments 1 comment 1 min read Adding local jar files to a Maven project Marcos Henrique Marcos Henrique Marcos Henrique Follow Mar 12 '20 Adding local jar files to a Maven project # todayilearned # java 15 reactions Comments 1 comment 1 min read Health Check with Java Spring easier than walking backwards Marcos Henrique Marcos Henrique Marcos Henrique Follow Mar 12 '20 Health Check with Java Spring easier than walking backwards # todayilearned # java 7 reactions Comments Add Comment 2 min read How to uncouples emit events from connection event into socket.io and express Marcos Henrique Marcos Henrique Marcos Henrique Follow Mar 7 '20 How to uncouples emit events from connection event into socket.io and express # todayilearned # node # javascript 44 reactions Comments 8 comments 2 min read The safest way to download a private repository Marcos Henrique Marcos Henrique Marcos Henrique Follow Feb 26 '20 The safest way to download a private repository # todayilearned # git # github # security 7 reactions Comments Add Comment 1 min read The [12 + 3] Commandments of Software Engineering 👨💻 Marcos Henrique Marcos Henrique Marcos Henrique Follow Feb 14 '20 The [12 + 3] Commandments of Software Engineering 👨💻 # todayilearned # codequality # computerscience # adventofcode 36 reactions Comments 1 comment 1 min read 12 tips/terms used by engineers when designing systems 🔮 Marcos Henrique Marcos Henrique Marcos Henrique Follow Feb 6 '20 12 tips/terms used by engineers when designing systems 🔮 # todayilearned # architecture # design 48 reactions Comments 1 comment 1 min read 7 Valuable tips for Object Oriented Design 😏 Marcos Henrique Marcos Henrique Marcos Henrique Follow Jan 28 '20 7 Valuable tips for Object Oriented Design 😏 # todayilearned # oop # productivity # codequality 85 reactions Comments 2 comments 1 min read Refactoring many OR statements like a ninja 🤘 Marcos Henrique Marcos Henrique Marcos Henrique Follow Jan 26 '20 Refactoring many OR statements like a ninja 🤘 # todayilearned # refactorit # javascript # codequality 10 reactions Comments 1 comment 1 min read Currying in JS 🤠 Marcos Henrique Marcos Henrique Marcos Henrique Follow Dec 29 '19 Currying in JS 🤠 # todayilearned # javascript 133 reactions Comments 13 comments 2 min read Git fast-forward merge strategy 😎 Marcos Henrique Marcos Henrique Marcos Henrique Follow Dec 29 '19 Git fast-forward merge strategy 😎 # todayilearned # git 13 reactions Comments Add Comment 3 min read Running Linux Bash Shell in Windows 🤯 Marcos Henrique Marcos Henrique Marcos Henrique Follow Dec 20 '19 Running Linux Bash Shell in Windows 🤯 # todayilearned # linux # ubuntu # bash 7 reactions Comments 9 comments 2 min read Um pouco de clean code com Clojure 🔮 Marcos Henrique Marcos Henrique Marcos Henrique Follow Nov 16 '19 Um pouco de clean code com Clojure 🔮 # 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https://docs.suprsend.com/docs/embeddable-inbox#updating-configuration-dynamically | Integration - SuprSend, Notification infrastructure for Product teams Skip to main content SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Community Trust Center Platform Status Postman Collection GETTING STARTED What is SuprSend? Quick Start Guide Best Practices Plan Your Integration Go-live checklist CORE CONCEPTS Templates Users Events Workflow Notification Categories Preferences Tenants Lists Broadcast Objects Translations DLT Guidelines Whatsapp Template Guidelines WORKFLOW BUILDER Design Workflow Node List Workflow Settings Trigger Workflow Validate Trigger Payload Tenant Workflows Notification Inbox Overview Multi Tabs React Javascript (Angular, Vuejs etc) Integration Customization options React Native Flutter (Headless) PREFERENCE CENTRE Embedded Preference Centre Javascript Angular React VENDOR INTEGRATION GUIDE Overview Email Integrations SMS Integrations Android Push Whatsapp Integrations iOS Push Chat Integrations Vendor Fallback Tenant Vendor INTEGRATIONS Webhook Connectors MONITORING & DEBUGGING Logs Audit Logs Error Guides MANAGE YOUR ACCOUNT Authentication Methods Contact Us Get Started SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Ask AI Contact Us Get Started Get Started Search... Navigation Javascript (Angular, Vuejs etc) Integration Documentation API Reference Management API CLI Reference Developer Resources Changelog Documentation API Reference Management API CLI Reference Developer Resources Changelog Javascript (Angular, Vuejs etc) Integration OpenAI Open in ChatGPT How to integrate SuprSend inbox/feed components in Angular, Vue, VanillaJS, and other non-React frameworks. OpenAI Open in ChatGPT End of Support for @suprsend/web-inbox . Migrate to @suprsend/web-components We have upgraded authentication of inbox from HMAC to JWT as it is more secure. Please migrate to newer SDK if you are on old one. There are 2 ways in which you can implement inbox functionality: Drop-in components: Pre-built UI with many customizable options which require minimal effort to build. Headless implementation: For more advanced use cases where you want to build UI/UX from scratch. This guide help you integrate drop-in components in your non-react frameworks (angular, vuejs, vanillajs etc). If you want to build your own UI (headless) instead of using drop-in components please refer docs . Integration Integrate using script tag This integration is used in Vanillajs, Django, Laravel, ruby etc where npm is not used. Copy Ask AI <!-- for dropin inbox with bell --> < div id = "suprsend-inbox" ></ div > <!-- for feed without bell as a fullscreen notification etc --> < div id = "suprsend-feed" ></ div > < script > window . suprsendConfig = { distinctId: "YOUR_DISTINCT_ID" , publicApiKey: "YOUR_PUBLIC_API_KEY" , userAuthenticationHandler : ({ response }) => { console . log ( "User Authentication Response" , response ); }, }; let scriptElem = document . createElement ( "script" ); scriptElem . async = 1 ; scriptElem . src = "https://web-components.suprsend.com/v0.3.0/bundle.umd.js" ; scriptElem . onload = () => { console . log ( "SuprSend SDK loaded" , window . suprsend ); }; document . body . appendChild ( scriptElem ); </ script > Integrate as npm package This integration is used in framework based applications like angular, vuejs etc. Copy Ask AI npm install @suprsend/web-components@latest Copy Ask AI import { initSuprSend , clearSuprSend } from "@suprsend/web-components" ; // for dropin inbox with bell < div id = "suprsend-inbox" ></ div > // for feed without bell as a fullscreen notification etc < div id = "suprsend-feed" ></ div > const suprsendConfig = { distinctId: "YOUR_DISTINCT_ID" , publicApiKey: "YOUR_PUBLIC_API_KEY" , userAuthenticationHandler : ({ response }) => { console . log ( "User Authentication Response" , response ); }, }; initSuprSend ( suprsendConfig ) // for creating instance and rendering component console . log ( "Instance created but user authentication pending" , window . suprsend ) NOTE: If you are using suprsend-feed , specify height for the container for infinite scroll to work properly. Copy Ask AI const suprsendConfig = { distinctId: "YOUR_DISTINCT_ID" , publicApiKey: "YOUR_PUBLIC_API_KEY" , feed: { theme: { notificationsContainer: { container: { height: "100vh" } } }, // add this to specify height }, }; Removing instance Components will be removed automatically if you navigate away from the page (on unmounting). If you want to remove them manually, you can use below methods. Using script tag Using npm package Copy Ask AI window . suprsend . clearSuprSend (); // clears instance and remove all components window . suprsend . clearSuprSendInbox (); // unmount only inbox component window . suprsend . clearSuprSendFeed (); // unmount only feed component Updating configuration dynamically Copy Ask AI window . suprsend . updateSuprSendConfig ( config : IUpdateSuprSendConfigOptions ); // refresh userToken, change locale, translations dymanically window . suprsend . updateInboxConfig ( config : IInbox ); window . suprsend . updateFeedConfig ( config : IFeed ); window . suprsend . updateToastConfig ( config : IToastNotificationProps ); Accessing other instance methods SDK internally calls new SuprSend() when you call initSuprSend() then you can access instance using window.suprsend.client . This instance has methods like preferences , webpush , event and user updates . Copy Ask AI // example methods window . suprsend . client . isIdentified (); window . suprsend . client . user . addEmail ( email : string ); window . suprsend . client . track ( event : string , properties ?: Dictionary ) window . suprsend . client . webpush . registerPush (); window . suprsend . client . user . preferences . getPreferences ( args ?: {tenantId? : string }); Config options To customise SuprSend components you can pass config object. Config Options Inbox Config Options Feed Config Options Toast Config Options Copy Ask AI interface ConfigProps { publicApiKey : string ; distinctId ? : unknown ; userToken ? : string ; host ? : string ; initOnLoad ? : boolean ; // pass false if you don't want to initialise instance just after loading script refreshUserToken ? : ( oldUserToken : string , tokenPayload : Dictionary ) => Promise < string > ; vapidKey ? : string ; swFileName ? : string ; userAuthenticationHandler ? : ({ response : ApiResponse }) => void ; inbox ? : IInbox ; // inbox config options feed ? : IFeed ; // feed config options toast ? : IToastNotificationProps ; // toast config options shadowRoot ?: ShadowRoot ; //shadowRoot reference } Parameter Description publicApiKey Public API Key is mandatory field without which error will be thrown by SuprSendProvider. You can get this from SuprSend Dashboard . distinctId Unique identifier to identify a user across platform. If a value is passed SDK will create user and authenticate user. If null value is passed authenticated user’s instance data will be cleared in your application, kind of logout. userToken Mandatory when enhanced security mode is on. This is ES256 JWT token generated in your server-side. Refer docs to create userToken. refreshUserToken This function is called by SDK internally to get new userToken before existing token is expired. The returned JWT token string is used as the new userToken. userAuthenticationHandler This callback will be called after authenticating user internally when you pass distinctId field to give you back the response of user creation API call. host Customise the host url. vapidKey This key is needed only if you are implementing WebPush notifications. You can get it in SuprSend Dashboard —> Vendors —> WebPush swFileName This key is needed only if you are implementing WebPush notifications and want to customise default serviceworker.js file name with your own service worker file name. shadowRoot Shadow root reference to render components inside shadow dom For further component specific customisations please refer to the docs . Was this page helpful? Yes No Suggest edits Raise issue Previous Customization options How to customize the styling, CSS, and layout of the Inbox Feed to match your product’s design in non-React websites. Next ⌘ I x github linkedin youtube Powered by On this page Integration Integrate using script tag Integrate as npm package Removing instance Updating configuration dynamically Accessing other instance methods Config options | 2026-01-13T08:49:44 |
https://dev.to/savagepixie/butchering-arrays-or-not-with-splice-and-slice-1c1f | Butchering Arrays (or not) with splice() and slice() - 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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Report Abuse SavagePixie Posted on Jul 27, 2019 Butchering Arrays (or not) with splice() and slice() # javascript # beginners # todayilearned Learning JavaScript (8 Part Series) 1 My New Friends filter() and map() 2 Destructuring Assignment in JavaScript ... 4 more parts... 3 Butchering Arrays (or not) with splice() and slice() 4 Lookaheads in Javascript 5 Wielding replace with all its power 6 Understanding reduce in JavaScript 7 An introduction to promises in JavaScript 8 Cool Object methods in JavaScript Today I've been delving into different ways to add to, subtract from and in general modify arrays. The methods splice() and slice() have particularly caught my attention, especially because their names are so similar that when I first read about slice() I thought, Wait, what? I thought splice() removed and changed stuff within an array? A few minutes later I noticed the missing P. How To Remove Elements from an Array with splice() If we want to remove the last element from an array, we can use pop() (I love its suggestive name). For the first element, we can use shift() (I don't like this name as much, but it's still useful). But what happens when we want to remove elements in the middle of the array? Or if we want to remove more than one element at once? Here's where splice() comes into play. This method takes at least one argument and up to... as many as you want, really, but for removing purposes, it'll take up to two. The first argument is the index where splice() will start removing and the second one is the number of elements it'll delete. In return, splice() will give us an array with the removed elements. So let's look at an example: let ourArr = [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ] //An array with 8 elements let splicedStuff = ourArr . splice ( 3 , 3 ) //We remove 3 elements starting from index 3 console . log ( ourArr ) //1,2,3,7,8 console . log ( splicedStuff ) //4,5,6 As we can see in the log, ourArr has now five elements instead of eight, and the values from the indexes 3-5 have been copied into splicedStuff . If we omitted the second argument, however, splice() would delete all the elements from the starting index to the end of the array. let ourArr = [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]; //Our known array let splicedStuff = ourArr . splice ( 3 ) //We remove all the elements starting from index 3 console . log ( ourArr ) //1,2,3 console . log ( splicedStuff ) //4,5,6,7,8 But wait, there's more to it! It can also replace the elements it removes with new elements. How To Replace Elements in an Array using splice() We just need to add one or more arguments after the second with elements to be added in place of the ones we remove. Note that we don't need to replace all the elements we remove. let ourArr = [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]; let splicedStuff = ourArr . splice ( 3 , 3 , ' four ' , ' five ' ) //This time, we replace two of the three elements we remove console . log ( ourArr ) //1,2,3,four,five,7,8 console . log ( splicedStuff ) //4,5,6 How To Partially Copy an Array with slice() If we want to copy something from an array but leave the original array untouched, we can use the method slice() indicating the beginning and the end. Note that the beginning is inclusive but the end is exclusive (that is, it won't be copied). let ourArr = [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]; //Let's start with the same array, 'cause why not? let slicedStuff = ourArr . slice ( 3 , 6 ) //We take elements from indexes 3-5 console . log ( ourArr ) //1,2,3,4,5,6,7,8 original array untouched console . log ( slicedStuff ) //4,5,6 Conclusion Sometimes we might want to remove one or more elements in the middle of an array. When pop() and shift() aren't up to the task, splice() rises to the challenge. It can even replace the removed elements with new ones. If we want to copy something from an array but leave the original untouched, we can use slice() . Learning JavaScript (8 Part Series) 1 My New Friends filter() and map() 2 Destructuring Assignment in JavaScript ... 4 more parts... 3 Butchering Arrays (or not) with splice() and slice() 4 Lookaheads in Javascript 5 Wielding replace with all its power 6 Understanding reduce in JavaScript 7 An introduction to promises in JavaScript 8 Cool Object methods in JavaScript 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 SavagePixie Follow Always learning new things. I love web development and coding in general. Joined Jul 23, 2019 More from SavagePixie Best practices to authenticate with Passport.js # javascript # node # help # discuss Mutability and reassignability in JavaScript # beginners # javascript # tips Asteroid App: Project Submission # twiliohackathon # javascript # node 💎 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. 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https://www.algolia.com/fr/use-cases/enterprise | Solutions de recherche Enterprise pour clients et employés 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. 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https://opensource.org/ai#share | Open Source AI – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Source AI Open Source AI OSAID 1.0 Process Timeline Open Weights FAQ Endorsements Open Main Menu THE OPEN SOURCE AI DEFINITION 1.0 We have released the first stable version of the Definition. Read version 1.0 What’s Open Source AI? Following the same idea behind Open Source Software, an Open Source AI is a system made available under terms that grant users the freedoms to: Use Study Modify Share Use the system for any purpose and without having to ask for permission. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Study how the system works and understand how its results were created. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Modify the system for any purpose, including to change its output. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Share the system for others to use with or without modifications, for any purpose. Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it. Benefits of Open Source AI Transparency & Safety Open Source AI provides information essential for auditing systems and to mitigate bias, ensures accountability and transparency of data sources, and accelerates AI safety research. Competition & Polyculture Open Source AI makes more models available, spurs innovation and quality due to increased competition and tackles AI monoculture by providing more stakeholders access to foundational technology. Diverse Applications Open Source AI gives developers access to resources crucial for developing context-specific, localized applications that are representative of cultural and linguistic diversity and allow for model aligned with different value systems. Read the white paper The Open Source Initiative and Open Future have taken a significant step toward addressing this challenge by releasing this white paper. The document is the culmination of a global co-design process, enriched by insights from a vibrant two-day workshop held in Paris in October 2024. Read the white paper Why Open Source AI needs a definition? Open Source Frontier The traditional view of Open Source code and licenses when applied to AI components are not sufficient to guarantee the freedoms to use, study, share and modify the systems. Informing Regulators Government regulations have begun in Europe, the United States, and elsewhere. Communities need a common understanding to educate policy makers. Combat Openwashing Companies are calling AI systems “Open Source” even though their licenses contain restrictions that go against the accepted principles and freedoms of Open Source. Who’s behind the Open Source AIDefinition View all Endorsers Overall process 0 Supporting Organizations 0 Supporting Individuals 0 Co-designers 0 Systems reviewed Representation in the co-design process 0 Nationalities 0 People Of Color 0 Global South 0 Femme, Trans, & Nonbinary Co-design 2023 – 2024 In 2023, we started the co-design process hosting several online and in-person activities around the world. Research 2022 – 2023 Alongside AI experts from various fields we produced a podcast , panels and webinars . Endorsements 2024 – 2025 Late 2024 into 2025, the OSI is gathering endorsements from various individuals and organizations, including Mozilla, Suse, Eleuther AI, Ai2, Eclipse Foundation, and the OpenInfra Foundation, among many others. Which AI systems comply with the OSAID 1.0? As part of our validation and testing of the OSAID, the volunteers checked whether the Definition could be used to evaluate if AI systems provided the freedoms expected. The list of models that passed the Validation phase are: Pythia (Eleuther AI), OLMo (AI2), Amber and CrystalCoder (LLM360), and T5 (Google). There are a couple of others that were analyzed and would probably pass if they changed their licenses/legal terms: BLOOM (BigScience), Starcoder2 (BigCode), Falcon (TII). Those that have been analyzed and don’t pass because they lack required components and/or their legal agreements are incompatible with the Open Source principles: Llama2 (Meta), Grok (X/Twitter), Phi-2 (Microsoft), Mixtral (Mistral). These results should be seen as part of the definitional process, a learning moment; they’re not certifications of any kind. OSI will continue to validate only legal documents, and will not validate or review individual AI systems, just as it does not validate or review software projects. If you are wondering about Open Weights models , please refer to our dedicated page . The OSAID co-design process was open to everyone interested in collaborating . How to participate There are many ways to get involved: Endorse the Open Source AI Definition : have your organization appended to the list of supporters of version 1.0. Join the forum : support and comment on the releases, record your approval or concerns to new and existing threads. Subscribe to our newsletter and read our blog to be kept up-to-date. Watch the town hall recordings to learn more about the process. Join the workshops and scheduled conferences : meet the OSI and other participants at in-person events around the world. Open Source AI Definition Governance Governance for the Open Source AI Definition is provided by the OSI Board of Directors . The OSI board members have expertise in business, legal, and open source software development, as well as experience across a range of commercial, public sector, and non-profit organizations. Formal progress reports including achievements, budget updates, and next steps are provided monthly by the Program Lead for advice and guidance as part of regular Board business. Additionally, informal updates on the outcomes of key meetings and milestones are provided via email to the Board as required. Supported by OSI’s efforts wouldn’t be possible without the support of our sponsors and thousands of individual members. 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https://dev.to/raphael_jambalos | Raphael Jambalos - 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 Follow User actions Raphael Jambalos Avid Reader, Curious Learner. AWS Certified ☁️ Location Manila Joined Joined on May 20, 2018 github website twitter website Education University of the Philippines Work Senior Cloud Developer at eCloudvalley Seven Year Club This badge celebrates the longevity of those who have been a registered member of the DEV Community for at least seven years. 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 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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I currently have 4 AWS Certifications, and more to come! 1 AWS Specialty - Certified Security 3 AWS Associate - Certified Solutions Architect - Certified Developer - Certified SysOps Administrator Currently learning - Python - VueJS - NodeJS - Ruby - AWS Post 48 posts published Comment 156 comments written Tag 14 tags followed Pin Pinned Welcome to DEV, AWS Community ASEAN! Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Apr 21 '21 Welcome to DEV, AWS Community ASEAN! # aws # devrel # cloud # community 15 reactions Comments 1 comment 3 min read More than "Hello World" in Docker: Build Rails + Sidekiq web apps in Docker Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Jul 28 '19 More than "Hello World" in Docker: Build Rails + Sidekiq web apps in Docker # docker # rails # sidekiq # postgres 206 reactions Comments 23 comments 14 min read Coding Sidekiq Workers the Right Way Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow May 20 '18 Coding Sidekiq Workers the Right Way # rails # sidekiq # ruby # refactoring 110 reactions Comments 10 comments 5 min read AWS Network Challenge 2: Deploy File Upload App on EC2, RDS, DocumentDB Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Nov 15 '24 AWS Network Challenge 2: Deploy File Upload App on EC2, RDS, DocumentDB # webdev # aws # beginners 4 reactions Comments 3 comments 8 min read Want to connect with Raphael Jambalos? 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Sign in Full Stack Serverless Challenge #1: AWS Amplify Gen 2 Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community Builders Jun 29 '24 Full Stack Serverless Challenge #1: AWS Amplify Gen 2 # aws # webdev # typescript # fullstack 7 reactions Comments Add Comment 4 min read AWS Network Challenge 1: Deploy Web App to EC2 / Two-Tier VPC Architecture Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for ECV PH Tech Team May 25 '24 AWS Network Challenge 1: Deploy Web App to EC2 / Two-Tier VPC Architecture # aws # webdev # network 15 reactions Comments Add Comment 6 min read How to Hack (and secure) Serverless Applications Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for ECV PH Tech Team Mar 28 '24 How to Hack (and secure) Serverless Applications # aws # security # webdev # serverless 4 reactions Comments Add Comment 6 min read Trigger AWS Lambda with TCP Traffic + Static IP Address Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community Builders Mar 27 '24 Trigger AWS Lambda with TCP Traffic + Static IP Address 5 reactions Comments Add Comment 3 min read Top 10 Security Best Practices we learned the hard way Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community Builders Mar 3 '24 Top 10 Security Best Practices we learned the hard way # aws # security # webdev # coding 8 reactions Comments 2 comments 9 min read Amazon Cognito: The Ugly Parts (and our workarounds) Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Jan 10 '24 Amazon Cognito: The Ugly Parts (and our workarounds) 26 reactions Comments 2 comments 6 min read Challenge #4: Create CI/CD for Serverless Apps Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Nov 17 '22 Challenge #4: Create CI/CD for Serverless Apps # aws # devops # cicd # serverless 9 reactions Comments Add Comment 6 min read Field Guide to Surviving DDoS Attacks in your application Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Nov 9 '22 Field Guide to Surviving DDoS Attacks in your application # aws # security # devops # ddos 15 reactions Comments 3 comments 8 min read Use EventBridge for your event-driven architecture in 8 steps Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Sep 25 '22 Use EventBridge for your event-driven architecture in 8 steps # aws # serverless # eventbride # eventdriven 12 reactions Comments Add Comment 7 min read How we embraced Amazon EventBridge for our event-driven architecture Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Sep 15 '22 How we embraced Amazon EventBridge for our event-driven architecture # aws # serverless # eventdriven 16 reactions Comments 4 comments 4 min read ElasticSearch: Zero to Hero in 12 Commands Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Aug 27 '22 ElasticSearch: Zero to Hero in 12 Commands # elasticsearch # codenewbie # aws # programming 29 reactions Comments Add Comment 9 min read Why we need an MVC-like web framework on top of Serverless Framework Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community Builders Mar 18 '22 Why we need an MVC-like web framework on top of Serverless Framework 14 reactions Comments Add Comment 5 min read Optimize your Application's performance with Amazon X-Ray Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Feb 19 '22 Optimize your Application's performance with Amazon X-Ray 6 reactions Comments Add Comment 9 min read Black Belt Techniques in Serverless Framework App Deployments Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Feb 1 '22 Black Belt Techniques in Serverless Framework App Deployments # aws # devops # serverless 16 reactions Comments Add Comment 11 min read AWS Community ASEAN - Quarterly Awards for Contributors in 2022 Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Jan 25 '22 AWS Community ASEAN - Quarterly Awards for Contributors in 2022 # aws # devrel # cloud # community 4 reactions Comments Add Comment 2 min read 6 Books from Silicon Valley Authors to help you become a better Tech Manager Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Jan 8 '22 6 Books from Silicon Valley Authors to help you become a better Tech Manager # books # management # career # leadership 10 reactions Comments 1 comment 5 min read Lessons I wish I knew when I started dealing with clients as a Dev Solutions Architect Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Oct 17 '21 Lessons I wish I knew when I started dealing with clients as a Dev Solutions Architect # career # startup # softskills # devops 19 reactions Comments 6 comments 5 min read Challenge #3: Using Offline Tools to speed up dev in Serverless Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Jul 17 '21 Challenge #3: Using Offline Tools to speed up dev in Serverless # challenge # aws # python 7 reactions Comments 1 comment 3 min read VueJS Challenge #1: Create a 3-page app to create/display loyalty cards Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow for AWS Community ASEAN Jul 4 '21 VueJS Challenge #1: Create a 3-page app to create/display loyalty cards # challenge # vue # javascript # tutorial 8 reactions Comments 1 comment 4 min read Why you should volunteer for a tech user group Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow May 6 '21 Why you should volunteer for a tech user group # watercooler # career # motivation # discus 1 reaction Comments Add Comment 3 min read Challenge 2: Add SQS + S3 Event Triggers for Lambda Apps Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Apr 28 '21 Challenge 2: Add SQS + S3 Event Triggers for Lambda Apps # challenge # aws # tutorial # serverless 8 reactions Comments Add Comment 3 min read Challenge 1: Create a simple app in Lambda + DynamoDB + Serverless Framework Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Apr 20 '21 Challenge 1: Create a simple app in Lambda + DynamoDB + Serverless Framework # challenge # aws # tutorial # serverless 24 reactions Comments Add Comment 3 min read Simulate Real-World User Behavior in Apache JMeter Load Tests Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Apr 15 '21 Simulate Real-World User Behavior in Apache JMeter Load Tests # devops # jmeter # tutorial # aws 27 reactions Comments 1 comment 8 min read When was the last time you let yourself be a beginner? Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Apr 2 '21 When was the last time you let yourself be a beginner? # watercooler # discuss # webdev 31 reactions Comments 12 comments 3 min read Quick and Visual Guide to API Performance Testing with Apache JMeter Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Apr 2 '21 Quick and Visual Guide to API Performance Testing with Apache JMeter # devops # webdev # jmeter 9 reactions Comments 1 comment 9 min read Develop Frontend Apps Faster with OpenAPI 3.0 and Prism Mock Server Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Mar 24 '21 Develop Frontend Apps Faster with OpenAPI 3.0 and Prism Mock Server # devops # webdev # aws # api 12 reactions Comments Add Comment 7 min read Create Reliable & Easy-to-use APIs with API First Design Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Mar 19 '21 Create Reliable & Easy-to-use APIs with API First Design # devops # webdev # aws # api 122 reactions Comments 10 comments 7 min read Basics of Load Testing Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Jan 2 '21 Basics of Load Testing # testing # devops # jmeter # tutorial 8 reactions Comments 4 comments 6 min read User Review: Learning VueJS from vuemastery.com Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Aug 27 '20 User Review: Learning VueJS from vuemastery.com # vue # javascript 8 reactions Comments 2 comments 5 min read Tips for Customizable CloudFormation Templates Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Aug 20 '20 Tips for Customizable CloudFormation Templates # aws # devops # cloudformation # iac 12 reactions Comments Add Comment 9 min read User-Centric CloudFormation Templates Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Aug 20 '20 User-Centric CloudFormation Templates # aws # devops # cloudformation # iac 5 reactions Comments Add Comment 8 min read Deploy Lambda Applications with the Serverless Framework Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow May 21 '20 Deploy Lambda Applications with the Serverless Framework # aws # serverless # devops # python 21 reactions Comments Add Comment 6 min read Automate Deployments with AWS CodePipeline Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow May 17 '20 Automate Deployments with AWS CodePipeline # aws # devops # ruby 39 reactions Comments 16 comments 9 min read 9 Things I Wish I Knew When I Started Managing AWS Workloads 🤷 Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Apr 3 '20 9 Things I Wish I Knew When I Started Managing AWS Workloads 🤷 # aws # devops # beginners # architecture 44 reactions Comments 6 comments 9 min read More than 'Hello World in Lambda': Build and Deploy Python Flask APIs in AWS Lambda via CDK Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Mar 23 '20 More than 'Hello World in Lambda': Build and Deploy Python Flask APIs in AWS Lambda via CDK # aws # devops # lambda # python 28 reactions Comments 2 comments 9 min read Enforce security best practices in your AWS environment with AWS Config Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Feb 17 '20 Enforce security best practices in your AWS environment with AWS Config # aws # devops # security 4 reactions Comments 5 comments 8 min read Secure AWS Environments by deploying apps in Private/Public Subnets Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Feb 2 '20 Secure AWS Environments by deploying apps in Private/Public Subnets # aws # security 10 reactions Comments Add Comment 10 min read Deep Dive on AWS CodeBuild Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Oct 3 '19 Deep Dive on AWS CodeBuild # aws # devops # codebuild # docker 20 reactions Comments 2 comments 15 min read Deploy Rails in Amazon ECS: Part 5 - Integrate Sidekiq and Redis for background processing Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Sep 15 '19 Deploy Rails in Amazon ECS: Part 5 - Integrate Sidekiq and Redis for background processing # aws # ruby # devops # docker 36 reactions Comments 4 comments 7 min read How we lost $800/mo with Amazon ECS Fargate Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Aug 18 '19 How we lost $800/mo with Amazon ECS Fargate # aws # devops # docker # ecs 75 reactions Comments 22 comments 3 min read Deploy Rails in Amazon ECS: Part 4 - Create an ECS Cluster Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Aug 8 '19 Deploy Rails in Amazon ECS: Part 4 - Create an ECS Cluster # aws # ruby # docker # devops 35 reactions Comments 24 comments 7 min read Deploy Rails in Amazon ECS: Part 3 - Create the RDS database, Task Definition, and Load Balancer Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Aug 8 '19 Deploy Rails in Amazon ECS: Part 3 - Create the RDS database, Task Definition, and Load Balancer # aws # docker # ruby # devops 34 reactions Comments 5 comments 7 min read Deploy Rails in Amazon ECS: Part 2 - Push an image to ECR Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Aug 8 '19 Deploy Rails in Amazon ECS: Part 2 - Push an image to ECR # aws # docker # ruby # devops 23 reactions Comments 13 comments 6 min read Deploy Rails in Amazon ECS: Part 1 - Concepts Raphael Jambalos Raphael Jambalos Raphael Jambalos Follow Aug 8 '19 Deploy Rails in Amazon ECS: Part 1 - Concepts # aws # docker # ruby # devops 72 reactions Comments 1 comment 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 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://opensource.org/blog/focusing-on-legal-aspects-of-ai | Focusing on legal aspects of AI – Open Source Initiative Skip to content Get involved About Licenses Open Source Definition Open Source AI Programs Blog Get involved About Licenses Open Source Definition Open Source AI Programs Blog Open Main Menu Home Open Source AI Panels Focusing on legal aspects of AI Focusing on legal aspects of AI Transcript from October 18th Deep Dive: AI Legal panel Stefano Maffulli: All right, Well, thanks everyone, and welcome to Deep Dive AI. This is the third panel of an event series organized by the Open Source Initiative. We started with a podcast series with exploring how artificial intelligence impacts open source software from developers to businesses and the rest of us. And the objectives of the panels that we – or the series, the series of panel interviews is to better understand the similarities and differences between AI and classic software, Let’s call it that way, particularly open source software. Today’s panel is the third of four discussions. The next one is gonna be on Thursday and will be the final one. I’m Stefano Maffulli and I’m the Executive Director of the Open Source Initiative. And today I’m joined by Pamela Chestek, who’s the principal of Chester Legal in Raleigh North Carolina. She consults creative communities on open source brand marketing and copyright matters and prior to returning to private practice, she held in now’s position on, on footwear, apparel and high technology companies. And she’s she’s a frequent author of scholarly articles and you’ll find her on our blog. She’s an expert in intellectual property case law and certified by the North Carolina Board of Legal specialization trademark law. And she’s also a member of the OSI board. Stefano Maffulli: Thank you Pam for joining and you’re muted. So, Danish Contractor is an AI researcher working on problems in multi sentence question answering and dialogue systems. Very on point for the conversation today. Danish also chairs the Responsible AI Licensing Initiative and is also chair of the IEEE-SA working group on the responsible AI licensing. He’s also served as co-chair on the model governance working group at Big Science, which is an initiative of Hugging Face I guess, or hosted at Hugging Face. And he’s also was also named as the Top Innovators, one of the Top Innovators under 35 in India By the Indian Institute of Technology Daily, Oh, sorry, by the MIT Technology Review and Mint. Very, very impressive curriculum. Thanks for joining Danish. I’m very pleased to have you. Stefano Maffulli: Then we have Jennifer Lee. Jennifer is the Technology and Liberty Project Manager of the American Civil Liberties Union of Washington State. She advocates for state and local legislation to regulate powerful surveillance and AI based technologies. She leads the working group for implementing community focused policies related to technology, privacy, and civil liberties. She’s working with researchers, activists, and technologists to develop for communities the capacity to build a counter surveillance, I would say, and AI policy toolkits. So, thank you, Jennifer. New Speaker: Finally Adrin Jalali who we have misspelled his name on on the, on the panel here Jalali with an I. He has a PhD on machine learning for cancer diagnostics, and consultant for different companies on focusing on algorithmic privacy and fairness. Is also working currently for Hugging Face and where he maintains libraries related to fairness in machine learning. Stefano Maffulli: And ML Ops. Is also a core contributor to the open source packages scikit-learn and fair-learn. He’s also a member of the technical committee, contributor of NumFocus, the nonprofit supporting open code for better science. He’s also an organizer of PyData Berlin. Thank you, Adrin. Thank you for being here. Very pleased. So today I’d like to focus mainly on three topics. One is the AI, the fact that AI introduces new artifacts. And I’d like to understand the role of the current legal frameworks for intellectual property for these artifacts. And another topic is with AI and machine learning requiring lots of data. Copyright claims are appealing from every front, including unsuspected ones. What are the alternatives, if there are any, or are we going down the right path? And finally I’d like to talk about the governance and uses of AI systems and these individual artifacts, why it’s important and what’s the role of developers, society and regulators for this? Stefano Maffulli: So let’s start from the beginning. AI doesn’t seem to have, you know, doesn’t have source code or executable code like classic software. And with these new artifacts starting from datasets or models, the way it’s the outputs generated by the models themself, copyright seems to be applied now to all these artifacts, despite the fact that when software, let’s call it classic software, was introduced, it was, it was a conscious policy choice to go that way. So what are – what’s happening here? What are, is this a good choice to apply copyright and to models to all the pieces and components, or what are the options we have available? Pamela Chestek: I’m happy to jump in on that one. I wanted to first sort of convey this sort of interesting exchange I had with a client yesterday who was attending an I dunno, machine learning or AI seminar. And she said, Oh, the speaker for IBM said that the datasets used to train AI, that’s fair use. And I said, Well, not so quick. I don’t think, like, no, he’s definitive. And it all comes out of basically this one, you know, Google, Google’s use of data in order to provide its search engine results was, was where this comes from. So, and since, since I had that conversation with her, I saw a Twitter thread that was sort of, you know, questioning that thesis. I saw there was a blog post that the R I – the Recording Institute of America is, is claiming that the use of their music for training is a copyright infringement. Pamela Chestek: So first off, just even on that very fundamental, that first piece of this data that we use, is it fair use? Which, you know, I think that’s really a big question to start with. I personally would not advise my client who’s gonna build a business on it and say, You’re home free. It’s fair use. I don’t, I think there’s a lot, a lot to, a lot to go into sort of this, your second premise that copyright covers all of this.I don’t think that’s been explored in any sense or way whatsoever. I think everybody’s just kind of speculating and assuming, but when the courts get to this, I think there will be a lot of unpacking because of this premise that ideas and functions are not copyrightable. I think that it’s gonna tease out and, you know, maybe it will be a matter of how good the advocacy on it is in favor of, of, for or against the fact that this is, is covered by copyright. I just, I think that’s still, we just don’t have a clue whether or not where and what pieces of this copyright will will apply to Stefano Maffulli: Danish. I see your mic up Danish Contractor: Yeah. So I think that that’s fair. You know, like it’s, it’s an area to be explored in terms of, I don’t think we’ve got definitive answers of what can be covered at the copyright and what can’t just yet. But I think at the end of the day, we have as researchers, we are releasing code data models and applications every day. And they’re, and it’s just by precedent that we’ve been all releasing them under open source licenses. So I guess if we unravel the debate on what’s really copyrightable or not, I think we’d have to go across everything that, you know, the software industry has been doing. And I think that could play a part in how that shapes up over time. Stefano Maffulli: There is definitely that. I saw that Twitter thread last night too. It was fascinating because there is, like Danish was saying, there is already a huge amount of conversations, not only conversation, but also elements artifact, let’s call it artifacts released assuming that the copyright applies. And what’s surprising to me is that the conversations we are hearing from people who have been promoting open access, open data, open science, and the Motion Picture Association sound start to sound very similar. Like there is, there is on one hand the intention to progress and to collaborate. On the other hand, there are restrictions that are being lifted all around with different explanations. Like Jennifer, one of the, one of the conversations I’ve heard, one of the topics I’ve heard mention multiple times is these balance of power like this fact that there is so many picture, for example, so many pictures available to people that have been data mind that have been and these are being used for surveillance like for nasty uses by either government or bad actors in general. Stefano Maffulli: So what are your thoughts on this front, like this massive availability of data? Jennifer Lee: I mean, it’s really concerning and I think, you know, whoever holds these data sets, whoever’s collecting the data you know, however it’s being used. I think when we think about developing new tools and collecting information, we really need to be thinking about the end impacts on who’s going to be the most harmed. And I, you know, though technology has advanced tremendously over time, I think it’s really important to remember that surveillance isn’t a new concept. People have always been surveilled over history and new ways of gathering enormous amounts of data just make it really easier to target historically marginalized communities. So, you know, one thing that we’re working on with the ACLU of Washington with our tech equity coalition is to bring that history of surveillance to the forefront of any conversations we have about technology development, deployment and regulation. Jennifer Lee: I think also this topic of data collection in conjunction with automated decision systems and algorithms is intricately tied to the conversation on data privacy and how we can stem the flow of data, data being collected and shared by both government and corporate actors. So there’s, it’s really interconnected. I think the surveillance laws that we’re working on, the privacy laws that we’re working on and the AI regulation laws we’re working on are, you know, they all impact each other. I hope that answers your question. Happy to elaborate. Stefano Maffulli: Indeed. And Adrin speaking of – okay, go ahead. I see you raise your hand. Adrin Jalali: Like another thing that I I think about going back to, to the kind of licenses, not every data that is used has no license or is vague. For example, when you look at GitHub’s Copilot most probably, most of the code used, most of the data used to train that model is licensed. And to me, when I license my software, I’m giving a sort of consent. It’s like I’m allowing people to use my product, my work in certain ways. And I don’t think I’ve ever answered the question, do I think that another organization should be allowed to train a model and make a profit out of it from the code that I have? And those things are licensed. It’s just that in those licenses, those questions are not answered. So another question is both with creative commons and the open source licenses, should we go and answer these questions in those licenses as well? Or are we just waiting for the court to come and say, according to that license, this is fair use? Or you’re allowed to do that or not. Stefano Maffulli: Pam? Pamela Chestek: Yeah, I think the, you know, relying on fair use, I think this discussion sort of elaborates, sort of illuminates, why fair use is sort of, it’s not maybe the right bucket or it’s not a great bucket because so as because, so for example, what Jennifer – there’s a huge difference in my mind. There’s a huge difference between collecting data on people’s images and then using that for the police, for example, using that to identify someone. They’re not doing any training with that. They’re just copying those images for – there’s no transformative use, which is sort of a primary piece of fair use. There’s no transformation there. They’re just using it for comparison purposes. So I think the fair use claim for that kind of use is quite different from a machine learning fair use. On the flip side, kind of to respond to Adrin yes, a lot of this stuff is licensed, but the problem with the license is you know, that is someone who is, who has agreed to use this data, and that isn’t necessarily going to be a great data set to be used to, to use for training. Pamela Chestek: So for example, you know, if copilot is trained on open source on all open source license, but no proprietary license software, is that really a great way to do it? Is that going to be, is that great, is that the best way to train the model? Is that a model that’s going to come out to be a well trained model? So, on one hand, I understand and I believe this is happening in other countries. I understand the value in not allowing the, the sort of owner of that data or the owner of those copyrighted works to have a say in whether or not their works are used for training. Because the training requires, you know, a reliable data set and if you are and if, if you’re only getting it voluntarily, maybe that’s not the best choice. Pamela Chestek: So I just wanted to kind of tease out sort of the problems on both sides of, you know, choosing, you know, of relying on fair uses. It would actually, I think, be great for Jennifer’s case. Cuz I don’t think that’s a fair use case to say that these images can be used for comparison. But maybe it should be for machine learning. So maybe fair use is the best way to tease that out. I don’t know. But fair use, again, notoriously difficult, only courts know when something is a fair use. You only know at the end of the lawsuit whether or not it was a fair use and you just spent, you know, if you’re Google and Oracle, you just spent a hundred million dollars to find out the answer to that question. So anyway, just pointing out sort of the legal problems we’re facing, Stefano Maffulli: Those are non-trivial legal problems. But I wanna go back a little bit to the original thought that I have that in the end, copyright was a sign consciously, it was some somewhat of a conscious decision to decide that software had to be covered by copyright. And it took like 15 years before a court case basically settled the argument in the United States. Now we are, like Danish was saying, we are basically rolling with the idea that we can assemble data sets and release them with licenses that have been threatened with the and even specify sometimes the concept of source code or executable code. And these artifacts don’t seem to matter that much. Are we really going in the right direction here or shall we stop and think and maybe come up with policies, suggestions? Danish, what are your thoughts on copyright? Is that, you know, acceptable? What, what do you hear from your communities? Danish Contractor: So I think, so, I’m not a lawyer, so I’m not sure, you know, like what the principal stance is on how copyright has been interpreted in different judgments. But I think as a community it’s, we’ve just been accepting that these are copyrightable artifacts, right? Because otherwise, if you’re applying creative commons licenses or data sets, if you’re applying apache 2.0 licenses on models, if you’re applying RAIL licenses more recently, there is an implicit assumption that the community has already made that these are copyrightable artifacts. Now, if that were to change either by law or by a court judgment, then I guess this would turn a lot of the arguments that, you know, the whole community relies on upside down. So like, I don’t know, I think the community as a whole has more or less accepted that a lot of these are copyright. Danish Contractor: But I think only now that we are starting to see generated models, for example, use art or code in some instances of, or without consent for particular applications. Is the question of copyright coming for even more, because if I’m copying somebody’s artwork in a particular style, do I have, is it transformative work? Am I, did I have permission to be able to do that? Was that permission explicitly granted, I think that’s what’s really leading to the discussion on copyright, because otherwise when you know, for all these years we’ve been releasing bill sets models, code with copyright and as a given. Stefano Maffulli: Right, bringing a very interesting, you know, widening the topic here and talking about the models and the output of those models also they involved conversations around copyright and what’s protectable. What are your – where are your thoughts, Pam, on this front? Pamela Chestek: Yeah, I – it’s interesting, I think it’s an interesting social, I think it’s very different in terms, again, I guess, and I’m still focused on the data used to train models we haven’t even gotten yet to kind of the discussion on the rest of it. So, you know, the data to train models, I think, I don’t think there’s a one size fits all answer because training models on artwork, artwork is very clearly copyrightable and no one would dispute that. What about training a model on weather data, for example? So that’s, I don’t, nobody would say that the weather data is copyrightable, the compilation of the data – the compilation of it, no data point is copyrightable compilation maybe to that’s true under US law, true under EU law. But, but it’s, you know, may not, it may also may not be it would be a very small a very tight copyright that would, you know, a very thin copyright we would call it on if there is and only on, so it would only be on the selection and arrangement and coordination of that data, how that data is assembled, which you then, you know, unpeel to, to do the training. Pamela Chestek: So even kind of looking at it that way, and I think Danish, you know, what we see as a manifestation here of, I’m gonna put a license on it, on the assumption because it’s beneficial, because then people are clear, They don’t have to ask, they have to worry about this question, is it copyrightable? They don’t have to worry about is it okay if I use this? They have the answer for it. Because the person who has control of that data has expressed an opinion on it, and we can rely on that opinion and we can rely on that opinion in court, for that permission. So that’s one of the reasons that those licenses get applied. I think that’s, it’s, it’s net positive in this case. We can talk in other situations about, or the appropriateness of applying licenses to stuff that should be freely available to use for everyone. But it makes sense because then, you know, if you want people to use this data, they know they can. So that’s very beneficial. So I think applying a license, you know, is the safest thing to do in this world of we don’t know what’s going on. Stefano Maffulli: Adrin? Adrin Jalali: I think we can also look at different areas where these discussions have been had for a while. For example, when you look at healthcare, if I take DNA samples from a bunch of patients and I develop a drug, do I, can I own the drug which is derived from the data that clearly I don’t own? It’s somebody, somebody else’s DNA, or if I go to the doctor, can that data then be used by researchers to do healthcare related research? And different countries have very different approaches on that. Like I think if you go to Denmark by default, like researchers can use that. If you go to like other countries, they can’t. They have to have the explicit consent. And one to me, one of the resistance that we’ve had these discussions is because of the potential harms of using and leaking using healthcare related data and that data being leaked. Whereas we haven’t been having that discussion on voice and image and like I dunno, everything that people produce because we haven’t necessarily thought about the potential harms, but these harms are now real deep, fake is very real. Producing somebody, like producing art, using somebody else’s art is really real. And these have either financial or reputational harm like to people somehow the part of the community working on this part is not necessarily connected with these potential harms as much as the healthcare communities, or at least that’s how I feel. Stefano Maffulli: Jennifer, what do you see in terms of potential harms also? Like what are your thoughts on this? Jennifer Lee: I mean, financial and reputational harms are definitely just a few of so many harms that arise from non-consensual data collection. Whether or not that’s used by companies or just individuals trying to use that data for whatever purpose. Those harms can lead to stalking, domestic violence. It can lead to police violence, just like data can be used in so many harmful ways that just can seriously lead to life or death consequences. I think the healthcare example is a good one because we do have strong healthcare laws, but you know, currently we don’t have healthcare laws that cover like non HIPPA covered data that’s like data with healthcare apps or just, you know, fitness apps or just data collected location data collected by your phone, even via a weather app that could, that could be used for health purposes. Jennifer Lee: That’s concern, that could be data used to track people who seek abortions, for example, in the US. So I think thinking about the harms is really, really important when we’re talking about, you know, whether or not we’re talking about proprietary algorithms or open source algorithms or, you know, what types of datasets there, what is the end result of the impact of how that data’s going to be used. And I think that’s something that companies, governments, and individuals all really need to be thinking very carefully about. And you know, to answer your earlier question about policy regulations, I don’t think that in the policy space where even at the point of thinking about regulating different types of data sets or different types of algorithms like open source or, or not. So you know, I think the conversation about AI regulation is just starting. People have been talking about it for years, but in terms of policy, like actual laws around regulation, at least in the United States, that’s something we’re getting to just broadly. Stefano Maffulli: Right. No, there is definitely a lot of action on, on the regulation front, and I think we’ll, we’ll get back to that conversation given towards later on because I, I’m interested in diving a little bit deeper, and I see a little question also from the from the chat here about the another artifact that is, that is being distributed and is being applied copyright. I don’t know if you, if anybody wants to, wants to take it from the, from the chat. Pamela Chestek: Yeah, I’m, I’m happy to, I’m happy to. Cause I would like, I’m really curious, like I think that the datasets is, the datasets is sort of the, what, what’s most familiar to us, so maybe it’s the easiest to cope with. And so I, for the benefit of people who aren’t reading the chat, Emily wrote, I’ve seen copyright licenses applied to values for parameters of trained ML models. I was under the impression that this application of copyright would be similar to data, which is thin or none, As Pamela mentioned, I’m curious what the panelists think about whether copyright is a suitable legal protection for parameter values for ML models. So that, this is where I’m sort of going back, going back to what I originally said was, we don’t even, you know, we don’t we have, I have no idea. Pamela Chestek: I mean, personally, my view would be, no, I don’t, I don’t think that, I guess exactly what Emily says is if there’s any, I don’t, my inclination is there’s no copyright protection of parameters, copyright, creative works. So, you know, if we go back to that very sort of fundamental principle of copyright, what’s it for? I’m like, I, you know, I don’t know whether I sort of doubt that parameters would be covered by it. And my question back to Emily was who was doing this? Like and kind of go back to what I said to Danish, which is, are they doing it out of good faith to try to, you know, to put a license on it so that everybody understands, or were they doing it to be exclusionary? Which I think, which is also going to be the case, is people are going to try to claim some kind of, or exclusive, right? Pamela Chestek: You know, they’re gonna be over, over expansive. And that’s where the courts would theoretically come in and say, No, actually, you know, that’s not entitled to cover it. There’s no way to protect this. You know, just for example, parameters. And Emily replies, the example I’m thinking of is AlphaFold. They had the parameters subject to a Creative Commons license with noncommercial use restrictions, but recently changed it to allow for commercial use. I’m not familiar with AlphaFold, but that kind of you know, that kind of illuminates what I’m saying is, you know, what, what was their motive was benevolent or was it, you know, exclusionary. Danish Contractor: So I think you know, we have to just think about this or broadly, right? Without going into the specifics of say, AlphaFold or any particular machine learning model. So when you’re doing machine learning, what are you doing? You’re taking your data set and you are training a bunch of matrices in, you know, in today’s terms and getting values for those matrices that result in certain outputs, right? Effectively, that’s really what’s happening. And you’re learning what those matrices should do, and some other functions basically to just transform your input data in a way that’s stored in some mathematical values and numbers, which then lets you do what you’re trying to do for your end task. Now, I don’t know whether courts would view this as a transformation of data or not. It’s hard to say. But then this, the fact that I have got this model with certain values is something that I have got after figuring out what architecture, which is what code blocks I want to use, what what data I want to use, how long do I wanna train this, what is, what should be my learning rate, what should be my bat sizes. Danish Contractor: I’ve done a lot of thinking behind how to create this model and the parameters values that’ve been satisfied with for a particular end task or for whatever I’m evaluating a model for. And that learned state of a model is basically what we call a model when we are releasing a model as we speak a, as we call it. So now the question of copyrights. So, this artifact is not something that, you know, I could have done or how I could have done without really spending all of the time and effort that I just described. And to share that particular artifact. We could call that maybe software, maybe that could be one interpretation, or maybe it’s the form of transformative data, I don’t know. But it is an artifact nonetheless that can be shared and distributed. And if I’m doing so I guess, you know, like you said, Pam, it’s not something that we know how, how courts would view, but it’s, it’s still an artifact that researchers tend to distribute as a whole. Danish Contractor: And just by norms of the community, they have been attached with licenses that view them as copyrightable. Now, once they’re viewed as copyrightable by the community there have been open licenses, there have been licenses that restrict certain applications just because of the harm they could do. For instance the big signs Bloom LLM anticipated certain harms from a particular model and attached some restrictions on use on, on the weights of the model. I believe Stability AI also has done, has done some, and there are a whole bunch of other models that have also been released open source, some have restricted commercial use. It’s not just AlphaFold, OPT-175B from Meta has done this and, and a whole bunch of other models have also applied the same paradigm or approach to copyright as researchers view these as copyrightable artifacts. Yeah, I guess that’s what I would say. Stefano Maffulli: Yeah. Thanks. Adrin? Adrin Jalali: I think we also can’t completely separate the license of the model, the weights of the model from the license of the dataset, especially for very large models. Those models are in effect a database. They, they, they are really good at memorizing the data. When you go in the, in the, like the privacy field, you can extract a lot of the dataset from just the weights. Which is why, like, like, a lot of privacy concerns have been raised from there. And therefore, if I don’t, like, if I don’t have a dataset that I can release, then the question is, can I release my model? If, if people can extract a lot of information from that model, and I know that that is causes some harm, or I don’t have, I’m not allowed to release the data, how can I then release the model? Adrin Jalali: That’s one aspect that I think we don’t necessarily have answers to. The other one is we have talked a little bit about use. For example, in the OpenRAIL license, we talk about like, what are the uses that we want to avoid and what are the harms we want to avoid, but what are the types of modifications that we would like to allow or avoid from having? For example, if I have a model that I put some safety mechanisms in, for example, these days we talk a lot about certain biases that are like creeped into the model. And imagine that I could have mechanisms to avoid those. Can somebody take my weight and release my model? Can somebody take my model and remove those mechanisms and create a really harm, harmful, really biased model as people do? For example, we have this bottle that like goes and like generates, gets like further fine tuned on a terrible data set and starts being about producing really terrible content. Could I avoid that? Can I avoid that by only limiting the uses? Or can I start talking about what are the types of transformations that people are allowed to do on this model that I’m releasing? Stefano Maffulli: You’re introducing two wonderful topics that I wanted to talk about. One is the concept of harm to these AI models that or systems that I hear mentioned very often as of necessity to create special cases for AI different from any other dangerous tools that we have deployed in the past. So who, who of you wants to take it you know, introduce this concept? Why is AI so much more harmful than anything we’ve seen before? Danish Contractor: Without saying whether it’s helpful or not, it’s very different from software that we’ve seen previously, especially when it’s machine learning software. There, you know, so a lot of the times when we think about restrictions on use on AI systems a question that props up is what’s so special about ai, right? What, what, we could have done this with software harm can be derived from software. I could have a simple sorting algorithm that could sort people by height, and I could just have a threshold to say, I’m not going to allow people below six feet to apply for a job that’s harmful, right? And I have not used any ai, and it’s a simple source code. Now, do you wanna license sorting algorithms with restrictions of use? That’s, Stefano Maffulli: Yeah, I heard more specifically comments around software that guess is password, like password crackers or anything that is related to security research where, you know, these are dangerous tools and they’re freely available without going into conversations about, you know, gene editing or other technologies that are potentially harmful but still regulated in different ways than than software. Danish Contractor: Yeah, so AI is not regulated, right? It’s hard to define what is harm for regulation. So now in, in the interim period, right, you do – we do recognize that AI systems are different from traditional software in the sense that at least machine learning based systems, you don’t have the same amount of testing that you can have with regular software systems. So for instance, if I knew that my regular source code was having a particular error, I could probably reliably fix it once it’s identified. Now, if you tell me my generative model is producing a harmful piece of text, stop that. I don’t know whether as a machine learning creator, I know how to do that. I can maybe suppress that output, but if I try retraining, I don’t know what else I’m gonna be breaking. Danish Contractor: And there is nobody who can give you those guarantees about what’s happening. Even things like confidence. You can’t even be sure that if you want to have thresholds around confidence, that’s gonna be a reliable measure of restricting harm. So I think because of the fundamentally different ambiguity that you have with the operation of AI systems and the lack of guarantees, or even quantification of how good or bad an AI system might be for particular use cases barring some evaluations, some test sets which already have some bias built to them, there may not be reflective of real world harms and so on. It’s different, which is why as machine learning developers and creators, we need to anticipate possible harms, even if by even if, based on the limitations of the work that we’re developing. Stefano Maffulli: Right. Adrin I was going to you because I wanted to hear about your fairness. How do you assess that? But go ahead. Adrin Jalali: Oh, before even assessing I think one fundamental difference between AI and software or AI and humans is we, humans are very causal creatures. We understand those causal relations, and if you tell me why you make a decision, then it’s much, much easier and much more intuitive for me and a society to decide whether it was fair or not. Whether that was a harm that was okay for me, like, was it okay for me not to hire you or not? Whereas when we talk about AI systems, in most cases, we can’t necessarily interpret them. We don’t necessarily have tools or we don’t use the tools that would give us the explanations on why a system made a decision the way they did. And I think that’s where regulation would be really, really useful. Cause as long as this is not regulated, I don’t see companies going and trying to figure out, okay, like when I have a model, I have to also have the explanation. Adrin Jalali: If a customer comes and asks, Why did you not give me the loan? I can just tell them, Well, the computer said no and nobody questions that. Whereas if I force everybody, no, you have to give an answer to that question then I think the field would move towards a very different direction and we would be much more comfortable regulating them. But I don’t think this is something that would necessarily go in the license. Like when it comes to harm, to me it’s a, it’s a lot more of the matter of regulation than it is licenses. We shouldn’t be doing certain things no matter whether the person releasing that software or that model was okay with us doing that or not. Stefano Maffulli: I saw Pam and Jennifer Pamela Chestek: Actually, I was – I was going to, Am I frozen? Stefano Maffulli: No, no. Pamela Chestek: Okay. I was going to, I was going to ask a question for Jennifer because this is, and Adrin sort of led, it was kind of leading exactly where my question was, is sort of what is the, what is the role of regulation and what is the role of, you know, an individual one-on-one relationship of a license and, you know, how do we decide where the appropriate control for harm should lie? Jennifer Lee: That’s a really good question. And when the question was asked about how do we define harm, I, you know, the first question I thought of was, who actually gets to determine what’s harmful? Is it, you know, is it regulators? Is it the developers? Is it people who are using these technologies? It’s typically not the people who are actually experiencing the harm. It’s usually a very top down approach that you know, I think leads to the exacerbation of existing societal biases and existing harms. And you know, as it was mentioned, I think it’s, it’s concerning because the use of these types of systems often hides and legitimizes biases that the people who are developing, regulating these systems may take as the status quo, may take as a norm when those are norms that are quite harmful to individuals. Jennifer Lee: You know, I think of harms. So I should just give you a little bit of context. So I mentioned the tech equity coalition earlier, and these coalition members are not technologists, many of them are not lawyers or policymakers, they’re people who have lived experience of harms caused by surveillance and automated systems and artificial intelligence and just technologies in general. And, you know, I, I think that there’s a disconnect between people who are trying to regulate these systems via either by licenses or by laws or litigation. There’s a disconnect between people who are experiencing these harms and, and saying, Don’t build this technology. Not just like, how do you mitigate those harms? A lot of people are saying, don’t build it at all. You know, don’t use this, don’t use data. You know, for us, we don’t have control. And, and that’s really harmful. Jennifer Lee: A lot of volunteer developers who are using open source data sets are, are not going to be from these communities either. So I think there’s a larger question of power, and it’s, it’s, you know, it’s not an easy fix. It’s structural, it’s societal. But I think regulation can go a long way in mitigating some of those harms. Requiring transparency and accountability for these sorts of technologies is really important. But ultimately I think the question I ask when I look at proposals is how is power distributed? Like, who, who’s going to ultimately have to say in whether or not a system is deployed? It’s often not going to be solved completely by regulation, but, you know, I think it is, it is a step in the right direction. We’ve seen a number of proposals but I, I think what one thing that might help you know, is, is requiring that transparency, but people who are developing these technologies, people who have understanding of of artificial intelligence really partnering with community and being led by community, impacted community in, in decisions about, you know, whether not just like how to deploy, but whether that technology should even be used and the limitations mentioned by Adrin should be, should be led by those, those who experience those harms. Stefano Maffulli: Right? Yeah. There is an interesting system of incentives here at play also that society needs to think about. I guess the issue – you raised your hand? Danish Contractor: Yeah. I just wanted to make a quick comment on something that Jennifer just said, right? So I think working with – so anticipating harms and limitations of technology is an important aspect that all I think developers should consider especially in AI, just because of how they can be repurposed for things and how they can be repackaged into larger software systems that they were not originally designed for. And I think I would, you know, I would argue that, you know, it’s not just regulation because I think not everything can be regulated. It’ll take forever to really piece out every small possible use case with different circumstances and then have regulation for it. I think even at the point of release, if developers are aware of certain limitations and restrictions, I think those should be made part of terms of use, because that only just gives enforceable mechanisms for preventing harm. Otherwise, if you don’t even put that in your terms of use as, as a creator, as a model creator, as a developer, I don’t, don’t even have rights to enforce anything. Regardless of whether that’s copyright, believe me, you could always rely on contractual law if it’s framed appropriately. Stefano Maffulli: Yeah it’s an interesting thought and one question that keeps dancing in my mind is whether we are ready, like basically Jennifer has, has mentioned this a couple of times, don’t release it. And between Adrin and Danish also, you, you somewhat said, we don’t know how to inspect this. We don’t know how to verify this. We don’t know how to fix this in case it’s creating harm. So I, I can understand the, the push of, of putting terms of services and, and sort of other limitations or being, being somewhat more careful than if you were, or as you were handing a loaded gun – you were about to say something? Pamela Chestek: I understand Danish’s sentiment, but I think the reality is perhaps somewhat different. So just because you have an enforceable mechanism, you have a mechanism are you actually going to deploy that mechanism? Are you going to enforce that mechanism? Are you going to go after, say, if someone uses one of your models for harmful purposes, are you going to pursue them or not? And will they care? And you’re gonna spend a ton of money to maybe achieve nothing? And I, I, this, this, we actually know this happens because we know in the open source industry, there is very, very little enforcement. And one of the reasons is that it, that it’s, the thought was, you know, it’s, it’s the licensor who has the right to enforce that license, and they’re not motivated to enforce it then, you know, so GPL violations are observed more in the breach than the observants, you know, that, that, that GPL violations happen, You know, I mean, I can’t even tell you the, you know, sort of the order of magnitude that they’re happening on. So you know, that’s, I think that maybe government clout maybe is a little more worrisome to people than the thought of a, of a license enforcement Stefano Maffulli: Danish. Danish Contractor: So I think enforcement of licenses Pamela, I think our – is a universal problem, right? Whether or not you’re putting terms of use, I think that’s true for open source. I mean, software piracy by itself is a multi-billion dollar industry. And, you know, there are things that people can do. I mean, you can only do as much as you can, but I think, I wouldn’t view that as an argument or not in your terms of use. I think terms of use – cause every is – a bad actor by intention, but it could also be just inappropriate use because I’m not fully aware of the limitation of model. I may want to, so I think for instance, you know, you had this, I’ll just make up an example. For instance, you had this, I’m sure you all see the story where, you know, like a hand washer or a hand dryer does not work for certain skin colors. Danish Contractor: It’s innocuous, perhaps less harmful. I mean, it’s exclusionary, but probably less harmful if it’s deployed in a bathroom to dry your hands. But it’s extremely exclusionary. If it was just as a, you know, wave and a door will open for an accessibility option. And so if, for instance, the model developers had even done some testing and release terms of use to say, you know, this has not been tested in the wild, This is just a sensor that we’ve developed for a certain, with, with this particular dataset. Do not use this outside unless you really test it for certain applications. It’s a terms of use question. And if it’s being repackaged and reused by somebody else, for certain thing, least I have developer rights to enforce, which I wouldn’t have otherwise. I’m intentionally picking relatively less time, for example. But it’s not hard to imagine how these harms can be excavated even for things like machine translation systems or, or things that you would otherwise view as innocuous not applied in real world, high risk situations that caused bodily harm. Stefano Maffulli: There’s definitely that sort of question. I wanna go back a little bit into to the objective also of creating these datasets and sharing the knowledge that research community and users in general, they been they’ve been doing with the, for the open source, in the open source world, the intention has been created, a commons – so create rules that are shared and understand and remove friction so that science can progress much more quickly even, and taking for granted the fact that, or you know, that the risk of of misuse or the risk of of harm would be handled in some other, in a different way. How might, how much consciousness is there into choosing to put barriers? Here it is, you know, is this a conscious choice or iis – it’s more like, oh, I wanna stand back and be safe, rather, because there are no tools to, you know, to fix this if this goes outta hand. Is there more fear than there was maybe at the, at when software and open source software was coded? Stefano Maffulli: Adrin, what, what do you think? I mean, you’re, if the concept of fairness in AI still amuses me, I, I’d like to hear more about that. Like how do you measure all of that? Adrin Jalali: So, fairness is very, very closely tied to harm. I don’t, I’m of the position that it doesn’t make much sense to measure any kind, fairness if we don’t know what are the potential harms. And that has a lot to do with the use case. If anything more to do with the use case, then it has to do with the model and the dataset itself. You can, you can imagine the same model that can be useful in a certain scenario, and it can be extremely harmful in the other one. So if I don’t, if as a model developer, if I don’t know where it’s going to be used, I don’t, I should not know what kind of like, what, what to measure in terms of sense. So that’s one aspect. The other aspect is different when we talk a lot about fairness, but I feel like the community talks about fairness as if it’s a well-defined measurable concept as, as a construct, as a social construct. Adrin Jalali: Fairness is contested, it’s not well defined, and different definitions are contradictory with the other ones. There are so many different versions that, for example, if you tell me that I have this model and I don’t want it to be sexist, for example, I can go and come up with a fairness metric that according to which your model is not sexist there are enough to choose from. The other issue is ideologically those fairness metrics also don’t necessarily agree with one another. And one thing that I don’t think we talk enough about is what would society look like if I optimize for this specific fairness metric? What are the implicit assumptions that I’m making? What are the normative assumptions that I’m taking and judgements when I take a metric and I try to optimize for that. Those are the conversations that I don’t think we’re having like enough of these things. Adrin Jalali: For example, if you take a hiring example, this is a very common, like I’ve had this conversation multiple times, which one is fed? I should hire proportionally… Like I should hire STEM graduates proportionate to the graduates that they are out there based on their demographic. If I look at gender, if I look at ethnicity if there is X percent of people graduating from this field, I should also hire X percent. That reflects reality. Is that a fair system or would you rather have an organization that reflects more of your ideal world rather than the world that exists out there? Do you want to be pushing the world forward towards what you think is a better world, or do you want to have a system that simply reflects what it’s out there? So when we talk about fairness, I like, I don’t, what is the, what is the harm that we are talking about and what is the world that you want to build? Stefano Maffulli: So it looks like everything goes back into incentives and how society needs to, needs to adapt. And Jennifer is, is this, what do you see from regulators in this space?Is there consciousness in their choices when it gets to adopting tools or systems that are AI machine learning based? Jennifer Lee: I think there is, and I think the question of fairness and, and the way Adrin laid it out is, it really is really clear. And, and I think there’s a difference between fairness and justice. And I think what we’re aiming for is justice, not just pure fairness. It’s not just division and equal proportions. It’s not just reflecting the world that we see because our existing world is incredibly unjust and it’s a fair upset of the population, but not really fair to others. So it’s an unjust situation. I think regulators are, and lawmakers, they’re, they’re increasingly realizing this you know, after the murder of George Floyd in 2020, a number of not just regulators, but companies started to place self-imposed moratoria on their sale of facial recognition to police, for example. And in the past few years, we’ve seen bands on different types of technologies like predictive policing tools and facial recognition, but there are just many, many other tools in use that, you know, have some beneficial applications, but also have both intended and unintended consequences that cause real harm to communities that have always been over surveilled, over police and marginalized. Jennifer Lee: And I think that’s why, you know, I talk about the history of | 2026-01-13T08:49:44 |
https://zeroday.forem.com/shiva_c74698f901616e10e57/tor-or-onion-browser-which-one-truly-protects-your-privacy-in-2025-3kf | Tor or Onion Browser: Which One Truly Protects Your Privacy in 2025 - 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. 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 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 shiva Posted on Dec 1, 2025 Tor or Onion Browser: Which One Truly Protects Your Privacy in 2025 # discuss # networksec Online privacy has become a major concern in 2025. With tracking systems, data brokers, advertisers, and cyber-surveillance growing stronger, people are looking for secure ways to browse the internet anonymously. Two of the most popular tools in this space are the Tor Browser and the Onion Browser. What Is Tor Browser? The Tor Browser is a fully featured, open-source web browser designed to keep users anonymous online. Tor routes your connection through multiple volunteer-operated servers called nodes. Each node only knows the previous and next step in the chain, making it nearly impossible to trace your identity, device, or location. Tor Browser comes with built-in privacy protections such as: Automatic tracker blocking Anti-fingerprinting features No browsing history stored Multi-layer encryption Protection against ISP monitoring Access to .onion sites (Deep Web) It is available for Windows, macOS, Linux, and Android. Tor is widely considered the best option for strong anonymity. What Is an Onion Browser? Onion Browser is a privacy-focused web browser made specifically for iOS (iPhone/iPad). Since Apple restricts the use of certain networking features, the Onion Browser cannot operate exactly like the official Tor Browser. However, it still uses Tor network connections and provides a good level of anonymity for Apple device users. Key features include: Tor network routing Basic anti-tracking protections Option to choose security levels Protection against most IP leaks Encrypted browsing on iPhones Onion Browser is ideal for users who want Tor-like privacy but are strictly using an iPhone or iPad. Tor Browser vs Onion Browser: Major Differences Platform Compatibility Tor Browser: Works on PC, laptop, and Android. Onion Browser: Only for iOS devices. If you’re using Windows, Mac, Linux, or Android, the Onion Browser isn’t even an option. Level of Anonymity Tor Browser offers higher anonymity because: It includes advanced fingerprint protection It blocks scripts by default It uses modified Firefox built for privacy It removes metadata and reduces browser variation Onion Browser provides strong protection, but its limitations on iOS mean it cannot fully match Tor Browser's anonymity features. Security Features Tor Browser lets you choose strict security modes, disable JavaScript, and prevent most browser-based attacks. For journalists, activists, or high privacy seekers, Tor Browser is considered more secure. Onion Browser is secure but depends heavily on iOS system restrictions, which limits customization and deeper protections. Speed and Performance Tor Browser is slower because of its stronger, multi-layer routing. Onion Browser may feel slightly faster but still slow compared to normal browsing. Remember: More privacy = Less speed. Which One Should You Use in 2025? Choose Tor Browser if: You want maximum anonymity You use Windows, Mac, Linux, or Android You visit .onion websites You need strong anti-fingerprinting protections You are concerned about surveillance or tracking Choose Onion Browser if: You are an iPhone or iPad user You want Tor-like privacy on iOS You browse casually but want to avoid trackers and surveillance You’re not accessing high-risk or sensitive websites Conclusion In 2025, both Tor and Onion Browser offer strong privacy protection, but Tor Browser remains the superior tool for true anonymity. Its advanced security features, wide compatibility, and deep privacy layers make it ideal for anyone serious about protecting their identity. Onion Browser is still a great choice for iPhone users but cannot fully replace Tor Browser’s power due to iOS limitations. 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 shiva Follow Joined Nov 27, 2025 More from shiva Why Medical Devices Are Now Prime Targets for Cyberattacks # iot # networksec # news 💎 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 | 2026-01-13T08:49:44 |
https://dev.to/pockit_tools/pnpm-vs-npm-vs-yarn-vs-bun-the-2026-package-manager-showdown-51dc#yarn-4x-berry | 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. 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 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 . 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https://zeroday.forem.com/shiva_c74698f901616e10e57/why-medical-devices-are-now-prime-targets-for-cyberattacks-191g#comments | Why Medical Devices Are Now Prime Targets for Cyberattacks - 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 shiva Posted on Nov 28, 2025 Why Medical Devices Are Now Prime Targets for Cyberattacks # iot # networksec # news In today’s digital healthcare ecosystem, medical devices have quietly moved to the center of cybercriminal activity. Devices that once operated in isolation like pacemakers, insulin pumps, ECG machines, ventilators, and infusion pumps are now fully connected to hospital networks. The Shift from Offline Machines to Connected Medical Technology Years ago, most medical devices functioned offline, storing data internally and rarely communicating with external systems. Today, healthcare has shifted toward cloud-based monitoring, real-time data analytics, remote reporting, and IoT-enabled devices. This digital transformation creates a massive attack surface. Weak Security Architecture in Legacy Devices Many medical devices in hospitals are outdated. They run on old operating systems like Windows XP or unpatched Linux versions. Because replacing medical equipment is expensive, hospitals continue to use legacy machines for years, even decades. No encryption Weak or hard-coded passwords No firewall No automatic updates Unpatched, known vulnerabilities Medical Devices Store Highly Valuable Data Cybercriminals want data that can be used, sold, or exploited. Medical devices store some of the most sensitive information available: Patient medical records Real-time biometric data Treatment history Hospital credentials Device configuration files This type of data is more valuable than credit card information. Medical records sell for a high price on the dark web because they can be used for identity theft, insurance fraud, and targeted attacks. Ransomware Attacks Are Increasing in Healthcare Ransomware is now the biggest cybersecurity threat for hospitals. By encrypting networks, attackers can shut down systems used for: Monitoring patients Dispensing medication Running diagnostic tests Operating life-support devices When a medical device is compromised, it can directly affect patient safety. Hospitals are more likely to pay ransom quickly because every second counts in critical care environments. This makes healthcare a highly profitable target. Lack of Security Awareness Among Healthcare Staff Doctors, nurses, and technicians focus on saving lives, not cybersecurity. Hackers exploit this by launching attacks using: Phishing emails Malware-infected USB drives Fake software updates Compromised remote access tools Once the attacker gets inside the hospital network through a staff member, they can move laterally and target medical devices that lack protection. Remote Care and Telemedicine Increase Vulnerability Since the rise of telemedicine and remote patient monitoring, more devices are connected through home networks. These networks typically lack strong security. Intercept device data Manipulate device readings Gain access to hospital systems through remote devices The High-Stakes Impact on Patient Safety Unlike most cyberattacks, breaches involving medical devices can cause physical harm. A compromised medical device can: Alter medication dosage Change heart-rate monitoring Disable ventilators Shut down diagnostic equipment Provide false readings to doctors Medical devices have become prime targets for cyberattacks due to their connectivity, outdated security, high-value data, and critical role in patient care. As hospitals continue to rely on digital health systems, the security of these devices will become even more important. Strengthening defenses, updating legacy equipment, and training staff are essential steps in protecting both patient data and patient lives. 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 shiva Follow Joined Nov 27, 2025 More from shiva Tor or Onion Browser: Which One Truly Protects Your Privacy in 2025 # discuss # networksec 💎 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 | 2026-01-13T08:49:44 |
https://www.algolia.com/de/use-cases/voice-search/ | Optimieren Sie Ihre Sprachsuche mit NLP- und KI-Tools | 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 Sprachsuche Sprachsuch-API für Apps, mobile Webseiten und Sprachassistenten Algolia bietet Benutzern relevante Sprachsuchergebnisse mit kontextbezogenem Verständnis, NLP, natürlichem Sprachverständnis und KI-Tools. Demo Anfordern Kostenlos Starten *]:border-l md:[&>*:nth-child(1)]:border-none md:[&>*:nth-child(4n+1)]:border-none"> 1.7+ Trillion searches every year 99.999% uptime SLA available 382% ROI according to Forrester Research 18,000+ customers across 150+ countries 100.000 Suchanfragen/Sek. in der Spitze 99,999% Verfügbarkeit mit SLA 382% Kapitalrendite 17.000+ Kunden Sprachsuche für mehrere Plattformen Intelligente Sprachassistenten Wenn Ihr Unternehmen über eine Amazon Alexa-Fähigkeit oder eine Google Assistant-Aktion verfügt, können unsere Sprachsuchfunktionen diese verbessern. Anstatt Ihre Nutzer zu zwingen, endlose Entscheidungen darüber zu treffen, was sie wollen, können Sie sie per Sprache suchen lassen und erhalten sofort, genaue Ergebnisse von unserer erweiterten Sprachsuchfunktion. Mobile Geräte Wir bieten Sprachsuche sowohl für mobile Apps als auch für mobile Webseiten. Am häufigsten wird die Sprachsuche nicht mit intelligenten Lautsprechern, sondern auf mobilen Geräten wie Apple iPhones (mit Siri) und Android-Telefonen verwendet. Mehr als die Hälfte der Sprachsuchenden sprechen täglich Sprachbefehle auf ihren Mobilgeräten. Sie suchen nach Informationen über Veranstaltungen in sozialen Medien oder führen lokale Suchen durch, z. B. nach lokalen Geschäften, die ein gewünschtes Produkt führen. Zwanzig Prozent aller Suchvorgänge auf mobilen Geräten erfolgen per Sprache, sodass es sich lohnt, für diese Art der Suche zu optimieren. Technologien für die Sprachsuche Sie können die Sprachfunktion nutzen, um Mitarbeitern die gewünschten Informationen zu geben oder Ihren Kunden zu helfen, die gewünschten Details zu finden. Super Sprache-zu-Text-Bibliotheken Wir stellen kostenlose Open-Source-Bibliotheken zur Verfügung, die Sie nutzen können, wenn Sie Ihre mobilen Anwendungen und Websites mit der Sprachsuchtechnologie versehen. Wir bieten Bibliotheken für iOS- und Android-Sprachsuche sowie für die Verwendung in mobilen Browsern. Hervorragende Textrelevanz Die beste Sprache-zu-Text-Verarbeitung oder das beste Verständnis natürlicher Sprache versagt, wenn die Suchmaschine die Relevanz des Textes nicht versteht. Unsere Technologie zur Suchmaschinenoptimierung kombiniert Textrelevanz und Ihre Geschäftskennzahlen mit unserer Verarbeitung natürlicher Sprache und natürlichem Sprachverständnis, um ein beispielloses Sprachsuch-Erlebnis zu bieten. Unterstützung für viele Sprachen Unsere Funktionen zur Verarbeitung natürlicher Sprache funktionieren in Dutzenden von Sprachen auf der ganzen Welt, und unsere Infrastruktur ist dezentralisiert, sodass wir Ihre Sprachsuchenden unabhängig von ihrem Standort unterstützen können. Erstklassige Personalisierung Wenn Menschen Sprachbefehle geben, erwarten sie, so verstanden zu werden, als würden sie mit einem Menschen sprechen. Deshalb bieten wir personalisierte, KI-gestützte Funktionalität. Wir lernen die Vorlieben Ihrer Nutzer kennen und stimmen die Ergebnisse auf ihre Interessen ab. Erste Schritte mit der Algolia-Sprachsuche Laden Sie Ihre Daten auf unseren Server Wir lassen sie von unserer Suchmaschine indizieren, hosten und stellen sie überall mit unvergleichlicher Geschwindigkeit zur Verfügung. Sagen Sie uns, was Ihnen wichtig ist Auf welche Textinhalte legen Sie bei der Sprachsuche am meisten Wert? Welche Kennzahlen bestimmen Ihre beliebten Inhalte? Mit unserer Sprachsuchplattform haben Sie die Kontrolle über Ihre Ranking-Formel. Fügen Sie Eingaben zu Ihren Anwendungen und Websites hinzu Nutzen Sie unsere Client-Bibliotheken, um die Vorteile unserer nativen Sprache-zu-Text-Funktionen zu nutzen, oder integrieren Sie über einen Drittanbieter Ihre eigenen. Optimieren Sie Ihre Ergebnisse im Laufe der Zeit Setzen Sie Personalisierung, dynamisches Re-Ranking und Lernen in natürlicher Sprache ein, um KI-Lernverfahren auf die Sprachsuche Ihrer Nutzer anzuwenden und die Sprachsuchergebnisse kontinuierlich zu verbessern. Recommended content How to use Algolia Voice Search Read more How to harness voice search in the retail sector Learn about the state of voice search today and how Harry Rosen brought in this technology to improve conversions and overall order sizes. Read more Search: the secret weapon to great omnichannel experiences Search and discovery can increase your conversion rate up to 50%. Learn how you can also build relevant omnichannel experiences with Algolia. Read more See more Häufig gestellte Fragen zur Sprachsuche Suchen Menschen oft per Sprache? 0 Die Menschen suchen immer häufiger per Stimme. Während die Sprachsuche vor fünf Jahren noch nicht von vielen Menschen genutzt wurde, hat sich die Sprachsuche mit der Verbesserung der Spracherkennung auf iOS, Android und im Internet auf all diesen Plattformen durchgesetzt. Die Trends deuten alle auf das Gleiche hin: Wenn Unternehmen eine Spracheingabeoption in der Suchleiste ihrer Apps und auf Webseiten anbieten, werden die Menschen diese nutzen. Müssen Nutzer etwas herunterladen, um die Sprachsuche von Algolia zu nutzen? 0 Um die Sprachsuche von Algolia zu nutzen, ist kein Download erforderlich. Als Entwickler einer App oder Webseite können Sie wählen, wie Sie die Sprache-zu-Text-Bibliotheken integrieren möchten. Algolia bietet Sprachbibliotheken für iOS und Android , sowie ein Widget für die Sprachsuche im Browser über JavaScript. Wie kann ich den Inhalt meiner Webseite für die Sprachsuche optimieren? 0 Sie müssen nicht viel tun, um Ihre Inhalte für die Sprachsuche vorzubereiten. Wenn Sie Algolias Leitfaden zur Suchoptimierung befolgen, sind Sie bestens gerüstet. Sie können auch Synonyme und Regeln hinzufügen, um alle Möglichkeiten der Sprachsuche zu nutzen. Dynamische Synonymvorschläge sind eine weitere Möglichkeit, Ihre Inhalte auf die Nutzerabsicht hin zu optimieren. Ist die Sprachsuche dasselbe wie die Suche in natürlicher Sprache? 0 Nicht notwendigerweise. Zwar verwenden die Menschen bei der Sprachsuche oft eine natürlichere Sprache, aber sie suchen genauso oft nach Schlüsselwörtern. Während Sie also Algolias bewährte Verfahren für die natürliche Sprachsuche befolgen sollten, müssen Sie nicht befürchten, dass die natürliche Sprachsuche „zu fortgeschritten“ ist. Algolia macht die Suche per Stimme und natürlicher Sprache einfach. Was ist Sprachsuche? 0 Bei der Sprachsuche verwenden die Menschen ihre Stimme, um zu suchen, unabhängig von der Plattform oder den Schlüsselwörtern, die sie verwenden. Während viele Menschen bei dem Begriff „Sprachsuche“ an intelligente Lautsprecher wie Alexa und Google Assistant denken, suchen Menschen häufiger per Sprache auf ihren Smartphones oder auf Webseiten. Nutzer schauen in Android- und iOS-Apps und auf Webseiten nach, ob sie die Sprachsuche implementiert haben. Was kann die Sprachsuche leisten? 0 Die beste Sprachsuchtechnologie bietet Nutzern und Kunden die Möglichkeit, per Sprache zu suchen und dabei natürliche Sprache oder Schlüsselwörter abzufragen. Dies eröffnet mehr interaktive Möglichkeiten, wenn Menschen unterwegs sind, öffentliche Verkehrsmittel benutzen oder einfach alle Hände voll zu tun haben. Um diese Art der Sprachsuche optimal zu bedienen, ist es wichtig, über eine Technologie zu verfügen, die versteht, was Menschen sagen, und die Ergebnisse für sie personalisiert. Wer verwendet die Sprachsuche? 0 Die Sprachsuche ist für alle Bevölkerungsgruppen geeignet. Junge Menschen nutzen sie, während sie mit ihren Smartphones unterwegs sind, und ältere Menschen schätzen die Vorteile der Barrierefreiheit. Praktisch jeder kann die Sprachsuche verwenden, um Artikel oder Produkte schneller zu finden oder wenn Tippen unpraktisch ist. Warum ist die Sprachsuche so wichtig? 0 Die Sprachsuche erleichtert nicht nur die Zugänglichkeit, sondern kann auch bequemer sein als das Eintippen, insbesondere auf Plattformen wie Mobiltelefonen oder Smart-TVs, wo das Eintippen fehleranfällig oder mühsam sein kann. 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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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 # communication Follow Hide Tips for talking effectively with your children at every age. Create Post Older #communication posts 2 3 4 5 6 7 8 9 10 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu #LearnedToday: One month of #LearnedToday Daniel Zotti Daniel Zotti Daniel Zotti Follow Jul 29 '23 #LearnedToday: One month of #LearnedToday # learnedtoday # webdev # communication # socialmedia 2 reactions Comments Add Comment 2 min read Improve your messengers communication experience with these 7 small tips Alexey Kramin Alexey Kramin Alexey Kramin Follow Jun 22 '23 Improve your messengers communication experience with these 7 small tips # productivity # team # communication # management 14 reactions Comments 8 comments 3 min read Architectural documentation and communication Anders Tornblad Anders Tornblad Anders Tornblad Follow Jun 8 '23 Architectural documentation and communication # architecture # documentation # communication 1 reaction Comments 3 comments 12 min read How detailed should requirements be?🤔 akazah akazah akazah Follow May 25 '23 How detailed should requirements be?🤔 # softwareengineering # requirements # communication # productowner Comments Add Comment 3 min read Systems Thinking and Technical Debt Aman Agrawal Aman Agrawal Aman Agrawal Follow for Coolblue Apr 6 '23 Systems Thinking and Technical Debt # systemsthinking # communication # technicaldebt # models Comments Add Comment 8 min read 5 principles of effective communication with a team-lead romanwrites romanwrites romanwrites Follow Apr 3 '23 5 principles of effective communication with a team-lead # softskill # communication # teamlead Comments Add Comment 1 min read CRUD Routing with Sinatra Stephanie Sison Stephanie Sison Stephanie Sison Follow Feb 11 '23 CRUD Routing with Sinatra # communication # management # career # productivity 1 reaction Comments Add Comment 4 min read Unlock the Power of CSS - 10 Tricks to Get Started! Helitha Rupasinghe Helitha Rupasinghe Helitha Rupasinghe Follow Jan 14 '23 Unlock the Power of CSS - 10 Tricks to Get Started! # writing # communication # productivity 21 reactions Comments 1 comment 9 min read Source of most problems in projects? Miscommunication! Michał Żurakowski Michał Żurakowski Michał Żurakowski Follow for Emphie Nov 16 '22 Source of most problems in projects? Miscommunication! # devjournal # programming # communication # teamwork 5 reactions Comments Add Comment 4 min read Where do developers hang out in 2022? Salman Shaikh Salman Shaikh Salman Shaikh Follow Oct 28 '22 Where do developers hang out in 2022? # generaladvice # generalprogramming # communication # community 6 reactions Comments Add Comment 11 min read Asynchronous Communication for remote teams Olumide Akinremi Olumide Akinremi Olumide Akinremi Follow Aug 26 '22 Asynchronous Communication for remote teams # communication # remote 7 reactions Comments 1 comment 4 min read 5 types of decision-makers Matheus Gomes 👨💻 Matheus Gomes 👨💻 Matheus Gomes 👨💻 Follow Apr 28 '22 5 types of decision-makers # softskills # communication # decisionmakers 1 reaction Comments Add Comment 2 min read Communications in Remote Teams Svitla Systems Inc. Svitla Systems Inc. Svitla Systems Inc. Follow Apr 22 '22 Communications in Remote Teams # remote # communication # remoteteams # teamcommunication 4 reactions Comments 4 comments 11 min read Lots Of Thoughts But Can’t Verbalise? Here’s How To Jumpstart Your Expression Channel Seow Yan Yi Seow Yan Yi Seow Yan Yi Follow Mar 16 '22 Lots Of Thoughts But Can’t Verbalise? Here’s How To Jumpstart Your Expression Channel # communication # softskills # speaking # personaldevelopment 3 reactions Comments 1 comment 3 min read The 3 Step Approach To Make Initiating Conversations With Strangers Less Scary Seow Yan Yi Seow Yan Yi Seow Yan Yi Follow Mar 16 '22 The 3 Step Approach To Make Initiating Conversations With Strangers Less Scary # conversations # communication # softskills # personaldevelopment 4 reactions Comments Add Comment 4 min read Why More Knowledge Isn’t Making Your Conversations Interesting (And How Associations Can Help) Seow Yan Yi Seow Yan Yi Seow Yan Yi Follow Mar 16 '22 Why More Knowledge Isn’t Making Your Conversations Interesting (And How Associations Can Help) # communication # personaldevelopment # career # speaking 2 reactions Comments Add Comment 3 min read The Most Effective Way to Reach Your Users - Building the Omnichannel Michael Bogan Michael Bogan Michael Bogan Follow Mar 14 '22 The Most Effective Way to Reach Your Users - Building the Omnichannel # tutorial # webdev # communication # channels 5 reactions Comments Add Comment 5 min read S T A H P Nathan Kallman Nathan Kallman Nathan Kallman Follow Mar 11 '22 S T A H P # productivity # communication 11 reactions Comments 5 comments 2 min read Communication Skills and Coding Akash Nigam Akash Nigam Akash Nigam Follow Feb 28 '22 Communication Skills and Coding # communication # interaction 4 reactions Comments Add Comment 1 min read This email should have been a document rinaarts rinaarts rinaarts Follow Feb 19 '22 This email should have been a document # productivity # communication 3 reactions Comments 1 comment 10 min read These 5 Tips Will Improve Your Designer To Developer Communications Today heymichellemac heymichellemac heymichellemac Follow Feb 14 '22 These 5 Tips Will Improve Your Designer To Developer Communications Today # design # communication # team # guide 3 reactions Comments Add Comment 6 min read 5 types of communication a senior developer does Sandor Dargo Sandor Dargo Sandor Dargo Follow Feb 9 '22 5 types of communication a senior developer does # watercooler # career # communication # responsibilities 63 reactions Comments Add Comment 8 min read How to write an effective design document rinaarts rinaarts rinaarts Follow Jan 12 '22 How to write an effective design document # softwaredesign # writing # communication 6 reactions Comments Add Comment 9 min read Handle scope changes without coming across as a naysayer 🙌 Valentin Sawadski (he/him) Valentin Sawadski (he/him) Valentin Sawadski (he/him) Follow Dec 28 '21 Handle scope changes without coming across as a naysayer 🙌 # career # management # communication # softskills 4 reactions Comments Add Comment 2 min read Content Fatigue and How to Avoid It Lilly Lilly Lilly Follow for Contenda Mar 30 '23 Content Fatigue and How to Avoid It # contentcreation # teamwork # communication 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://zeroday.forem.com/sovarcneo/i-put-an-air-gapped-neural-network-in-my-pocket-python-on-android-1pci#comments | I put an Air-Gapped Neural Network in my pocket (Python on Android) - 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. 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 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 I put an Air-Gapped Neural Network in my pocket (Python on Android) # discuss # beginners # tools # devsecops The Pocket Mainframe I shared my desktop AI defense system. Now I'm sharing the mobile unit. This is NEXUS v9 , a sovereign network intelligence tool designed to run natively on Android (via Pydroid3 or Termux). Most "AI apps" on your phone are just wrappers sending your data to a cloud API. This is different. This is a complete, self-contained neural network running locally on my device. The Mobile Engineering Challenge Running a complex defense system on a phone presents unique challenges: No Root Access: I can't easily access system-level process data. Dependency Hell: Installing numpy or scipy on Android can be tricky. Battery/Resources: Spawning 50 threads kills a battery instantly. The Architecture To make this work, I had to rewrite the core engine: AsyncIO Network Scanner: Replaced threading with asyncio . It scans hundreds of ports/hosts concurrently without locking up the UI or draining the battery. Pure Python Fallbacks: I wrote a custom NeuralNetwork class that checks for numpy . If it's missing, it seamlessly degrades to a pure Python implementation of the dense layers and activation functions. It trains on-device . Synthetic Baselines: Since I can't always read raw CPU/RAM on non-rooted devices, the system builds its own baseline of "normal" behavior and detects anomalies relative to its own process state. Why do this? Because "Personal Security" shouldn't require a server rack. I can walk into a network environment, pull out my phone, and have the same level of anomaly detection and analysis as I do at my desk—completely offline. Repo updated with the Android Branch: https://github.com/SovArcNeo 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 Building an Air-Gapped AI Defense System in Python (No Cloud APIs) # blueteam # beginners # discuss # tools 💎 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 | 2026-01-13T08:49:44 |
https://dev.to/t/sqlite3 | Sqlite3 - 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 # sqlite3 Follow Hide Create Post Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu Creating Native Desktop Apps with SQLite and GTK Misael Braga de Bitencourt Misael Braga de Bitencourt Misael Braga de Bitencourt Follow May 26 '25 Creating Native Desktop Apps with SQLite and GTK # c # sqlite3 # gtk # gtk3 Comments Add Comment 5 min read ActiveRecord / SQlite3 Compatibility Matrix Peter H. Boling Peter H. Boling Peter H. Boling Follow Jun 6 '25 ActiveRecord / SQlite3 Compatibility Matrix # ruby # rails # sqlite3 # activereccrd 5 reactions Comments 1 comment 3 min read Quick SQLite to PostgreSQL Migration with pgloader Wedson Lima Wedson Lima Wedson Lima Follow May 1 '25 Quick SQLite to PostgreSQL Migration with pgloader # postgres # sqlite3 # pgloader Comments Add Comment 1 min read Learning About Security: SQL Injection kaazzu kaazzu kaazzu Follow Nov 7 '24 Learning About Security: SQL Injection # bunjs # sqlite3 # sqlinjection # hono Comments Add Comment 3 min read Building a Fast and Compact SQLite Cache Store sjdonado sjdonado sjdonado Follow Jul 24 '24 Building a Fast and Compact SQLite Cache Store # bunjs # sqlite3 # msgpackr # cbor 2 reactions Comments Add Comment 3 min read Migrating a SQLite3 Database to PostgreSQL in Dokku Steve Steve Steve Follow May 30 '24 Migrating a SQLite3 Database to PostgreSQL in Dokku # dokku # sqlite3 # postgres # rails Comments Add Comment 2 min read Unir tablas en SQL con JOIN Salvador HM Salvador HM Salvador HM Follow Jun 19 '23 Unir tablas en SQL con JOIN # sql # join # sqlite3 Comments Add Comment 4 min read Update postgres to sqlite3 for rails 6 using Docker Compose radin reth radin reth radin reth Follow Jun 2 '21 Update postgres to sqlite3 for rails 6 using Docker Compose # rails # postgres # sqlite3 # docker 6 reactions Comments Add Comment 1 min read I've done TUI (Terminal User Interface) app. This is what it looks like Mike Skoe Mike Skoe Mike Skoe Follow Apr 16 '21 I've done TUI (Terminal User Interface) app. This is what it looks like # ocaml # tui # notty # sqlite3 10 reactions Comments Add Comment 3 min read Creating a Basic Sinatra Application Cristal Cristal Cristal Follow Dec 7 '19 Creating a Basic Sinatra Application # flatironschool # sinatra # activerecord # sqlite3 8 reactions Comments Add Comment 4 min read The problem with blindly using the latest Node Michel Michel Michel Follow Nov 26 '19 The problem with blindly using the latest Node # node # version # sqlite3 5 reactions Comments 4 comments 6 min read Making a full-text search module that works on both desktop and mobile (Pt. 2) Takuya Matsuyama Takuya Matsuyama Takuya Matsuyama Follow Dec 16 '18 Making a full-text search module that works on both desktop and mobile (Pt. 2) # sqlite3 # reactnative # electron 10 reactions Comments 2 comments 3 min read Making a full-text search module that works on both desktop and mobile (Pt. 1) Takuya Matsuyama Takuya Matsuyama Takuya Matsuyama Follow Dec 14 '18 Making a full-text search module that works on both desktop and mobile (Pt. 1) # sqlite3 # reactnative # electron 16 reactions Comments 3 comments 3 min read Receiving data from ESP8266 sensors Jake Fenton Jake Fenton Jake Fenton Follow Apr 27 '18 Receiving data from ESP8266 sensors # python # sockets # sqlite3 # threading 9 reactions Comments Add Comment 14 min read Recent changes in go-sqlite3 Yasuhiro Matsumoto Yasuhiro Matsumoto Yasuhiro Matsumoto Follow Nov 15 '17 Recent changes in go-sqlite3 # go # sqlite3 29 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 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/t/mentalhealth/page/5 | Mental Health Page 5 - 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 Mental Health Follow Hide Mental health matters! Break the stigma. We can empower ourselves and each other to invest in our mental health. We can give support and care to ourselves and each other while we struggle. Let's talk about making our mental health priority. Create Post about #mentalhealth Posts should be related to mental health. This is a pretty wide category but some things that are included are: Managing mental health as a developer Living with mental illness and how it affects your work Ways to cope with mental health issues Avoiding burn out Tools, apps, and methods that help you with your mental health ...and more “Your mental health is a priority. Your happiness is an essential. Your self-care is a necessity.” Struggling? Help is out there. Click here to find a list of global mental health resources and hotlines. Older #mentalhealth posts 2 3 4 5 6 7 8 9 10 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu Khủng hoảng nghề nghiệp tuổi 30 Kelvyn Thai Kelvyn Thai Kelvyn Thai Follow Oct 22 '25 Khủng hoảng nghề nghiệp tuổi 30 # crisis # career # mentalhealth Comments Add Comment 4 min read I use AI when I code. And sometimes it makes me feel like I’m cheating. Christoffer Madsen Christoffer Madsen Christoffer Madsen Follow Nov 10 '25 I use AI when I code. And sometimes it makes me feel like I’m cheating. # ai # selfimprovement # webdev # mentalhealth 83 reactions Comments 57 comments 2 min read Pourquoi changer sera difficile… (Réflexion personnelle) Elvis Ansima Elvis Ansima Elvis Ansima Follow Nov 25 '25 Pourquoi changer sera difficile… (Réflexion personnelle) # mentalhealth Comments 1 comment 2 min read October Taught Me to Show Up Hamzat Abdul-muizz Hamzat Abdul-muizz Hamzat Abdul-muizz Follow Nov 2 '25 October Taught Me to Show Up # devjournal # mentalhealth # motivation 6 reactions Comments Add Comment 4 min read The Beginning : Hello World! cz writes cz writes cz writes Follow Oct 25 '25 The Beginning : Hello World! # codenewbie # motivation # devjournal # mentalhealth 1 reaction Comments 1 comment 2 min read 🌱La maternidad, la pausa laboral… y un clic Laura Montironi Laura Montironi Laura Montironi Follow Nov 20 '25 🌱La maternidad, la pausa laboral… y un clic # career # devjournal # mentalhealth 5 reactions Comments Add Comment 4 min read Sleep Patterns Predict Mental Health Episodes 3-7 Days Ahead: Building SleepMind.ai Alex Chen Alex Chen Alex Chen Follow Oct 21 '25 Sleep Patterns Predict Mental Health Episodes 3-7 Days Ahead: Building SleepMind.ai # mentalhealth # ai # python # opensource Comments Add Comment 2 min read Has AI made programming less satisfying for you? Ava Nichols Ava Nichols Ava Nichols Follow Nov 6 '25 Has AI made programming less satisfying for you? # discuss # ai # productivity # mentalhealth 4 reactions Comments 4 comments 1 min read Lesson 25: Risk Control and Psychology Management Henry Lin Henry Lin Henry Lin Follow Nov 20 '25 Lesson 25: Risk Control and Psychology Management # management # mentalhealth # productivity Comments Add Comment 14 min read From Imposter Syndrome to Confidence: My Journey as a Senior Angular Developer Gouranga Das Samrat Gouranga Das Samrat Gouranga Das Samrat Follow Nov 16 '25 From Imposter Syndrome to Confidence: My Journey as a Senior Angular Developer # angular # career # mentalhealth 4 reactions Comments 2 comments 9 min read The Commit I Almost Didn't Push Evan Lausier Evan Lausier Evan Lausier Follow Nov 18 '25 The Commit I Almost Didn't Push # beginners # career # mentalhealth 1 reaction Comments Add Comment 2 min read Turning emotional struggles into “bug reports” — my side project, Life.exe SoulInMind Labs SoulInMind Labs SoulInMind Labs Follow Oct 26 '25 Turning emotional struggles into “bug reports” — my side project, Life.exe # showdev # webdev # sideprojects # mentalhealth 1 reaction Comments Add Comment 1 min read Building Eunoia: A Mental Wellbeing Companion Oluwadahunsi Ifeoluwa Oluwadahunsi Ifeoluwa Oluwadahunsi Ifeoluwa Follow Nov 3 '25 Building Eunoia: A Mental Wellbeing Companion # gemini # ai # mentalhealth # go Comments 3 comments 9 min read We Need to Stop Calling It 'Imposter Syndrome' Tombri Bowei Tombri Bowei Tombri Bowei Follow Nov 6 '25 We Need to Stop Calling It 'Imposter Syndrome' # discuss # career # mentalhealth # programming 21 reactions Comments 12 comments 4 min read I Reduced My Panic Attacks by 90% With Data—Here's How I Built the Tool Alex Chen Alex Chen Alex Chen Follow Oct 20 '25 I Reduced My Panic Attacks by 90% With Data—Here's How I Built the Tool # wellbeing # mentalhealth # productivity # data Comments Add Comment 2 min read Stuck Between Studies and Career Manasvi Pal Manasvi Pal Manasvi Pal Follow Oct 31 '25 Stuck Between Studies and Career # help # beginners # mentalhealth # career Comments 1 comment 1 min read The Thirst of Learning Everything Mourya Vamsi Modugula Mourya Vamsi Modugula Mourya Vamsi Modugula Follow Nov 7 '25 The Thirst of Learning Everything # webdev # programming # career # mentalhealth Comments Add Comment 2 min read 🎧 The Silent Crisis: Using Speech Recognition and Sentiment AI for Real-Time Emotional Support shambhavi525-sudo shambhavi525-sudo shambhavi525-sudo Follow Nov 6 '25 🎧 The Silent Crisis: Using Speech Recognition and Sentiment AI for Real-Time Emotional Support # showdev # ai # mentalhealth # machinelearning 1 reaction Comments 2 comments 2 min read Developing Secure Healthcare Apps in 2026: Standards, Tech & Best Practices CHILLICODE CHILLICODE CHILLICODE Follow Oct 6 '25 Developing Secure Healthcare Apps in 2026: Standards, Tech & Best Practices # mentalhealth # mobile # development Comments Add Comment 7 min read Reflections on Learning, Mentorship, and Growth Fredrick Ogutu Fredrick Ogutu Fredrick Ogutu Follow Oct 13 '25 Reflections on Learning, Mentorship, and Growth # learning # mentalhealth # career # productivity Comments Add Comment 5 min read MoodFeed: Building an AI-Powered Social Feed That Actually Gets You Nadim Chowdhury Nadim Chowdhury Nadim Chowdhury Follow Nov 1 '25 MoodFeed: Building an AI-Powered Social Feed That Actually Gets You # showdev # sideprojects # mentalhealth # ai 1 reaction Comments Add Comment 6 min read What Keeps Me Coding When It Gets Tough Churchill Emmanuel Churchill Emmanuel Churchill Emmanuel Follow Sep 29 '25 What Keeps Me Coding When It Gets Tough # discuss # coding # mentalhealth Comments Add Comment 2 min read What I See When I Watch You Build Dev TNG Dev TNG Dev TNG Follow Oct 30 '25 What I See When I Watch You Build # discuss # mentalhealth # startup 7 reactions Comments 1 comment 2 min read Enhancing Critical Thinking Skills: The Power of Online Learning Platforms for Students Ghumman Tech Ghumman Tech Ghumman Tech Follow Oct 12 '25 Enhancing Critical Thinking Skills: The Power of Online Learning Platforms for Students # ghummantech # mentalhealth # financialliteracy Comments Add Comment 3 min read 1% 0 — Sobre la improbabilidad estadística de conservar la conciencia en un sistema que la penaliza Bernard Uriza Bernard Uriza Bernard Uriza Follow Oct 31 '25 1% 0 — Sobre la improbabilidad estadística de conservar la conciencia en un sistema que la penaliza # webdev # security # mentalhealth # analytics 1 reaction Comments Add Comment 4 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 | 2026-01-13T08:49:44 |
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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 # yarn Follow Hide Create Post Older #yarn posts 1 2 3 4 5 6 7 8 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . 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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 Mental Health Follow Hide Mental health matters! Break the stigma. We can empower ourselves and each other to invest in our mental health. We can give support and care to ourselves and each other while we struggle. Let's talk about making our mental health priority. Create Post about #mentalhealth Posts should be related to mental health. This is a pretty wide category but some things that are included are: Managing mental health as a developer Living with mental illness and how it affects your work Ways to cope with mental health issues Avoiding burn out Tools, apps, and methods that help you with your mental health ...and more “Your mental health is a priority. Your happiness is an essential. Your self-care is a necessity.” Struggling? Help is out there. 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Right menu 🧠 How Small Daily Habits Improve Mental Health (NIMH-Backed Self-Care Strategies) NVelUp NVelUp NVelUp Follow Oct 11 '25 🧠 How Small Daily Habits Improve Mental Health (NIMH-Backed Self-Care Strategies) # mentalhealth # productivity # selfcare # wellness Comments Add Comment 4 min read I Built an AI Prompt to Debug My Post-Holiday Brain Fog—Here's How It Works Hui Hui Hui Follow Oct 8 '25 I Built an AI Prompt to Debug My Post-Holiday Brain Fog—Here's How It Works # ai # productivity # mentalhealth # promptengineering 2 reactions Comments Add Comment 8 min read Burnout in Tech: How to recognize it and build a sustainable career Gonzalo Terzano Gonzalo Terzano Gonzalo Terzano Follow Sep 1 '25 Burnout in Tech: How to recognize it and build a sustainable career # worklife # mentalhealth # career # developers Comments Add Comment 4 min read Coping After Trauma: Practical Strategies to Rebuild Resilience 🌱 NVelUp NVelUp NVelUp Follow Sep 30 '25 Coping After Trauma: Practical Strategies to Rebuild Resilience 🌱 # mentalhealth # burnout # trauma # resilience Comments Add Comment 2 min read Work hard, ship code, go home: Why hustle culture is cringe <devtips/> <devtips/> <devtips/> Follow Sep 29 '25 Work hard, ship code, go home: Why hustle culture is cringe # discuss # career # mentalhealth # productivity 4 reactions Comments 1 comment 25 min read Migraine Awareness Week 2025: Living With New Daily Persistent Headache (NDPH) Dumebi Okolo Dumebi Okolo Dumebi Okolo Follow Sep 24 '25 Migraine Awareness Week 2025: Living With New Daily Persistent Headache (NDPH) # watercooler # mentalhealth # wellbeing # devhealth 17 reactions Comments 9 comments 4 min read 🏆003. Brainstorming is key Valacor Valacor Valacor Follow Sep 26 '25 🏆003. Brainstorming is key # softwareengineering # learning # mentalhealth # tooling Comments Add Comment 1 min read Why Your Relationships Matter as Much as Code Reviews: Social Connection & Mental Health NVelUp NVelUp NVelUp Follow Sep 23 '25 Why Your Relationships Matter as Much as Code Reviews: Social Connection & Mental Health # mentalhealth # productivity # wellness # developer Comments 1 comment 2 min read Building Aliō: How I Shipped an App with AI Tools Evgeniy Molozhenko Evgeniy Molozhenko Evgeniy Molozhenko Follow Sep 21 '25 Building Aliō: How I Shipped an App with AI Tools # vibecoding # ios # mobile # mentalhealth 4 reactions Comments 3 comments 2 min read Detecting Depression Using Deep Learning on Facial Expression Images -Mini Project Michael Quelazar Michael Quelazar Michael Quelazar Follow Aug 19 '25 Detecting Depression Using Deep Learning on Facial Expression Images -Mini Project # deeplearning # tensorflow # cnn # mentalhealth Comments Add Comment 5 min read The Feeling of Momentum Can Be a Trap (Bite-size Article) koshirok096 koshirok096 koshirok096 Follow Aug 15 '25 The Feeling of Momentum Can Be a Trap (Bite-size Article) # mentalhealth # productivity Comments Add Comment 3 min read How a Simple Dashboard Can Transform TMS Therapy Management OlhaStfn OlhaStfn OlhaStfn Follow Aug 15 '25 How a Simple Dashboard Can Transform TMS Therapy Management # ai # tms # mentalhealth # healthtech Comments Add Comment 2 min read Common Challenges for developers and learners davinceleecode davinceleecode davinceleecode Follow Aug 19 '25 Common Challenges for developers and learners # developer # learning # career # mentalhealth 6 reactions Comments Add Comment 1 min read StrangEars Pratham Dhyani Pratham Dhyani Pratham Dhyani Follow Aug 14 '25 StrangEars # kiro # mentalhealth # webdev # ai Comments Add Comment 3 min read 🧠 5 Evidence-Based Anxiety Coping Strategies (For Developers & Tech Professionals) NVelUp NVelUp NVelUp Follow Sep 17 '25 🧠 5 Evidence-Based Anxiety Coping Strategies (For Developers & Tech Professionals) # mentalhealth # wellness # productivity # career Comments 1 comment 2 min read 🧠 When to Seek Help: 5 Clear Signs You Might Need a Therapist NVelUp NVelUp NVelUp Follow Sep 15 '25 🧠 When to Seek Help: 5 Clear Signs You Might Need a Therapist # mentalhealth # productivity # career # burnout Comments 1 comment 2 min read How to get a job without losing your mind! JEstebanDev JEstebanDev JEstebanDev Follow Sep 14 '25 How to get a job without losing your mind! # career # mentalhealth # motivation Comments Add Comment 5 min read New Angle : Serious Games cutieyunny-tech cutieyunny-tech cutieyunny-tech Follow Sep 14 '25 New Angle : Serious Games # showdev # mentalhealth # gamedev # discuss Comments Add Comment 1 min read 🔥 Leading Through Burnout: What They Don’t Tell You Yusuf Saifurahman Yusuf Saifurahman Yusuf Saifurahman Follow Aug 24 '25 🔥 Leading Through Burnout: What They Don’t Tell You # mentalhealth # webdev # programming 5 reactions Comments 1 comment 3 min read The Future of Self-Care: How AI is Revolutionizing Personalized Affirmations with Affirmi WolfOf420Stret WolfOf420Stret WolfOf420Stret Follow Aug 10 '25 The Future of Self-Care: How AI is Revolutionizing Personalized Affirmations with Affirmi # ai # mentalhealth # buildinpublic # startup Comments Add Comment 6 min read WorryBox Andrew Hewitt Andrew Hewitt Andrew Hewitt Follow Sep 12 '25 WorryBox # ai # opensource # mentalhealth # kiro Comments Add Comment 8 min read 🧠Too much on my mind🧠 Valacor Valacor Valacor Follow Sep 11 '25 🧠Too much on my mind🧠 # softwaredevelopment # sideprojects # mentalhealth 1 reaction Comments Add Comment 2 min read 🧠 Sleep Your Way to Mental Wellness: A Developer’s Guide NVelUp NVelUp NVelUp Follow Sep 11 '25 🧠 Sleep Your Way to Mental Wellness: A Developer’s Guide # mentalhealth # productivity # selfcare # wellness 1 reaction Comments Add Comment 2 min read Boost Your Productivity: A Sleep Debt Calculator for Devs Chirag Patel Chirag Patel Chirag Patel Follow Sep 11 '25 Boost Your Productivity: A Sleep Debt Calculator for Devs # productivity # mentalhealth # webdev # developer Comments Add Comment 3 min read Mentorship in Motion: How a Summer Break Sparked a Pivot Bala Madhusoodhanan Bala Madhusoodhanan Bala Madhusoodhanan Follow Sep 8 '25 Mentorship in Motion: How a Summer Break Sparked a Pivot # mentorship # mentalhealth # womenintech 5 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://vibe.forem.com/dhren2019/ai-news-hub-a-complete-aitech-news-aggregator-saas-you-fully-own-480i#comments | AI News Hub — A Complete AI/Tech News Aggregator SaaS You Fully Own 🔥 - 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 Dhren Posted on Dec 2, 2025 AI News Hub — A Complete AI/Tech News Aggregator SaaS You Fully Own 🔥 # ai # cloud # api # database I built AI News Hub as a complete platform that automatically collects, organizes, and publishes the latest content from the AI world, programming, machine learning, dev tools, and tech tutorials. Every 2 hours, the system scrapes trusted sources, cleans the data, generates SEO-optimized posts, and updates a fully featured dashboard. It also sends push notifications to users whenever new content is available. The whole project is designed to be a plug-and-launch SaaS: it includes authentication, subscriptions, blog system, PRO mode (ads removed), backend API, scraper, SEO, and everything needed to run a polished production website. ⚙️ Scraper · Backend · Dashboard · Push Notifications · Authentication · SEO · Blog · Friendly URLs React 18 · FastAPI Python · TailwindCSS · shadcn/ui · MongoDB Atlas · OneSignal · Clerk Auth 🧩 FEATURES INCLUDED 🎨 Frontend (React + Tailwind + shadcn) What I built on the front: SEO-ready homepage /hub dashboard with all scraped news /subscription page for plans /profile for user details /post/:slug for individual articles /blog with a complete technical blogging system SEO: dynamic titles, meta descriptions, OpenGraph, JSON-LD, sitemap, robots, and clean URLs like: /post/openai-new-model-2025 🧠 Backend (FastAPI + Python) The backend exposes clean endpoints: /api/articles /api/post/{slug} /api/dashboard /api/notifications/send I included Pydantic models, error handling, and optional Clerk token validation. 🤖 Automated Scraper This part is fully automated: Runs every 2 hours Normalizes and deduplicates content Inserts everything into MongoDB Triggers push notifications when new posts appear 📡 Push Notifications Built-in OneSignal integration: Automatic registration Service worker included Works for new article alerts 💸 Monetization (Monthly Subscriptions) I added subscription billing using Clerk + Clerk Billing. 🔐 PRO Mode: Paying users don’t see ads Free users see ads Automatic monthly billing You can set any price you want. 📝 SEO Package Auto-generated titles Optimized meta descriptions Clean SEO-friendly slugs Article schema Dynamic sitemap + robots.txt 🎯 Perfect For Developers wanting a ready SaaS Makers shipping a fast MVP Freelancers reselling SaaS to clients Students learning real-world architecture 🧾 Summary of What I Built Full frontend Backend API Automated scraper Blog system Push notifications OAuth + Auth Subscriptions + PRO mode SEO + deployment-ready FULL PROJECT HERE and if you are interested you can purchase here ! It’s a complete, fully connected SaaS—ready to run or sell. 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 Dhren Follow Location Spain Joined Jan 11, 2021 💎 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. 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 . Vibe Coding Forem © 2025 - 2026. Where anyone can code, with a bit of creativity and some AI help. Log in Create account | 2026-01-13T08:49:44 |
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https://github.com/nikic/scalar_objects | GitHub - nikic/scalar_objects: Extension that adds support for method calls on primitive types in PHP 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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Dismiss alert {{ message }} nikic / scalar_objects Public Notifications You must be signed in to change notification settings Fork 43 Star 1.1k Extension that adds support for method calls on primitive types in PHP License MIT license 1.1k stars 43 forks Branches Tags Activity Star Notifications You must be signed in to change notification settings Code Issues 10 Pull requests 2 Actions Projects 0 Wiki Security Uh oh! There was an error while loading. Please reload this page . Insights Additional navigation options Code Issues Pull requests Actions Projects Wiki Security Insights nikic/scalar_objects 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 106 Commits .appveyor .appveyor .github/ workflows .github/ workflows doc doc handlers handlers tests tests .appveyor.yml .appveyor.yml .gitignore .gitignore .travis.yml .travis.yml LICENSE LICENSE README.md README.md config.m4 config.m4 config.w32 config.w32 php_scalar_objects.h php_scalar_objects.h scalar_objects.c scalar_objects.c View all files Repository files navigation README MIT license Add support for method calls on primitive types in PHP This extension implements the ability to register a class that handles the method calls to a certain primitive type (string, array, ...). As such it allows implementing APIs like $str->length() . The main purpose of this repo is to provide a proof of concept implementation that can be used to design the new APIs. The switch to object syntax for operations on primitive types is a unique opportunity for PHP to redesign many of its inconsistent core APIs. This repo provides the means to quickly prototype and test new APIs as userland code. Once the APIs are figured out it will be proposed for inclusion into PHP. Note: The ability to register type handlers from userland is just for prototyping. It's not something I would actually want in PHP in the end. Registering type handlers Type handlers are registered through register_primitive_type_handler . The function takes a type name (like "string" or "array") and a class name. The class should contain static methods, which receive the primitive type as the first parameter: <?php class StringHandler { public static function length ( $ self ) { return strlen ( $ self ); } public static function startsWith ( $ self , $ other ) { return strpos ( $ self , $ other ) === 0 ; } } register_primitive_type_handler ( ' string ' , ' StringHandler ' ); $ string = " abc " ; var_dump ( $ string -> length ()); // int(3) var_dump ( $ string -> startsWith ( " a " )); // bool(true) The valid type names are: null , bool , int , float , string , array and resource . Not all of those will make sense in practice, but for now they are all supported. Implemented APIs As already pointed out in the introduction the main purpose of this repo is designing good APIs for the primitive types. The implemented APIs are available in the handlers/ folder (and are obviously work in progress). In order to load these APIs just include the handlers/bootstrap.php file. Installation The master branch supports PHP version 7.0 to 8.1. The extension is incompatible with the JIT compiler. Unix In order to compile and install the extension run the following commands: phpize ./configure make sudo make install Windows Download a prebuilt Windows DLL that matches your PHP version and move it into the ext/ directory of your PHP installation. Furthermore you'll have to add extension=php_scalar_objects.dll to your php.ini . Testing the extension The extension comes with a run-tests.php file to run the tests. (To see examples of the implemented APIs you should also look in the tests.) The script is run as follows: php run-tests.php -q -p php Where php is the path to your PHP executable. Limitations This extension has a number of limitations: Due to technical limitations, it is not possible to create mutable APIs for primitive types. Modifying $self within the methods is not possible (or rather, will have no effect, as you'd just be changing a copy). About Extension that adds support for method calls on primitive types in PHP Resources Readme License MIT license Uh oh! There was an error while loading. Please reload this page . Activity Stars 1.1k stars Watchers 50 watching Forks 43 forks Report repository Releases No releases published Packages 0 No packages published Uh oh! There was an error while loading. Please reload this page . Contributors 8 Uh oh! There was an error while loading. Please reload this page . Languages PHP 73.9% C 18.1% PowerShell 7.1% Other 0.9% Footer © 2026 GitHub, Inc. Footer navigation Terms Privacy Security Status Community Docs Contact Manage cookies Do not share my personal information You can’t perform that action at this time. | 2026-01-13T08:49:44 |
https://github.com/nikic/FastRoute | GitHub - nikic/FastRoute: Fast request router for PHP 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... Search code, repositories, users, issues, pull requests... --> Search Clear Search syntax tips Provide feedback --> We read every piece of feedback, and take your input very seriously. Include my email address so I can be contacted Cancel Submit feedback Saved searches Use saved searches to filter your results more quickly --> Name Query To see all available qualifiers, see our documentation . Cancel Create saved search Sign in Sign up Appearance settings Resetting focus You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert {{ message }} nikic / FastRoute Public Notifications You must be signed in to change notification settings Fork 454 Star 5.3k Fast request router for PHP License View license 5.3k stars 454 forks Branches Tags Activity Star Notifications You must be signed in to change notification settings Code Issues 18 Pull requests 8 Actions Projects 0 Wiki Security Uh oh! There was an error while loading. Please reload this page . Insights Additional navigation options Code Issues Pull requests Actions Projects Wiki Security Insights nikic/FastRoute 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 235 Commits .github/ workflows .github/ workflows benchmark benchmark src src test test .editorconfig .editorconfig .gitattributes .gitattributes .gitignore .gitignore LICENSE LICENSE Makefile Makefile README.md README.md composer-require-checker.json composer-require-checker.json composer.json composer.json composer.lock composer.lock phpbench.json phpbench.json phpcs.xml.dist phpcs.xml.dist phpstan.neon.dist phpstan.neon.dist phpunit.xml.dist phpunit.xml.dist View all files Repository files navigation README License FastRoute - Fast request router for PHP This library provides a fast implementation of a regular expression based router. Blog post explaining how the implementation works and why it is fast. Install To install with composer: composer require nikic/fast-route Requires PHP 8.1 or newer. Usage Here's a basic usage example: <?php require ' /path/to/vendor/autoload.php ' ; $ dispatcher = FastRoute \simpleDispatcher ( function ( FastRoute \ ConfigureRoutes $ r ) { $ r -> addRoute ( ' GET ' , ' /users ' , ' get_all_users_handler ' ); // {id} must be a number (\d+) $ r -> addRoute ( ' GET ' , ' /user/{id:\d+} ' , ' get_user_handler ' ); // The /{title} suffix is optional $ r -> addRoute ( ' GET ' , ' /articles/{id:\d+}[/{title}] ' , ' get_article_handler ' ); }); // Fetch method and URI from somewhere $ httpMethod = $ _SERVER [ ' REQUEST_METHOD ' ]; $ uri = $ _SERVER [ ' REQUEST_URI ' ]; // Strip query string (?foo=bar) and decode URI if ( false !== $ pos = strpos ( $ uri , ' ? ' )) { $ uri = substr ( $ uri , 0 , $ pos ); } $ uri = rawurldecode ( $ uri ); $ routeInfo = $ dispatcher -> dispatch ( $ httpMethod , $ uri ); switch ( $ routeInfo [ 0 ]) { case FastRoute \Dispatcher:: NOT_FOUND : // ... 404 Not Found break ; case FastRoute \Dispatcher:: METHOD_NOT_ALLOWED : $ allowedMethods = $ routeInfo [ 1 ]; // ... 405 Method Not Allowed break ; case FastRoute \Dispatcher:: FOUND : $ handler = $ routeInfo [ 1 ]; $ vars = $ routeInfo [ 2 ]; // ... call $handler with $vars break ; } Defining routes The routes are defined by calling the FastRoute\simpleDispatcher() function, which accepts a callable taking a FastRoute\ConfigureRoutes instance. The routes are added by calling addRoute() on the collector instance: $ r -> addRoute ( $ method , $ routePattern , $ handler ); The $method is an uppercase HTTP method string for which a certain route should match. It is possible to specify multiple valid methods using an array: // These two calls $ r -> addRoute ( ' GET ' , ' /test ' , ' handler ' ); $ r -> addRoute ( ' POST ' , ' /test ' , ' handler ' ); // Are equivalent to this one call $ r -> addRoute ([ ' GET ' , ' POST ' ], ' /test ' , ' handler ' ); By default, the $routePattern uses a syntax where {foo} specifies a placeholder with name foo and matching the regex [^/]+ . To adjust the pattern the placeholder matches, you can specify a custom pattern by writing {bar:[0-9]+} . Some examples: // Matches /user/42, but not /user/xyz $ r -> addRoute ( ' GET ' , ' /user/{id:\d+} ' , ' handler ' ); // Matches /user/foobar, but not /user/foo/bar $ r -> addRoute ( ' GET ' , ' /user/{name} ' , ' handler ' ); // Matches /user/foo/bar as well $ r -> addRoute ( ' GET ' , ' /user/{name:.+} ' , ' handler ' ); Custom patterns for route placeholders cannot use capturing groups. For example {lang:(en|de)} is not a valid placeholder, because () is a capturing group. Instead you can use either {lang:en|de} or {lang:(?:en|de)} . Furthermore, parts of the route enclosed in [...] are considered optional, so that /foo[bar] will match both /foo and /foobar . Optional parts are only supported in a trailing position, not in the middle of a route. // This route $ r -> addRoute ( ' GET ' , ' /user/{id:\d+}[/{name}] ' , ' handler ' ); // Is equivalent to these two routes $ r -> addRoute ( ' GET ' , ' /user/{id:\d+} ' , ' handler ' ); $ r -> addRoute ( ' GET ' , ' /user/{id:\d+}/{name} ' , ' handler ' ); // Multiple nested optional parts are possible as well $ r -> addRoute ( ' GET ' , ' /user[/{id:\d+}[/{name}]] ' , ' handler ' ); // This route is NOT valid, because optional parts can only occur at the end $ r -> addRoute ( ' GET ' , ' /user[/{id:\d+}]/{name} ' , ' handler ' ); The $handler parameter does not necessarily have to be a callback, it could also be a controller class name or any other kind of data you wish to associate with the route. FastRoute only tells you which handler corresponds to your URI, how you interpret it is up to you. Shortcut methods for common request methods For the GET , POST , PUT , PATCH , DELETE and HEAD request methods shortcut methods are available. For example: $ r -> get ( ' /get-route ' , ' get_handler ' ); $ r -> post ( ' /post-route ' , ' post_handler ' ); Is equivalent to: $ r -> addRoute ( ' GET ' , ' /get-route ' , ' get_handler ' ); $ r -> addRoute ( ' POST ' , ' /post-route ' , ' post_handler ' ); Route Groups Additionally, you can specify routes inside a group. All routes defined inside a group will have a common prefix. For example, defining your routes as: $ r -> addGroup ( ' /admin ' , function ( FastRoute \ ConfigureRoutes $ r ) { $ r -> addRoute ( ' GET ' , ' /do-something ' , ' handler ' ); $ r -> addRoute ( ' GET ' , ' /do-another-thing ' , ' handler ' ); $ r -> addRoute ( ' GET ' , ' /do-something-else ' , ' handler ' ); }); Will have the same result as: $ r -> addRoute ( ' GET ' , ' /admin/do-something ' , ' handler ' ); $ r -> addRoute ( ' GET ' , ' /admin/do-another-thing ' , ' handler ' ); $ r -> addRoute ( ' GET ' , ' /admin/do-something-else ' , ' handler ' ); Nested groups are also supported, in which case the prefixes of all the nested groups are combined. Caching The reason simpleDispatcher accepts a callback for defining the routes is to allow seamless caching. By using cachedDispatcher instead of simpleDispatcher you can cache the generated routing data and construct the dispatcher from the cached information: <?php $ dispatcher = FastRoute \cachedDispatcher ( function ( FastRoute \ ConfigureRoutes $ r ) { $ r -> addRoute ( ' GET ' , ' /user/{name}/{id:[0-9]+} ' , ' handler0 ' ); $ r -> addRoute ( ' GET ' , ' /user/{id:[0-9]+} ' , ' handler1 ' ); $ r -> addRoute ( ' GET ' , ' /user/{name} ' , ' handler2 ' ); }, [ ' cacheKey ' => __DIR__ . ' /route.cache ' , /* required */ // 'cacheFile' => __DIR__ . '/route.cache', /* will still work for v1 compatibility */ ' cacheDisabled ' => IS_DEBUG_ENABLED , /* optional, enabled by default */ ' cacheDriver ' => FastRoute \ Cache \FileCache::class, /* optional, class name or instance of the cache driver - defaults to file cache */ ]); The second parameter to the function is an options array, which can be used to specify the cache key (e.g. file location when using files for caching), caching driver, among other things. Dispatching a URI A URI is dispatched by calling the dispatch() method of the created dispatcher. This method accepts the HTTP method and a URI. Getting those two bits of information (and normalizing them appropriately) is your job - this library is not bound to the PHP web SAPIs. The dispatch() method returns an array whose first element contains a status code. It is one of Dispatcher::NOT_FOUND , Dispatcher::METHOD_NOT_ALLOWED and Dispatcher::FOUND . For the method not allowed status the second array element contains a list of HTTP methods allowed for the supplied URI. For example: [FastRoute\Dispatcher::METHOD_NOT_ALLOWED, ['GET', 'POST']] NOTE: The HTTP specification requires that a 405 Method Not Allowed response include the Allow: header to detail available methods for the requested resource. Applications using FastRoute should use the second array element to add this header when relaying a 405 response. For the found status the second array element is the handler that was associated with the route and the third array element is a dictionary of placeholder names to their values. For example: /* Routing against GET /user/nikic/42 */ [FastRoute\Dispatcher::FOUND, 'handler0', ['name' => 'nikic', 'id' => '42']] Overriding the route parser and dispatcher The routing process makes use of three components: A route parser, a data generator and a dispatcher. The three components adhere to the following interfaces: <?php namespace FastRoute ; interface RouteParser { public function parse ( $ route ); } interface DataGenerator { public function addRoute ( $ httpMethod , $ routeData , $ handler ); public function getData (); } interface Dispatcher { const NOT_FOUND = 0 , FOUND = 1 , METHOD_NOT_ALLOWED = 2 ; public function dispatch ( $ httpMethod , $ uri ); } The route parser takes a route pattern string and converts it into an array of route infos, where each route info is again an array of its parts. The structure is best understood using an example: /* The route /user/{id:\d+}[/{name}] converts to the following array: */ [ [ '/user/', ['id', '\d+'], ], [ '/user/', ['id', '\d+'], '/', ['name', '[^/]+'], ], ] This array can then be passed to the addRoute() method of a data generator. After all routes have been added the getData() of the generator is invoked, which returns all the routing data required by the dispatcher. The format of this data is not further specified - it is tightly coupled to the corresponding dispatcher. The dispatcher accepts the routing data via a constructor and provides a dispatch() method, which you're already familiar with. The route parser can be overwritten individually (to make use of some different pattern syntax), however the data generator and dispatcher should always be changed as a pair, as the output from the former is tightly coupled to the input of the latter. The reason the generator and the dispatcher are separate is that only the latter is needed when using caching (as the output of the former is what is being cached.) When using the simpleDispatcher / cachedDispatcher functions from above the override happens through the options array: <?php $ dispatcher = FastRoute \simpleDispatcher ( function ( FastRoute \ ConfigureRoutes $ r ) { /* ... */ }, [ ' routeParser ' => ' FastRoute \\ RouteParser \\ Std ' , ' dataGenerator ' => ' FastRoute \\ DataGenerator \\ MarkBased ' , ' dispatcher ' => ' FastRoute \\ Dispatcher \\ MarkBased ' , ]); The above options array corresponds to the defaults. By replacing MarkBased with GroupCountBased you could switch to a different dispatching strategy. A Note on HEAD Requests The HTTP spec requires servers to support both GET and HEAD methods : The methods GET and HEAD MUST be supported by all general-purpose servers To avoid forcing users to manually register HEAD routes for each resource we fallback to matching an available GET route for a given resource. The PHP web SAPI transparently removes the entity body from HEAD responses so this behavior has no effect on the vast majority of users. However, implementers using FastRoute outside the web SAPI environment (e.g. a custom server) MUST NOT send entity bodies generated in response to HEAD requests. If you are a non-SAPI user this is your responsibility ; FastRoute has no purview to prevent you from breaking HTTP in such cases. Finally, note that applications MAY always specify their own HEAD method route for a given resource to bypass this behavior entirely. Credits This library is based on a router that Levi Morrison implemented for the Aerys server. A large number of tests, as well as HTTP compliance considerations, were provided by Daniel Lowrey . About Fast request router for PHP Resources Readme License View license Uh oh! There was an error while loading. Please reload this page . Activity Stars 5.3k stars Watchers 176 watching Forks 454 forks Report repository Releases 12 FastRoute 1.3.0 Latest Feb 13, 2018 + 11 releases Packages 0 No packages published Uh oh! There was an error while loading. Please reload this page . Contributors 50 Uh oh! There was an error while loading. Please reload this page . + 36 contributors Languages PHP 99.4% Makefile 0.6% Footer © 2026 GitHub, Inc. Footer navigation Terms Privacy Security Status Community Docs Contact Manage cookies Do not share my personal information You can’t perform that action at this time. | 2026-01-13T08:49:44 |
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Right menu n8n: Credential - OpenAI account codebangkok codebangkok codebangkok Follow Dec 10 '25 n8n: Credential - OpenAI account # automation # openai # tutorial Comments Add Comment 1 min read Create OpenAI API key codebangkok codebangkok codebangkok Follow Dec 10 '25 Create OpenAI API key # openai # beginners # api # tutorial Comments Add Comment 1 min read Supercharge Your LLMs: Turn Basic APIs into 3D AI Desktop Companions with Zero Code Change GitHubOpenSource GitHubOpenSource GitHubOpenSource Follow Nov 4 '25 Supercharge Your LLMs: Turn Basic APIs into 3D AI Desktop Companions with Zero Code Change # ai # llm # openai # multimodal Comments Add Comment 3 min read CodeMate - An AI Coding Agent for developers chinecherem rose chinecherem rose chinecherem rose Follow Nov 4 '25 CodeMate - An AI Coding Agent for developers # ai # agents # openai # javascript Comments Add Comment 4 min read I Classified the Soft Skills I've Posted So Far sta sta sta Follow Dec 7 '25 I Classified the Soft Skills I've Posted So Far # softskills # ai # openai # category Comments Add Comment 10 min read The headline? 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OpenAI DevDay just changed the enterprise AI game. # ai # openai # b2b Comments Add Comment 2 min read StudyMate AI – Your Personal AI Study Assistant for Smarter Learning StudyMateAI StudyMateAI StudyMateAI Follow Dec 4 '25 StudyMate AI – Your Personal AI Study Assistant for Smarter Learning # ai # openai # nextjs # learning Comments Add Comment 1 min read How Agentic Browsers Can Break Your Security Model Rodrigo Fernandez Rodrigo Fernandez Rodrigo Fernandez Follow Oct 28 '25 How Agentic Browsers Can Break Your Security Model # ai # openai # jailbreak # browser Comments Add Comment 5 min read 🚀 Why the AWS–OpenAI Partnership Redefines the Future of AI Infrastructure Matias Santoro Matias Santoro Matias Santoro Follow Nov 3 '25 🚀 Why the AWS–OpenAI Partnership Redefines the Future of AI Infrastructure # aws # ai # cloud # openai 3 reactions Comments Add Comment 3 min read Conselhos para Startups Lucas Rafaldini Lucas Rafaldini Lucas Rafaldini Follow Oct 27 '25 Conselhos para Startups # startup # empreendedorismo # openai # tradução Comments Add Comment 6 min read OpenAI will make its ChatGPT Go model free for Indian users for a year Maulik Thakrar Maulik Thakrar Maulik Thakrar Follow for Ekantik Technologies Oct 28 '25 OpenAI will make its ChatGPT Go model free for Indian users for a year # news # openai # chatgpt # ekantiktechnologies Comments Add Comment 1 min read Breaking News: OpenAI Rebrands to OpaqueAI Rich Jeffries Rich Jeffries Rich Jeffries Follow Nov 22 '25 Breaking News: OpenAI Rebrands to OpaqueAI # ai # openai # mcp # llm 16 reactions Comments 9 comments 10 min read OpenAI launches ChatGPT feature to integrate company knowledge from multipleconnected business tools Saiki Sarkar Saiki Sarkar Saiki Sarkar Follow Oct 28 '25 OpenAI launches ChatGPT feature to integrate company knowledge from multipleconnected business tools # news # openai # chatgpt # rag Comments Add Comment 2 min read Implementing Token Count Optimization in repo-contextr Dharam Ghevariya Dharam Ghevariya Dharam Ghevariya Follow Oct 26 '25 Implementing Token Count Optimization in repo-contextr # openai # tooling # opensource # llm Comments Add Comment 3 min read The Evolution of GPT: How AI Became Your Everyday Assistant Chetana Desai Chetana Desai Chetana Desai Follow Nov 19 '25 The Evolution of GPT: How AI Became Your Everyday Assistant # ai # openai # chatgpt # gpt3 Comments Add Comment 7 min read AI Recipe Generator: Turn Food Photos into Instant Recipes with AI 🍳✨ Nithin Pradeep Nithin Pradeep Nithin Pradeep Follow Oct 30 '25 AI Recipe Generator: Turn Food Photos into Instant Recipes with AI 🍳✨ # ai # openai # nextjs # webdev 3 reactions Comments Add Comment 2 min read Talking About Things I Dislike About Atlas KazooTTT KazooTTT KazooTTT Follow Oct 25 '25 Talking About Things I Dislike About Atlas # openai # chatgpt # browser Comments Add Comment 2 min read DeepSeek-OCR: The New 'Black Tech' in AI, How It's Changing Our Interaction with AI Models? baboon baboon baboon Follow Oct 24 '25 DeepSeek-OCR: The New 'Black Tech' in AI, How It's Changing Our Interaction with AI Models? # deepseek # ai # llm # openai Comments Add Comment 5 min read How ChatGPT Apps Really Work: Inside the OpenAI Apps SDK Zach Park Zach Park Zach Park Follow Oct 24 '25 How ChatGPT Apps Really Work: Inside the OpenAI Apps SDK # openai # chatgptapps # opensource # python Comments Add Comment 2 min read The OpenAI Mixpanel Security Incident Explained Ali Farhat Ali Farhat Ali Farhat Follow Nov 27 '25 The OpenAI Mixpanel Security Incident Explained # openai # mixpanel # security # ai 33 reactions Comments 8 comments 3 min read Choosing Between Agent Builder and n8n? Here’s the Breakdown That Would’ve Saved Me Months Developer Harsh Developer Harsh Developer Harsh Follow for Composio Nov 24 '25 Choosing Between Agent Builder and n8n? Here’s the Breakdown That Would’ve Saved Me Months # ai # openai # automation # agents 1 reaction Comments Add Comment 7 min read You Have No Idea How Screwed OpenAI Is Mashraf Aiman Mashraf Aiman Mashraf Aiman Follow Nov 24 '25 You Have No Idea How Screwed OpenAI Is # ai # futurechallenge # openai 7 reactions Comments Add Comment 4 min read A Client Asked Me to Add AI. I Spent 2 Weeks Researching the Costs. Here's What I Found. Arbythecoder Arbythecoder Arbythecoder Follow Nov 24 '25 A Client Asked Me to Add AI. I Spent 2 Weeks Researching the Costs. Here's What I Found. # ai # webdev # openai # cloudcomputing 2 reactions Comments Add Comment 4 min read OpenAI dévoile les Prompt Packs : plus de 300 prompts pour booster votre productivité avec ChatGPT babi mumba babi mumba babi mumba Follow Oct 20 '25 OpenAI dévoile les Prompt Packs : plus de 300 prompts pour booster votre productivité avec ChatGPT # openai # resources # chatgpt # productivity Comments Add Comment 2 min read Built Datapizza-AI in PHP on 2011 Raspberry Pi: Edge AI Without GPU Paolo Paolo Paolo Follow Nov 2 '25 Built Datapizza-AI in PHP on 2011 Raspberry Pi: Edge AI Without GPU # php # ai # raspberrypi # openai 1 reaction Comments Add Comment 1 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.suprsend.com/docs/embeddable-inbox#accessing-other-instance-methods | Integration - SuprSend, Notification infrastructure for Product teams Skip to main content SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Community Trust Center Platform Status Postman Collection GETTING STARTED What is SuprSend? Quick Start Guide Best Practices Plan Your Integration Go-live checklist CORE CONCEPTS Templates Users Events Workflow Notification Categories Preferences Tenants Lists Broadcast Objects Translations DLT Guidelines Whatsapp Template Guidelines WORKFLOW BUILDER Design Workflow Node List Workflow Settings Trigger Workflow Validate Trigger Payload Tenant Workflows Notification Inbox Overview Multi Tabs React Javascript (Angular, Vuejs etc) Integration Customization options React Native Flutter (Headless) PREFERENCE CENTRE Embedded Preference Centre Javascript Angular React VENDOR INTEGRATION GUIDE Overview Email Integrations SMS Integrations Android Push Whatsapp Integrations iOS Push Chat Integrations Vendor Fallback Tenant Vendor INTEGRATIONS Webhook Connectors MONITORING & DEBUGGING Logs Audit Logs Error Guides MANAGE YOUR ACCOUNT Authentication Methods Contact Us Get Started SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Ask AI Contact Us Get Started Get Started Search... Navigation Javascript (Angular, Vuejs etc) Integration Documentation API Reference Management API CLI Reference Developer Resources Changelog Documentation API Reference Management API CLI Reference Developer Resources Changelog Javascript (Angular, Vuejs etc) Integration OpenAI Open in ChatGPT How to integrate SuprSend inbox/feed components in Angular, Vue, VanillaJS, and other non-React frameworks. OpenAI Open in ChatGPT End of Support for @suprsend/web-inbox . Migrate to @suprsend/web-components We have upgraded authentication of inbox from HMAC to JWT as it is more secure. Please migrate to newer SDK if you are on old one. There are 2 ways in which you can implement inbox functionality: Drop-in components: Pre-built UI with many customizable options which require minimal effort to build. Headless implementation: For more advanced use cases where you want to build UI/UX from scratch. This guide help you integrate drop-in components in your non-react frameworks (angular, vuejs, vanillajs etc). If you want to build your own UI (headless) instead of using drop-in components please refer docs . Integration Integrate using script tag This integration is used in Vanillajs, Django, Laravel, ruby etc where npm is not used. Copy Ask AI <!-- for dropin inbox with bell --> < div id = "suprsend-inbox" ></ div > <!-- for feed without bell as a fullscreen notification etc --> < div id = "suprsend-feed" ></ div > < script > window . suprsendConfig = { distinctId: "YOUR_DISTINCT_ID" , publicApiKey: "YOUR_PUBLIC_API_KEY" , userAuthenticationHandler : ({ response }) => { console . log ( "User Authentication Response" , response ); }, }; let scriptElem = document . createElement ( "script" ); scriptElem . async = 1 ; scriptElem . src = "https://web-components.suprsend.com/v0.3.0/bundle.umd.js" ; scriptElem . onload = () => { console . log ( "SuprSend SDK loaded" , window . suprsend ); }; document . body . appendChild ( scriptElem ); </ script > Integrate as npm package This integration is used in framework based applications like angular, vuejs etc. Copy Ask AI npm install @suprsend/web-components@latest Copy Ask AI import { initSuprSend , clearSuprSend } from "@suprsend/web-components" ; // for dropin inbox with bell < div id = "suprsend-inbox" ></ div > // for feed without bell as a fullscreen notification etc < div id = "suprsend-feed" ></ div > const suprsendConfig = { distinctId: "YOUR_DISTINCT_ID" , publicApiKey: "YOUR_PUBLIC_API_KEY" , userAuthenticationHandler : ({ response }) => { console . log ( "User Authentication Response" , response ); }, }; initSuprSend ( suprsendConfig ) // for creating instance and rendering component console . log ( "Instance created but user authentication pending" , window . suprsend ) NOTE: If you are using suprsend-feed , specify height for the container for infinite scroll to work properly. Copy Ask AI const suprsendConfig = { distinctId: "YOUR_DISTINCT_ID" , publicApiKey: "YOUR_PUBLIC_API_KEY" , feed: { theme: { notificationsContainer: { container: { height: "100vh" } } }, // add this to specify height }, }; Removing instance Components will be removed automatically if you navigate away from the page (on unmounting). If you want to remove them manually, you can use below methods. Using script tag Using npm package Copy Ask AI window . suprsend . clearSuprSend (); // clears instance and remove all components window . suprsend . clearSuprSendInbox (); // unmount only inbox component window . suprsend . clearSuprSendFeed (); // unmount only feed component Updating configuration dynamically Copy Ask AI window . suprsend . updateSuprSendConfig ( config : IUpdateSuprSendConfigOptions ); // refresh userToken, change locale, translations dymanically window . suprsend . updateInboxConfig ( config : IInbox ); window . suprsend . updateFeedConfig ( config : IFeed ); window . suprsend . updateToastConfig ( config : IToastNotificationProps ); Accessing other instance methods SDK internally calls new SuprSend() when you call initSuprSend() then you can access instance using window.suprsend.client . This instance has methods like preferences , webpush , event and user updates . Copy Ask AI // example methods window . suprsend . client . isIdentified (); window . suprsend . client . user . addEmail ( email : string ); window . suprsend . client . track ( event : string , properties ?: Dictionary ) window . suprsend . client . webpush . registerPush (); window . suprsend . client . user . preferences . getPreferences ( args ?: {tenantId? : string }); Config options To customise SuprSend components you can pass config object. Config Options Inbox Config Options Feed Config Options Toast Config Options Copy Ask AI interface ConfigProps { publicApiKey : string ; distinctId ? : unknown ; userToken ? : string ; host ? : string ; initOnLoad ? : boolean ; // pass false if you don't want to initialise instance just after loading script refreshUserToken ? : ( oldUserToken : string , tokenPayload : Dictionary ) => Promise < string > ; vapidKey ? : string ; swFileName ? : string ; userAuthenticationHandler ? : ({ response : ApiResponse }) => void ; inbox ? : IInbox ; // inbox config options feed ? : IFeed ; // feed config options toast ? : IToastNotificationProps ; // toast config options shadowRoot ?: ShadowRoot ; //shadowRoot reference } Parameter Description publicApiKey Public API Key is mandatory field without which error will be thrown by SuprSendProvider. You can get this from SuprSend Dashboard . distinctId Unique identifier to identify a user across platform. If a value is passed SDK will create user and authenticate user. If null value is passed authenticated user’s instance data will be cleared in your application, kind of logout. userToken Mandatory when enhanced security mode is on. This is ES256 JWT token generated in your server-side. Refer docs to create userToken. refreshUserToken This function is called by SDK internally to get new userToken before existing token is expired. The returned JWT token string is used as the new userToken. userAuthenticationHandler This callback will be called after authenticating user internally when you pass distinctId field to give you back the response of user creation API call. host Customise the host url. vapidKey This key is needed only if you are implementing WebPush notifications. You can get it in SuprSend Dashboard —> Vendors —> WebPush swFileName This key is needed only if you are implementing WebPush notifications and want to customise default serviceworker.js file name with your own service worker file name. shadowRoot Shadow root reference to render components inside shadow dom For further component specific customisations please refer to the docs . Was this page helpful? Yes No Suggest edits Raise issue Previous Customization options How to customize the styling, CSS, and layout of the Inbox Feed to match your product’s design in non-React websites. Next ⌘ I x github linkedin youtube Powered by On this page Integration Integrate using script tag Integrate as npm package Removing instance Updating configuration dynamically Accessing other instance methods Config options | 2026-01-13T08:49:44 |
https://docs.devcycle.com/sdk/#server-side-sdks | SDK Overview | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up SDK Overview SDK Lifecycle SDK Features Client-side SDKS Server-side SDKS SDK Proxy SDK Overview SDK Overview DevCycle has both client-side and server-side SDKs. This page describes the differences between these SDK types. Implementation and usage change depending on which type of SDK is being used. tip Explore our SDK Features and Functionality to discover how to implement your solutions using the DevCycle SDKs. Client Side SDKs DevCycle client-side SDKs are meant for single-user contexts, such as web browsers and mobile apps. These SDKs retrieve their configuration for the current user when they are initialized and any time the user is re-identified. They also receive updates in real time when configuration is changed in the DevCycle platform. The current Client-Side SDKs are: JavaScript SDK React SDK Next.js SDK Angular SDK iOS SDK Android SDK React Native SDK Flutter SDK Roku SDK Server-Side SDKs Server-side SDKs are used in multi-user contexts such as backend services, where each call to the SDK will likely be for a different user. The user's ID and any other targeting information must be passed in on every SDK function call. The current Server-Side SDKs are: NodeJS SDK NestJS SDK Go SDK PHP SDK Python SDK Ruby SDK Java SDK .NET SDK OpenFeature Providers OpenFeature is an open standard that provides a vendor-agnostic, community-driven SDKs for feature flagging that works natively with DevCycle. Client-Side JavaScript React Angular iOS Android Server-Side NodeJS NestJS Go Ruby Java .NET / C# Python PHP Edit this page Last updated on Jan 9, 2026 Next SDK Lifecycle DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:44 |
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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 Mental Health Follow Hide Mental health matters! Break the stigma. We can empower ourselves and each other to invest in our mental health. We can give support and care to ourselves and each other while we struggle. Let's talk about making our mental health priority. Create Post about #mentalhealth Posts should be related to mental health. This is a pretty wide category but some things that are included are: Managing mental health as a developer Living with mental illness and how it affects your work Ways to cope with mental health issues Avoiding burn out Tools, apps, and methods that help you with your mental health ...and more “Your mental health is a priority. Your happiness is an essential. Your self-care is a necessity.” Struggling? Help is out there. 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Right menu AI Chatbots and Mental Health: The Hidden Crisis Developers Need to Know shiva shanker shiva shanker shiva shanker Follow Sep 8 '25 AI Chatbots and Mental Health: The Hidden Crisis Developers Need to Know # ethics # mentalhealth # psychology # usersafety 40 reactions Comments 1 comment 3 min read 🧠 Breaking Negative Thinking Patterns with Cognitive Behavioral Therapy (CBT): A Practical Guide NVelUp NVelUp NVelUp Follow Sep 6 '25 🧠 Breaking Negative Thinking Patterns with Cognitive Behavioral Therapy (CBT): A Practical Guide # mentalhealth # productivity # cbt # selfimprovement 1 reaction Comments Add Comment 3 min read Life’s Answers Are Only Visible in Hindsight (Bite-size Article) koshirok096 koshirok096 koshirok096 Follow Aug 1 '25 Life’s Answers Are Only Visible in Hindsight (Bite-size Article) # mentalhealth Comments Add Comment 2 min read Is Burnout Inevitable in the World of Tech? Let's Talk About It Cesar Aguirre Cesar Aguirre Cesar Aguirre Follow Aug 25 '25 Is Burnout Inevitable in the World of Tech? Let's Talk About It # discuss # career # programming # mentalhealth 17 reactions Comments 8 comments 2 min read The Trigger… Sayman Lal Sayman Lal Sayman Lal Follow Jul 29 '25 The Trigger… # developer # lifeexperience # motivation # mentalhealth 1 reaction Comments Add Comment 1 min read Zeigarnik Effect (Bite-size Article) koshirok096 koshirok096 koshirok096 Follow Jul 25 '25 Zeigarnik Effect (Bite-size Article) # productivity # mentalhealth 1 reaction Comments Add Comment 4 min read Дефицит железа у разработчиков: Невидимый баг производительности 🐛 Sport Store Sport Store Sport Store Follow Jul 24 '25 Дефицит железа у разработчиков: Невидимый баг производительности 🐛 # mentalhealth # supplements # ionic # programming Comments Add Comment 1 min read HEY!! Don't Burnout 🤗 David James David James David James Follow Aug 18 '25 HEY!! Don't Burnout 🤗 # webdev # mentalhealth # workplace # career Comments 1 comment 2 min read Doom Vibe Coding: How AI is Changing the Way We Work—and Think BeFede BeFede BeFede Follow Aug 13 '25 Doom Vibe Coding: How AI is Changing the Way We Work—and Think # ai # programming # productivity # mentalhealth 1 reaction Comments Add Comment 5 min read Why Meditation Matters More Than You Think Jorge Sequeira Jorge Sequeira Jorge Sequeira Follow Aug 13 '25 Why Meditation Matters More Than You Think # psychology # mentalhealth # productivity # science 1 reaction Comments Add Comment 5 min read From Economics to Engineering My Future — Day Two of the Journey Slobodan Jevtić Slobodan Jevtić Slobodan Jevtić Follow Jul 7 '25 From Economics to Engineering My Future — Day Two of the Journey # python # coding # 100daysofcode # mentalhealth Comments Add Comment 1 min read Day Two – Sleep Tracker Tab Built and Working Slobodan Jevtić Slobodan Jevtić Slobodan Jevtić Follow Jul 7 '25 Day Two – Sleep Tracker Tab Built and Working # programming # 100daysofcode # python # mentalhealth 1 reaction Comments Add Comment 2 min read Data Privacy in AI Mental Health Tools: What You Should Know bvanderbilt0033 bvanderbilt0033 bvanderbilt0033 Follow Jul 3 '25 Data Privacy in AI Mental Health Tools: What You Should Know # mental # health # mentalhealth # mentalhealthtools Comments Add Comment 4 min read Building an AI Health Platform That Could Save the NHS £8.5M+ Annually shiva shanker shiva shanker shiva shanker Follow Aug 4 '25 Building an AI Health Platform That Could Save the NHS £8.5M+ Annually # healthydebate # mentalhealth # uk # software 16 reactions Comments 2 comments 3 min read Build Something You Can Eat: Raised Garden Beds for Devs potager project potager project potager project Follow Jul 1 '25 Build Something You Can Eat: Raised Garden Beds for Devs # sideprojects # gardeningfordevelopers # sustainability # mentalhealth Comments Add Comment 1 min read Hello...Just Wondering If There Are Others Out There Like Me? Ivan Krause Ivan Krause Ivan Krause Follow Jun 29 '25 Hello...Just Wondering If There Are Others Out There Like Me? # intro # devlife # mentalhealth # ireland Comments Add Comment 1 min read Why I’m All In on Burnout Prevention and What I’m Building with DustOff Reset Gaofenngwe Kabubi Gaofenngwe Kabubi Gaofenngwe Kabubi Follow Jun 28 '25 Why I’m All In on Burnout Prevention and What I’m Building with DustOff Reset # founder # burnout # ai # mentalhealth Comments Add Comment 2 min read The Ones Who Tried, and Disappeared Anyway versiqcontent versiqcontent versiqcontent Follow Jun 24 '25 The Ones Who Tried, and Disappeared Anyway # discuss # mentalhealth # community # coding 1 reaction Comments Add Comment 1 min read Sundays Are My AI Recharge Day Accio by Alibaba Group Accio by Alibaba Group Accio by Alibaba Group Follow Jul 27 '25 Sundays Are My AI Recharge Day # mentalhealth Comments Add Comment 1 min read Book reflection: 5 killer habits be a rebel Ansu Shaw Ansu Shaw Ansu Shaw Follow Jul 25 '25 Book reflection: 5 killer habits be a rebel # kindnessisstrength # books # mentalhealth # emotionalwellbeing Comments Add Comment 2 min read Organizing Your “Mental Logs”: A Meditation Approach That Turns Distractions Into Allies (Bite-size Article) koshirok096 koshirok096 koshirok096 Follow Jun 20 '25 Organizing Your “Mental Logs”: A Meditation Approach That Turns Distractions Into Allies (Bite-size Article) # mentalhealth # productivity Comments Add Comment 4 min read Building Slurp: A Fruit-Themed AI Mood Tracker with Supabase and Next.js Arka Arka Arka Follow Jun 20 '25 Building Slurp: A Fruit-Themed AI Mood Tracker with Supabase and Next.js # mentalhealth # webdev # ai Comments Add Comment 2 min read 🚀 How Kiro Helped Me Build BreezaAI – an AI Mental Wellness Tutor in Record Time Miiu Miiu Miiu Follow Jul 23 '25 🚀 How Kiro Helped Me Build BreezaAI – an AI Mental Wellness Tutor in Record Time # kiro # hackathon # mentalhealth # ai 3 reactions Comments 1 comment 2 min read How Social Media Subconsciously Controls Your Mind: An Academic Review on Dopamine, FOMO & Algorithms Sarthak Wakade Sarthak Wakade Sarthak Wakade Follow Jul 20 '25 How Social Media Subconsciously Controls Your Mind: An Academic Review on Dopamine, FOMO & Algorithms # socialmedia # mentalhealth # algorithms # psychology 6 reactions Comments 1 comment 4 min read Beating the Monday Blues as a CTO (Without Losing Your Mind) Akshay Joshi Akshay Joshi Akshay Joshi Follow Jun 16 '25 Beating the Monday Blues as a CTO (Without Losing Your Mind) # productivity # leadership # mentalhealth # workculture Comments Add Comment 1 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 Mental Health Follow Hide Mental health matters! Break the stigma. We can empower ourselves and each other to invest in our mental health. We can give support and care to ourselves and each other while we struggle. Let's talk about making our mental health priority. Create Post about #mentalhealth Posts should be related to mental health. This is a pretty wide category but some things that are included are: Managing mental health as a developer Living with mental illness and how it affects your work Ways to cope with mental health issues Avoiding burn out Tools, apps, and methods that help you with your mental health ...and more “Your mental health is a priority. Your happiness is an essential. Your self-care is a necessity.” Struggling? Help is out there. Click here to find a list of global mental health resources and hotlines. Older #mentalhealth posts 3 4 5 6 7 8 9 10 11 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu I tracked 7 daily habits for 3 weeks. The data surprised me. Alex Chen Alex Chen Alex Chen Follow Oct 30 '25 I tracked 7 daily habits for 3 weeks. The data surprised me. # productivity # mentalhealth # data # habits 6 reactions Comments 1 comment 2 min read What I Learned From 60 Days of Brutal Honesty in My Work Journal Ed Ed Ed Follow Oct 31 '25 What I Learned From 60 Days of Brutal Honesty in My Work Journal # devjournal # mentalhealth # productivity 1 reaction Comments 2 comments 4 min read 🌊 Part 3.5 — Calm Between Waves Masato Kato Masato Kato Masato Kato Follow Oct 29 '25 🌊 Part 3.5 — Calm Between Waves # ai # creativity # poetry # mentalhealth 2 reactions Comments Add Comment 3 min read Ted talks that everyone should hear - part 1 Or Yaacov Or Yaacov Or Yaacov Follow Sep 23 '25 Ted talks that everyone should hear - part 1 # discuss # learning # mentalhealth Comments Add Comment 3 min read Practical Strategies to Improve Your Mental Health Every Day Oliver Davis Oliver Davis Oliver Davis Follow Sep 23 '25 Practical Strategies to Improve Your Mental Health Every Day # mentalhealth # therapy # selfcare # wellnesstips Comments Add Comment 4 min read How Endless Scrolling Broke My Focus — and Why I Built Block Scroll to Fix It Block Scroll Block Scroll Block Scroll Follow Oct 26 '25 How Endless Scrolling Broke My Focus — and Why I Built Block Scroll to Fix It # productivity # mentalhealth # digitalwellbeing # startup 6 reactions Comments 1 comment 3 min read 🖥️ Cut the Screen, Boost Your Mood: A Developer’s Guide to Digital Wellness NVelUp NVelUp NVelUp Follow Sep 22 '25 🖥️ Cut the Screen, Boost Your Mood: A Developer’s Guide to Digital Wellness # digitalwellness # mentalhealth # productivity # selfcare Comments Add Comment 2 min read We Need to Talk About How Toxic Dev Culture Has Become (And How We Fix It) Elvis Sautet Elvis Sautet Elvis Sautet Follow Oct 23 '25 We Need to Talk About How Toxic Dev Culture Has Become (And How We Fix It) # culture # community # mentalhealth # webdev 26 reactions Comments 3 comments 15 min read Unleashing Developer Potential: Balancing Cognitive Load for Peak Productivity George Udonte George Udonte George Udonte Follow Oct 23 '25 Unleashing Developer Potential: Balancing Cognitive Load for Peak Productivity # mentalhealth # productivity # softwareengineering Comments 1 comment 2 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 🌱 Acupuncture for Anxiety? 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Here’s What Developers Should Know 🧠 # mentalhealth # productivity # selfcare # wellness Comments Add Comment 2 min read Some rejections hit hard Manasvi Pal Manasvi Pal Manasvi Pal Follow Oct 26 '25 Some rejections hit hard # career # interview # mentalhealth Comments 5 comments 1 min read 🧘♂️ Mindfulness Meditation: Science-Backed Path to a Calmer Mind NVelUp NVelUp NVelUp Follow Sep 18 '25 🧘♂️ Mindfulness Meditation: Science-Backed Path to a Calmer Mind # mentalhealth # mindfulness # wellness # neuroscience Comments Add Comment 2 min read When Passion Becomes Commodity Andrew Reese Andrew Reese Andrew Reese Follow Oct 8 '25 When Passion Becomes Commodity # discuss # mentalhealth # inclusion # watercooler 50 reactions Comments 16 comments 9 min read Balancing Focus and Fatigue as a Full-Time Developer Miriam Miriam Miriam Follow Oct 19 '25 Balancing Focus and Fatigue as a Full-Time Developer # help # productivity # mentalhealth # career Comments 2 comments 1 min read 💬 Breaking the Stigma: Why Open Mental Health Conversations Matter (and How You Can Help) NVelUp NVelUp NVelUp Follow Sep 16 '25 💬 Breaking the Stigma: Why Open Mental Health Conversations Matter (and How You Can Help) # mentalhealth # selfimprovement # productivity # wellbeing Comments Add Comment 2 min read I Built a Mental Ritual That Doesn’t Explain Itself katorymnddev katorymnddev katorymnddev Follow Sep 16 '25 I Built a Mental Ritual That Doesn’t Explain Itself # mentalhealth # mindfulness # webdev Comments Add Comment 4 min read Misunderstood: Notice What You’ve Been Feeling, Not Just What You Showed Rajat Singh Rajat Singh Rajat Singh Follow Sep 13 '25 Misunderstood: Notice What You’ve Been Feeling, Not Just What You Showed # misunderstoodbook # mentalhealth # sreekrishnaseelam # readforgrowth Comments Add Comment 3 min read Talk Therapy & Mental Wellbeing: A Developer’s Guide to Debugging the Mind 🧠 NVelUp NVelUp NVelUp Follow Sep 13 '25 Talk Therapy & Mental Wellbeing: A Developer’s Guide to Debugging the Mind 🧠 # mentalhealth # productivity # therapy # cbt Comments Add Comment 2 min read Moriah Behavioral Health’s Commitment to Reducing the Stigma Around Mental Health Moriah Behavioral Health Moriah Behavioral Health Moriah Behavioral Health Follow Sep 13 '25 Moriah Behavioral Health’s Commitment to Reducing the Stigma Around Mental Health # discuss # mentalhealth Comments Add Comment 4 min read A Review of 'Misunderstood': Insights into Mental Health Literature Preetinder Kaur Preetinder Kaur Preetinder Kaur Follow Sep 12 '25 A Review of 'Misunderstood': Insights into Mental Health Literature # misunderstood # sreekrishnaseelam # mentalhealth # awareness Comments Add Comment 2 min read How Gratitude Can Rewire Your Brain (and Why Devs Should Care) NVelUp NVelUp NVelUp Follow Sep 25 '25 How Gratitude Can Rewire Your Brain (and Why Devs Should Care) # productivity # mentalhealth Comments Add Comment 2 min read Introducing… Git Pushups Justin Higgins ➡️ Justin Higgins ➡️ Justin Higgins ➡️ Follow Sep 12 '25 Introducing… Git Pushups # git # github # mentalhealth # mobile Comments Add Comment 1 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 Congested Mind vs. Fresh Mind: The Hidden Factor in Problem Solving Muhammed Shafeeque AKP Muhammed Shafeeque AKP Muhammed Shafeeque AKP Follow Sep 9 '25 Congested Mind vs. Fresh Mind: The Hidden Factor in Problem Solving # programming # mentalhealth # career # productivity 1 reaction 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://docs.devcycle.com/sdk/client-side-sdks/android | Android SDK | DevCycle Docs Skip to main content Home SDKs APIs Management API Bucketing API Integrations CLI / MCP Best Practices Community Blog Discord Search Sign Up SDK Overview SDK Lifecycle SDK Features Client-side SDKS JavaScript SDK React SDK Next.js SDK Angular SDK iOS SDK Android SDK Installation Getting Started Usage Example App OpenFeature React Native Flutter SDK Roku SDK Server-side SDKS SDK Proxy Client-side SDKS Android SDK DevCycle Android SDK The DevCycle Android Client SDK! This SDK uses our Client SDK APIs to perform all user segmentation and bucketing for the SDK, providing fast response times using our globally distributed edge workers all around the world. Installation Installing the SDK Getting Started Initializing the SDK Usage Using the SDK Example App Try it out for yourself OpenFeature How to implement the OpenFeature Provider for Android The SDK is available as a package on MavenCentral. It is also open source and can be viewed on Github. Edit this page Last updated on Jan 9, 2026 Previous OpenFeature Next Installation DevCycle Dashboard Blog Privacy Policy Twitter Discord GitHub Copyright © 2026 DevCycle. All rights reserved. | 2026-01-13T08:49:44 |
https://www.algolia.com/fr/industries/grocery/ | Algolia pour la distribution alimentaire 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. 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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We got you covered! Brand guidelines Download logo pack Your browser does not support the video tag. Distribution alimentaire Solution intelligente pour la grande distribution Une recherche qui comprend les goûts uniques de vos consommateurs. Planifier une démonstration Les plus grandes ensignes font confiance à Algolia “Une vitesse et des performances de recherche exceptionnelles sont devenues une attente grâce à Algolia. Mais pour nous, Algolia est devenu bien plus que de la recherche. L’utilisation des fonctionnalités avancées d’Algolia nous a permis d’améliorer les résultats pour nos clients, d’augmenter les ventes et de concentrer nos efforts sur la création d’une expérience exceptionnelle.” Responsable du commerce digital H-E-B México Lire l'étude de cas "Le grand avantage d’Algolia, c’est qu’avec une implémentation de données simples, nous avons pu obtenir d’excellents résultats. Le reclassement dynamique en a été un élément clé. Nous l’avons A/B testé dans les premières semaines du lancement et avons constaté des résultats positifs. Cela a vraiment été un énorme avantage pour nous : cela nous fait gagner beaucoup de temps puisque nous n’avons pas à ajouter manuellement des règles, tout en offrant une expérience client positive." Gareth Cottiss Responsable du développement e-commerce @ Co-op Lire l'étude de cas “Avant Algolia, notre équipe passait beaucoup de temps à gérer et à ajuster notre ancienne solution de recherche, ce qui entraînait souvent des résultats incohérents pour les utilisateurs. Depuis la mise en place d’Algolia, nous avons non seulement réduit la charge de travail de nos ingénieurs, mais nous avons aussi constaté que nos clients trouvent plus rapidement les produits, avec beaucoup moins de frustration. En tant que chaîne de magasins de spiritueux, Algolia a également facilité la découverte de nouveaux produits similaires à ceux que nos clients appréciaient déjà, ce qui a stimulé la découverte et encouragé l’essai de nouvelles marques.” Kelby Fritz Responsable produit technique @ Binny’s Read their story “Même si la langue officielle est l’espagnol, beaucoup de gens parlent un mélange d’espagnol et d’anglais — le spanglish. Les clients n’entrent pas de descriptions techniques lorsqu’ils font une recherche. Algolia nous a aidés à combler l’écart entre les mots utilisés par nos chefs de catégorie et les termes réellement saisis par les clients dans le champ de recherche. Nous adorons cette fonctionnalité.” Felipe Alonso Valverde Responsable e-commerce @ Auto Mercado Lire l'étude de cas Transformez votre recherche et votre découverte Créez une recherche e-commerce personnalisée, propulsée par l’IA, qui comprend réellement ce que recherchent vos clients. Une expérience de distribution alimentaire online au service de vos clients et votre entreprise Saisissez l’opportunité avec une recherche par IA intelligente. Augmentez vos marges et la taille du panier Mettez en avant les marques maison à forte marge et les offres groupées grâce à des recommandations pilotées par l’IA et un marchandisage intelligent. Obtenir une démo Des listes faciles, rapidement Transformez les commandes vocales, les recettes et les listes de courses importées en paniers prêts à l’achat. Recherche et personnalisation par IA Adaptez les résultats de recherche à chaque client en fonction de ses préférences en temps réel, du stock, de la localisation et des intentions. Obtenir une démo De l’inspiration au panier Une IA conversationnelle qui transforme les idées et les intentions en achats — pour aider les clients à faire leurs courses comme ils pensent. Visibilité de l’inventaire en temps réel Aidez les clients à trouver ce qui est réellement disponible près de chez eux — et réduisez la frustration et l’attrition. Recherche et personnalisation par IA Adaptez les résultats de recherche à chaque client en fonction de ses préférences en temps réel, du stock, de la localisation et des intentions. Obtenir une démo Des rayons à l’IA : comment les distributeurs alimentaires alimentent la prochaine génération de croissance du retail Le secteur de l’alimentation est transformé par la recherche, la découverte et le merchandising propulsés par l’IA, avec des résultats remarquables. Plébiscité par les pionniers de la distribution alimentaire Maximisez les lancements de produits Réduisez la charge cognitive, éliminez les impasses et aidez les clients à trouver exactement ce qu’ils veulent — rapidement. Apportez une vraie valeur Transformez les informations en actions grâce à une expérience de recherche qui reflète la disponibilité en temps réel, la saisonnalité et la personnalisation. Réinventez vos marges Utilisez l’IA pour promouvoir les bons produits au bon moment — en équilibrant satisfaction client et objectifs de rentabilité. L’avantage du retail media Avec des dépenses mondiales en retail media estimées à 180 milliards de dollars en 2025, l’opportunité est immense. Mais le succès dépend de la manière dont les distributeurs utilisent les données, le design et la technologie pour servir à la fois les clients et les marques. Dans notre dernier rapport, nous avons interrogé des dirigeants sur leurs projets d’investissement dans les réseaux de retail media. 76% les annonces sponsorisées sur les pages de résultats de recherche constituent la principale stratégie de retail media. 30% un meilleur ciblage d’audience est l’objectif principal des annonces sponsorisées sur les pages de résultats de recherche. 11% des acheteurs de retail media utilisent plusieurs plateformes de retail media. Télécharger le rapport Contenus recommandés AI-powered Solutions for Smarter Grocery Shopping Online grocery shopping is exploding, driven by the next generation of shoppers' buying habits. To compete, grocers need AI-driven, experiences that are easy, convenient, and tailored to each individual shopper's preferences. Read more Beyond the Basket: Algolia’s Shopping Assistant for Grocers Algolia’s intelligent grocery solution helps you provide your customers with a simple, intuitive online grocery shopping experience that they can navigate using natural language. Read this eBook to find out how. Read more Retail Media Strategies Ranked by Popularity Get a firsthand look at the trends shaping retail media purchasing decisions for online grocers. Read more La recherche par IA qui comprend vos utilisateurs Demandez 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://www.algolia.com/fr/resources/asset/building-agentic-ai/ | RESOURCE CENTER LANDING TEMPLATE --> Building Agentic AI 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. 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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http://www.alan-g.me.uk/l2p2 | Learning to Program Learning to Program email: learn2program@gmail.com Contents Introduction Concepts What do I need? What is 'Programming'? Getting Started The Basics Simple Sequences The Raw Materials More Sequences Loops Add a little style Talking to the user Branching Modules & Functions Handling Files Handling Text Error Handling Advanced Topics What's in a name? Regular Expressions Object Oriented Programming Event Driven Programming GUI Programming Recursion - or doing it to yourself Functional Programming A Case Study Applications Python in Practice Working with Databases Using the Operating System Inter-process communications Network programming Working with the Web Writing web clients Writing Web Server Applications Using Web Application Frameworks Concurrent processing Appendices References, Books and Projects | 2026-01-13T08:49:44 |
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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 Mental Health Follow Hide Mental health matters! Break the stigma. We can empower ourselves and each other to invest in our mental health. We can give support and care to ourselves and each other while we struggle. Let's talk about making our mental health priority. Create Post about #mentalhealth Posts should be related to mental health. This is a pretty wide category but some things that are included are: Managing mental health as a developer Living with mental illness and how it affects your work Ways to cope with mental health issues Avoiding burn out Tools, apps, and methods that help you with your mental health ...and more “Your mental health is a priority. Your happiness is an essential. Your self-care is a necessity.” Struggling? Help is out there. Click here to find a list of global mental health resources and hotlines. Older #mentalhealth 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 Rejection: The Gateway to Growth Mike Sorrenti Mike Sorrenti Mike Sorrenti Follow Nov 24 '25 Rejection: The Gateway to Growth # career # mentalhealth # motivation Comments Add Comment 4 min read Should developers meditate? Andreas Müller Andreas Müller Andreas Müller Follow Dec 13 '25 Should developers meditate? # discuss # mentalhealth # productivity 13 reactions Comments 5 comments 3 min read Why Tech Brains Struggle With Money Simplicity (And How to Fix It) James Patterson James Patterson James Patterson Follow Nov 19 '25 Why Tech Brains Struggle With Money Simplicity (And How to Fix It) # watercooler # mentalhealth # productivity Comments Add Comment 3 min read Hallucinating Help Rich Jeffries Rich Jeffries Rich Jeffries Follow Dec 1 '25 Hallucinating Help # discuss # ai # safety # mentalhealth 1 reaction Comments Add Comment 9 min read The Debugger’s Guide to Understanding Your Financial Biases Allen Bailey Allen Bailey Allen Bailey Follow Nov 19 '25 The Debugger’s Guide to Understanding Your Financial Biases # watercooler # learning # mentalhealth Comments Add Comment 4 min read The Cost of Stopping When We Hit the Wall Yodit Weldegeorgise Yodit Weldegeorgise Yodit Weldegeorgise Follow Dec 22 '25 The Cost of Stopping When We Hit the Wall # mentalhealth # motivation # productivity 5 reactions Comments 4 comments 2 min read The Story Behind Peaceful Night, a Calm Space for Tired Minds Thoma Nani Thoma Nani Thoma Nani Follow Nov 15 '25 The Story Behind Peaceful Night, a Calm Space for Tired Minds # showdev # mentalhealth # ux Comments Add Comment 2 min read Cybersecurity Fatigue Isn’t a Bug — It’s a Systemic Failure in MSP Engineering Donald Betancourt Donald Betancourt Donald Betancourt Follow Nov 13 '25 Cybersecurity Fatigue Isn’t a Bug — It’s a Systemic Failure in MSP Engineering # discuss # mentalhealth # career # cybersecurity Comments Add Comment 3 min read The Coffee Bean Yodit Weldegeorgise Yodit Weldegeorgise Yodit Weldegeorgise Follow Dec 15 '25 The Coffee Bean # career # mentalhealth # motivation 4 reactions Comments 6 comments 2 min read How to Prepare for the Future of Programming Clara Maine Clara Maine Clara Maine Follow Dec 10 '25 How to Prepare for the Future of Programming # discuss # mentalhealth # career # programming 16 reactions Comments 4 comments 6 min read The Environment Includes Soft, Hard, and Body sta sta sta Follow Dec 12 '25 The Environment Includes Soft, Hard, and Body # devex # developer # mentalhealth 2 reactions Comments Add Comment 7 min read Respiration Pascal CESCATO Pascal CESCATO Pascal CESCATO Follow Dec 3 '25 Respiration # watercooler # devjournal # writing # mentalhealth 30 reactions Comments 10 comments 6 min read 🌟 My First Hackathon Journey: My Mental Health Buddy at Hackaura 2025 Lavender Princess Lavender Princess Lavender Princess Follow Nov 7 '25 🌟 My First Hackathon Journey: My Mental Health Buddy at Hackaura 2025 # hackathon # beginners # python # mentalhealth Comments Add Comment 3 min read Beyond Burnout: A 5-Step Action Plan for Stressed-Out Developers Mikhail Dorokhovich Mikhail Dorokhovich Mikhail Dorokhovich Follow Dec 9 '25 Beyond Burnout: A 5-Step Action Plan for Stressed-Out Developers # burnout # careerdevelopment # mentalhealth # productivity Comments Add Comment 3 min read When the Spark is Done: The ADHD Energy Cycle No One Talks About ✨ Ashley Childress Ashley Childress Ashley Childress Follow Dec 4 '25 When the Spark is Done: The ADHD Energy Cycle No One Talks About ✨ # discuss # mentalhealth # productivity # adhd 18 reactions Comments 6 comments 5 min read LLM Context Window Stress Testing: Reliability Under Load Rich Jeffries Rich Jeffries Rich Jeffries Follow Nov 21 '25 LLM Context Window Stress Testing: Reliability Under Load # ai # mentalhealth # safety 9 reactions Comments 1 comment 4 min read Two People, Same Body: A Developer's Crisis Architecture CrisisCore-Systems CrisisCore-Systems CrisisCore-Systems Follow Dec 8 '25 Two People, Same Body: A Developer's Crisis Architecture # mentalhealth # opensource # webdev # react Comments Add Comment 4 min read From Cancer Cures to Pornography: The Six-Month Descent of AI Denis Stetskov Denis Stetskov Denis Stetskov Follow Nov 18 '25 From Cancer Cures to Pornography: The Six-Month Descent of AI # ai # mentalhealth # programming # webdev 5 reactions Comments 2 comments 7 min read When Someone Dismisses Your Work. Why You Should Keep Building Anyway Ed Ed Ed Follow Dec 7 '25 When Someone Dismisses Your Work. Why You Should Keep Building Anyway # discuss # motivation # mentalhealth # softwaredevelopment 4 reactions Comments Add Comment 3 min read The Dopamine Hit of git push vs. The Agony of git revert Tombri Bowei Tombri Bowei Tombri Bowei Follow Nov 30 '25 The Dopamine Hit of git push vs. The Agony of git revert # programming # webdev # career # mentalhealth 15 reactions Comments 6 comments 5 min read Building Confessly: The Anonymous Confession Platform That Actually Listens Nadim Chowdhury Nadim Chowdhury Nadim Chowdhury Follow Nov 1 '25 Building Confessly: The Anonymous Confession Platform That Actually Listens # showdev # opensource # mentalhealth # ai Comments Add Comment 7 min read I Built MoodFeed: An AI That Actually Knows When You're Having a Sh*t Day Nadim Chowdhury Nadim Chowdhury Nadim Chowdhury Follow Nov 1 '25 I Built MoodFeed: An AI That Actually Knows When You're Having a Sh*t Day # showdev # sideprojects # mentalhealth # ai Comments Add Comment 9 min read Finding Strength in Code, Part 2: Lessons from Loss and the Power of Reflection Sagrario Meneses Sagrario Meneses Sagrario Meneses Follow Nov 28 '25 Finding Strength in Code, Part 2: Lessons from Loss and the Power of Reflection # mentalhealth # postmortem # softwareengineering # problemsolving 5 reactions Comments 1 comment 5 min read Trauma-informed design left everyone asking: "How does it actually know I'm struggling without spying?" CrisisCore-Systems CrisisCore-Systems CrisisCore-Systems Follow Nov 29 '25 Trauma-informed design left everyone asking: "How does it actually know I'm struggling without spying?" # privacy # mentalhealth # opensource # webdev Comments Add Comment 7 min read The Silent Struggle of the High-Achiever: Why your own high standards are making you tired Cathy Lai Cathy Lai Cathy Lai Follow Nov 26 '25 The Silent Struggle of the High-Achiever: Why your own high standards are making you tired # discuss # mentalhealth # career # productivity 5 reactions Comments 4 comments 1 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 Mental Health Follow Hide Mental health matters! Break the stigma. We can empower ourselves and each other to invest in our mental health. We can give support and care to ourselves and each other while we struggle. Let's talk about making our mental health priority. Create Post about #mentalhealth Posts should be related to mental health. This is a pretty wide category but some things that are included are: Managing mental health as a developer Living with mental illness and how it affects your work Ways to cope with mental health issues Avoiding burn out Tools, apps, and methods that help you with your mental health ...and more “Your mental health is a priority. Your happiness is an essential. Your self-care is a necessity.” Struggling? Help is out there. Click here to find a list of global mental health resources and hotlines. Older #mentalhealth 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 Why "Productivity" Won't Save You: How I Built My Way Out of a Rut ElevenApril ElevenApril ElevenApril Follow Dec 10 '25 Why "Productivity" Won't Save You: How I Built My Way Out of a Rut # devjournal # mentalhealth # productivity Comments Add Comment 3 min read Staying Sharp After 40 SGTSanjay SGTSanjay SGTSanjay Follow Dec 11 '25 Staying Sharp After 40 # career # learning # mentalhealth 1 reaction Comments Add Comment 3 min read Debugging the Mind's Legacy Code: A Cognitive Architect's Log Aleksei Sergeevich Aleksei Sergeevich Aleksei Sergeevich Follow Dec 10 '25 Debugging the Mind's Legacy Code: A Cognitive Architect's Log # mentalhealth # productivity # softwaredevelopment Comments Add Comment 2 min read Why I didn't build in public JohannKrugell JohannKrugell JohannKrugell Follow Dec 12 '25 Why I didn't build in public # discuss # motivation # mentalhealth # devjournal Comments Add Comment 1 min read Kelime Eda Deniz Sarsılmaz Eda Deniz Sarsılmaz Eda Deniz Sarsılmaz Follow Dec 9 '25 Kelime # discuss # mentalhealth # watercooler Comments Add Comment 1 min read Failing LeetCode Over and Over? Here's How to Stay Motivated Alex Hunter Alex Hunter Alex Hunter Follow Dec 12 '25 Failing LeetCode Over and Over? Here's How to Stay Motivated # beginnerguide # motivation # mentalhealth # learningstrategy Comments Add Comment 9 min read 🚀 How Media Misrepresents Mental Health — And Why It Matters in a Digital World NVelUp NVelUp NVelUp Follow Dec 8 '25 🚀 How Media Misrepresents Mental Health — And Why It Matters in a Digital World # mentalhealth Comments Add Comment 3 min read The Double-Edged Sword of "Flow State" in Game Dev ⚔️ crow crow crow Follow Dec 11 '25 The Double-Edged Sword of "Flow State" in Game Dev ⚔️ # productivity # mentalhealth # web3 # developers 1 reaction Comments 1 comment 3 min read How Dopamine Detox Made Me a Better Developer yusuf yonturk yusuf yonturk yusuf yonturk Follow Dec 26 '25 How Dopamine Detox Made Me a Better Developer # discuss # productivity # mentalhealth # devlife 2 reactions Comments 4 comments 4 min read How to Deal with LeetCode Anxiety and Interview Stress: A Mental Health Guide Alex Hunter Alex Hunter Alex Hunter Follow Dec 6 '25 How to Deal with LeetCode Anxiety and Interview Stress: A Mental Health Guide # mentalhealth # anxietymanagement # interviewstress # beginnerguide Comments Add Comment 8 min read The Invisible Bonds: Understanding Hatred’s Deep Roots DriftLens team DriftLens team DriftLens team Follow Dec 7 '25 The Invisible Bonds: Understanding Hatred’s Deep Roots # mentalhealth # emotion # productivity # career 1 reaction Comments Add Comment 3 min read 🧠 [Memory Leak] Why I Felt Stupid at 2 PM: Debugging My Brain's RAM Vital Access Vital Access Vital Access Follow Dec 1 '25 🧠 [Memory Leak] Why I Felt Stupid at 2 PM: Debugging My Brain's RAM # productivity # career # mentalhealth # softskills 2 reactions Comments Add Comment 5 min read Why Reflection Needs Structure: A Cognitive Model from Buddhism DriftLens team DriftLens team DriftLens team Follow Dec 2 '25 Why Reflection Needs Structure: A Cognitive Model from Buddhism # learning # mentalhealth # productivity Comments Add Comment 4 min read Surviving Cancer Doesn't Suck Abbey Perini Abbey Perini Abbey Perini Follow Jan 6 Surviving Cancer Doesn't Suck # watercooler # devjournal # mentalhealth # motivation 9 reactions Comments Add Comment 10 min read Why Psychological Safety Matters in Dev Workplaces Aarti Jangid Aarti Jangid Aarti Jangid Follow Dec 25 '25 Why Psychological Safety Matters in Dev Workplaces # management # career # productivity # mentalhealth 2 reactions Comments 2 comments 3 min read Understanding the Attention Economy: Why Your Focus Is the New Currency Martin Ghazaryan Martin Ghazaryan Martin Ghazaryan Follow Dec 1 '25 Understanding the Attention Economy: Why Your Focus Is the New Currency # learning # mentalhealth # productivity Comments Add Comment 4 min read The Reset Button That Wasn't: Why 2026 Demands a New Kind of Resolution Tombri Bowei Tombri Bowei Tombri Bowei Follow Dec 30 '25 The Reset Button That Wasn't: Why 2026 Demands a New Kind of Resolution # newyearchallenge # career # productivity # mentalhealth 7 reactions Comments 1 comment 3 min read When Sleep Became a Problem Nimesh Thakur Nimesh Thakur Nimesh Thakur Follow Dec 25 '25 When Sleep Became a Problem # discuss # mentalhealth # productivity # motivation 9 reactions Comments 1 comment 3 min read The Divine Algorithm: A Developer’s Confession crow crow crow Follow Nov 22 '25 The Divine Algorithm: A Developer’s Confession # watercooler # web3 # showdev # mentalhealth Comments Add Comment 3 min read Improving Developer Productivity Without Burning Out Your Team Practical engineering habits that actually scale SYFOX dev SYFOX dev SYFOX dev Follow Dec 25 '25 Improving Developer Productivity Without Burning Out Your Team Practical engineering habits that actually scale # management # productivity # devops # mentalhealth Comments Add Comment 2 min read Digital Sabbatical and Outernet (Bite-size Article) koshirok096 koshirok096 koshirok096 Follow Nov 21 '25 Digital Sabbatical and Outernet (Bite-size Article) # discuss # mentalhealth # productivity Comments Add Comment 3 min read Replacing Phone Addiction with Building a Real Project Vigneshwaralingam Vigneshwaralingam Vigneshwaralingam Follow Dec 25 '25 Replacing Phone Addiction with Building a Real Project # fullstack # productivity # motivation # mentalhealth 8 reactions Comments Add Comment 1 min read Why I Keep My Kids Away From Screens (Even Though I Work in Tech) Juan Rueda Juan Rueda Juan Rueda Follow Dec 23 '25 Why I Keep My Kids Away From Screens (Even Though I Work in Tech) # discuss # mentalhealth # watercooler 4 reactions Comments 1 comment 5 min read The Growing Crisis of Burnout Among Cybersecurity Professionals GuardingPearSoftware GuardingPearSoftware GuardingPearSoftware Follow Nov 24 '25 The Growing Crisis of Burnout Among Cybersecurity Professionals # career # cybersecurity # mentalhealth Comments Add Comment 5 min read The Afternoon I Found Comfort In A Small Conversation Milo Anders Milo Anders Milo Anders Follow Nov 19 '25 The Afternoon I Found Comfort In A Small Conversation # watercooler # devjournal # mentalhealth Comments Add Comment 8 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://openfuture.eu/blog/digital-commons-on-the-eus-digital-sovereignty-agenda/ | Digital Commons on the EU’s Digital Sovereignty Agenda – Open Future --> About Us Our Approach People Transparency Blog Events Publications Our Work AI and Creative Labor Copyright Infrastructure Open Movement Public AI Public Digital Infrastructure Observatory AI Act and Open Source Digital Decade Blog / Digital Commons on the EU’s Digital Sovereignty Agenda From Underdog to Strategic Asset? Opinion November 24, 2025 #Digital Commons Zuzanna Warso As discussions around digital sovereignty in the EU mature, Digital Commons are emerging as a visible part of that agenda. They appeared not only on the main stage of the Franco-German Summit on European Digital Sovereignty last week but were also included in the final declaration , which lists them among “the seven strategic and promising areas to unlock EU’s competitiveness and build EU’s digital sovereignty.” Later in the week, this momentum carried into Brussels, where the NGI Commons consortium in collaboration with the European Commission hosted the Digital Commons Policy Summit . Commission representatives were clear: the issue is no longer whether Digital Commons fit into Europe’s digital agenda, but how to put this into practice. The discussions at the policy summit showed that expectations are increasingly centred on the newly established Digital Commons EDIC as the institutional vehicle capable of coordinating Member State efforts, scaling proven commons-based models and providing long-term stewardship for Europe’s shared digital resources. Speakers stressed that the DC EDIC matters precisely because many Digital Commons lack a stable governance home, and because coordination is essential for uptake. They also stressed that the DC EDIC must demonstrate early successes in order to attract additional countries (and funding) and consolidate a European approach to shared infrastructure. This inevitably brings the debate back to a challenge familiar to many Digital Commons projects: sustainability. There is now a clear recognition that Europe must invest in public digital infrastructure, including the Digital Commons, in a way that reflects their strategic importance and operational realities. In this context, the European Competitiveness Fund (ECF), proposed in the context of the MFF negotiation as the EU’s flagship instrument for competitiveness and sovereignty, was mentioned as a key framework. Its ambition to provide “a single investment journey from research to deployment” aligns closely with what commons-based infrastructure requires: stable, multi-year support rather than fragmented, project-based grants. Importantly, the current ECF draft already references EDICs as potential vehicles for delivering Europe’s digital leadership agenda, but this connection remains underdeveloped. If the DC-EDIC is to fulfil its mandate, the ECF should explicitly recognise Digital Commons as part of Europe’s strategic infrastructure and provide the level of financial continuity required to maintain and operate them over time. The ECF framework also acknowledges that different challenges call for different types of investment, and this applies equally to the Digital Commons ecosystem. There is no single model for supporting Digital Commons. Some projects, such as foundational open-source software components or non-profit community-driven systems, may require stable grants for maintenance, security and governance. But as providers and representatives of European industry often emphasise, many do not need subsidies; they need customers. This is where the conversation naturally shifts to public procurement. At the Digital Commons Policy Summit, this point surfaced repeatedly: strategic public procurement can create the demand necessary for commons-based solutions to scale, strengthen their viability and compete with entrenched proprietary systems. Yet procurement is also a matter of coordination. Public authorities need clear guidance and consistent ways to assess whether solutions are reliable, well-supported and capable of integrating into public-sector environments. Without this support, procurement processes tend to revert to familiar proprietary solutions, locking public bodies into systems that undermine Europe’s long-term sovereignty goals. As the DC-EDIC takes shape , the ECF opens a pathway for investment, and procurement gains recognition as a strategic lever, a coordinated EU approach to sustaining Digital Commons is finally within reach. Zuzanna Warso keep up to date and subscribe to our newsletter Subscribe --> hello@openfuture.eu Unless otherwise indicated all content is published under the Creative Commons Attribution License | Privacy statement Website by panGenerator Hosted by Greenhost --> | 2026-01-13T08:49:44 |
https://llvm.org/docs/OpaquePointers.html | Opaque Pointers — LLVM 22.0.0git documentation Navigation index next | previous | LLVM Home | Documentation » User Guides » Opaque Pointers Documentation Getting Started/Tutorials User Guides Reference Getting Involved Contributing to LLVM Submitting Bug Reports Mailing Lists Discord Meetups and Social Events Additional Links FAQ Glossary Publications Github Repository This Page Show Source Quick search Opaque Pointers ¶ The Opaque Pointer Type ¶ Traditionally, LLVM IR pointer types have contained a pointee type. For example, i32* is a pointer that points to an i32 somewhere in memory. However, due to a lack of pointee type semantics and various issues with having pointee types, there is a desire to remove pointee types from pointers. The opaque pointer type project aims to replace all pointer types containing pointee types in LLVM with an opaque pointer type. The new pointer type is represented textually as ptr . Some instructions still need to know what type to treat the memory pointed to by the pointer as. For example, a load needs to know how many bytes to load from memory and what type to treat the resulting value as. In these cases, instructions themselves contain a type argument. For example the load instruction from older versions of LLVM load i64 * %p becomes load i64 , ptr %p Address spaces are still used to distinguish between different kinds of pointers where the distinction is relevant for lowering (e.g. data vs function pointers have different sizes on some architectures). Opaque pointers are not changing anything related to address spaces and lowering. For more information, see DataLayout . Opaque pointers in non-default address space are spelled ptr addrspace(N) . This was proposed all the way back in 2015 . Issues with explicit pointee types ¶ LLVM IR pointers can be cast back and forth between pointers with different pointee types. The pointee type does not necessarily represent the actual underlying type in memory. In other words, the pointee type carries no real semantics. Historically LLVM was some sort of type-safe subset of C. Having pointee types provided an extra layer of checks to make sure that the Clang frontend matched its frontend values/operations with the corresponding LLVM IR. However, as other languages like C++ adopted LLVM, the community realized that pointee types were more of a hindrance for LLVM development and that the extra type checking with some frontends wasn’t worth it. LLVM’s type system was originally designed to support high-level optimization. However, years of LLVM implementation experience have demonstrated that the pointee type system design does not effectively support optimization. Memory optimization algorithms, such as SROA, GVN, and AA, generally need to look through LLVM’s struct types and reason about the underlying memory offsets. The community realized that pointee types hinder LLVM development, rather than helping it. Some of the initially proposed high-level optimizations have evolved into TBAA due to limitations with representing higher-level language information directly via SSA values. Pointee types provide some value to frontends because the IR verifier uses types to detect straightforward type confusion bugs. However, frontends also have to deal with the complexity of inserting bitcasts everywhere that they might be required. The community consensus is that the costs of pointee types outweigh the benefits, and that they should be removed. Many operations do not actually care about the underlying type. These operations, typically intrinsics, usually end up taking an arbitrary pointer type i8* and sometimes a size. This causes lots of redundant no-op bitcasts in the IR to and from a pointer with a different pointee type. No-op bitcasts take up memory/disk space and also take up compile time to look through. However, perhaps the biggest issue is the code complexity required to deal with bitcasts. When looking up through def-use chains for pointers it’s easy to forget to call Value::stripPointerCasts() to find the true underlying pointer obfuscated by bitcasts. And when looking down through def-use chains passes need to iterate through bitcasts to handle uses. Removing no-op pointer bitcasts prevents a category of missed optimizations and makes writing LLVM passes a little bit easier. Fewer no-op pointer bitcasts also reduces the chances of incorrect bitcasts in regards to address spaces. People maintaining backends that care a lot about address spaces have complained that frontends like Clang often incorrectly bitcast pointers, losing address space information. An analogous transition that happened earlier in LLVM is integer signedness. Currently there is no distinction between signed and unsigned integer types, but rather each integer operation (e.g. add) contains flags to signal how to treat the integer. Previously LLVM IR distinguished between unsigned and signed integer types and ran into similar issues of no-op casts. The transition from manifesting signedness in types to instructions happened early on in LLVM’s timeline to make LLVM easier to work with. Opaque Pointers Mode ¶ During the transition phase, LLVM can be used in two modes: In typed pointer mode all pointer types have a pointee type and opaque pointers cannot be used. In opaque pointers mode (the default), all pointers are opaque. The opaque pointer mode can be disabled using -opaque-pointers=0 in LLVM tools like opt , or -Xclang -no-opaque-pointers in clang. Additionally, opaque pointer mode is automatically disabled for IR and bitcode files that explicitly mention i8* style typed pointers. In opaque pointer mode, all typed pointers used in IR, bitcode, or created using PointerType::get() and similar APIs are automatically converted into opaque pointers. This simplifies migration and allows testing existing IR with opaque pointers. define i8 * @test ( i8 * %p ) { %p2 = getelementptr i8 , i8 * %p , i64 1 ret i8 * %p2 } ; Is automatically converted into the following if -opaque-pointers ; is enabled: define ptr @test ( ptr %p ) { %p2 = getelementptr i8 , ptr %p , i64 1 ret ptr %p2 } Migration Instructions ¶ In order to support opaque pointers, two types of changes tend to be necessary. The first is the removal of all calls to PointerType::getElementType() and Type::getPointerElementType() . In the LLVM middle-end and backend, this is usually accomplished by inspecting the type of relevant operations instead. For example, memory access related analyses and optimizations should use the types encoded in the load and store instructions instead of querying the pointer type. Here are some common ways to avoid pointer element type accesses: For loads, use getType() . For stores, use getValueOperand()->getType() . Use getLoadStoreType() to handle both of the above in one call. For getelementptr instructions, use getSourceElementType() . For calls, use getFunctionType() . For allocas, use getAllocatedType() . For globals, use getValueType() . For consistency assertions, use PointerType::isOpaqueOrPointeeTypeEquals() . To create a pointer type in a different address space, use PointerType::getWithSamePointeeType() . To check that two pointers have the same element type, use PointerType::hasSameElementTypeAs() . While it is preferred to write code in a way that accepts both typed and opaque pointers, Type::isOpaquePointerTy() and PointerType::isOpaque() can be used to handle opaque pointers specially. PointerType::getNonOpaquePointerElementType() can be used as a marker in code-paths where opaque pointers have been explicitly excluded. To get the type of a byval argument, use getParamByValType() . Similar method exists for other ABI-affecting attributes that need to know the element type, such as byref, sret, inalloca and preallocated. Some intrinsics require an elementtype attribute, which can be retrieved using getParamElementType() . This attribute is required in cases where the intrinsic does not naturally encode a needed element type. This is also used for inline assembly. Note that some of the methods mentioned above only exist to support both typed and opaque pointers at the same time, and will be dropped once the migration has completed. For example, isOpaqueOrPointeeTypeEquals() becomes meaningless once all pointers are opaque. While direct usage of pointer element types is immediately apparent in code, there is a more subtle issue that opaque pointers need to contend with: A lot of code assumes that pointer equality also implies that the used load/store type or GEP source element type is the same. Consider the following examples with typed and opaque pointers: define i32 @test ( i32 * %p ) { store i32 0 , i32 * %p %bc = bitcast i32 * %p to i64 * %v = load i64 , i64 * %bc ret i64 %v } define i32 @test ( ptr %p ) { store i32 0 , ptr %p %v = load i64 , ptr %p ret i64 %v } Without opaque pointers, a check that the pointer operand of the load and store are the same also ensures that the accessed type is the same. Using a different type requires a bitcast, which will result in distinct pointer operands. With opaque pointers, the bitcast is not present, and this check is no longer sufficient. In the above example, it could result in store to load forwarding of an incorrect type. Code making such assumptions needs to be adjusted to check the accessed type explicitly: LI->getType() == SI->getValueOperand()->getType() . Frontends ¶ Frontends need to be adjusted to track pointee types independently of LLVM, insofar as they are necessary for lowering. For example, clang now tracks the pointee type in the Address structure. Frontends using the C API through an FFI interface should be aware that a number of C API functions are deprecated and will be removed as part of the opaque pointer transition: LLVMBuildLoad -> LLVMBuildLoad2 LLVMBuildCall -> LLVMBuildCall2 LLVMBuildInvoke -> LLVMBuildInvoke2 LLVMBuildGEP -> LLVMBuildGEP2 LLVMBuildInBoundsGEP -> LLVMBuildInBoundsGEP2 LLVMBuildStructGEP -> LLVMBuildStructGEP2 LLVMBuildPtrDiff -> LLVMBuildPtrDiff2 LLVMConstGEP -> LLVMConstGEP2 LLVMConstInBoundsGEP -> LLVMConstInBoundsGEP2 LLVMAddAlias -> LLVMAddAlias2 Additionally, it will no longer be possible to call LLVMGetElementType() on a pointer type. It is possible to control whether opaque pointers are used (if you want to override the default) using LLVMContext::setOpaquePointers . Temporarily disabling opaque pointers ¶ In LLVM 15, opaque pointers are enabled by default, but it it still possible to use typed pointers using a number of opt-in flags. For users of the clang driver interface, it is possible to temporarily restore the old default using the -DCLANG_ENABLE_OPAQUE_POINTERS=OFF cmake option, or by passing -Xclang -no-opaque-pointers to a single clang invocation. For users of the clang cc1 interface, -no-opaque-pointers can be passed. Note that the CLANG_ENABLE_OPAQUE_POINTERS cmake option has no effect on the cc1 interface. Usage for LTO can be disabled by passing -Wl,-plugin-opt=no-opaque-pointers to the clang driver. For users of LLVM as a library, opaque pointers can be disabled by calling setOpaquePointers(false) on the LLVMContext . For users of LLVM tools like opt, opaque pointers can be disabled by passing -opaque-pointers=0 . Version Support ¶ LLVM 14: Supports all necessary APIs for migrating to opaque pointers and deprecates/removes incompatible APIs. However, using opaque pointers in the optimization pipeline is not fully supported. This release can be used to make out-of-tree code compatible with opaque pointers, but opaque pointers should not be enabled in production. LLVM 15: Opaque pointers are enabled by default. Typed pointers are still supported. LLVM 16: Opaque pointers are enabled by default. Typed pointers are supported on a best-effort basis only and not tested. LLVM 17: Only opaque pointers are supported. Typed pointers are not supported. Transition State ¶ As of July 2023: Typed pointers are not supported on the main branch. The following typed pointer functionality has been removed: The CLANG_ENABLE_OPAQUE_POINTERS cmake flag is no longer supported. The -no-opaque-pointers cc1 clang flag is no longer supported. The -opaque-pointers opt flag is no longer supported. The -plugin-opt=no-opaque-pointers LTO flag is no longer supported. C APIs that do not support opaque pointers (like LLVMBuildLoad ) are no longer supported. The following typed pointer functionality is still to be removed: Various APIs that are no longer relevant with opaque pointers. Navigation index next | previous | LLVM Home | Documentation » User Guides » Opaque Pointers © Copyright 2003-2026, LLVM Project. Last updated on 2026-01-13. Created using Sphinx 7.2.6. | 2026-01-13T08:49:44 |
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We got you covered! Brand guidelines Download logo pack Enterprise Suche Unternehmens-Suchlösungen für Kunden und den Arbeitsplatz Die Unternehmenssuche von Algolia verbindet Ihre Kunden mit den richtigen Inhalten und Ihre Mitarbeiter mit den richtigen Informationen. Demo Anfordern Kostenlos Starten *]:border-l md:[&>*:nth-child(1)]:border-none md:[&>*:nth-child(4n+1)]:border-none"> 1,7+ Billionen Suchanfragen pro Jahr 99,999 % SLA-Verfügbarkeit 382 % ROI laut Forrester Research 18.000+ Kunden in über 150 Ländern Eine leistungsstarke, gehostete Suchmaschine für Unternehmen Bauen Sie schnell und mit weniger IT-Ressourcen ein führendes digitales Search and Discovery-Erlebnis auf und senken Sie Ihre Gesamtbetriebskosten (TCO). Personalisieren Sie die Kundenerlebnisse und achten Sie gleichzeitig auf die Verwirklichung Ihrer Geschäftsziele. APIs, Front-End-Bibliotheken, Integrationen und ein Dashboard Ermöglichen Sie Ihren Teams das Erstellen, Konfigurieren und Anpassen von Such- und Ermittlungsanwendungen für jeden Anwendungsfall und jede Branche. Schnell live gehen Der Aufbau eines internen Suchteams und von Fachwissen ist nicht erforderlich. Nutzen Sie unsere Algorithmen, Integrationen, Bibliotheken für die Benutzeroberfläche und Skalierbarkeit. Entdecken Sie KI-Such-APIS von Algolia Preisgestaltung für Unternehmen Software für die Unternehmenssuche bedeutet nicht zwangsläufig hohe Kosten. Pay-As-You-Scale-Flexibilität. Mengenrabatte und Ermäßigungen bei verbindlicher Nutzung verfügbar. Informationen zur Preisgestaltung Intranetsuche und Wissensmanagement für Unternehmen neu konzipiert Steigern Sie die Effizienz am Arbeitsplatz und die Produktivität Ihrer Mitarbeiter, indem Sie Ihre internen Unternehmensinhalte mit modernen, flexiblen Suchfunktionen für Websites verwalten. Leiten Sie Ihre Mitarbeiter systemübergreifend zum richtigen Organisationswissen. Unbegrenzte Indizierung Aggregieren und intelligentes Anzeigen von organisierten Informationen, unabhängig von Ihrem Back-End-System Anpassungen und Flexible Integrationen Integrieren Sie maßgeschneiderte Search-as-Service-Funktionalitäten in Anwendungen von Drittanbietern. Anbindung von Back-Office-Systemen Integrieren Sie die Unternehmenssuche (und Empfehlungen!) in Ihre Content-Management-Systeme (CMS), ERP-, CRM- und andere Geschäftsplattformen und -prozesse. Führende Arbeitsplatzsuche und -ermittlung zur Maximierung der Mitarbeiterproduktivität Geschäftskunden 0 Mit unserem intuitiven Dashboard können Sie ganz einfach Änderungen vornehmen, ohne dass Sie dafür Programmierer oder IT-Mitarbeiter benötigen. Händler oder PMs 0 Mit Merchandising Studio können Sie Produkte und Inhalte einfach und in Echtzeit vermarkten und verwalten. 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Künstliche Intelligenz auf Knopfdruck Interne KI und Machine-Learning-Suchteams sind nicht erforderlich. Erhalten Sie führendes Fachwissen ohne kostspielige Investitionen. Entdecken Sie Die KI-Suche von Algolia Flexibel Über 10.000 Server weltweit Zuverlässig 99,999 % SLA für Unternehmenskunden Skalierbar Weltweit schnell, mit über 55 Datenzentren auf sechs Kontinenten Globale Infrastruktur Sicher SSO-, SAML-, SOC2-, SOC3- und ISO27001-konform Sicherheit und Konformität “Die Zusammenarbeit mit Algolia ist unkompliziert. Der Austausch des alten Back-End-Systems gegen einen Prototyp dauerte nur ein paar Tage. Um die gewünschten Ergebnisse zu erzielen, haben wir mit dem Ranking und der Facettierung experimentiert. Journalisten waren in der Lage, ihre Texte dreihundert- bis fünfhundertmal schneller zu veröffentlichen als mit unserer vorherigen Lösung.” Matt Taylor Redaktioneller Produktmanager @ The Times Zur Fallstudie “Algolia ist das Kernstück des WeWork-Navigationssystems für die Bestandssuche... Anstatt zu versuchen, unsere eigene Back-End-API zu verwalten, tut Agolia genau das, was wir möchten und leistet großartige Arbeit. Es ist ein Index in der Cloud, der effizient verteilt wird... Es ist einfach eine coole Funktion, an der man nicht vorbeikommt.” Fernando Padilla Software-Engineer @ WeWork Die modernsten Unternehmen nutzen Algolia Recommended content Understanding AI transparency This white paper for business and technology leaders explores real world examples of how to incorporate transparent AI into their business. Read more Want to sound really smart about AI personalization? 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http://www.videolan.org/ | VLC: Official site - Free multimedia solutions for all OS! - VideoLAN * { behavior: url("/style/box-sizing.htc"); } Toggle navigation VideoLAN Team & Organization Consulting Services & Partners Events Legal Press center Contact us VLC Download Features Customize Get Goodies Projects DVBlast x264 x262 x265 multicat dav1d VLC Skin Editor VLC media player libVLC libdvdcss libdvdnav libdvdread libbluray libdvbpsi libaacs libdvbcsa biTStream vlc-unity All Projects Contribute Getting started Donate Report a bug Support donate donate Donate donate donate VideoLAN, a project and a non-profit organization. Partners VLC media player VLC is a free and open source cross-platform multimedia player and framework that plays most multimedia files as well as DVDs, Audio CDs, VCDs, and various streaming protocols. VLC is a free and open source cross-platform multimedia player and framework that plays most multimedia files, and various streaming protocols. Download VLC Windows Windows 64bit Windows ARM 64 macOS macOS (Apple Silicon) Linux Android Other Systems Version 3.0.23 • Windows • 38 MB Other projects from VideoLAN For Everyone VLC media player VLC is a powerful media player playing most of the media codecs and video formats out there. VLMC VideoLAN Movie Creator is a non-linear editing software for video creation. For Professionals DVBlast DVBlast is a simple and powerful MPEG-2/TS demux and streaming application. multicat multicat is a set of tools designed to easily and efficiently manipulate multicast streams and TS. x264 x264 is a free application for encoding video streams into the H.264/MPEG-4 AVC format. For Developers libdvdcss libdvdnav libdvdpsi libVLC vlc-unity libbluray libaacs libdca biTStream View All Projects Help us out! donate VideoLAN is a non-profit organization. All our costs are met by donations we receive from our users. If you enjoy using a VideoLAN product, please donate to support us. Learn More Contribute VideoLAN is open-source software. This means that if you have the skill and the desire to improve one of our products, your contributions are welcome Learn More Spread the Word We feel that VideoLAN has the best video software available at the best price: free. If you agree please help spread the word about our software. Learn More News & Updates VLC 3.0.23 2026-01-08 VideoLAN and the VLC team are publishing the 3.0.23 release of VLC today, which is the 24th update to VLC's 3.0 branch: it updates codecs, adds a dark mode option on Windows and Linux, support for Windows ARM64 and improves support for Windows XP SP3. This is the largest bug fix release ever with a large number of stability and security improvements to demuxers (reported by rub.de, oss-fuzz and others) and updates to most third party libraries. Additional details on the release page . The security impact of this release is detailed here . The major maintenance effort of this release to strengthen VLC's overall stability as well as the compatibility with old releases of Windows and macOS was made possible by a generous sponsorship of the Sovereign Tech Fund by Germany's Federal Ministry for Digital Transformation and Government Modernisation. VLC for iOS, iPadOS and tvOS 3.7.0 2026-01-08 Alongside the 3.0.23 release for desktop, VideoLAN and the VLC team are publishing a larger update for Apple's mobile platforms to include the latest improvements of VLC's 3.0 branch plus important bug fixes and amendments for the 26 versions of the OS. Previously, we added pCloud as a European choice for cloud storage allowing direct streaming and downloads within the app. New releases for biTStream, DVBlast and multicat 2025-12-01 We are pleased to release versions 1.6 of biTStream , 3.5 of DVBlast and 2.4 of multicat . DVBlast and multicat had major improvements and new features. New releases for libdvdcss, libdvdread and libdvdnav 2025-11-09 New releases of libdvdread , libdvdnav and libdvdcss have been published today. The biggest features of those releases (libdvdread/nav 7 and libdvdcss 1.5) are related to DVD-Audio support, including DRM decryption. VLC for Android 3.6.0 2025-01-13 We are pleased to release version 3.6.0 of the VLC version for the Android platform. It comes with the new Remote Access feature, a parental control and a lot of fixes. See our Android page . More News Development Blogs Rémi Denis-Courmont: VLC 3.0.22-rc2 plays Doom music files With the back-port of the VLC DMXMUS plugin from VLC 4.0, versions 3.0.22-RC2 and later are able to play music files from the 1990's DMX sound library such as Doom, Doom II and Heretic. [...] Rémi Denis-Courmont: SpacemiT's Integrated Matrix Extension Breaking down the SpacemiT X60's matrix product. [...] Rémi Denis-Courmont: C908 & X60 HCF instructions Decoding the HCF instructions from the GhostWrite paper. [...] Social media Mastodon VLC media player VLC VLC for Windows VLC for Mac OS X VLC for Ubuntu VLC for Android VLC for iOS Skins Extensions Features Screenshots VLC Skin Editor All Projects VideoLan Movie Creator DVBlast x264 x262 x265 multicat dav1d VLMa libVLC libdvdcss libdvdnav libdvdread libbluray libdvbpsi libaacs libdvbcsa biTStream vlc-unity Community Wiki Forums Mailing-Lists FAQ Donate money Donate time Get Goodies VideoLAN Project and Organization Team Legal Contact us Partners Mirrors Press center Events Security center Get Involved News Language: Afrikaans | Afrikaans Amharic | አማርኛ Aragonese | Aragonese Arabic | العربية Assamese (India) | অসমীয়া (ভাৰত) Asturian | asturianu Azerbaijani | azərbaycan Belarusian | беларуская Bulgarian | български Bangla (Bangladesh) | বাংলা (বাংলাদেশ) Bangla (India) | বাংলা (ভারত) Breton | brezhoneg Bosnian | bosanski Catalan | català Corsican | Corsican Czech | čeština Welsh | Cymraeg Danish | dansk German | Deutsch Divehi | Divehi Greek | Ελληνικά English (United Kingdom) | English (United Kingdom) Spanish | español Spanish (Mexico) | español (México) Estonian | eesti Basque | euskara Persian | فارسی Finnish | suomi Faroese | føroyskt French | français Western Frisian | Frysk Irish | Gaeilge Scottish Gaelic | Gàidhlig Galician | galego Gujarati (India) | ગુજરાતી (ભારત) Hebrew | עברית Hindi | हिन्दी Croatian | hrvatski Hungarian | magyar Indonesian | Indonesia Icelandic | íslenska Italian | italiano Japanese | 日本語 Georgian | ქართული Khmer | ខ្មែរ Kannada | ಕನ್ನಡ Korean | 한국어 Kyrgyz | кыргызча Luxembourgish | Lëtzebuergesch Lao | ລາວ Lithuanian | lietuvių Latvian | latviešu Macedonian | македонски Malayalam | മലയാളം Mongolian | монгол Marathi | मराठी Malay | Melayu Burmese | မြန်မာ Burmese (Myanmar [Burma]) | မြန်မာ (မြန်မာ) Norwegian Bokmål | norsk bokmål Nepali | नेपाली Dutch | Nederlands Norwegian Nynorsk | nynorsk Odia (India) | ଓଡ଼ିଆ (ଭାରତ) Polish | polski Portuguese (Brazil) | português (Brasil) Portuguese (Portugal) | português (Portugal) Romanian | română Russian | русский Sardinian | Sardinian Sinhala | සිංහල Slovak | slovenčina Slovenian | slovenščina Albanian | shqip Serbian | српски Swedish | svenska Tamil | தமிழ் Telugu | తెలుగు Thai | ไทย Turkish | Türkçe Tatar | татар Uyghur | ئۇيغۇرچە Ukrainian | українська Urdu | اردو Uzbek | o‘zbek Vietnamese | Tiếng Việt Walloon | Walloon Chinese (China) | 中文（中国） Chinese (Taiwan) | 中文（台灣） Legal | Report Trademark Abuse VideoLAN, VLC, VLC media player and x264 are trademarks internationally registered by the VideoLAN non-profit organization. VideoLAN software is licensed under various open-source licenses: use and distribution are defined by each software license. Design by Made By Argon . Some icons are licensed under the CC BY-SA 3.0+ . The VLC cone icon was designed by Richard Øiestad. Icons for VLMC, DVBlast and x264 designed by Roman Khramov . | 2026-01-13T08:49:44 |
https://zeroday.forem.com/shiva_c74698f901616e10e57/tor-or-onion-browser-which-one-truly-protects-your-privacy-in-2025-3kf#comments | Tor or Onion Browser: Which One Truly Protects Your Privacy in 2025 - 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. 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 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 shiva Posted on Dec 1, 2025 Tor or Onion Browser: Which One Truly Protects Your Privacy in 2025 # discuss # networksec Online privacy has become a major concern in 2025. With tracking systems, data brokers, advertisers, and cyber-surveillance growing stronger, people are looking for secure ways to browse the internet anonymously. Two of the most popular tools in this space are the Tor Browser and the Onion Browser. What Is Tor Browser? The Tor Browser is a fully featured, open-source web browser designed to keep users anonymous online. Tor routes your connection through multiple volunteer-operated servers called nodes. Each node only knows the previous and next step in the chain, making it nearly impossible to trace your identity, device, or location. Tor Browser comes with built-in privacy protections such as: Automatic tracker blocking Anti-fingerprinting features No browsing history stored Multi-layer encryption Protection against ISP monitoring Access to .onion sites (Deep Web) It is available for Windows, macOS, Linux, and Android. Tor is widely considered the best option for strong anonymity. What Is an Onion Browser? Onion Browser is a privacy-focused web browser made specifically for iOS (iPhone/iPad). Since Apple restricts the use of certain networking features, the Onion Browser cannot operate exactly like the official Tor Browser. However, it still uses Tor network connections and provides a good level of anonymity for Apple device users. Key features include: Tor network routing Basic anti-tracking protections Option to choose security levels Protection against most IP leaks Encrypted browsing on iPhones Onion Browser is ideal for users who want Tor-like privacy but are strictly using an iPhone or iPad. Tor Browser vs Onion Browser: Major Differences Platform Compatibility Tor Browser: Works on PC, laptop, and Android. Onion Browser: Only for iOS devices. If you’re using Windows, Mac, Linux, or Android, the Onion Browser isn’t even an option. Level of Anonymity Tor Browser offers higher anonymity because: It includes advanced fingerprint protection It blocks scripts by default It uses modified Firefox built for privacy It removes metadata and reduces browser variation Onion Browser provides strong protection, but its limitations on iOS mean it cannot fully match Tor Browser's anonymity features. Security Features Tor Browser lets you choose strict security modes, disable JavaScript, and prevent most browser-based attacks. For journalists, activists, or high privacy seekers, Tor Browser is considered more secure. Onion Browser is secure but depends heavily on iOS system restrictions, which limits customization and deeper protections. Speed and Performance Tor Browser is slower because of its stronger, multi-layer routing. Onion Browser may feel slightly faster but still slow compared to normal browsing. Remember: More privacy = Less speed. Which One Should You Use in 2025? Choose Tor Browser if: You want maximum anonymity You use Windows, Mac, Linux, or Android You visit .onion websites You need strong anti-fingerprinting protections You are concerned about surveillance or tracking Choose Onion Browser if: You are an iPhone or iPad user You want Tor-like privacy on iOS You browse casually but want to avoid trackers and surveillance You’re not accessing high-risk or sensitive websites Conclusion In 2025, both Tor and Onion Browser offer strong privacy protection, but Tor Browser remains the superior tool for true anonymity. Its advanced security features, wide compatibility, and deep privacy layers make it ideal for anyone serious about protecting their identity. Onion Browser is still a great choice for iPhone users but cannot fully replace Tor Browser’s power due to iOS limitations. 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 shiva Follow Joined Nov 27, 2025 More from shiva Why Medical Devices Are Now Prime Targets for Cyberattacks # iot # networksec # news 💎 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. 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https://dev.to/mitul3737/unlocking-the-power-of-auth0-streamlining-authentication-and-authorization-for-modern-apps-2o0h | Unlocking the Power of Auth0: Streamlining Authentication and Authorization for Modern Apps - 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 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 Jan 18, 2024 Unlocking the Power of Auth0: Streamlining Authentication and Authorization for Modern Apps # auth0 Auth0 is a powerful tool that can help streamline the authentication and authorization process for modern applications. With Auth0, developers can easily support different flows in their own applications and APIs without worrying about OIDC/OAuth 2.0 specifications or other technical aspects of authentication and authorization . Auth0 uses the OpenID Connect (OIDC) Protocol and OAuth 2.0 Authorization Framework to authenticate users and get their authorization to access protected resources . This means that developers can easily implement login, signup, and secure API calls using modern security protocols including OAuth 2.0, OpenID Connect, and JWT tokens. Auth0 supports scenarios for server-side, mobile, desktop, client-side, machine-to-machine, and device applications . This makes it easy for developers to use Auth0 in a variety of different contexts. One of the key benefits of Auth0 is that it provides a universal login experience for users . This means that users can log in once and then access all of the applications that they have access to without having to log in again. This can help to improve the user experience and reduce the amount of time that users spend logging in and out of different applications. Another benefit of Auth0 is that it provides a range of different authentication and authorization flows that developers can use in their own applications and APIs . These flows include the Authorization Code Flow, Implicit Flow, Hybrid Flow, and Client Credentials Flow. Each of these flows is designed to support different use cases and scenarios. In summary, Auth0 is a powerful tool that can help streamline the authentication and authorization process for modern applications. With Auth0, developers can easily support different flows in their own applications and APIs without worrying about OIDC/OAuth 2.0 specifications or other technical aspects of authentication and authorization. Auth0 supports scenarios for server-side, mobile, desktop, client-side, machine-to-machine, and device applications. It provides a universal login experience for users and a range of different authentication and authorization flows that developers can use in their own applications and APIs . 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 What is Identity : Auth0 # identity # security # auth0 Creating Your First Auth0 Action # auth0 💎 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.suprsend.com/docs/embeddable-inbox#integration | Integration - SuprSend, Notification infrastructure for Product teams Skip to main content SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Community Trust Center Platform Status Postman Collection GETTING STARTED What is SuprSend? Quick Start Guide Best Practices Plan Your Integration Go-live checklist CORE CONCEPTS Templates Users Events Workflow Notification Categories Preferences Tenants Lists Broadcast Objects Translations DLT Guidelines Whatsapp Template Guidelines WORKFLOW BUILDER Design Workflow Node List Workflow Settings Trigger Workflow Validate Trigger Payload Tenant Workflows Notification Inbox Overview Multi Tabs React Javascript (Angular, Vuejs etc) Integration Customization options React Native Flutter (Headless) PREFERENCE CENTRE Embedded Preference Centre Javascript Angular React VENDOR INTEGRATION GUIDE Overview Email Integrations SMS Integrations Android Push Whatsapp Integrations iOS Push Chat Integrations Vendor Fallback Tenant Vendor INTEGRATIONS Webhook Connectors MONITORING & DEBUGGING Logs Audit Logs Error Guides MANAGE YOUR ACCOUNT Authentication Methods Contact Us Get Started SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Ask AI Contact Us Get Started Get Started Search... Navigation Javascript (Angular, Vuejs etc) Integration Documentation API Reference Management API CLI Reference Developer Resources Changelog Documentation API Reference Management API CLI Reference Developer Resources Changelog Javascript (Angular, Vuejs etc) Integration OpenAI Open in ChatGPT How to integrate SuprSend inbox/feed components in Angular, Vue, VanillaJS, and other non-React frameworks. OpenAI Open in ChatGPT End of Support for @suprsend/web-inbox . Migrate to @suprsend/web-components We have upgraded authentication of inbox from HMAC to JWT as it is more secure. Please migrate to newer SDK if you are on old one. There are 2 ways in which you can implement inbox functionality: Drop-in components: Pre-built UI with many customizable options which require minimal effort to build. Headless implementation: For more advanced use cases where you want to build UI/UX from scratch. This guide help you integrate drop-in components in your non-react frameworks (angular, vuejs, vanillajs etc). If you want to build your own UI (headless) instead of using drop-in components please refer docs . Integration Integrate using script tag This integration is used in Vanillajs, Django, Laravel, ruby etc where npm is not used. Copy Ask AI <!-- for dropin inbox with bell --> < div id = "suprsend-inbox" ></ div > <!-- for feed without bell as a fullscreen notification etc --> < div id = "suprsend-feed" ></ div > < script > window . suprsendConfig = { distinctId: "YOUR_DISTINCT_ID" , publicApiKey: "YOUR_PUBLIC_API_KEY" , userAuthenticationHandler : ({ response }) => { console . log ( "User Authentication Response" , response ); }, }; let scriptElem = document . createElement ( "script" ); scriptElem . async = 1 ; scriptElem . src = "https://web-components.suprsend.com/v0.3.0/bundle.umd.js" ; scriptElem . onload = () => { console . log ( "SuprSend SDK loaded" , window . suprsend ); }; document . body . appendChild ( scriptElem ); </ script > Integrate as npm package This integration is used in framework based applications like angular, vuejs etc. Copy Ask AI npm install @suprsend/web-components@latest Copy Ask AI import { initSuprSend , clearSuprSend } from "@suprsend/web-components" ; // for dropin inbox with bell < div id = "suprsend-inbox" ></ div > // for feed without bell as a fullscreen notification etc < div id = "suprsend-feed" ></ div > const suprsendConfig = { distinctId: "YOUR_DISTINCT_ID" , publicApiKey: "YOUR_PUBLIC_API_KEY" , userAuthenticationHandler : ({ response }) => { console . log ( "User Authentication Response" , response ); }, }; initSuprSend ( suprsendConfig ) // for creating instance and rendering component console . log ( "Instance created but user authentication pending" , window . suprsend ) NOTE: If you are using suprsend-feed , specify height for the container for infinite scroll to work properly. Copy Ask AI const suprsendConfig = { distinctId: "YOUR_DISTINCT_ID" , publicApiKey: "YOUR_PUBLIC_API_KEY" , feed: { theme: { notificationsContainer: { container: { height: "100vh" } } }, // add this to specify height }, }; Removing instance Components will be removed automatically if you navigate away from the page (on unmounting). If you want to remove them manually, you can use below methods. Using script tag Using npm package Copy Ask AI window . suprsend . clearSuprSend (); // clears instance and remove all components window . suprsend . clearSuprSendInbox (); // unmount only inbox component window . suprsend . clearSuprSendFeed (); // unmount only feed component Updating configuration dynamically Copy Ask AI window . suprsend . updateSuprSendConfig ( config : IUpdateSuprSendConfigOptions ); // refresh userToken, change locale, translations dymanically window . suprsend . updateInboxConfig ( config : IInbox ); window . suprsend . updateFeedConfig ( config : IFeed ); window . suprsend . updateToastConfig ( config : IToastNotificationProps ); Accessing other instance methods SDK internally calls new SuprSend() when you call initSuprSend() then you can access instance using window.suprsend.client . This instance has methods like preferences , webpush , event and user updates . Copy Ask AI // example methods window . suprsend . client . isIdentified (); window . suprsend . client . user . addEmail ( email : string ); window . suprsend . client . track ( event : string , properties ?: Dictionary ) window . suprsend . client . webpush . registerPush (); window . suprsend . client . user . preferences . getPreferences ( args ?: {tenantId? : string }); Config options To customise SuprSend components you can pass config object. Config Options Inbox Config Options Feed Config Options Toast Config Options Copy Ask AI interface ConfigProps { publicApiKey : string ; distinctId ? : unknown ; userToken ? : string ; host ? : string ; initOnLoad ? : boolean ; // pass false if you don't want to initialise instance just after loading script refreshUserToken ? : ( oldUserToken : string , tokenPayload : Dictionary ) => Promise < string > ; vapidKey ? : string ; swFileName ? : string ; userAuthenticationHandler ? : ({ response : ApiResponse }) => void ; inbox ? : IInbox ; // inbox config options feed ? : IFeed ; // feed config options toast ? : IToastNotificationProps ; // toast config options shadowRoot ?: ShadowRoot ; //shadowRoot reference } Parameter Description publicApiKey Public API Key is mandatory field without which error will be thrown by SuprSendProvider. You can get this from SuprSend Dashboard . distinctId Unique identifier to identify a user across platform. If a value is passed SDK will create user and authenticate user. If null value is passed authenticated user’s instance data will be cleared in your application, kind of logout. userToken Mandatory when enhanced security mode is on. This is ES256 JWT token generated in your server-side. Refer docs to create userToken. refreshUserToken This function is called by SDK internally to get new userToken before existing token is expired. The returned JWT token string is used as the new userToken. userAuthenticationHandler This callback will be called after authenticating user internally when you pass distinctId field to give you back the response of user creation API call. host Customise the host url. vapidKey This key is needed only if you are implementing WebPush notifications. You can get it in SuprSend Dashboard —> Vendors —> WebPush swFileName This key is needed only if you are implementing WebPush notifications and want to customise default serviceworker.js file name with your own service worker file name. shadowRoot Shadow root reference to render components inside shadow dom For further component specific customisations please refer to the docs . Was this page helpful? Yes No Suggest edits Raise issue Previous Customization options How to customize the styling, CSS, and layout of the Inbox Feed to match your product’s design in non-React websites. Next ⌘ I x github linkedin youtube Powered by On this page Integration Integrate using script tag Integrate as npm package Removing instance Updating configuration dynamically Accessing other instance methods Config options | 2026-01-13T08:49:44 |
https://zeroday.forem.com/agardnerit/osquery-opentelemetry--55c#main-content | osquery + OpenTelemetry = ❤️ - 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. 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 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 Adam Gardner Posted on Nov 16, 2025 osquery + OpenTelemetry = ❤️ # devsecops # tools # soc As you probably know by now, osquery effectively turns your endpoints into SQL endpoints that you can query: SELECT * FROM processes or SELECT * FROM users etc. But, that data is much more useful if it's tied to other telemetry data coming from your VMs, endpoints or Kubernetes clusters. This is typically the domain of APM tools. Using OpenTelemetry (and specifically the OpenTelemetry collector) we can bring those two worlds together. In this video I show you how that's done. 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 Adam Gardner Follow CNCF Ambassador, DevRel working in Observability. I blog at https://agardner.net and YouTube at https://www.youtube.com/@agardnerit Work DevRel @ Dynatrace / CNCF Ambassador Joined Dec 8, 2023 💎 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 | 2026-01-13T08:49:44 |
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