id int64 5 1.93M | title stringlengths 0 128 | description stringlengths 0 25.5k | collection_id int64 0 28.1k | published_timestamp timestamp[s] | canonical_url stringlengths 14 581 | tag_list stringlengths 0 120 | body_markdown stringlengths 0 716k | user_username stringlengths 2 30 |
|---|---|---|---|---|---|---|---|---|
1,873,475 | 🕒 Task vs Promise: Encadenación | El primer lenguaje en el que aprendí a trabajar de forma asíncrona fue JavaScript. Al principio, me... | 0 | 2024-06-04T15:00:00 | https://oscarlp6.dev/blogs/task-vs-promises/ | csharp, javascript, async | El primer lenguaje en el que aprendí a trabajar de forma *asíncrona* fue *JavaScript*. Al principio, me costó mucho trabajo porque era una forma de pensar completamente distinta a la que aprendí en la universidad. Una vez que logré interiorizar los principios de la programación *asíncrona*, me fue mucho más sencillo. Entonces, cuando comencé a trabajar en *C#*, de inmediato detecté las similitudes entre las `Task` y las `Promise`, pues son prácticamente equivalentes.
Pero al intentar encadenar promesas de la misma forma en la que se hace en *JavaScript*, me topé con una peculiaridad. La función que se recibe en el método `.then` de *JavaScript* es una función que espera el valor que está envuelto en la promesa. Es decir, si tenemos una `Promise<number>`, la función del `.then` es una función que recibe un `number`. En cambio, en *C#* el "equivalente" a `.then` es `.ContinueWith`, pero este método espera una función que recibe una `Task` del mismo tipo que la `Task` *original*. Es decir, si tenemos un `Task<string>`, el método `.ContinueWith` recibe una función que recibe un `Task<string>`. Esto me causó mucha confusión, y conversando con **ChatGPT** logré tener más claridad en el caso.
En caso de querer revisar mi proceso, esta es la [conversación](https://chatgpt.com/share/909c4bb1-d514-4279-a25a-05ce7d71103d)
### `.then` en *Javascript*
En *JavaScript*, el método `.then` se utiliza para manejar el resultado de una **promesa**. El manejador de `.then` recibe directamente el *valor resuelto* de la promesa. Además, *JavaScript* proporciona el método `.catch` para manejar errores.
**Ejemplo en JavaScript:**
```javascript
fetch('http://example.com')
.then(response => response.json())
.then(data => {
console.log(data);
})
.catch(error => {
console.error('Error:', error);
});
```
En este ejemplo, si la promesa se resuelve, el manejador en el primer `.then` recibe el response y lo procesa. Si ocurre un error, el manejador en `.catch` se ejecuta.
### `.ContinueWith` en *C\#*
En C#, el método `.ContinueWith` de una `Task` se utiliza para continuar con la ejecución de código después de que una tarea se complete. A diferencia de `.then`, el manejador de `.ContinueWith` recibe una instancia de `Task<T>`, lo que permite acceder a más detalles acerca de la tarea, incluyendo su *estado, excepciones y resultado*.
**Ejemplo básico en C#:**
```csharp
Task<int> task = Task.Run(() => {
// Simulando una operación asíncrona
return 42;
});
task.ContinueWith(t => {
if (t.IsFaulted)
{
// Manejar excepciones
Console.WriteLine($"Error: {t.Exception.InnerException.Message}");
}
else if (t.IsCompletedSuccessfully)
{
// Manejar el resultado exitoso
Console.WriteLine($"Resultado: {t.Result}");
}
});
```
En este ejemplo, ContinueWith maneja tanto el resultado exitoso como las posibles excepciones. Esto es posible porque ContinueWith proporciona acceso a la tarea completa.
### El porqué
#### No hay `.catch` en *C\#*
En *C#*, no existe un método equivalente a `.catch` de las promesas en *JavaScript* que se encadene directamente a una `Task`. En cambio, los errores se manejan dentro del mismo manejador `ContinueWith` o utilizando bloques *try-catch* en combinación con `await`.
#### Opciones de `.ContinueWith` en *C\#*
El método `.ContinueWith` también permite especificar opciones que controlan *cuándo* se debe ejecutar el manejador de continuación, tales como `OnlyOnRanToCompletion` y `OnlyOnFaulted`.
**Ejemplo con opciones de ContinueWith:**
```csharp
Task<int> task = Task.Run(() => {
// Simulando una operación que puede lanzar una excepción
throw new InvalidOperationException("Error simulado");
return 42;
});
task.ContinueWith(t => {
Console.WriteLine($"Resultado: {t.Result}");
}, TaskContinuationOptions.OnlyOnRanToCompletion);
task.ContinueWith(t => {
Console.WriteLine($"Error: {t.Exception.InnerException.Message}");
}, TaskContinuationOptions.OnlyOnFaulted);
```
En este ejemplo, se definen dos manejadores de continuación: uno que se ejecuta solo si la tarea se completa con éxito (`OnlyOnRanToCompletion`) y otro que se ejecuta solo si la *tarea falla* (`OnlyOnFaulted`).
### Conclusiones
Aunque tanto `.ContinueWith` en *C#* como `.then` en *JavaScript* sirven para continuar con la ejecución de código después de una operación *asíncrona*, hay diferencias importantes:
1. **Manejador de Continuación:** En *JavaScript*, el manejador `.then` recibe el *valor resuelto* de la **promesa**. En *C#*, el manejador `.ContinueWith` recibe una instancia de `Task<T>`, proporcionando acceso a más detalles de la tarea.
2. **Manejo de Errores:** *JavaScript* utiliza `.catch` para manejar **errores**. En *C#*, se manejan dentro del manejador `ContinueWith` o mediante bloques try-catch cuando se usa `await`.
3. **Opciones de Continuación:** *C#* permite especificar opciones en `.ContinueWith` para controlar cuándo se debe ejecutar el manejador de continuación, ofreciendo un control más granular.
Estas diferencias reflejan las distintas filosofías y capacidades de los lenguajes, proporcionando a los desarrolladores herramientas poderosas para manejar operaciones asíncronas en cada entorno.
Espero que este artículo te sea útil para entender mejor las diferencias entre .ContinueWith en C# y .then en JavaScript, así como las opciones para manejar errores y acceder a los detalles de las tareas en C#. | oscareduardolp6 |
1,864,508 | How to unit test a private method in Kotlin without making it public | Table of contents The problem I am facing Why not make it public? My app on... | 0 | 2024-06-04T14:57:56 | https://dev.to/theplebdev/how-to-unit-test-a-private-method-in-kotlin-without-making-it-public-glc | android, kotlin, mobile, tristan | ### Table of contents
1. [The problem I am facing](#problem)
2. [Why not make it public?](#make)
### My app on the Google play store
- [The app](https://play.google.com/store/apps/details?id=elliott.software.clicker)
### My app's GitHub code
- [The GitHub ](https://github.com/thePlebDev/Clicker)
### The problem I am facing <a name="problem"></a>
- I have a relatively simple method that I want to test but its private. Why test it in the first place? Well, despite its simplicity it is very important to the project and I want the to be able to test it and ensure that it will run how I want it to. Even if myself or others change it. Further more, if the test fails it will be a sign to me that something has gone wrong and it needs to be fixed before the next release.
- The function I want to test:
```kotlin
class TwitchEmoteImpl @Inject constructor(
private val twitchEmoteClient: TwitchEmoteClient,
): TwitchEmoteRepo {
// THE FUNCTION I WANT TO TEST
private fun createMapValue(
emoteValue: EmoteNameUrl,
innerInlineContentMap: MutableMap<String, InlineTextContent>
){
val url = emoteValue.url
val value = InlineTextContent(
Placeholder(
width = 35.sp,
height = 35.sp,
placeholderVerticalAlign = PlaceholderVerticalAlign.Center
)
) {
AsyncImage(
model = url,
contentDescription = stringResource(R.string.moderator_badge_icon_description),
modifier = Modifier
.fillMaxSize()
.padding(2.dp)
)
}
innerInlineContentMap[emoteValue.name] = value
}
}
```
- The obvious question is, `how do I test a private method without making it public?`. The answer is, `Dependencies!!!!!`
- To clarify, we take all of the logic out of this private method, put it into a public dependency that we can then test. Our updated code will look like this:
```kotlin
class TwitchEmoteImpl @Inject constructor(
private val twitchEmoteClient: TwitchEmoteClient,
private val emoteParsing:EmoteParsing = EmoteParsing()
): TwitchEmoteRepo {
private fun createMapValue(
emoteValue: EmoteNameUrl,
innerInlineContentMap: MutableMap<String, InlineTextContent>
){
// WE CAN NOW UNIT TEST THE EmoteParsing CLASS
emoteParsing.createMapValueForComposeChat(
emoteValue,
innerInlineContentMap
)
}
}
```
- As you can see the dependency (`emoteParsing`) we created is literally just a clone to help us aid in testing:
```kotlin
class EmoteParsing {
fun createMapValueForComposeChat(
emoteValue: EmoteNameUrl,
innerInlineContentMap: MutableMap<String, InlineTextContent>
){
val url = emoteValue.url
val value = InlineTextContent(
Placeholder(
width = 35.sp,
height = 35.sp,
placeholderVerticalAlign = PlaceholderVerticalAlign.Center
)
) {
AsyncImage(
model = url,
contentDescription = stringResource(R.string.moderator_badge_icon_description),
modifier = Modifier
.fillMaxSize()
.padding(2.dp)
)
}
innerInlineContentMap[emoteValue.name] = value
}
}
```
- We can now unit test this createMapValueForComposeChat() function through the EmoteParsing class.
### Why not just make it public? <a name="make"></a>
- You obviously could make the method public. However, this would give developers(myself or others) the ability to use the code not as it was intended to. Essentially, increasing the scope of the class. By keeping the method private we are establishing clear boundaries between classes and leave smaller possibilities of misuses.
### Conclusion
- Thank you for taking the time out of your day to read this blog post of mine. If you have any questions or concerns please comment below or reach out to me on [Twitter](https://twitter.com/AndroidTristan).
| theplebdev |
1,876,800 | AI indoor navigation service | Infrastructure development for indoor tracking application, the project from A to Z. Abto Software... | 0 | 2024-06-04T14:57:54 | https://dev.to/abtosoftware/ai-indoor-navigation-service-2a1p | webdev, ai, devops, machinelearning | _[Infrastructure development for indoor tracking application](https://www.abtosoftware.com/portfolio/ai-indoor-navigation-service), the project from A to Z._
Abto Software was contracted by a mapping and accessibility organization providing indoor navigation services. Our company has covered application refactoring, software development, cloud development, cloud migration, and several other services to host the implemented inference service.
During the project’s course, our team was extended to provide DevOps practices and legacy code refactoring. Moving further over the project’s course, we provided MLOps practices to deliver a quality control solution, which allowed our team to evaluate the accuracy of the inference service.
## Navigation app infrastructure development: Our goals
The indoor navigation service was presenting several challenges that impacted the overall business efficiency, so we primarily focused on optimizing:
### Response times and costs
- by ensuring efficient allocation of directed computational resources
- by allowing the system to scale on demand and avoid unnecessary consumption
### Software development and release, thereby facilitating:
- Competitive advantage
- Meeting evolving customer demands
- Shorter time-to-market
- Greater maintainability and adaptivity to changes
## Navigation app infrastructure development: The solution in details
The indoor navigation service is designed and developed to allow any company guide visitors though spaces, thus bringing additional benefit to organizations across transportation, retail, recreation, and others.
Having built-in visual and audio guidance for accessibility, the solution can provide autonomous navigation. What’s more, the system can provide additional information about facilities, thus improving user experience and enhancing business reputation and image.
The concept is simple:
- The user first scans the location to capture required information
- The data is stitched to create a map
- The user then manages the map to control the permissions and options for interaction
- And, finally, the visitors can navigate the location using both visual and audio clues
The service is made up of:
1. A custom web application for the client’s internal tech personnel
2. A custom mobile application for the end user
3. And the inference service that extracts specific features from images to match those features with the scanned model of the added building or point of interest
## Our contribution
We covered:
- Business analysis
- Software development
- AI development
- Cloud development
- Code refactoring
- DevOps services
- Thorough testing (manual testing, unit testing)
- Technical support and maintenance
In particular:
- In-depth research of models considering performance and accuracy
- Event-driven architecture for optimized response times and costs
- DevOps practices and legacy code refactoring
- MLOps services for quality control solution
**Web and mobile stack:** TypeScript, Node.js, JavaScript, DynamoDB, Postgres, Python, PyTorch, Snowflake, Grafana, Plotly Dash, API Gateway Kong
**DevOps and MLOps services:** OpenVino, PyColMap, Fast API, EKS stack, AWS, Azure, Flask, Kubernetes, Prometheus, Bitbucket, RabbitMQ, Redis, Knative, Kserve, GraphQL, Airflow, Terraform, HELM
## The challenges
During the project’s scope, we faced multiple challenges associated with:
### The improvement of the SLAM system
The size and similarity of spaces within some larger buildings has complicated image capture and processing. To handle this challenge, our team has autotuned the thresholds for each pipeline microservice and minimized the false positive responses.
### The hosting of the SLAM system
The application requires intensive computational resources for adequate response times at an acceptable cost. To solve this challenge, our engineers have built message brokers and divided the components into individual pipeline microservices.
### The scaling of the delivered solution
To scale the capabilities and enable the scanning of different facility types, including airports, train stations, schools, museums, and more, we delivered a custom Infrastructure-as-a-code system for quick, one-click setup of clusters.
### The monitoring of performance, user behavior, and scaling
To ensure high uptime and facilitate user experience, we established individual monitoring within clusters.
## Summing up
Abto Software was focused around replacing an inefficient inference service to accelerate user experience, simultaneously optimize response times and costs, and streamline localization precision.
Our client can now explore wider, forward-looking opportunities and leverage:
### New customers, higher demand, and profitability
By accelerating navigation precision, thus improving user experience, the client can attract more customers and leverage future opportunities for growth. and profit
### Business agility
By streamlining software development and release, optimizing time and cost, and achieving greater scalability, the client can introduce new features and updates more frequently, invest strategically, and adapt to changes without compromising business performance and reliability.
| abtosoftware |
1,876,799 | Frontend challenge June edition | This is a submission for Frontend Challenge v24.04.17, CSS Art: June. ... | 0 | 2024-06-04T14:56:14 | https://dev.to/sharmi2020/frontend-challenge-june-edition-51ma | frontendchallenge, devchallenge, css | _This is a submission for [Frontend Challenge v24.04.17](https://dev.to/challenges/frontend-2024-05-29), CSS Art: June._
## Inspiration
<!-- What are you highlighting today? -->
Sizzling Summer
## Demo
<!-- Show us your CSS Art! You can directly embed an editor into this post (see the FAQ section of the challenge page) or you can share an image of your project and share a public link to the code. -->
https://codepen.io/sharmi2020-the-styleful/pen/LYoyrKQ
website URL
https://challengedev.netlify.app/
## Journey
<!-- Tell us about your process, what you learned, anything you are particularly proud of, what you hope to do next, etc. -->
<!-- Team Submissions: Please pick one member to publish the submission and credit teammates by listing their DEV usernames directly in the body of the post. -->
<!-- We encourage you to consider adding a license for your code. -->
<!-- Don't forget to add a cover image to your post (if you want). -->
<!-- Thanks for participating! --> | sharmi2020 |
1,876,795 | ToonCrafter: Amazing AI Cartoon Generator | In the world of animation, a revolutionary new tool is changing the game - ToonCrafter. This... | 0 | 2024-06-04T14:46:19 | https://dev.to/christianhappygo/tooncrafter-amazing-ai-cartoon-generator-1b4e | animation, ai, aigc |
In the world of animation, a revolutionary new tool is changing the game - [ToonCrafter](https://www.tooncrafter.app). This innovative technology uses a generative interpolation method to create smooth transitions between different cartoon frames, greatly simplifying the traditional animation production process.
Traditional cartoon animation requires animators to draw each intermediate frame one by one, a process that is extremely time-consuming and prone to errors. With ToonCrafter, however, you simply input the start and end frames, and it automatically generates natural, seamless in-between frames, saving a tremendous amount of human effort and time.
**The Power of Breakthrough Technology**
At the core of ToonCrafter is a novel generative interpolation method. Unlike traditional correspondence-based interpolation, it uses a generative approach to directly create smooth transitions between frames, overcoming the limitations of traditional methods.
**Experience the Magic Firsthand**
Want to experience the magic of ToonCrafter for yourself? Visit [ToonCrafter](https://www.tooncrafter.app) now, upload your start and end frames, and witness how ToonCrafter creates fluid, lifelike animation transitions. Whether you're a professional animator or a hobbyist, ToonCrafter will be your powerful ally, making animation creation simpler and more efficient than ever before. | christianhappygo |
1,876,021 | Recapping the AI, Machine Learning and Data Science Meetup - May 30, 2024 | We just wrapped up the May '24 AI, Machine Learning and Data Science Meetup, and if you missed it or... | 0 | 2024-06-04T14:44:22 | https://voxel51.com/blog/recapping-the-ai-machine-learning-and-data-science-meetup-may-30-2024/ | computervision, machinelearning, datascience, ai | We just wrapped up the May '24 AI, Machine Learning and Data Science Meetup, and if you missed it or want to revisit it, here's a recap!
In this blog post you'll find the playback recordings, highlights from the presentations and Q&A, as well as the upcoming Meetup schedule so that you can join us at a future event.
## First, Thanks for Voting for Your Favorite Charity!
In lieu of swag, we gave Meetup attendees the opportunity to help guide a $200 donation to charitable causes. The charity that received the highest number of votes this month was [Heart to Heart International](https://www.hearttoheart.org/), an organization that ensures quality care is provided equitably in medically under-resourced communities and in disaster situations. We are sending this event's charitable donation of $200 to Heart to Heart International on behalf of the Meetup members!
Missed the Meetup? No problem. Here are playbacks and talk abstracts from the event.
## Lessons Learned fine-tuning Llama2 for Autonomous Agents
{% embed https://www.youtube.com/watch?v=EX8rn1aZsVw %}
In this talk, Rahul Parundekar, Founder of A.I. Hero, Inc. does a deep dive into the practicalities and nuances of making LLMs more effective and efficient. He'll share hard-earned lessons from the trenches of LLMOps on Kubernetes, covering everything from the critical importance of data quality to the choice of fine-tuning techniques like LoRA and QLoRA. Rahul will share insights into the quirks of fine-tuning LLMs like Llama2, the need for looking beyond loss metrics and benchmarks for model performance, and the pivotal role of iterative improvement through user feedback - all learned through his work on fine-tuning an LLM for retrieval-augmented generation and autonomous agents. Whether you're a seasoned AI professional or just starting, this talk will equip you with the knowledge of when and why you should fine-tune, to the long-term strategies to push the boundaries of what's possible with LLMs, to building a performant framework on top of Kubernetes for fine-tuning at scale.
**Speaker:** [Rahul Parundekar](https://www.linkedin.com/in/rparundekar/) is the founder of A.I. Hero, Inc., a seasoned engineer, and architect with over 15 years of experience in AI development, focusing on Machine Learning and Large Language Model Operations (MLOps and LLMOps). AI Hero automates mundane enterprise tasks through agents, utilizing a framework for fine-tuning LLMs with both open and closed-source models to enhance agent autonomy.
## Combining Hugging Face Transformer Models and Image Data with FiftyOne
{% embed https://www.youtube.com/watch?v=0bdU4yrdiLE %}
Datasets and Models are the two pillars of modern machine learning, but connecting the two can be cumbersome and time-consuming. In this lightning talk, you will learn how the seamless integration between Hugging Face and FiftyOne simplifies this complexity, enabling more effective data-model co-development. By the end of the talk, you will be able to download and visualize datasets from the Hugging Face hub with FiftyOne, apply state-of-the-art transformer models directly to your data, and effortlessly share your datasets with others.
**Speaker:** [Jacob Marks](https://www.linkedin.com/in/jacob-marks/) is a Senior Machine Learning Engineer and Researcher at Voxel51, where he leads open source efforts in vector search, semantic search, and generative AI for the FiftyOne data-centric AI toolkit. Prior to joining Voxel51, Jacob worked at Google X, Samsung Research, and Wolfram Research. In a past life, he was a theoretical physicist: in 2022, he completed his Ph.D. at Stanford, where he investigated quantum phases of matter.
### Resource links
- [Colab Notebook used in the demo](https://colab.research.google.com/drive/1OIvESVbxNaXRPv0riaB905xPIimEOn2E?usp=sharing#scrollTo=soJBW3Bvjr67)
- [Blog post based on the talk](https://huggingface.co/blog/jamarks/fiftyone-datasets-come-to-hf-hub)
- [Hugging Face and FiftyOne integration documentation](https://docs.voxel51.com/integrations/huggingface.html)
## Multi-Modal Visual Question Answering (VQA) using UForm tiny models with Milvus vector database (Parts One and Two)
{% embed https://www.youtube.com/watch?v=TuBK1WAWlgg %}
{% embed https://www.youtube.com/watch?v=TuBK1WAWlgg %}
UForm is a multimodal AI library that will help you understand and search visual and textual content across various languages. UForm not only supports RAG chat use-cases, but is also capable of Visual Question Answering (VQA). Compact custom pre-trained transformer models can run anywhere from your server farm down to your laptop. I’ll be giving a demo of RAG and VQA using Milvus vector database.
**Speaker:** [Christy Bergman](https://www.linkedin.com/in/christybergman/) is a passionate Developer Advocate at Zilliz. She previously worked in distributed computing at Anyscale and as a Specialist AI/ML Solutions Architect at AWS. Christy studied applied math, is a self-taught coder, and has published papers, including one with ACM Recsys. She enjoys hiking and bird watching. [Ash Vardanian](https://www.linkedin.com/in/ashvardanian/) is the Founder of Unum Cloud. With a background in Astrophysics, his work today primarily lies in the intersection of Theoretical Computer Science, High-Performance Computing, and AI Systems Design, including everything from GPU algorithms and SIMD Assembly to Linux kernel bypass technologies and multimodal perception.
### Resource links
- [UForm: Pocket-Sized Multimodal AI for Content Understanding and Generation](https://github.com/unum-cloud/uform)
- [Milvus open source vector database](https://milvus.io/)
## Strategies for Enhancing the Adoption of Open Source Libraries: A Case Study on Albumentations.ai
{% embed https://www.youtube.com/watch?v=tZN4nEg_CJ8 %}
In this presentation, we explore key strategies for boosting the adoption of open-source libraries, using [Albumentations.ai](https://albumentations.ai/) as a case study. We will cover the importance of community engagement, continuous innovation, and comprehensive documentation in driving a project’s success. Through the lens of Albumentations.ai’s growth, attendees will gain insights into effective practices for promoting their open source projects within the machine learning and broader developer communities.
**Speaker:** [Vladimir Iglovikov](https://www.linkedin.com/in/iglovikov/) is a co-creator of Albumentations.ai, a Kaggle Grandmaster, and an advocate for open source AI technology. With a Ph.D. in physics and deep expertise in deep learning, he has significantly contributed to advancing the machine learning field.
## Join the AI, Machine Learning and Data Science Meetup!
The combined membership of the [Computer Vision and AI, Machine Learning and Data Science Meetups](https://voxel51.com/computer-vision-ai-meetups/) has grown to over 20,000 members! The goal of the Meetups is to bring together communities of data scientists, machine learning engineers, and open source enthusiasts who want to share and expand their knowledge of AI and complementary technologies.
Join one of the 12 Meetup locations closest to your timezone.
- [Athens](https://www.meetup.com/athens-ai-machine-learning-data-science)
- [Austin](https://www.meetup.com/austin-ai-machine-learning-data-science)
- [Bangalore](https://www.meetup.com/bangalore-ai-machine-learning-data-science)
- [Boston](https://www.meetup.com/boston-ai-machine-learning-data-science)
- [Chicago](https://www.meetup.com/chicago-ai-machine-learning-data-science)
- [London](https://www.meetup.com/london-ai-machine-learning-data-science)
- [New York](https://www.meetup.com/new-york-ai-machine-learning-data-science)
- [Peninsula](https://www.meetup.com/peninsula-ai-machine-learning-data-science)
- [San Francisco](https://www.meetup.com/sf-ai-machine-learning-data-science)
- [Seattle](https://www.meetup.com/seattle-ai-machine-learning-data-science)
- [Silicon Valley](https://www.meetup.com/sv-ai-machine-learning-data-science)
- [Toronto](https://www.meetup.com/toronto-ai-machine-learning-data-science)
## What’s Next?
Up next on June 27, 2024 at 10 AM Pacific, we have four great speakers lined up!
- Leveraging Pre-trained Text2Image Diffusion Models for Zero-Shot Video Editing - [Barışcan Kurtkaya](https://www.linkedin.com/in/bariscankurtkaya/), KUIS AI Fellow at Koc University
- Improved Visual Grounding through Self-Consistent Explanations - [Dr. Paola Cascante-Bonilla](https://www.linkedin.com/in/paola-cascante/), Rice University and [Ruozhen (Catherine) He](https://www.linkedin.com/in/ruozhen-he-906666236/), Rice University
- Combining Hugging Face Transformer Models and Image Data with FiftyOne - [Jacob Marks](https://www.linkedin.com/in/jacob-marks/), PhD - ML Engineer/Researcher at Voxel51
Register for the Zoom [here](https://voxel51.com/computer-vision-events/june-27-2024-ai-machine-learning-computer-vision-meetup/). You can find a complete schedule of upcoming Meetups on the [Voxel51 Events page](https://voxel51.com/computer-vision-events/).
## Get Involved!
There are a lot of ways to get involved in the Computer Vision Meetups. Reach out if you identify with any of these:
- You’d like to speak at an upcoming Meetup
- You have a physical meeting space in one of the Meetup locations and would like to make it available for a Meetup
- You’d like to co-organize a Meetup
- You’d like to co-sponsor a Meetup
Reach out to Meetup co-organizer Jimmy Guerrero on Meetup.com or ping me over [LinkedIn](https://www.linkedin.com/in/jiguerrero/) to discuss how to get you plugged in.
_These Meetups are sponsored by [Voxel51](https://voxel51.com/), the company behind the open source [FiftyOne](https://github.com/voxel51/fiftyone) computer vision toolset. FiftyOne enables data science teams to improve the performance of their computer vision models by helping them curate high quality datasets, evaluate models, find mistakes, visualize embeddings, and get to production faster. It’s easy to [get started](https://voxel51.com/docs/fiftyone/index.html), in just a few minutes._ | jguerrero-voxel51 |
1,876,789 | Lifting State 🏋🏻♀️ | WHY ? Think of an online grocery store containing 100,000+ products. Each product has a... | 26,254 | 2024-06-04T14:41:41 | https://dev.to/jorjishasan/lifting-state-31g3 | webdev, beginners, react, programming | ## WHY ?
Think of an online grocery store containing 100,000+ products. Each product has a common feature, which is the toggle feature. They will show certain things on click. To function that feature in React, each product must have a state variable. Now, the popup question is, if 100,000 products have 100,000 state variables, would our app be efficient? And what about MILLION?
The answer is **BIG NO** ❌.
> ✅ What if there is just one state variable instead of **100,000** ? That state would be at the top of the parent component. When any product is clicked, only that product will get a share of the state variable and enable the toggle feature.
Doesn't it sound like solution? Yes, the Lifting state does that solution. Well, now you know the lifting state in theory. Let's give it a technical shot!
---
Before diving deep into State lifting, give it a well-read [(controlled & uncontrolled components)](https://dev.to/jorjishasan/controlled-uncontrolled-component-3h3h).
These 2 things are the same :
- Lifting State
- State Sharing
Lifting up state is a technique used in React to share states between multiple components. Instead of each component having its own local state, the state is lifted up to their closest ancestor. This common ancestor then passes the state down to the components via props.
---
Let it make easy steps for you to understand this example illustration:
1. `Accordion` is the parent component here if you see the hierarchy over the picture.
2. We create a state variable `activeIndex` with `null` value at the parent(`Accordion`) component. Why `null`? Because by default, no accordion is clicked.
3. We'll give children(`AccordianFrame`) access to modify the `activeIndex` state variable by sharing the `setActiveIndex()` function via props.
4. Now, when any accordion is clicked, it triggers `setActiveIndex()` with its own index value. The `activeIndex` variable will update accordingly.
5. When it matches the `index === activeIndex, ' the accordion that was clicked will receive the `isActive = true` value through props and be expanded.
Dev View: [Live ✨](https://doubleaccordion.netlify.app/)
CODE: [GITHUB](https://github.com/jorjishasan/Accordions)
File-structures followed here ⬇️
```
├── src/
│ ├── AccordionFrame.jsx
│ ├── App.jsx
│ ├── constant.jsx
│ ├── index.css
├── tailwind.config.js
└── vite.config.js
```
| jorjishasan |
1,876,787 | How we migrated our codebase from fp-ts to Effect | Summary At Inato, we migrated from fp-ts to Effect in early 2024. Given our substantial... | 0 | 2024-06-04T14:30:41 | https://medium.com/inato/how-we-migrated-our-codebase-from-fp-ts-to-effect-b71acd0c5640 | fpts, effect, migration, typescript | ## Summary
At Inato, we migrated from fp-ts to Effect in early 2024. Given our substantial codebase (around 500k lines of typescript code), we needed a way of ensuring any new code could be written using Effect while allowing existing fp-ts code to coexist. We achieved this goal in just two months, dedicating around 10% of our time to it. In this article, you will find our detailed migration strategy, the helpers we developed (which you can find in this [repository](https://github.com/inato/effect-fpts-interop)), and how we ensured a smooth transition of our codebase.
## Migrate to Effect, why?
At Inato we were very motivated early on to adopt functional programming, so we started using fp-ts in our codebase at the beginning of 2020. If you want to know more about this, have a look at [Our journey to functional programming](https://medium.com/inato/our-journey-to-functional-programing-36854a370de1).
Let’s now get to the heart of the matter: at the beginning of this year, we officially decided to switch to Effect! Why?
* The main maintainer of fp-ts (gcanti 👋) [joined the Effect team](https://dev.to/effect/a-bright-future-for-effect-455m) which presumably means less active development on the fp-ts side and positions Effect as a rather obvious next step.
* Because of the learning curve associated with fp-ts and the lack of documentation. Developers who joined Inato in the last years have frequently mentioned it: learning fp-ts is not really straightforward. This is a strong point for Effect with top-notch documentation and a lot of resources for training.
* For even more reasons, visit the [Effect website which compares fp-ts and Effect](https://effect.website/docs/other/fp-ts#comparison-table)!
Migrating our codebase to Effect is a great goal, but doing it turned out to be more challenging and required careful planning. We also wanted to limit the time spent on this project, so we agreed on a 2.5-month deadline. With all this in mind, we came up with the following strategy.
## The Migration Strategy
First of, here’s a representation of our server-side codebase: we have use cases that represent our business actions, these use cases have multiple dependencies (services, repositories, etc. — we’ll refer to them as ports), and we also have runners that will execute our use cases:
When we started the migration we had around 400 use cases and 80 ports and their adapters to migrate.
Our objective for this migration was clear: by the end of our 2.5-month window, any new use case or port will be written using Effect. To have a smooth transition that would allow us to have fp-ts and Effect code cohabitating, we came up with the following plan:
1. Ensure our ports return `ReaderTaskEither` to facilitate the transition to Effect \[\*\]
2. Create Effect proxies of our ports: only one implementation in fp-ts, but the ability to use an fp-ts “real” version and an Effect proxy version of each port
3. Start (re)writing use cases in Effect
4. Create fp-ts proxies of Effect use cases
5. Start (re)writing ports in Effect
6. Create fp-ts proxies of Effect ports: at this point, we would already have fulfilled our objective of writing new use cases and ports with Effect. But we wanted to go the extra mile to have the full flow covered!
7. Be able to run both Effect and fp-ts use cases
\[\*\] `ReaderTaskEither` (we will refer to it as `RTE` later on) was a prerequisite to facilitate the migration to Effect. Why? Conceptually, a `ReaderTaskEither` can be represented as follows:
```ts
ReaderTaskEither<R, E, A>
= Reader<R, TaskEither<E, A>>
= (context: R) => () => Promise<Either<E, A>>
```
If we look at the representation of an effect given on the [official Effect website](https://effect.website/docs/guides/essentials/the-effect-type), we can see that these are very similar concepts (which is something that we will leverage during our migration):
```ts
Effect<A, E, R> ~ (context: Context<R>) => E | A
```
## The Migration Process
Let’s deep dive into the code now! Here are the steps we are going to follow:
* [The program to migrate](#the-program-to-migrate)
* [Create Effect proxies of the ports](#create-effect-proxies-of-the-ports)
* [Rewrite a use case in Effect](#rewrite-a-use-case-in-effect)
* [Convert ports to Effect](#convert-ports-to-effect)
* [Use ManagedRuntime to run Effect usecases](#use-managedruntime-to-run-effect-usecases)
* [Bonus: simplify fp-ts ↔ effect tag mapping management](#bonus-simplify-fpts-%E2%86%94-effect-tag-mapping-management)
To illustrate our migration process, we will focus on an example program that is representative of how our codebase is organized.
_Note: all the code and helpers that will be presented are available in 👉_ [_this repository_](https://github.com/inato/effect-fpts-interop) _👈_
The program to migrate
----------------------
Let say that our domain model is composed of a simple `Foo` class:
```ts
// domain.ts
export class Foo {
constructor(readonly id: string) {}
static make = (id = "random-id") => new Foo(id);
}
```
We define a repository port to get and store a `Foo`:
```ts
// FooRepository.ts
export interface FooRepository {
getById: (id: string) => RTE<unknown, Error, Foo>;
store: (foo: Foo) => RTE<unknown, Error, void>;
}
export interface FooRepositoryAccess {
fooRepository: FooRepository;
}
export declare const FooRepository: {
getById: (id: string) => RTE<FooRepositoryAccess, Error, Foo>;
store: (foo: Foo) => RTE<FooRepositoryAccess, Error, void>;
};
export declare const makeFooRepository: () => Promise<FooRepository>;
```
Note:
* We follow the [module pattern](https://degoes.net/articles/zio-environment#the-module-pattern) when defining the `FooRepositoryAccess` interface to enable context aggregation when composing multiple `ReaderTaskEither`:
```ts
declare const a: RTE<{ serviceA: ServiceA },never,void>
declare const b: RTE<{ serviceB: ServiceB },never,void>
const ab: RTE<{ serviceA: ServiceA; serviceB: ServiceB },never,void>
= rte.flatMap(a,() => b)
```
* We define a [companion object](https://stefan-bauer.online/blog/posts/writing-better-type-script#know-and-use-the-companion-object-pattern) `FooRepository` that exposes the same methods as the repository itself, except that they each require a context with `FooRepositoryAccess`. This makes for more concise code later on:
```ts
const theLongWay: RTE<FooRepositoryAccess, Error, Foo> = pipe(
rte.ask<FooRepositoryAccess>(),
rte.flatMap(({ fooRepository }) => fooRepository.getById('id'))
);
const theEasyWay: RTE<FooRepositoryAccess, Error, Foo>
= FooRepository.getById('id')
```
We also define a service port to transform a `Foo`:
```ts
// TransformFooService.ts
export interface TransformFooService {
transform: (foo: Foo) => RTE<unknown, Error, Foo>;
}
export interface TransformFooServiceAccess {
transformFooService: TransformFooService;
}
export declare const TransformFooService: {
transform: (foo: Foo) => RTE<TransformFooServiceAccess, Error, Foo>;
};
declare const makeTransformFooService: () => Promise<TransformFooService>;
```
Next we can write two use cases: one to create a new `Foo` , and another one to transform a `Foo`:
```ts
// usecases.ts
export const createFooUseCase = (id:string) =>
pipe(
rte.of(Foo.make(id)),
rte.tap(FooRepository.store)
);
export const transformFooUseCase = (id: string) =>
pipe(
FooRepository.getById(id),
rte.flatMap(TransformFooService.transform),
rte.flatMap(FooRepository.store)
);
```
Finally, we can write our `main` that will create all the port adapters and invoke our use cases:
```ts
// index.ts
const main = async () => {
const fooRepository = await makeFooRepository();
const transformFooService = await makeTransformFooService();
await createFooUseCase("my-foo-id")({
transformFooService,
fooRepository,
})();
await transformFooUseCase("my-foo-id")({
transformFooService,
fooRepository,
})();
};
main();
```
Create Effect proxies of the ports
----------------------------------
This step consists in generating new companion objects `FooRepository` and `TransformFooService` for our ports that are exposing an Effect version of the member methods.
First we rename the companion objects, adding a `Fpts` suffix:
```ts
// FooRepository.ts
e̶x̶p̶o̶r̶t̶ ̶d̶e̶c̶l̶a̶r̶e̶ ̶c̶o̶n̶s̶t̶ ̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶:̶ ̶{̶
export declare const FooRepositoryFpts: {
getById: (id: string) => RTE<FooRepositoryAccess, Error, Foo>;
store: (foo: Foo) => RTE<FooRepositoryAccess, Error, void>;
};
// TransformFooService.ts
e̶x̶p̶o̶r̶t̶ ̶d̶e̶c̶l̶a̶r̶e̶ ̶c̶o̶n̶s̶t̶ ̶T̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶:̶ ̶{̶
export declare const TransformFooServiceFpts: {
transform: (foo: Foo) => RTE<TransformFooServiceAccess, Error, Foo>;
};
// usecases.ts
export const createFooUseCase = (id:string) =>
pipe(
rte.of(Foo.make(id)),
r̶t̶e̶.̶t̶a̶p̶(̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶.̶s̶t̶o̶r̶e̶)̶
rte.tap(FooRepositoryFpts.store)
);
export const transformFooUseCase = (id: string) =>
pipe(
F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶.̶g̶e̶t̶B̶y̶I̶d̶(̶i̶d̶)̶,̶
r̶t̶e̶.̶f̶l̶a̶t̶M̶a̶p̶(̶T̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶.̶t̶r̶a̶n̶s̶f̶o̶r̶m̶)̶,̶
r̶t̶e̶.̶f̶l̶a̶t̶M̶a̶p̶(̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶.̶s̶t̶o̶r̶e̶)̶
FooRepositoryFpts.getById(id),
rte.flatMap(TransformFooServiceFpts.transform),
rte.flatMap(FooRepositoryFpts.store)
);
```
Then we use the `portToEffect` helper function to generate the Effect companion objects from the previous companion objects:
```ts
// FooRepository.ts
export const FooRepositoryTag = Context.GenericTag<FooRepository>(
"FooRepository"
);
export const FooRepository = portToEffect(FooRepositoryFpts, {
fooRepository: FooRepositoryTag,
}); // { getById: (id: string) => Effect<Foo, Error, FooRepository> ... }
// TransformFooService.ts
export const TransformFooServiceTag = Context.GenericTag<TransformFooService>(
"TransformFooService"
);
export const TransformFooService = portToEffect(TransformFooServiceFpts, {
transformFooService: TransformFooServiceTag,
}); // { transform: (foo: Foo) => Effect<Foo, Error, TransformFooService> }
```
Rewrite a use case in Effect
----------------------------
At this point we can start using our newly generated Effect companion objects to rewrite the `transformFooUseCase` use case in Effect. Note that we voluntarily leave the `createFooUseCase` use case as is to simulate a migration that is ongoing, as opposed to a “big-bang” migration where we would convert all of our use cases to Effect in one go (much harder and riskier).
```ts
// usecases.ts
export const transformFooUseCase = (id: string) =>
pipe(
F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶F̶p̶t̶s̶.̶g̶e̶t̶B̶y̶I̶d̶(̶i̶d̶)̶,̶
r̶t̶e̶.̶f̶l̶a̶t̶M̶a̶p̶(̶T̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶F̶p̶t̶s̶.̶t̶r̶a̶n̶s̶f̶o̶r̶m̶)̶,̶
r̶t̶e̶.̶f̶l̶a̶t̶M̶a̶p̶(̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶F̶p̶t̶s̶.̶s̶t̶o̶r̶e̶)̶
FooRepository.getById(id),
Effect.flatMap(TransformFooService.transform),
Effect.flatMap(FooRepository.store)
); // Effect<void, Error, TransformFooService | FooRepository>
```
Since we don’t want to impact our `main` program yet, we must maintain an fp-ts version of this use case, for backward compatibility. We can generate it from the Effect version thanks to the `functionToFpts` helper function:
```ts
// usecases.ts
export const transformFooUseCaseFpts = functionToFpts(transformFooUseCase, {
fooRepository: FooRepositoryTag,
transformFooService: TransformFooServiceTag,
}); // RTE<TransformFooServiceAccess & FooRepositoryAccess, Error, void>
// index.ts
const main = async () => {
const fooRepository = await makeFooRepository();
const transformFooService = await makeTransformFooService();
await createFooUseCase("my-foo-id")({
transformFooService,
fooRepository,
})();
a̶w̶a̶i̶t̶ ̶t̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶U̶s̶e̶C̶a̶s̶e̶(̶"̶m̶y̶-̶f̶o̶o̶-̶i̶d̶"̶)̶(̶{̶
await transformFooUseCaseFpts("my-foo-id")({
transformFooService,
fooRepository,
})();
};
main();
```
Convert ports to Effect
-----------------------
Next we convert our `FooRepository` port to Effect directly:
```ts
// FooRepository.ts
export interface FooRepository {
g̶e̶t̶B̶y̶I̶d̶:̶ ̶(̶i̶d̶:̶ ̶s̶t̶r̶i̶n̶g̶)̶ ̶=̶>̶ ̶R̶T̶E̶<̶u̶n̶k̶n̶o̶w̶n̶,̶ ̶E̶r̶r̶o̶r̶,̶ ̶F̶o̶o̶>̶;̶
s̶t̶o̶r̶e̶:̶ ̶(̶f̶o̶o̶:̶ ̶F̶o̶o̶)̶ ̶=̶>̶ ̶R̶T̶E̶<̶u̶n̶k̶n̶o̶w̶n̶,̶ ̶E̶r̶r̶o̶r̶,̶ ̶v̶o̶i̶d̶>̶;̶
getById: (id: string) => Effect.Effect<Foo, Error>;
store: (foo: Foo) => Effect.Effect<void, Error>;
}
```
We can now generate the Effect companion object using `Effect.serviceFunctions`:
```ts
// FooRepository.ts
e̶x̶p̶o̶r̶t̶ ̶c̶o̶n̶s̶t̶ ̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶ ̶=̶ ̶p̶o̶r̶t̶T̶o̶E̶f̶f̶e̶c̶t̶(̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶F̶p̶t̶s̶,̶ ̶{̶
̶ ̶ ̶f̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶:̶ ̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶T̶a̶g̶,̶
̶}̶)̶;̶
export const FooRepository = Effect.serviceFunctions(FooRepositoryTag);
```
Finally, for backward compatibility, we must maintain the fp-ts companion object. We can generate it using the `portToFpts` helper function:
```ts
// FooRepository.ts
e̶x̶p̶o̶r̶t̶ ̶d̶e̶c̶l̶a̶r̶e̶ ̶c̶o̶n̶s̶t̶ ̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶F̶p̶t̶s̶:̶ ̶{̶
̶ ̶ ̶g̶e̶t̶B̶y̶I̶d̶:̶ ̶(̶i̶d̶:̶ ̶s̶t̶r̶i̶n̶g̶)̶ ̶=̶>̶ ̶R̶T̶E̶<̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶A̶c̶c̶e̶s̶s̶,̶ ̶E̶r̶r̶o̶r̶,̶ ̶F̶o̶o̶>̶;̶
̶ ̶ ̶s̶t̶o̶r̶e̶:̶ ̶(̶f̶o̶o̶:̶ ̶F̶o̶o̶)̶ ̶=̶>̶ ̶R̶T̶E̶<̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶A̶c̶c̶e̶s̶s̶,̶ ̶E̶r̶r̶o̶r̶,̶ ̶v̶o̶i̶d̶>̶;̶
̶}̶;̶
export const FooRepositoryFpts = portToFpts(FooRepository, {
fooRepository: FooRepositoryTag,
}); // { getById: (id: string) => RTE<FooRepositoryAccess, Error, Foo>; ... }
```
We do the same for the `TransformFooService` port:
```ts
// TransformFooService.ts
export interface TransformFooService {
t̶r̶a̶n̶s̶f̶o̶r̶m̶:̶ ̶(̶f̶o̶o̶:̶ ̶F̶o̶o̶)̶ ̶=̶>̶ ̶R̶T̶E̶<̶u̶n̶k̶n̶o̶w̶n̶,̶ ̶E̶r̶r̶o̶r̶,̶ ̶F̶o̶o̶>̶;̶
transform: (foo: Foo) => Effect<Foo, Error>;
}
e̶x̶p̶o̶r̶t̶ ̶c̶o̶n̶s̶t̶ ̶T̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶ ̶=̶ ̶p̶o̶r̶t̶T̶o̶E̶f̶f̶e̶c̶t̶(̶T̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶F̶p̶t̶s̶,̶ ̶{̶
̶ ̶ ̶t̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶:̶ ̶T̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶T̶a̶g̶,̶
̶}̶)̶;̶
export const TransformFooService = Effect.serviceFunctions(
TransformFooServiceTag
);
e̶x̶p̶o̶r̶t̶ ̶d̶e̶c̶l̶a̶r̶e̶ ̶c̶o̶n̶s̶t̶ ̶T̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶F̶p̶t̶s̶:̶ ̶{̶
̶ ̶ ̶t̶r̶a̶n̶s̶f̶o̶r̶m̶:̶ ̶(̶f̶o̶o̶:̶ ̶F̶o̶o̶)̶ ̶=̶>̶ ̶R̶T̶E̶<̶T̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶A̶c̶c̶e̶s̶s̶,̶ ̶E̶r̶r̶o̶r̶,̶ ̶F̶o̶o̶>̶;̶
̶}̶;̶
export const TransformFooServiceFpts = portToFpts(TransformFooService, {
fooRepository: FooRepositoryTag,
}); // { transform: (foo: Foo) => RTE<unknown, Error, Foo>; }
```
Note that we have not changed our `main` in this step and it can still be run without a problem.
Use ManagedRuntime to run Effect usecases
-----------------------------------------
In order to run the `transformFooUseCase` as an Effect, we must be able to provide our ports via Layers:
```ts
// FooRepository.ts
e̶x̶p̶o̶r̶t̶ ̶d̶e̶c̶l̶a̶r̶e̶ ̶c̶o̶n̶s̶t̶ ̶m̶a̶k̶e̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶:̶ ̶(̶)̶ ̶=̶>̶ ̶P̶r̶o̶m̶i̶s̶e̶<̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶>̶;̶
export declare const FooRepositoryLive: Layer.Layer<FooRepository>;
// TransformFooService.ts
d̶e̶c̶l̶a̶r̶e̶ ̶c̶o̶n̶s̶t̶ ̶m̶a̶k̶e̶T̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶:̶ ̶(̶)̶ ̶=̶>̶ ̶P̶r̶o̶m̶i̶s̶e̶<̶T̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶>̶;̶
declare const TransformFooServiceLive: Layer.Layer<TransformFooService>;
```
Next we can create a `ManagedRuntime` and extract all the ports from the runtime context using the `contextToFpts` helper:
```ts
// index.ts
const main = async () => {
c̶o̶n̶s̶t̶ ̶f̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶ ̶=̶ ̶a̶w̶a̶i̶t̶ ̶m̶a̶k̶e̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶(̶)̶;̶
c̶o̶n̶s̶t̶ ̶t̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶ ̶=̶ ̶a̶w̶a̶i̶t̶ ̶m̶a̶k̶e̶T̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶(̶)̶;̶
const runtime = ManagedRuntime.make(
Layer.mergeAll(FooRepositoryLive, TransformFooServiceLive)
);
const { context } = await runtime.runtime();
const { fooRepository, transformFooService } = contextToFpts(context, {
fooRepository: FooRepositoryTag,
transformFooService: TransformFooServiceTag,
});
await createFooUseCase("my-foo-id")({
transformFooService,
fooRepository,
})();
await transformFooUseCaseFpts("my-foo-id")({
transformFooService,
fooRepository,
})();
};
main();
```
Finally, we can use the runtime to run the Effect `transformFooUseCase`:
```ts
// index.ts
const main = async () => {
const runtime = ManagedRuntime.make(
Layer.mergeAll(FooRepositoryLive, TransformFooServiceLive)
);
const { context } = await runtime.runtime();
const { fooRepository, transformFooService } = contextToFpts(context, {
fooRepository: FooRepositoryTag,
transformFooService: TransformFooServiceTag,
});
await createFooUseCase("my-foo-id")({
transformFooService,
fooRepository,
})();
a̶w̶a̶i̶t̶ ̶t̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶U̶s̶e̶C̶a̶s̶e̶F̶p̶t̶s̶(̶"̶m̶y̶-̶f̶o̶o̶-̶i̶d̶"̶)̶(̶{̶
̶t̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶,̶
̶f̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶,̶
}̶)̶(̶)̶;̶
await runtime.runPromise(transformFooUseCase("my-foo-id"));
};
main();
```
Note that, once again, we left the `createFooUseCase` use case as is to show that we can be in a hybrid state where only part of the use cases have been migrated to Effect.
Bonus: simplify fp-ts ↔ effect tag mapping management
-----------------------------------------------------
All of the helpers we have used throughout this migration require a mapping object to go from the key name of the fp-ts port Access interface (eg `transformFooService` of `TransformFooServiceAccess`) to the `Tag` of the corresponding Effect port. For example:
```ts
contextToFpts(context, {
fooRepository: FooRepositoryTag,
transformFooService: TransformFooServiceTag,
});
```
This mapping is essential for all the helpers to work correctly. It is not ideal to have to craft them like that all the time. To help us with that, we introduce:
```ts
const FptsConvertibleId = Symbol();
interface FptsConvertible<T extends string> {
[FptsConvertibleId]: T;
}
```
We can now embed this conversion information at the type level of our ports:
```ts
// FooRepository.ts
export interface FooRepository extends FptsConvertible<"fooRepository"> {
getById: (id: string) => Effect.Effect<Foo, Error>;
store: (foo: Foo) => Effect.Effect<void, Error>;
}
// TransformFooService.ts
export interface TransformFooService
extends FptsConvertible<"transformFooService"> {
transform: (foo: Foo) => Effect<Foo, Error>;
}
```
The first thing we can do with this is to simplify the definition of Access interfaces using a type helper `FptsAccess`:
```ts
// FooRepository.ts
export interface FooRepositoryAccess extends FptsAccess<FooRepository> {}
// TransformFooService.ts
export interface TransformFooServiceAccess
extends FptsAccess<TransformFooService> {}
```
And we can also define smaller atomic mapping objects using a new helper `getFptsMapping`:
```ts
// FooRepository.ts
const FooRepositoryFptsMapping = getFptsMapping(
FooRepositoryTag,
"fooRepository"
); // { fooRepository: FooRepositoryTag }
// TransformFooService.ts
const TransformFooServiceFptsMapping = getFptsMapping(
TransformFooServiceTag,
"transformFooService"
); // { transformFooService: TransformFooServiceTag }
```
Note: It looks like we are once again typing the key `"fooRepository"` or `"transformFooService"` but in fact, the function `getFptsMapping` is type-safe so that given `FooRepositoryTag` as first argument, only the string `"fooRepository"` is valid as second argument. So your code editor will autocomplete it for you. Moreover, the compiler will break if you change the definition in the `FptsConvertible` so it is not really an additional burden.
We can now combine these two mapping objects when calling `contextToFpts` or any other helper:
```ts
contextToFpts(context, {
f̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶:̶ ̶F̶o̶o̶R̶e̶p̶o̶s̶i̶t̶o̶r̶y̶T̶a̶g̶,̶
t̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶:̶ ̶T̶r̶a̶n̶s̶f̶o̶r̶m̶F̶o̶o̶S̶e̶r̶v̶i̶c̶e̶T̶a̶g̶,̶
...FooRepositoryFptsMapping,
...TransformFooServiceFptsMapping,
});
```
## Conclusion
Our objective of being able to write any new use case or port using Effect was accomplished in 2 months (working around 10% of our time on it)!
Teamwork was definitely a big part of this success: first, we have to mention [Stephane Ledorze](https://medium.com/u/c67fddd7ca1c?source=post_page-----b71acd0c5640--------------------------------) as he migrated all our repositories single-handedly and gave us great advice on how to define our migration strategy. We handled the rest with the whole team during dedicated “tech sessions” that we do every Wednesday afternoon at Inato: during those sessions, we stop delivering features to be able to focus on purely tech subjects, which was a great occasion to migrate the many ports we had to handle and onboard the team on Effect.
As we’re writing this article, we have around 150 full Effect use cases. The rest of the existing use cases will be migrated on the go whenever we need to update them!
We’re already seeing great improvements: for example, implementing rate limiting with just a few lines of code with Effect, whereas we needed a big amount of code to do it with fp-ts. We’re eager to leverage even more the Effect ecosystem now that we have officially migrated to it!
We hope this article motivated you to take the leap from fp-ts to Effect, don’t hesitate to comment if you have any questions or comments!
_This article was written by_ [_Jeremie Dayan_](https://medium.com/u/32aa24b2222c?source=post_page-----b71acd0c5640--------------------------------) _and_ [_Laure Retru-Chavastel_](https://medium.com/u/8fb677159f4f?source=post_page-----b71acd0c5640--------------------------------)
| laurerc |
1,876,783 | Top External Email Providers for Your Strapi Application | Email is one of the most critical communication channels between businesses and their users. It can... | 0 | 2024-06-04T14:23:55 | https://strapi.io/blog/top-external-email-providers-for-your-strapi-application | strapi, webdev, email, nextjs | Email is one of the most critical communication channels between businesses and their users. It can be used to send notifications, updates, newsletters, offers, or other communications. [A global study](https://sendgrid.com/en-us/why-sendgrid) showed that 83% of people prefer to receive communications from businesses over email.
Going with an external email provider has many benefits, and Strapi offers a wide array of options and ways to integrate them into your application.
This article aims to simplify your decision-making process by gathering all the necessary resources in one place. We will compare these providers, helping you choose the one that best suits your application.
Additionally, we will provide code examples and guide you through integrating this functionality into your Strapi projects, making the process more convenient and straightforward.
## What is Strapi?
Strapi is an open-source headless CMS (Content Management System) that allows you to quickly and easily create an API for content-rich applications. Unlike traditional CMS, it decouples the backend from the frontend, allowing you to pick whatever UI framework you're most comfortable with, meaning you can better create faster and more personalized experiences for your customers.
## External Email Providers
While Strapi provides us with powerful ways to manage content and user data, using an external email provider comes with several benefits.
### Benefits of Email Providers
* They are reliable as they invest heavily in infrastructure, anti-spam measures, and deliverability
* They are scalable: as your application grows, so too will the number of emails you send.
* An external email provider has the ability to handle large email volumes without compromising on performance or deliverability,
* They get rid of the need for your developers to manage email server infrastructure.
* They also offer a lot of different features for you to take advantage of and follow best practices when it comes to security.
### How to choose
Strapi's flexibility allows us to combine it with a wide range of email service providers, such as **Mailgun**, **Resend**, **Sendgrid**, **Nodemailer**, and **AWS SES**. We'll review and compare the advantages of each.
There are many things to consider when selecting an email service provider. For instance, imagine an up-and-coming e-commerce platform experiencing rapid growth in its customer base and transactional email volume. In this case, it may want to go for a more reliable service, such as AWS SES, which will ensure reliable handling of increased email volume without compromising on deliverability and speed.
On the other hand, let's consider a more content-focused website powered by Strapi, where personalised news letters and email campaigns are key to the business's success. In this case, a feature-rich solution like Sendgrid, with its segmentation, A/B testing, and campaign analytics, would be a great fit. This example underscores the importance of first understanding your project requirements and then aligning them with the most suitable provider, a strategy that can significantly enhance your email marketing efforts.
### Advantages of Using Strapi with Email Providers
Strapi provides a great content management system; we can easily pull in user-specific data, such as names or preferences, and inject them into our email templates to make them more engaging. Strapi is scalable and offers a user-friendly interface with built-in API capabilities, which will help streamline the development process and reduce the time and effort it would cost to build something similar with another framework such as [express.js](https://expressjs.com/).
Let's examine each provider individually to see what they offer and how they compare to each other.
## Resend Email Provider
First up, we have [Resend](https://www.resend.com/), which is an email service provider that helps optimise transactional emails and email campaigns. It offers a collection of tools to simplify the process of sending marketing emails, making sure they reach the correct recipients effectively and not the spam folder. With Resend's free plan, you can send 100 emails per day.
### Resend Features
Resend has a test mode, so you can experiment with their API without the risk of sending accidental emails. They also offer modular webhooks, so you can receive real-time notifications letting you know when emails are delivered, opened, bounced, or even if a link inside is clicked. Another great feature of Resend is that you can develop email templates with React, getting rid of the need to use confusing table layouts.
The paid version of Resend offers a multi-region option, which allows you to send emails from the region closest to your users, so you can be sure they receive your emails in a timely and efficient manner. They also offer a dedicated IP for those companies who wish to avoid a shared IP and maintain complete control over their reputation.
Resend also offers open and click tracking, which allows developers to track engagement and tune their sending approach. This allows you to identify which of your customers or users are most interested in receiving your messages.
Key features:
- Multi-language support.
- CRM integrations with Hubspot, ActiveCampaign, etc..
- Link tracking, open & click tracking.
- Behaviour-based subscriber segmentation.
- Automated welcome and follow-up flows.
- Inbox placement and spam testing.
## Strapi and Resend Integration
### Install Strapi
Create a folder that will contain the source code for the project. Open a terminal and navigate to a directory of your choice, and run the below commands:
```bash
mkdir strapi-email-tutorial
cd strapi-email-tutorial
```
Now, let's create our Strapi project with the command below:
```bash
npx create-strapi-app@latest strapi-email
```
You can enter `y` to proceed and select `quickstart`
Once that has finished installing, you should see a confirmation such as the below:
### Obtain Resend API Key
Now before we write any code, let's obtain an API key for Resend. Go to the following address: https://resend.com/overview, sign in, and get the API key; there should be an option to generate one on the dashboard.
Now navigate to the route of the strapi project in the terminal and run the following to install the [Strapi plugin for Resend](https://market.strapi.io/providers/strapi-provider-email-resend):
```bash
yarn add strapi-provider-email-resend
```
Copy the following code and replace with the code in the `config/plugins.js`. Make sure to add the `RESEND_API_KEY` variable to your `.env` file, with the value being the key you generated with Resend. Also, use your normal email account for the `defaultFrom` and `defaultTo` fields.
```js
module.exports = ({ env }) => ({
email: {
config: {
provider: 'strapi-provider-email-resend',
providerOptions: {
apiKey: env('RESEND_API_KEY'), // Required
},
settings: {
defaultFrom: 'YOUR_EMAIL_HERE',
defaultReplyTo: 'YOUR_EMAIL_HERE',
},
}
},
});
```
### Generate Custom Email API
Now we have the Resend plugin setup in our project we should be able to use the package in one of our API's, let's first generate a custom API and write the code, navigate to the terminal and run the below command in the root directory:
```bash
yarn strapi generate
```
This will begin the process of generating our own custom API. Choose the API option, give it the name `email-test`, and select **"no"** when it asks us if this is for a plugin.
Inside the `src` directory, If we check the `api` directory in our code editor, we should see the newly created API for `email-test` with it's route, controller, and service.
### Enable Public API Access
By default, Strapi requires authentication to query our API and receive information, but that is outside the scope of this tutorial. Instead, we will make our API publicly accessible. We can find more about authentication and REST API in this [blog post](https://strapi.io/blog/guide-on-authenticating-requests-with-the-rest-api).
Navigate to the Strapi admin dashboard, From the left sidebar, click on Settings. Again, on the left panel under **USERS & PERMISSIONS PLUGIN**, click on **Roles**, then click on **Public** from the table on the right. Now scroll down, click on **Email-test**, and tick **Select all** then save in the top right which will allow us to hit this endpoint without authentication.
### Modify Custom Email Route
Now under the `src/api/email-test` directory, locate the `routes` directory. There you will find `email-test.js` file. Replace the code inside this file with the following:
```js
module.exports = {
routes: [
{
method: "POST",
path: "/email-test/exampleAction",
handler: "email-test.exampleAction",
config: {
policies: [],
middlewares: [],
},
},
],
};
```
### Modify Custom Email Controller
Change the code in the `src/api/controllers/email-test.js` file to the following:
```js
module.exports = {
exampleAction: async (ctx, next) => {
try {
const res = await strapi
.service("api::email-test.email-test")
.emailService(ctx);
ctx.body = res.message;
} catch (err) {
ctx.body = err;
}
},
};
```
### Modify Custom Email Service
Also, change the code in the `src/api/services/email-test.js` directory to the following:
```js
module.exports = ({ strapi }) => ({
emailService: async (ctx) => {
try {
const input = ctx.request.body.data?.input;
const emailTo = ctx.request.body.data?.emailTo;
await strapi.plugins["email"].services.email.send({
from: "onboarding@resend.dev",
to: emailTo,
subject: "Hello World",
html: `<p>${input}</p>`,
});
return {
message: "Email sent!",
};
} catch (err) {
ctx.body = err;
}
},
});
```
You should now be able to test the Resend email integration with the following code in your terminal, make sure you replace `YOUR_EMAIL_HERE` with your actual email address.
```bash
curl -X POST \
http://localhost:1337/api/email-test/exampleAction \
-H 'Content-Type: application/json' \
-d '{
"data": {
"input": "This is a test email to check the integration.",
"emailTo": "YOUR_EMAIL_HERE"
}
}'
```
## Sendgrid Email Provider
[Sendgrid](https://sendgrid.com/) focuses on email delivery, automation and real-time analytics it allows users to send transactional and marketing emails at scale, it offers a 99% deliverability rate and has AI-powered deliverability, and email marketing, email automation and multichannel advertising products. With Sengrids free plan, you can send 100 emails per day.
### Sendgrid Features
Key features include
- Dedicated IP addresses to improve deliverability
- Integrations with e-commerce platforms like shopify and BigCommerce
- A/B testing capabilities
- Automated workflows with templates
- Contact segmentation for targeted campaigns
- In-depth email and click analytics
### Sendgrid vs Resend
In terms of deliverability, Sendgrid provides dedicated IPs for improved sender reputation and inboxing; it has average industry deliverability rates compared to Resend, which heavily optimises campaigns for maximum inbox placement, maintaining exceptional IP reputations. Both platforms provide good deliverability that meets or exceeds industry standards.
When we look at Automation and workflow for campaigns both Sendgrid and Resend provide easy to use drag-and-drop workflow builders to create sequences and triggers. For multi-step campaigns, Resend seems to be the best at building conditional logic across workflows but Sendgrid also allows multi-path workflows. In terms of segmentation and targeting Resend has the strongest features for dynamically inserting subscriber data into emails, Sendgrid allows some personalisation. Sendgrid imposes contact limits whereas Resend allows unlimited segments.
Sendgrid seems to have the most integrations and plugins available, with extensive documentation, Resend has fewer out of the box integrations.
In terms of analytics Sendgrid has the most powerful segmentation capabilities for analyzing your email data, allowing you to deeply filter your reports, while Resend visual reports and insights are simplest to digest Sendgrid provides greater raw data access.
As a conclusion to this quick comparison between these two email providers I think Resend is best for beginners, with easy workflow automation, campaign building and visual reporting whereas Sendgrid provides the greatest email volume scalability and add-on integrations, It also offers robust targeting and analytics for experts.
## Strapi and Sendgrid Integration
We can use a [Strapi plugin](https://market.strapi.io/providers/@strapi-provider-email-sendgrid) to integrate Sendgrid just like we did with Resend, first of all let's remove the Resend integration, navigate to your terminal and run the below command in the root directory.
### Install Sendgrid Strapi Plugin
```bash
yarn remove strapi-provider-email-resend
```
With that package removed, run the below to install the [Sendgrid plugin](https://market.strapi.io/providers/@strapi-provider-email-sendgrid).
```bash
yarn add @strapi/provider-email-sendgrid
```
### Modify Plugin Configuration
Now navigate to your `config/plugins.js` file and replace the code in there with the below:
```js
module.exports = ({ env }) => ({
email: {
config: {
provider: "sendgrid",
providerOptions: {
apiKey: env("SENDGRID_API_KEY"), // Required
},
settings: {
defaultFrom: "YOUR_EMAIL_HERE",
defaultReplyTo: "YOUR_EMAIL_HERE",
},
},
},
});
```
Make sure to head on over to [sendgrid](https://sendgrid.com/) and create a free account, then navigate to settings and create an API key.
### Add Sendgrid API Key to Environment Variable
You can then add it to your `.env` file so the above integration works.
```bash
SENDGRID_API_KEY=YOUR_SENDGRID_API_KEY
```
### Modify Email Service
Now restart the project and the only thing you will need to change is the `from` field in the `src/api/email-test/services/email-test.js` file as below:
```js
await strapi.plugins["email"].services.email.send({
from: "YOUR_EMAIL_HERE",
to: email,
subject: "Hello World",
html: `<p>${input}</p>`,
});
```
### Test Sendgrid Email Provider
Seeing that the rest of the code is in place from the previous Resend integration if you run the below code in the terminal it should send an email using Sendgrid.
```bash
curl -X POST \
http://localhost:1337/api/email-test/exampleAction \
-H 'Content-Type: application/json' \
-d '{
"data": {
"input": "This is a test email to check the integration.",
"emailTo": "YOUR_EMAIL_HERE"
}
}'
```
## Nodemailer Email Provider
[Nodemailer](https://nodemailer.com/) is a module developed in 2010 to simplify email sending. It has a flexible and easy-to-use interface and utilizes various email services or SMTP servers, including EmailEngine. This self-hosted email gateway allows you to make REST requests against IMAP and SMTP servers. This makes Nodemailer more flexible, as it gives you a choice to use different email services or SMTP servers based on your requirements. Note that while it is free to use Nodemailer, you may incur costs depending on the email service you choose.
### Differentiation From Hosted Email Services
Nodemailer allows us to send emails directly from our Node applications. Hosted email service providers like Sendgrid, on the other hand, differ. For example, hosted email services provide a dedicated and reliable infrastructure, typically offer a broad range of features beyond sending basic emails, are easier to use, and will more often than not operate on some sort of subscription-based model.
So in comparison, just to recap, Nodemailer gives us flexibility in choosing the email transport method and supports various options: `SMTP`,`sendmail`, `Amazon SES`, and more. Nodemailer focuses on core email sending functionality, allowing developers to send emails programatically from their applications. It gives us some basic features like email composition, attachment support, and error handling.
### Strapi and NodeMailer Integration
For Nodemailer to work we will need to provide it with an `SMTP` service and the auth credentials, let's use `Ethereal` which is a fake `SMTP` service. navigate to - https://ethereal.email and click the button to create an Ethereal account.
Now you should be directed to a page like the above, jot down the credentials somewhere as we will use these later.
### Install NodeMailer Strapi Plugin
Now navigate back to your terminal and go to the root directory. Remove the previous plugin with the following command:
```bash
yarn remove @strapi/provider-email-sendgrid
```
And add the Strapi n[odemailer plugin](https://www.npmjs.com/package/@strapi/provider-email-nodemailer) by running the below:
```bash
yarn add @strapi/provider-email-nodemailer
```
### Update Configuration
Once that has finished let's add the plugin integration code to the `config/plugins.js` file, remember you will need to create the `SMTP_USERNAME` and `SMTP_PASSWORD` variables in your .env file and fill their values with the info you obtained from ethereal mail:
```js
module.exports = ({ env }) => ({
email: {
config: {
provider: 'nodemailer',
providerOptions: {
host: env('SMTP_HOST', 'smtp.ethereal.email'),
port: env('SMTP_PORT', 587),
auth: {
user: env('SMTP_USERNAME'),
pass: env('SMTP_PASSWORD'),
},
},
settings: {
defaultFrom: 'hello@example.com',
defaultReplyTo: 'hello@example.com',
},
},
},
});
```
### Update Email Service
Update the email code in the `src/api/email-test/services/email-test.js` file with the following:
```js
await strapi.plugins["email"].services.email.send({
from: "YOUR_ETHEREAL_MAIL",
to: email,
subject: "Hello World",
html: `<p>${input}</p>`,
});
```
Run the below code in your terminal to see Nodemailer in action remembering to replace the emailTo with your email:
### Test NodeMailer Email Provider
```bash
curl -X POST \
http://localhost:1337/api/email-test/exampleAction \
-H 'Content-Type: application/json' \
-d '{
"data": {
"input": "This is a test email to check the integration.",
"emailTo": "YOUR_EMAIL_HERE"
}
}'
```
As ethereal is a fake SMTP provider, it won't actually send the email, but you can navigate back to ethereal and look under the `Messages` tab (this tab captures all of the outgoing and incoming mail), and you will be able to see the outgoing email that Nodemailer just connected with ethereal to send, confirming that the integration was successful.
## AWS SES Email Provider
Amazon Simple Email Service (SES) is a cloud-based email service provided by Amazon Web Services. It allows you to send transactional, marketing, and notification emails, much like Resend and Sendgrid. It offers an extensive set of features, such as improved inbox deliverability with a virtual deliverability manager so you can reach inboxes instead of spam or junk, flexible deployment options such as shared or dedicated IP addresses, a console so you can view important analytics such as number of sends, opens, clicks, bounces, etc, and it supports all industry-standard authentication mechanisms.
### AWS SES Features
key features include
- Reliable Email Sending
- High Deliverability
- SMTP and API-based Sending
- Email Templates
- Bounce and Complaint Handling
- Email Analytics
- Dedicated IP Addresses
- Integration with AWS Services
- Cost-effective Pricing
In terms of pricing, Amazon SES will allow you to send 3000 messages per month for 12 months completely free. In comparison, Resend and Sendgrid offer the same amount of emails but don't give you a 12-month time period, suggesting you can stay on their free tier until you scale up. However, upon closer inspection, Amazon's paid service is actually cheaper. 20,000 emails a month will cost you $2 per month, while with Resend or Sendgrid, that will set you back $20 per month. This suggests that if you intend to scale rapidly, Amazon would be a better choice.
I would say one of the main standout features of AWS SES is its ability to integrate with other services on the AWS platform. You can configure it to be used on a site hosted on EC2 and set up some custom notifications with Simple Notification Service (SNS), for example. It might also be worth mentioning if you plan to use AWS EC2 for your hosting. AWS gives you the first 62,000 emails per month absolutely free.
### Strapi and AWS SES Integration
Now to integrate this service with Strapi we can use a plugin but we will also need to create an AWS account to configure Amazon SES and get the correct credentials to connect.
First navigate to AWS at - https://aws.amazon.com create an account and then on the dashboard search for Amazon SES, click get started and then you should be directed to a dashboard like this
You can just add your email address here, then click next.
Here you can just add the domain of your email and click next on all of the following options.
Then you can click in the top right dropdown and navigate to `security credentials` scroll down and click to create an access key then copy the Access key and the Secret somewhere safe.
### Install NodeMailer Strapi Plugin
Now you can navigate to the root of your project and remove the previous integration with the following command:
```bash
yarn remove @strapi/provider-email-nodemailer
```
And then add the [AWS SES Strapi plugin](https://market.strapi.io/providers/@strapi-provider-email-amazon-ses) by running the below command
```bash
yarn add @strapi/provider-email-amazon-ses
```
### Update Configuration
Navigate to `./config/plugins.js` and paste in the following integration code
```js
module.exports = ({ env }) => ({
email: {
config: {
provider: 'amazon-ses',
providerOptions: {
key: env('AWS_SES_KEY'),
secret: env('AWS_SES_SECRET'),
amazon: 'https://email.us-east-1.amazonaws.com',
},
settings: {
defaultFrom: 'myemail@protonmail.com',
defaultReplyTo: 'myemail@protonmail.com',
},
},
},
});
```
### Update Email Service
Now add the AWS access and secret key you got earlier to the `.env` file with the relevant variable names.
And in `src/api/email-test/services/email-test.js` file, you will just need to change the email from the previous ethereal email to the one you used for SES:
```js
await strapi.plugins["email"].services.email.send({
from: "YOUR_EMAIL_HERE",
to: email,
subject: "Hello World",
html: `<p>${input}</p>`,
});
```
Now that we've set up the integration we can use this curl command in the terminal to simulate the api call and watch Amazon SES in action
### Test AWS SES Provider
```bash
curl -X POST \
http://localhost:1337/api/email-test/exampleAction \
-H 'Content-Type: application/json' \
-d '{
"data": {
"input": "This is a test email to check the integration.",
"emailTo": "YOUR_EMAIL_HERE"
}
}'
```
## Mailgun Email Provider
Like the other email automation services we have seen so far, [Mailgun](https://www.mailgun.com/) offers all of the usual features, such as email sending, tracking, and analytics. What sets it apart from the crowd is its email validation service, which ensures that the email addresses you're sending mail to actually exist, this helps overall email deliverability. For instance, if you integrated this into a contact form, you could use this feature for real-time validation, and the user would be quickly notified if they entered an invalid email address.
Mailgun offers a free plan which allows you to send up to 5000 emails per month (to five authorized users of your choice) but then on the paid plan it jumps to 50,000 emails per month priced at $35 per month, clearly Mailgun free tier has limited features and it's paid plan is a little on the pricey side.
### Mailgun Features
- Email sending via API or SMTP integration
- Email parsing and routing for inbound emails
- Email validation to maintain a clean email list
- Comprehensive email tracking and analytics
- Customizable email templates for personalized campaigns
- A/B testing to optimize email content and performance
- Developer-friendly APIs, SDKs, and libraries
- Scalable infrastructure for handling large email volumes
- Compliance with email regulations (e.g., GDPR, CAN-SPAM)
- Security features such as SPF/DKIM authentication and TLS encryption
### Strapi and Mailgun Integration
To integrate Mailgun we can use the Strapi plugin to proceed, but first let's generate the api key we will need to connect to mailgun, head over to the following - https://www.mailgun.com and click "Get started for free" in the top right for Mailgun login.
Go through the mailgun login or account creation process and once that's done you should be able to scroll down on the dashboard and select your domain.
Then if you click the dropdown for your account in the top right and click on "API security", and then you can scroll down to see your api key.
### Install Mailgun Strapi Plugin
Now navigate to the root of your project in the terminal and remove the previous plugin with the below command.
```bash
yarn remove @strapi/provider-email-amazon-ses
```
And add the [Mailgun Strapi plugin](https://market.strapi.io/providers/@strapi-provider-email-mailgun) with the following:
``` bash
yarn add @strapi/provider-email-mailgun
```
### Update Configuration
Navigate to `./config/plugins.js` and paste in the following integration code
``` js
module.exports = ({ env }) => ({
email: {
config: {
provider: 'mailgun',
providerOptions: {
key: env('MAILGUN_API_KEY'), // Required
domain: env('MAILGUN_DOMAIN'), // Required
url: env(https://api.mailgun.net'), //Optional. If domain region is Europe use 'https://api.eu.mailgun.net'
},
settings: {
defaultFrom: 'myemail@protonmail.com',
defaultReplyTo: 'myemail@protonmail.com',
},
},
}
});
```
Making sure you add the relevant variable to your `.env` file.
Now you should be all set to test out the integration by running the below code in your terminal
### Test Mailgun Email Provider
```bash
curl -X POST \
http://localhost:1337/api/email-test/exampleAction \
-H 'Content-Type: application/json' \
-d '{
"data": {
"input": "This is a test email to check the integration.",
"emailTo": "YOUR_EMAIL_HERE"
}
}'
```
## Choosing the Right Email Provider
First of all, thanks to the flexibility of Strapi, there was a quick and easy way to integrate every email provider we looked at, so complexity in integration, at least where your Strapi projects are concerned, isn't an issue. All the options we looked at are great, and the one you pick will ultimately come down to your priorities and the number of emails you're sending.
Amazon SES stands out if cost-effectiveness is your primary consideration, especially if you're hosting on AWS infrastructure like an EC2 instance and anticipate rapid scaling of your service. If your focus is on enhancing your marketing efforts, then the choice is between Sendgrid and Resend, both of which offer similar features. Resend, with its user-friendly interface and easy-to-understand analytics dashboard, may be more suitable for beginners. On the other hand, Sendgrid, with its more complex and detailed features, provides more granular data.
If customizability is important and you're only concerned with basic send and receive functionality, then Nodemailer is a good option. Lastly, we have Mailgun, which isn't the cheapest solution and doesn't have the most features; if email validation is essential, it could be an option.
## Conclusion
To recap, the benefits of integrating with an external email provider are numerous and include the following: enhanced email functionality, improved deliverability, scalability, analytics, and customization.
This article will help you select the right email provider for your product or business. If you still need to decide, experiment with different providers and see what works best.
## Additional Resources
* [Github link](https://github.com/gitChimp88/strapi-email) to the complete code.
| gitchimp88 |
1,873,237 | Heroku for ChatOps: Start and Monitor Deployments from Slack | In our last two articles, we explored how to configure CI/CD for Heroku using Heroku pipelines. When... | 0 | 2024-06-04T14:22:23 | https://dev.to/thawkin3/heroku-for-chatops-start-and-monitor-deployments-from-slack-3kle | webdev, programming, devops, heroku | In our last two articles, we explored how to [configure CI/CD for Heroku using Heroku pipelines](https://dev.to/thawkin3/how-i-finally-got-all-my-cicd-in-one-place-getting-my-cicd-act-together-with-heroku-flow-4fo2). When viewing a pipeline within the Heroku dashboard, you can easily [start a deployment or promote your code from one environment to the next](https://dev.to/thawkin3/going-with-the-flow-for-cicd-heroku-flow-with-gitflow-28oj) with the click of a button. From the dashboard, you can monitor the deployment and view its progress.
This all works really well, assuming that you have Heroku open in your browser. But, what if you wanted to do it all from Slack?
Software engineers use a lot of apps at work. Throughout the day, we are constantly bouncing between Zoom meetings, Jira tasks, Slack conversations, GitHub, email, our calendar, and our IDE. This context switching can be exhausting and also lead to a lot of visual clutter on our monitors.
Sometimes, it’s nice to just live in Slack, and that’s why many tools offer Slack integrations. With these Slack integrations, you can monitor various processes and even use shortcut commands to trigger actions.
[Heroku ChatOps](https://devcenter.heroku.com/articles/chatops), the Heroku Slack integration, allows you to start and monitor deployments directly from Slack. In this article, we’ll explore some of the Slack commands it offers.
---
## Getting Started
If you’d like to follow along throughout this tutorial, you’ll need a Heroku account and a GitHub account. You can [create a Heroku account here](https://www.heroku.com/), and you can [create a GitHub account here](https://github.com).
The demo app that we will use with our Heroku pipeline in this article is deployed to Heroku, and [the code is hosted on GitHub](https://github.com/thawkin3/heroku-flow-demo).
---
## Create Our Heroku Pipeline
We won’t go through the step-by-step process for creating a Heroku pipeline in this article. Refer to these articles for a walkthrough of creating a Heroku pipeline:
- [How to create a Heroku pipeline with a staging and production app and a single main branch](https://dev.to/thawkin3/how-i-finally-got-all-my-cicd-in-one-place-getting-my-cicd-act-together-with-heroku-flow-4fo2)
- [How to create a Heroku pipeline with a staging and production app with a dev branch and a main branch](https://dev.to/thawkin3/going-with-the-flow-for-cicd-heroku-flow-with-gitflow-28oj)
You can also read the [Heroku docs for Heroku pipelines](https://devcenter.heroku.com/articles/pipelines).
Configuring your Heroku pipeline includes the following steps:
1. Create a GitHub repo.
2. Create a Heroku pipeline.
3. Connect the GitHub repo to the Heroku pipeline.
4. Add a staging app to the pipeline.
5. Add a production app to the pipeline.
The other activities that you’ll see in those articles, such as configuring review apps, Heroku CI, or automated deployments are optional. In fact, for the purposes of this demo, I recommend _not_ configuring automated deployments, since we’ll be using some Slack commands to start the deployments.
When you’re done, you should have a Heroku pipeline that looks something like this:
<figcaption>Example Heroku pipeline</figcaption>
---
## Connect to Slack
Now that you have your Heroku pipeline created, it’s time for the fun part: integrating with Slack. You can [install the Heroku ChatOps Slack app here](https://chatops.heroku.com/auth/slack_install).
Clicking that link will prompt you to grant the Heroku ChatOps app permission to access your Slack workspace:
<figcaption>Grant Heroku ChatOps access to your Slack workspace</figcaption>
After that, you can add the Heroku ChatOps app to any Slack channel in your workspace.
<figcaption>Add the Heroku ChatOps app</figcaption>
After adding the app, type `/h login` and hit Enter. This will prompt you to connect your Heroku and GitHub accounts. You’ll see several Heroku OAuth and GitHub OAuth screens where you confirm connecting these accounts.
_(As a personal anecdote, I found that it took me several tries to connect my Heroku account and my GitHub account. It may be due to having several Slack workspaces to choose from, but I’m not sure.)_
After connecting your Heroku account and your GitHub account, you’re ready to start using Heroku in Slack.
<figcaption>Connect your Heroku and GitHub accounts</figcaption>
---
## View All Pipelines
To view all deployable pipelines, you can type `/h pipelines`:
<figcaption>View all pipelines</figcaption>
---
## View Pipeline Info
To see information about any given pipeline, type `/h info <PIPELINE_NAME>`. (Anything you see in angle brackets throughout this article should be replaced by an actual value. In this case, the value would be the name of a pipeline — for example, “heroku-flow-demo-pipeline”.)
<figcaption>View pipeline info</figcaption>
---
## View Past Releases
To view a history of past releases for any given pipeline, type `/h releases <PIPELINE_NAME>`.
<figcaption>View past releases</figcaption>
This command defaults to showing you past releases for the production app, so if you want to see the past releases for the staging app, you can type `/h releases <PIPELINE_NAME> in <STAGE_NAME>`, where `<STAGE_NAME>` is “staging”.
<figcaption>View past staging releases</figcaption>
---
## Deploy to Staging
Now that we know which pipelines are available, we can see information about any given pipeline along with when the code was last released for that pipeline. We’re ready to trigger a deployment.
Most engineering organizations have a Slack channel (or channels) where they monitor deployments. Imagine being able to start a deployment right from that channel and monitor it as it goes out! That’s exactly what we’ll do next.
To start a deployment to your staging environment, type `/h deploy <PIPELINE_NAME> to <STAGE_NAME>`, where `<STAGE_NAME>` is “staging”.
<figcaption>Deploy to staging</figcaption>
After running that command, an initial message is posted to communicate that the app is being deployed. Shortly after, you’ll also see several more messages, this time in a Slack thread on the original message:
<figcaption>Slack messages sent when deploying to staging</figcaption>
If you want to verify what you’re seeing in Slack, you can always check the Heroku pipeline in your Heroku dashboard. You’ll see the same information: The staging app has been deployed!
<figcaption>Staging app shown in the Heroku dashboard</figcaption>
---
## Promote to Production
Now, let’s promote our app to production. Without the Slack commands, we _could_ navigate to our Heroku pipeline, click the “Promote to production” button, and then confirm that action in the modal dialog that appears. However, we’d prefer to stay in Slack.
To promote the app to production from Slack, type `/h promote <PIPELINE_NAME>`.
<figcaption>Promote to production</figcaption>
Just like with the staging deployment, an initial Slack message will be sent, followed by several other messages as the production deployment goes out:
<figcaption>Slack messages sent when promoting to production</figcaption>
And — voilà — the latest changes to the app are now in production!
---
## Conclusion
Now you can start and monitor Heroku app deployments all from Slack — no need to context switch or move between multiple apps.
For more use cases and advanced setups, you can also [check out the docs](https://devcenter.heroku.com/articles/chatops).
Happy deploying! | thawkin3 |
1,876,725 | Do you know how the code you write in JavaScript gets executed? How JavaScript supports asynchronous execution? | JavaScript is quite unique in that it’s single-threaded, meaning it processes code in a specific... | 0 | 2024-06-04T14:20:13 | https://dev.to/afnan_ahmed/do-you-know-how-the-code-you-write-in-javascript-gets-executed-how-javascript-supports-asynchronous-execution-315d | webdev, javascript, programming, tutorial | JavaScript is quite unique in that it’s single-threaded, meaning it processes code in a specific order. But despite this, it manages to handle time-consuming tasks without freezing up. So, how does it do that? Well, let me break it down for you.
First off, there’s the call stack. Think of it like a stack of plates in a kitchen, where each plate represents a function being executed. When a function is called, a plate is added to the stack. Once that function finishes, its plate is removed. And it operates on a last-in, first-out basis.
Then, there’s the event loop. This is JavaScript’s way of handling asynchronous tasks. It keeps an eye on the call stack, a queue for bigger tasks (macrotasks), and another queue for smaller, immediate tasks (microtasks). It checks if the call stack is empty. If it is, it grabs the first task from the microtask queue and puts it on the stack. It keeps doing this until the microtask queue is empty, and only then does it move on to the macrotask queue.
Speaking of queues, there are macrotasks and microtasks. Microtasks are for urgent stuff, like Promise callbacks, while macrotasks are for things like I/O operations or timers. And microtasks always come before macrotasks, affecting the order of execution.
Lastly, there’s the Web API stack, which is part of the browser. When you use functions like setTimeout, they’re handed off to this stack. Once the specified time elapses, the callback function gets added to the macrotask queue. It’s kind of like waiting for a timer to go off before doing something else.
In a nutshell this happens:
Code execution in JavaScript flows from the call stack ➔ to the event loop ➔ where microtasks take priority over macrotasks ➔ ultimately handling asynchronous tasks while synchronous code continues to execute. | afnan_ahmed |
1,876,724 | Scripting vs. Programming Languages: Uncover the Key Differences Every Developer Should Know! | Discover key insights into scripting vs. programming languages that every developer should know!... | 0 | 2024-06-04T14:18:19 | https://dev.to/afnan_ahmed/scripting-vs-programming-languages-uncover-the-key-differences-every-developer-should-know-3haa | webdev, programming, learning, development | Discover key insights into scripting vs. programming languages that every developer should know! Understanding the difference between scripting and programming languages is key in software development.
Scripting languages are interpreter-based, while programming languages are compiler-based, defining their distinct uses and behaviors.
There are few exceptions here as well.
Scripting languages are ideal for combining existing components within an application, making them great for integration and automation tasks. They run within an existing program, converting high-level instructions to machine language on-the-fly, without needing prior compilation. This makes them simpler and faster to use, with shorter, easier-to-write code. They don’t create specific file types and typically involve lower maintenance costs. Examples include VB Script, Perl, Ruby, PHP, and JavaScript.
In contrast, programming languages are used to develop applications from scratch. They compile the entire program into machine language, resulting in standalone, self-executable code. This process requires more complex and lengthy code, offering comprehensive support for data types, UI design, and graphics. While more time-consuming and expensive to maintain, programming languages provide the robustness needed for building large-scale applications. Examples include C, C++, COBOL, Basic, VB, C#, Pascal, and Java.
Fun fact: The term “scripting language” originates from the early days of computing when scripts were used to automate the execution of tasks on mainframe computers. One of the first scripting languages was JCL (Job Control Language), used in the 1960s to manage and automate job processing on IBM mainframes.
#javascript #developemnt | afnan_ahmed |
1,876,262 | What is Automation Testing? | In the fast-paced world of software development, maintaining high quality and reliability of... | 0 | 2024-06-04T06:45:12 | https://dev.to/perfectqa/what-is-automation-testing-422e | testing | In the fast-paced world of software development, maintaining high quality and reliability of applications is crucial. As applications become more complex, manual testing can become time-consuming, error-prone, and inefficient. This is where automation testing comes into play. But [what is automation testing?](https://www.perfectqaservices.com/post/what-is-automation-testing) This comprehensive guide will explore the concept, benefits, types, tools, and best practices associated with automation testing.
Understanding Automation Testing
What is Automation Testing?
Automation testing is a software testing technique that uses specialized tools and scripts to automate the execution of test cases. Unlike manual testing, where a human tester executes the tests, automation testing leverages software tools to perform the tests automatically. This method helps to increase the efficiency, accuracy, and coverage of the testing process.
Key Features of Automation Testing
Automated Execution: Tests are executed by software tools rather than human intervention.
Repeatability: Tests can be repeated consistently across multiple iterations.
Scalability: Automation allows for the simultaneous execution of tests on different platforms and environments.
Speed: Automated tests are executed faster than manual tests, saving time and resources.
Benefits of Automation Testing
Automation testing offers numerous benefits that can significantly enhance the software development lifecycle.
Increased Efficiency and Speed
Faster Test Execution: Automated tests run much faster than manual tests, allowing for quicker identification of issues.
Continuous Testing: Automation supports continuous integration and continuous deployment (CI/CD) practices by enabling tests to run automatically after each code change.
Enhanced Accuracy and Consistency
Elimination of Human Error: Automation reduces the risk of human errors that can occur during manual testing.
Consistent Results: Tests are executed in a consistent manner every time, ensuring reliable and repeatable results.
Cost-Effectiveness
Reduced Manual Effort: Automation reduces the need for repetitive manual testing, saving time and resources.
Long-Term Savings: While the initial setup of automation can be costly, it leads to long-term savings by reducing manual testing efforts and speeding up the testing process.
Comprehensive Test Coverage
Broader Test Coverage: Automation allows for extensive test coverage, including complex test scenarios that are difficult to perform manually.
Regression Testing: Automated tests can easily perform regression testing to ensure new changes do not affect existing functionality.
Scalability
Parallel Testing: Automation supports the simultaneous execution of tests on multiple devices, browsers, and operating systems.
Large-Scale Projects: Automation is ideal for large-scale projects with extensive test cases and frequent updates.
Types of Automation Testing
There are several types of automation testing, each focusing on different aspects of the application.
Functional Testing
Functional testing verifies that the application functions as intended according to the specified requirements.
Key Aspects of Functional Testing
Unit Testing: Tests individual components or units of the application to ensure they work correctly.
Integration Testing: Verifies the interactions between different components or modules.
End-to-End Testing: Tests the entire application flow from start to finish to ensure all components work together seamlessly.
Regression Testing
Regression testing ensures that recent changes or updates to the application do not adversely affect the existing functionality.
Key Aspects of Regression Testing
Re-Execution of Test Cases: Automated test cases are re-executed to verify that new changes have not introduced new defects.
Impact Analysis: Identifies areas of the application that may be affected by recent changes and focuses testing efforts on those areas.
Performance Testing
Performance testing evaluates the application's performance under various conditions to ensure it meets performance requirements.
Key Aspects of Performance Testing
Load Testing: Assesses the application's performance under expected user loads to ensure it can handle peak traffic.
Stress Testing: Evaluates the application's behavior under extreme conditions to identify breaking points.
Scalability Testing: Ensures the application can scale to accommodate increased loads.
Security Testing
Security testing identifies vulnerabilities and ensures the application is secure from potential threats.
Key Aspects of Security Testing
Vulnerability Scanning: Scans the application for known vulnerabilities.
Penetration Testing: Simulates attacks to identify security weaknesses.
Compliance Testing: Ensures the application meets security standards and regulations.
User Interface (UI) Testing
UI testing verifies that the application's user interface is functional and user-friendly.
Key Aspects of UI Testing
Layout Testing: Ensures that UI elements are correctly displayed across different devices and screen sizes.
Usability Testing: Assesses the user experience and ensures the interface is intuitive and easy to use.
Accessibility Testing: Verifies that the application is accessible to users with disabilities.
Popular Automation Testing Tools
There are numerous automation testing tools available, each offering unique features and capabilities.
Selenium
Overview: Selenium is a popular open-source tool for automating web browsers. It supports multiple programming languages and browsers.
Key Features: Cross-browser testing, parallel execution, integration with CI/CD tools.
Use Cases: Web application testing, regression testing, cross-browser testing.
JUnit
Overview: JUnit is a widely used framework for unit testing Java applications.
Key Features: Annotations for defining test cases, support for assertions, integration with build tools.
Use Cases: Unit testing, integration testing.
TestNG
Overview: TestNG is a testing framework inspired by JUnit, designed to cover a wider range of test configurations.
Key Features: Annotations, parallel test execution, data-driven testing.
Use Cases: Functional testing, integration testing, end-to-end testing.
Appium
Overview: Appium is an open-source tool for automating mobile applications on Android and iOS.
Key Features: Cross-platform testing, support for native, hybrid, and mobile web apps, integration with CI/CD tools.
Use Cases: Mobile application testing, UI testing, regression testing.
LoadRunner
Overview: LoadRunner is a performance testing tool from Micro Focus that simulates user activity to test application performance.
Key Features: Load testing, stress testing, detailed performance analysis.
Use Cases: Performance testing, load testing, scalability testing.
Best Practices for Automation Testing
To maximize the benefits of automation testing, it is essential to follow best practices.
Define Clear Objectives
Set SMART Goals: Define Specific, Measurable, Achievable, Relevant, and Time-bound goals for automation testing.
Align with Business Objectives: Ensure that the objectives of automation testing align with the overall business goals and project requirements.
Select the Right Tools
Tool Evaluation: Evaluate different automation testing tools based on project requirements, ease of use, and compatibility with existing systems.
Tool Integration: Ensure that the selected tools integrate seamlessly with CI/CD pipelines and other development tools.
Develop Robust Test Scripts
Reusable Scripts: Create reusable test scripts that can be easily maintained and updated.
Modular Approach: Use a modular approach to develop test scripts, making it easier to manage and modify individual components.
Implement Continuous Integration
CI/CD Integration: Integrate automation testing with Continuous Integration and Continuous Deployment (CI/CD) pipelines to enable continuous testing.
Automated Builds: Configure automated builds and deployments to ensure tests are run automatically with each code change.
Monitor and Maintain Tests
Regular Monitoring: Regularly monitor automated tests to identify and resolve issues promptly.
Script Maintenance: Update and maintain test scripts to ensure they remain relevant and effective as the application evolves.
Measure and Analyze Results
Performance Metrics: Measure key performance metrics, such as test execution time, pass/fail rates, and defect density.
Continuous Improvement: Use the insights gained from test results to continuously improve the testing process.
Conclusion
Automation testing is a powerful technique that enhances the efficiency, accuracy, and coverage of the software testing process. By leveraging specialized tools and following best practices, organizations can achieve significant improvements in software quality and reliability. Understanding what automation testing is and how to implement it effectively is essential for any organization looking to streamline its testing efforts and deliver high-quality applications. Whether you are testing web, mobile, or desktop applications, automation testing can help you achieve your goals more efficiently and effectively.
| perfectqa |
1,876,723 | Fundamentals Of Set Theory | Definitions and Examples Sets Definition: A set is a collection of distinct objects,... | 0 | 2024-06-04T14:18:15 | https://dev.to/niladridas/fundamentals-of-set-theory-44ie | database, computerscience, programming, machinelearning | ## Definitions and Examples
- **Sets**
**Definition**: A set is a collection of distinct objects, known as elements of the set. Sets are typically denoted using curly braces.
**Example**: Consider "A = {2, 4, 6, 8, 10}". This represents a set of the first five even numbers. Another example might be "C = {apple, banana, cherry}", a set of fruits.
- **Subsets**
**Definition**: A subset is a set where every element is also contained in another set. This relationship is denoted as "B is a subset of A", indicating that "B" is a subset of "A".
**Example**: If "A = {1, 2, 3, 4}", then "B = {1, 2}" and "C = {3, 4}" are both subsets of "A". Importantly, every set is a subset of itself, so "A is a subset of A" is also true.
- **Unions**
**Definition**: The union of two sets "A" and "B", includes all elements from both "A" and "B", without any duplicates.
**Example**: If "A = {1, 2, 3}" and "B = {3, 4, 5}", the union "A union B = {1, 2, 3, 4, 5}" combines all elements from both sets, with the overlap only listed once.
- **Intersections**
**Definition**: The intersection of two sets "A" and "B", contains only the elements that are common to both sets.
**Example**: For the same sets "A" and "B" as above, "A intersect B = {3}" includes only the number 3, which is present in both sets.
- **Complements**
**Definition**: The complement of a set "A", includes all elements that are not in "A", relative to a defined universal set "U".
**Example**: If "U = {1, 2, 3, 4, 5, 6}" is the universal set and "A = {1, 2, 3}", then "A' = {4, 5, 6}" includes every element of "U" that is not in "A".
## Introduction to Venn Diagrams
```
A = {1, 2, 3} B = {3, 4, 5}
_______ _______
/ \ / \
/ 1 \ / 4 \
/ \ / \
/_______2_____\ /_______5_____\
/ 3 \ / 3 \
\ / \ /
\_______ / \_______ /
\ / \ /
\/ \/
\ /
\________C = {2, 3, 6}___/
```
Venn diagrams provide a visual way to represent set relationships, which can be particularly helpful in understanding complex interactions between multiple sets.
**Basic Example**: Drawing two overlapping circles for sets "A" and "B" helps visualize their union and intersection. If "A = {1, 2, 3}" and "B = {3, 4, 5}", the overlap contains "{3}", representing "A intersect B", while the total area covered by both circles represents "A union B".
**Complex Example**: Introduce a third set, "C = {2, 3, 6}", and add another circle to the diagram. The points where all three circles intersect can represent the intersection of all three sets, "A intersect B intersect C = {3}", highlighting the shared element. Each pair of overlaps shows the pairwise intersections, and the areas not overlapping with any other represent the unique elements of each set.
_Some dreams might start from nowhere so never give up_. Author [𝕏](https://x.com/niladrridas) | niladridas |
1,876,722 | Post de desarrollo web | A post by BYRON LOARTE | 0 | 2024-06-04T14:16:22 | https://dev.to/byrontosh/post-de-desarrollo-web-55lb | webdev | byrontosh | |
1,860,249 | Hosting Your Company's landing page (static website) on Google Cloud Using HTTPS & HTTP With Azure DNS Zone record | Introduction Imagine getting a job or being assigned a task in your organization, probably... | 0 | 2024-06-04T14:16:21 | https://dev.to/clouddiadem/hosting-your-companys-landing-page-static-website-on-google-cloud-using-https-http-with-azure-dns-zone-record-3n58 | googlecloud, dnsserver, dnsnamerecord, azure | ## Introduction
Imagine getting a job or being assigned a task in your organization, probably as a new intern to host the company's website on Google Cloud but you must use a load balancer to share network traffic appropriately and also secure it using an SSL certificate. Do you know how to do that? If not, let's walk through it together.
In today's digital environment, businesses of all sizes must manage cloud resources effectively and securely. Businesses depend more on cloud infrastructure, so grasping the fundamental ideas and resources is critical. This article aims to explain a few basic features of Google Cloud Platform (GCP) and offer a step-by-step tutorial for creating a landing page with GCP services and your domain name.
Definitions of important terms like landing pages, folders, projects, DNS zones, and static websites will be covered. After that, we'll go over the actual procedures for setting up a safe and orderly cloud environment. After reading this article, you should be able to deploy a static website on GCP using your domain name, manage access controls, and easily arrange cloud resources.
### Definitions
**Folders**: For your cloud resources, you can set up an orderly and controllable structure that will guarantee effective access control, billing management, and compliance.
**Projects**: They offer a mechanism to enforce quotas, manage billing, restrict access, and hierarchically arrange resources. To manage your GCP environment effectively and securely, you must understand how to use projects.
**DNS Zone**: You can arrange and manage DNS records for your domain names with DNS Zones. They can be private for internal use only within your VPC networks, or public for access to the internet. These zones are yours to make and maintain, and you can add different kinds of DNS records as needed. For domain names to correctly resolve to their corresponding IP addresses and for other services, DNS zone management is essential.
**Static Website**: Google Cloud Storage makes it simple to host a static website on Google Cloud Platform. Benefits include GCP's performance, dependability, and scalability. For websites where content doesn't need to be dynamically generated or updated often, this method is perfect.
**Landing Page**: With Google Cloud Storage, a landing page on Google Cloud Platform can be hosted effectively. This strategy makes use of GCP's strong infrastructure to provide your audience with high-performance static content while emphasizing simplicity, speed, and security.
## Steps
###Step 1: Create folder, project & Permission grant to Cloud Developer
i. Log into the [google console](https://console.cloud.google.com/) you created your company domain name. My own domain name for this project is **adaezennamdi.me**. You should have yours which should have been added to a record set on DNS Zone and also mapped on a name server.
ii. On your search in your console, search for **manage resource** and click on it. Click on **create folder**, then **folder**, and name it **Landing Page**. The **Organization** and **Location** should all be your website organization name then click **create**.
iii. Click on **Create Project** to create a project called **landing page**. The **Organization** should be your website organization name and the **Location**, click on **browse** and pick **Landing Page**. Go ahead and click **Create**.
It would be best if you had something like this below if you are following the steps.
iv. Grant permission to user (owner) `gcpcloudadmin`. I have created a cloud admin before. Go ahead and create yours and give the admin account owner permission because that is the account we are using to do this project. Click on the pencil icon and **edit principal**.
v. Click on Role>> Basic>>Owner. Click on **save**.
### Step 2: Download the Source Code and Test
i. We will be using a source code gotten from a template [here](https://www.tooplate.com/free-templates#google_vignette). Download any HTML template. I will be using this [one](https://www.tooplate.com/view/2131-wedding-lite).
ii. Open the downloaded file and unzip it. We will be using the folders and files in our bucket.
iii. Test the code with your vs code to see the way the website looks. Open the unzipped folder with VS Code and open it on the browser by clicking below it where it says **go live** from the image below.
This is what I have after going live to test the sourced codes.
### Step 3: Create a Bucket with the Company's domain and upload web files
i. Verify your company domain name first with this link below (https://search.google.com/search-console?authuser=1). Input your domain name and click **continue**
ii. Copy the code for verification and follow the prompt written. After which, come back to verify and you will see something like this.
iii. When you click on **go to property**, you will see an overview of what the performance of your website will be when it is hosted and visited.
iv. Create a bucket using the company's root domain name which you verified from the previous step. Go back to your google console and search for **cloud storage**. Click on it and click **bucket**. You will be asked to pick a project. Click the **landing page** project we created earlier in Step 1.
v. Click on enable billing before you proceed else you would not be able to use any of the services or resources.
.
vi. If you do not have up to 3 projects attached to your billing account, it should work for you and look like this.
vii. In your search bar, search for **IAM & Admin** and click on it. Click on **IAM** then on **Grant Access**. For **New Principal**, put your organization name. Mine is `adaezennamdi.me`. For **Role**, type and click on `Organization Policy Administrator`. Then click **Save**.
viii. You would see the policy just like this against your organization. This policy will give you permission to grant all users public access to your bucket storage.
ix. Under **IAM & Admin**, click on **Organization Policies** by your left-hand side. In the filter search bar, type domain and pick `constraints/iam.allowedPolicyMemberDomains`.
x. For **Policy source**,pick **overide parent's policy**.
xi. For Rule, edit it to **allow all**. Click **set policy**.
xii. You should have something like this after setting the policy.
xiii. Go back to your bucket. On your created bucket, click the 3 vertical dots by your extreme right and click on **edit access**.
xiv. For **New Principal**, put `allUsers`. For **Role**, type and click on `Storage Object Viewer`. Then click **Save**. This is to make all users over the internet see your website. Click **Save** and you will be prompted with **Allow public access**. Click on it.
xv. Under your created bucket, upload all the files of the template you want to use, then the folders.
xvi. Click on your bucket name and then scroll down through the files and click on the 3 vertical lines on **index.hmtl**. Click on **Copy Public URL**. copy the link and paste on your browser.
xvii. Your URL should look like this but with your own bucket name. This is the website I hosted shown below. You will see that your URL is not customized to carry your domain name. We will work on that.
### Step 4: Set up HTTPS Load Balancer with SSL Certificate)
i. On your google console, navigate to your load balancer page by searching for the service inside the console. If you are asked to enable API for **Compute Engine**, click the button.
ii. Click on **create load balancer** to start the process. Click on any of the **create load balancer** you see.
iii. For **Type of load balancer**,pick **Application Load Balancer (HTTP/HTTPS)**,then click **next**. For **Public facing or internal**, pick **Public facing (external)** and click **next**. For **Global or single region deployment**, click **Best for global workloads**. Click **next**. For **Load balancer generation**, click **Global external Application Load Balancer** and then **next**. Click **configure** to Create load balancer.
iv. For **load balancer name**,call it **landingpage-lb**. Under **Frontend configuration**,name it **landingpage-frontend**. For **protocol**,pick **HTTPS**. For **port**,put **443**. For **Ip address**, click on the drop-down and click **create IP address**. Name it **landing-pub-ip** and click **Reserve**.
v. Under **certificate**, click on **create a new certificate**.
vi. For name of certificate,let the name be **landing-cert**. For **Create mode**, click on **Create Google-managed certificate**. For **Domains**, input your domain name for the first one and add `www` to the domain name for the second domain name. Click on **Create**.
vii. Under Additional Certificates, let **SSL Policy** be **GCP Default**. Let **HTTP/3 (QUIC) negotiation** be left as **default**. Check the box **Enable HTTP to HTTPS redirect** and click **done**.
viii. For **Backend configuration**, click on the dropdown **backend services & backend buckets** and click **create a backend bucket**.
ix. For **Backend bucket name**, let it be **landing-backend**. For
**Cloud Storage bucket** click on **browse**. Select the bucket you create earlier on and click **select**.
x. For **Cloud CDN**, tick **Enable Cloud CDN**.For Client TTL, Default TTL, and Maximum TTL, leave them with the default value.Leave every other parameter as default and click **create**. Click **OK** on the next prompt you see.
xi. For **Routing rules**,leave everything as default. Click on **Review and finalize** and make sure everything is in order and then click **create**.
xii. You should see something like this with the green checkmarks. if someone is trying to visit your website,with the information below,the person will be redirected to https.
### Step 5: Create a domain record in Azure DNS Zone
i. Click on the link called **landingpage-lb** from the image above. Copy the IP address there. You will go and map it in your DNS server where you created the domain name and mapped your txt record. In my case, I used Azure DNS zone. If you did this [project](https://dev.to/clouddiadem/step-by-step-guide-setting-up-azure-entra-id-with-domain-names-and-user-management-3dno), under _step 5,number vi_,where we added a record for Azure. You will add same for google too in same place. That is why I am using same account carrying my domain name to map `A record` and `CNAME record`. The image below is for `A record`
ii. If you used azure to map your own records,then go to your azure portal, navigate to **DNS ZONE**, then click on **DNS Management** and **recordsets** under it. Click on **Add** to add record set.
iii. Name can be empty or you use @, Type should be A-Address Record, IP address should be the IP address from your load balancer you created from earlier step under **IP:Port**. Leave everything else at default and click **Add**.
iv. We will add another record type called CNAME.**Name** should be _www_ and **Type** should be **CNAME record**. Your domain name will automatically show as your **alias**. Then click **Add**.
V. It will be mapped to your domain name like this even if you don't add it, as long as you are doing it under the domain name record.
vi. Use a [DNS Checker](https://dnschecker.org/) to see that your domain name propagates across the world and it shows the IP address you inputted in the A record.
vii. Do the same for CNAME records too.
viii. You should see this on the certificate page after some minutes. It would have provisioned.
ix. You will see your website if you use the CNAME record with `www.your domain name`, and if you input just `your domain name`, you will see your website.
x. When you are done, delete your resources like bucket storage, load balancer, and SSL certificate in your google console.
## Conclusion
Multiple steps are involved in setting up and maintaining a landing page on Google Cloud Platform, all of which contribute to a stable and secure cloud environment. You can guarantee effective access control, billing, and compliance management through comprehension and application of GCP's resources, including Folders, Projects, and DNS Zones. Performance, dependability, and scalability are just a few advantages that make hosting a static website on GCP a great option for many businesses.
This detailed documented steps thoroughly explain how to create a GCP project, configure required permissions, confirm domain ownership, and launch a static webpage. By following these procedures, you can use GCP's robust infrastructure to provide your audience with high-performance static content and guarantee a smooth and effective experience.
I would love to know if you found this helpful and if you could try it out. Your likes and comments will go a long way in encouraging me to keep writing more. See you in my next article!
| clouddiadem |
1,876,721 | Fetch API React | import "./styles.css"; import React, { useEffect } from "react"; import { useState } from... | 0 | 2024-06-04T14:13:05 | https://dev.to/alamfatima1999/fetch-api-react-3787 | ```JS
import "./styles.css";
import React, { useEffect } from "react";
import { useState } from "react";
import axios from "axios";
export default function App() {
const URL = `https://jsonplaceholder.typicode.com/todos`;
const [todoList, setTodoList] = useState([]);
useEffect(() => {
// let todos;
// axios
// .get(URL)
// .then((res) => {
// // console.log(res);
// setTodoList(res);
// })
// .catch((err) => {
// console.log(err);
// });
// fetch(URL)
// .then((res) => {
// return res.json();
// })
// .then((res) => {
// // console.log(res);
// setTodoList(res);
// })
// .catch((err) => {
// console.log(err);
// });
const todos = async () => {
let todo = await fetch(URL);
let data = await todo.json();
// console.log(data);
setTodoList(data);
};
todos();
});
// console.log(todoList);
return <div className="App"></div>;
}
```
| alamfatima1999 | |
1,876,720 | Combing Kubeflow With A Dedicated Stack: Enter deployKF | In a world where there are thousands of tools in the cloud-native realm, we need methods to make our... | 0 | 2024-06-04T14:11:56 | https://dev.to/thenjdevopsguy/combing-kubeflow-with-a-dedicated-stack-enter-deploykf-3mak | kubernetes, devops, programming, git | In a world where there are thousands of tools in the cloud-native realm, we need methods to make our jobs more efficient. Software stacks have helped us out with this a ton. For example, LAMP (Linux, Apache, MySQL, Python) made deploying Linux web stacks far more efficient. Not because you **had** to use the specific stack, but because it gave you a great starting point.
Similar to other cloud-native stacks, engineers need a similar stack methodology for running AI and ML workloads on Kubernetes.
That’s where deployKF comes into play.
In this blog post, you’ll learn about what deployKF is and how to get it running in your Kubernetes cluster.
<aside>
💡 At the time of writing this, I believe that it’s still more beneficial to use Kubeflow instead of deployKF. There are a lot of assumptions and prerequisites along with using a particular application stack when it comes to deployKF. Nontheless, it’s still good to know about in the world of Kubernetes and ML/AI.
</aside>
## Prerequisites
To follow along with this blog post in a hands-on fashion, you’ll need a Kubernetes cluster running the following:
- 4 CPUs
- 16GB of RAM
- 64 GB of storage
- No ARM-based Worker Nodes
If you aren’t going to follow along with the installation of deployKF, you don’t need to deploy the cluster.
<aside>
💡 Please note that at the time of writing this, the steps in this blog post do not work on AKS due to a policy issue.
</aside>
## What Is deployKF
As with many complex implementations in the realm of Kubernetes, it’s always good to have a starting point. Some list or collection of tools that can help make your life easier when you install them, preferably at the same time.
When deploying AI and ML based workloads to perform Modeling on your cluster, you can install several tools separately to get the job done. Software like PyTorch and TensorFlow work on Kubernetes just fine and you can install them separately. However, what if there was a way to install all the necessary AI and ML tools on your cluster with one stack?
deployKF helps with this.
Much like Kubeflow, deployKF gives you a collection of resources to deploy on your Kubernetes cluster to ensure that you have everything you need to begin building Models. The biggest difference conceptually is that deployKF combines Kubeflow with a few other tools to make the entirety of managing the cluster from an AI and ML perspective a bit easier.
Below are the dependencies that you’ll see deployed with deployKF.
Source: https://www.deploykf.org/guides/cluster-dependencies/
All of the cluster dependencies are installed during the installation of deployKF.
Source: https://www.deploykf.org/guides/cluster-dependencies/
The gist is that deployKF combines Kubeflow with a couple of other tools that you may need into one installation/stack.
## Installing deployKF
Now that you have gone through the “why” behind deployKF, let’s learn how to install it.
As with most pre-defined stacks, there are pre-defined tools that you must use to get the deployment up and running. One of the tools that you must use for the installation and to use deployKF is ArgoCD, a popular leading GitOps tool.
<aside>
💡 Remember - all stacks that are pre-built for you require you to use specific tools. If you don’t like the “lock-in” piece, deployKF may not be for you along with just about any other pre-defined software stack.
</aside>
1. Clone the deploykf repo. This will contain the necessary installation for ArgoCD along with the `values.yaml` file you’ll use later.
```jsx
git clone -b main https://github.com/deployKF/deployKF.git ./deploykf
```
1. Modify permissions to make the script executable.
```jsx
chmod +x ./deploykf/argocd-plugin/install_argocd.sh
```
1. Run the installation script.
```jsx
bash ./deploykf/argocd-plugin/install_argocd.sh
```
1. Create a `deploykf.yaml` file for the deployKF deployment with the following code.
Please note that this is the default `Application` object/resource installation. It’s using the default values that are available from deployKF. For example, if you look at the array under `value_files`, you’ll see that it’s pointing to the default configurations within the deployKF repo you cloned in step 1.
```jsx
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: deploykf-app-of-apps
namespace: argocd
labels:
app.kubernetes.io/name: deploykf-app-of-apps
app.kubernetes.io/part-of: deploykf
spec:
project: "default"
source:
repoURL: "https://github.com/deployKF/deployKF.git"
targetRevision: "v0.1.4"
path: "."
plugin:
name: "deploykf"
parameters:
- name: "source_version"
string: "0.1.4"
- name: "values_files"
array:
- "./sample-values.yaml"
destination:
server: "https://kubernetes.default.svc"
namespace: "argocd"
```
If you want to change the values in the `sample-values.yaml` file, open the deployKF rep and within the default directory, you’ll see the file.
If you want to change the values within the `Application` object/resource YAML, you can via the sample values override link [here](https://github.com/deployKF/deployKF/blob/main/sample-values-overrides.yaml).
**If you aren’t going to change anything of the defaults, don’t worry about going to the link where the sample values are.**
<aside>
💡 deployKF is heavily dependent on ArgoCD for deployment.
</aside>
1. Within the same directory that you saved the `values.yaml` file in, run the `deploykf.yaml` file.
```jsx
kubectl apply -f deploykf.yaml
```
### Sync ArgoCD
Because deployKF is dependent on ArgoCD, you may need to sync the app or at least see the status of the deployment.
1. Download the sync script.
```jsx
curl -fL -o "sync_argocd_apps.sh" "https://raw.githubusercontent.com/deployKF/deployKF/main/scripts/sync_argocd_apps.sh"
```
1. Change the sync scripts permissions so you can run it locally.
```jsx
chmod +x ./sync_argocd_apps.sh
```
1. Run the sync script.
<aside>
💡 Make sure you’re on the latest version of the ArgoCD CLI before running the following script.
</aside>
```jsx
bash ./sync_argocd_apps.sh
```
After about 15-20 minutes, you should see an output similar to the below screenshot when viewing resources in the `deploykf-istio-gateway` Namespace, which you’ll need for the next steps.
<aside>
💡 You may have to run the script twice if you see a sync issue OR go into the ArgoCD dashboard and run the sync manually. My assumption is it’s a race condition issue. Some of the apps need to be running before the others, which the script is supposed to take care of, but the Pods may not be operational once the script moves on to another workload.
</aside>
## Accessing deployKF
deployKF is now installed and it’s time to access the dashboard. To access the dashboard, you’ll want to port-forward on the central dashboard.
```jsx
kubectl port-forward svc/central-dashboard -n deploykf-dashboard 8085:80
```
You should now see a screen similar to the one below.
## Closing Thoughts
As ML tooling for Kubernetes stands right now, you have two primary options that are Kubernetes centric:
1. Kubeflow
2. deployKF
Both are great, but they leave a lot to be desired from an installation and configuration perspective. Both deployKF and Kubeflow don’t have the easiest installation methods. It makes sense due to the complex nature of AI and ML software, but the hope is that this changes in the future.
deployKF is a great solution if you want a ready-to-use application stack out of the box without you having to do much configuration. Just keep in mind that you will be bound to certain third-party solutions like ArgoCD and Istio to use it.
Overall, I would still recommend using Kubeflow directly. | thenjdevopsguy |
1,876,719 | The Common Struggle of Automation Testing: Programming Knowledge | You’ve probably been thinking about exploring the field of automation testing for a long time. You... | 0 | 2024-06-04T14:10:28 | https://dev.to/sadia/the-common-struggle-of-automation-testing-programming-knowledge-3h50 | testing, automation, tester, programming | You’ve probably been thinking about exploring the field of automation testing for a long time. You even started working on it according to your thoughts. But when you started working, you got into trouble. This problem has been faced by more or less everyone, including me, you, and us. So let’s share some of my limited knowledge about this familiar problem with you.
As an automation tester, having a strong foundation in programming is very important. This strong foundation will help you design, write, and maintain automation scripts beautifully. You will need to know some Core Concepts of programming. These are:
**1. Syntax and Familiarity with data types, variables, operators, and expressions.**
**2. Conditional statements (if-else).**
**3. Looping constructs (for, while).**
**4. Functions and Methods -Function Invocation, Parameters and Return Values, local and global scope**
**5. Arrays and Lists**
**6. Object-Oriented Programming (OOP) — Classes and Objects, Inheritance, Polymorphism, Encapsulation, Abstraction**
**7. Exception handling mechanisms (try-catch/finally blocks/Custom Exception).**
If you have a good understanding of these Core Concepts, you can write automation scripts using any programming language. If you want, you can also take ideas about Advanced Concepts. | sadia |
1,876,716 | RTSP: Real Time Streaming Protocol Explained | In this article we are going to learn about RTSP RTSP (Real Time Streaming Protocol) is a network... | 0 | 2024-06-04T14:08:12 | https://www.metered.ca/blog/rtsp-real-time-streaming-protocol-explained-3/#meteredca-the-global-turn-server-solution | webdev, javascript, devops, webrtc | In this article we are going to learn about RTSP
RTSP (Real Time Streaming Protocol) is a network control protocol designed to use in communication systems.
RTSP protocol provides a framework for real time streaming of audio and video
RTSP provides efficient management of audio and video streams with extreamly low latency, thus making it perfect for live streaming applications
## **What is RTSP**
Real TIme Streaming Protocol. (RTSP) is a network control protocol to control streaming media servers.
It is perfect for streaming of real time data streams such as audio and video and other multimedia files over the internet
It is different from HTTP which is primarily designed for downloading content, RTSP is designed for live streaming
### **Historical context: How RTSP came to be**
RTSP was developed by the Internet Engineering Task Force (IETF) in 1998. The RTSP protocol allows client devices (browsers or web apps) to control a media streaming server such as pause, play, stop the media stream
Thus it is the preferred protocol for applicagtions that require real time media delivery and interactivity
## **Key features of RTSP**
### **1\. Session Control Capabilities**
* Play, Pause and Stop Commands: With RTSP the client devices cantrol the media streams similar to traditional cd / blu ray players. The interactivity is important for applications such as live broadcasts, movie rentals or any video streaming services
* Seek Functionality: RTSP also allows for seek functionality which enables users to go to a specific point in a media stream
These features are dependent on the implementation of the RTSP protocol. The owner of the live broadcast or the on demand streaming service provider can control which features that want to give to the viewer watching the stream
#### **2\. Low latency**
* RTSP is designed from the ground up to provide low latency streaming, which is very important for live streams such as sporting events or other live events the users might want to stream
#### **3\. Efficient bandwidth utilization**
* RTSP is designed for efficient bandwidth utilization, that does not mean it provides bad quality. With RTSP you can stream 4k and even 8k video.
* RTSP lets client request what data they want, if a client wants just the audio stream RTSP does not push the video to that client thus saving bandwidth.
#### **4\. Transport Agnostic**
* RTSP is transport agnostic meaning it can be used with TCP, UDP or any other protocol, although UDP is preferred.
* Thus RTSP has the flexibility to be used with different network conditions and requirements
#### **5\. Scalability**
* RTSP supports scalable streaming solutions. It does not matter if you are running small deplyments or large scale deplyments
* RTSP can handle multiple media streams
#### **6\. Support for different media types**
* RTSP can handle different types of media including audio, video and text.
#### **7\. Interoperability**
* RTSP works with various codecs and media formats, ensuring broad compatibility across different browsers and devices.
* This helps with reaching a wider audience which is important when you are broadcasting video.
### **8\. Extensibility**
* The Protocol is designed to be extensiable, which means that it allows for the addition of new features and improvements over time
* This means that the protocol is very much alive and in development and is able to cope with future streaming advancements and technologies
### **9\. Control over Network elements**
* RTSP can interact with networking elements such as NAT and firewalls, which inadvertantly come in the ways to communicating with devices on the internet.
### **10\. Security features**
* RTSP has security features built in, it has end to end encryption and auth mechanisms
* Because of the security features the streams can only be accessed by authorized viewers and audiences
## [**Metered.ca**](http://Metered.ca)**: The Global TURN Server solution**
## [**Metered TURN servers**](https://www.metered.ca/stun-turn)
1. **API:** TURN server management with powerful API. You can do things like Add/ Remove credentials via the API, Retrieve Per User / Credentials and User metrics via the API, Enable/ Disable credentials via the API, Retrive Usage data by date via the API.
2. **Global Geo-Location targeting:** Automatically directs traffic to the nearest servers, for lowest possible latency and highest quality performance. less than 50 ms latency anywhere around the world
3. **Servers in 14 Regions of the world:** Toronto, Miami, San Francisco, Amsterdam, London, Frankfurt, Bangalore, Singapore,Sydney, Seoul, Dallas, Warsaw.
4. **Low Latency:** less than 50 ms latency, anywhere across the world.
5. **Cost-Effective:** pay-as-you-go pricing with bandwidth and volume discounts available.
6. **Easy Administration:** Get usage logs, emails when accounts reach threshold limits, billing records and email and phone support.
7. **Standards Compliant:** Conforms to RFCs 5389, 5769, 5780, 5766, 6062, 6156, 5245, 5768, 6336, 6544, 5928 over UDP, TCP, TLS, and DTLS.
8. **Multi‑Tenancy:** Create multiple credentials and separate the usage by customer, or different apps. Get Usage logs, billing records and threshold alerts.
9. **Enterprise Reliability:** 99.999% Uptime with SLA.
10. **Enterprise Scale:** With no limit on concurrent traffic or total traffic. Metered TURN Servers provide Enterprise Scalability
11. **5 GB/mo Free:** Get 5 GB every month free TURN server usage with the Free Plan
12. Runs on port 80 and 443
13. Support TURNS + SSL to allow connections through deep packet inspection firewalls.
14. Support STUN
15. Supports both TCP and UDP
16. Free Unlimited STUN
## **Key Components of RTSP**
Real Time Streaming Protocol (RTSP) is protocol designed for streaming media over the internet
Let us learn some of the important componenets and their roles in the streaming, here is a detailed explanation of the components of RTSP
### **1\. RTSP Server**
* **Session management:** The RTSP server manages the streaming session, It establishes, maintains and terminates the session as required. It also keeps track of the state of on going session like playing, paused and stopped
* **Media Streaming:** The server sends the media stream to the client devices.
* **Command Processing:** The server processes RTSP commands requests like PLAY, PAUSE, STOP etc from client devices. This enable the client devices to control video streaming
### **2\. RTSP Client**
The RTSP client is any device or app that connects to the RTSP server and requests and controls the media streams
* **Session Control:** The client initiates a media streaming session by sending a request to the RTSP server to initiate a streaming session. It can also pause and stop the session by sending appropriate commands
* **Media Playback:** The client recieves the media stream and decodes media streams for playback on the device. The client device can by anything, a software application like VLC, a web browser like chrome, a hardware device such as a prime stick etc.
* **User Interface:** The client has a built in user interface for the user to interact with the media including playing the media or stoppping the media playback
### **3\. RTSP Commands**
RTSP defines several commands to control the media streams on the server. The commands include
* **SETUP:** Starts a session and allocates resources for the stream. It also decides of the transport mechanism to be used. For example RTP/UDP.
* **PLAY:** Starts the playback of a media stream from a specified point in time.
* **PAUSE:** Temprorily stop the playback of the media stream with releasing resources and remembers the point in time where the media stream is stoppped so that it can resume the playback from that point onwards.
* **TEARDOWN:** Terminates the session and releases the resources that were associated with the session.
* **DESCRIBE:** Requests a description of the media streams, this is typically in the SDP that is Session Description Protocol format. Detailing the available media streams and their formats
* **OPTIONS:** This function quries the media server for available methods and features that can be availed
* **ANNOUNCE:** This is used by the streaming server to notify the clients about changes in media streaming formats or session status.
### **4\. Media Transport protocols**
as we have already seen above the RTSP itself does not transport hte media streams but instead relies on other underlying protocols to transport the streams. These protocols include
* **RTP ( Real Time Transport Protocol):**
The RTP is the primary protocol that is used to transport media streams over the internet in real time. It provides mechanisms that can be used for time stamping, sequence numbering and ensuring the proper synchronization of packet order
* **RTCP (RTP Control Protocol):**
This protocol the RTCP protocol basically works alongside the RTP protocol in order to provide feedback on the quality of the media stream that is being delivered, with metrics such as packet loss and jitter. It helps in maintaining the quality of media streams.
### **5\. Session Description Protocol (SDP)**
SDP is used to describe the multimedia sessions in a standardized format. This is it provides detailed info on the media streams that are available for a streaming session, this includes information like
* Media Types: The type of media that is available for streaming such as audio video etc
* Codec Information: Codecs are used for media encoding, popular one include H.264, mp4 and others for video and mp3 or AAC for audio. This codec information is provided by the SDP protocol
* Network Information: Network information including the IP addresses, domain names, port numbers and other such information
* **Timing Information:** The start and end time for the media session
### **6\. Transport layer**
* **TCP (Transsmission Control Protocol)**: the TCP provides reliable, connection oriented delivery of RTSP messages. The TCP protocol ensures that the Commands from the devices and the responses from the server are delivered in order
* **UDP (User Datagram Protocol:** UDP provides low overhead and low latency delivery. It is preferred for real time media but one drawback is that it does not gaurantee delivery and order of data
* **HTTP Tunneling:** RTSP can be tunnelled through HTTP to traverse firewalls and NAT devices ensuring compatibility and delivery in extremly restrictive networks
### **7\. Control and Feedback Mechanism**
RTSP has many security mechamisms built in place to ensure secure streaming is secure
* **Session Identifiers:** There are unique identifies for each streaming session, allowing you to accurately measure and manage multiple sessions at once.
* **State Management:** RTSP saves the information regarding the current state of the streaming session like playing, paused stopped etc.
* **RTCP Reports:** Server and the client devices contineously exchange information regarding packet loss, jitter and other quality metrics to facilitate streaming and error correction
### **8\. Security Components**
RTSP has basic security mechanisms in place to secure the streams from un authorized access
* **Authentication:** RTSP has built in mechanisms to identify and verify the indentity of the client that is asking for the media streams.
* **Encryption:** RTSP uses protocols loke SRTP (Secure Real Time Transport Protocol) to encrypt media streams, thus preventing unauthorized access and download of media streams.
* **Access Control:** With RTSP you can create access control policies to restrict who can access the media streams
## **RTSP Vs Other Streaming Protocols**
#### **Comparison with HTTP Live Streaming (HLS), Dynamic Adaptive Streaming over HTTP (DASH), and Real-Time Messaging Protocol (RTMP)**
### **1\. RTSP (Real Time Streaming Protocol)**
* **Latency:** Low Latency, best suited for real time streaming applications
* **Control:** Provides client devices control over the streaming media such as play, pause, stop etc
* **Transport:** Can use a wide variety of protocols for transport but generally RTP/RTCP protocols are used
* **Interactivity:** Highly interactive, this is due to ultra low latency and real time client control over streaming media
### **2\. HLS (HTTP Live Streaming)**
* **Latency:** Higher latency than RTSP, this is usually 10 to 30 seconds
* **Control:** With HLS there is limited control, as this protocol is primarily designed with continuous playback in mind
* **Transport:** Uses HTTP for transport, which is universally supported but it is heavy and can introduce latecy and performance bottlenecks
* **Interactivity:** Limited interactivity as the stream cannot be controlled by users. The stream can be controlled by the server and the admin
### **3\. DASH (Dynamic Adaptive Streaming over HTTP)**
* **Latency:** It is similar to HLS but with higher latency as compared to HLS
* **Control:** Does provide more control over the media streams but not as interactive as RTSP
* **Transport:** Uses HTTP for transport, but with advanced adaptive bit rate technology that adapts to changes network conditions
* **Interactivity:** There is moderate interactivity, but it is much suitable for both live and on demand streaming with automatic adaptability to changing network conditions
### **4\. RTMP (Real Time Messaging Protocol)**
* **Latency:** Low latency, it is similar to RTSP
* **Control:** This provides a good control over media streams.
* **Transport:** RTMP typically uses TCP, which can be used for straming video and data over the internet
* **Interactivity:** Highly interactive, can be used for live streaming applications | alakkadshaw |
1,876,715 | TanStack Form Tutorial: Arrays & Dynamic Fields | Your form might not have a fixed number of fields, that's where arrays come in! Let's see today how... | 27,295 | 2024-06-04T14:07:50 | https://leonardomontini.dev/tanstack-form-arrays-dynamic-fields/ | react, angular, tutorial, codenewbie | Your form might not have a fixed number of fields, that's where arrays come in! Let's see today how TanStack Form behaves in this scenario.
This is Chapter 3 of the TanStack Form series, talking about this new Form library which supports React, Angular, Vue, Solid, and Lit.
We'll learn how to setup an array field and how to add, remove, and even move elements around. This works in both flavors with arrays of primitives (strings, numbers, etc) and objects with nested fields.
The video version for this chapter is available here:
{% youtube 0IPPHdjvrzk %}
Code is as usual on GitHub at the [03-dynamic-arrays](https://github.com/Balastrong/tanstack-form-demo/tree/03-dynamic-arrays) branch (leave a star if you like it! ⭐️).
The final result will look like this:
## Array of strings
Let's start from an array of primates, in our case strings. Here are some snippets of what we need to do:
### 1. Define the form schema
```tsx
const form = useForm({
defaultValues: {
...
interests: [] as string[],
...
});
```
### 2. Add the field to the form
This takes a few steps, let's see them one by one. First of all, you need to create a `Field` component with `mode="array"` as a container where all elements will have their own components.
```tsx
<form.Field
name="interests"
mode="array"
children={(field) => (
// your fields go here
)}
/>
```
From here, `field.state.value` is an array and we can cycle through it with the `map` function to render each element.
```tsx
field.state.value.map((_, index) => (
<form.Field
name={`interests[${index}]`}
children={(subField) => (
<Input
type="text"
value={subField.state.value}
autoFocus
onChange={(e) => subField.handleChange(e.target.value)}
/>
)}
/>
));
```
Notice how the first parameter of the `map` function is not used, we just need the index to create the correct name for the field and we'll use `subField` to control the input.
Side note, the `autoFocus` attribute is used to focus the input as soon as it's added. We'll get there in a moment.
### 3. Add the button to add a new element
You might want to add elements dynamically, for example with a button. The easiest way is to use the `field` object provided in the parent `Field` component and call `field.pushValue('')` from a button.
```tsx
<form.Field
name="interests"
mode="array"
children={(field) => (
<>
{field.state.value.map((_, index) =>
// your components here (the snippet above)
)}
<Button type="button" variant={'outline'} onClick={() => field.pushValue('')}>
Add
</Button>
</>
)}
/>
```
### 4. Add the button to remove an element
Lastly, you might want to remove elements. This time you'll need the index of the element you want to remove, that's why the easiest place to put the button is inside the `map` function using `field.removeValue(index)`.
```tsx
<form.Field
name="interests"
mode="array"
children={(field) =>
field.state.value.map((_, index) => (
<div key={index}>
...
<Button variant={'destructive'} onClick={() => field.removeValue(index)}>
<X />
</Button>
</div>
))
}
/>
```
### 5. Full example
Now, by assembling all the pieces of the puzzle, your array field with add/remove and single-element handling will be similar to this:
```tsx
<form.Field
name="interests"
mode="array"
children={(field) => (
<>
<Label className="mr-2">Interests</Label>
{field.state.value.map((_, index) => (
<div key={index} className="flex gap-2 my-2">
<form.Field
name={`interests[${index}]`}
children={(subField) => (
<Input
type="text"
value={subField.state.value}
autoFocus
onChange={(e) => subField.handleChange(e.target.value)}
/>
)}
/>
<Button variant={'destructive'} onClick={() => field.removeValue(index)}>
<X />
</Button>
</div>
))}
<Button type="button" variant={'outline'} onClick={() => field.pushValue('')}>
Add
</Button>
</>
)}
/>
```
## Moving elements
There's still one tiny detail we can implement, what if we want to let the user sort the elements? A cool interaction could be via drag & drop but let's keep it simple for now as we want to explore the API.
We can add a dropdown to move the element at a given index. The component in the example is a `Select` from shadcn but all that really matters here is the `field.moveValue(index, newIndex)` function.
Once again the full example, the only difference is the addition of the `Select` component inside the `map` function to display it on each row.
```tsx
<form.Field
name="interests"
mode="array"
children={(field) => (
<>
<Label className="mr-2">Interests</Label>
{field.state.value.map((_, index) => (
<div key={index} className="flex gap-2 my-2">
<Select value={`${index}`} onValueChange={(newIndex) => field.moveValue(index, +newIndex)}>
<SelectTrigger className="w-28">
<SelectValue />
</SelectTrigger>
<SelectContent>
{field.state.value.map((_, index) => (
<SelectItem key={index} value={`${index}`}>
# {index + 1}
</SelectItem>
))}
</SelectContent>
</Select>
<form.Field
name={`interests[${index}]`}
children={(subField) => (
<Input
type="text"
value={subField.state.value}
autoFocus
onChange={(e) => subField.handleChange(e.target.value)}
/>
)}
/>
<Button variant={'destructive'} onClick={() => field.removeValue(index)}>
<X />
</Button>
</div>
))}
<Button type="button" variant={'outline'} onClick={() => field.pushValue('')}>
Add
</Button>
</>
)}
/>
```
## Array of objects
Similarly to the array of strings, you can have an array of objects.
```tsx
const form = useForm({
defaultValues: {
...
skills: [] as { language: string; rating: number }[],
...
});
```
This time the subfields will no longer be primitives but objects with their own fields. The main difference is that while `interest[0]` is a valid name since it's a primitive, with the object you'll find that `skills[0]` does not work as it's not a value you can access directly.
The values are nested inside the object, so you'll need to use `skills[0].language` and `skills[0].rating` to access them.
In short, your `field.state.value.map` will render **two** `form.Field` components, one for each field of the object.
Here's the full example with also the add/remove buttons:
```tsx
<form.Field
name="skills"
mode="array"
children={(field) => (
<>
<Label className="mr-2">Skills</Label>
{field.state.value.map((_, index) => (
<div key={index} className="flex gap-2 my-2">
<form.Field
name={`skills[${index}].language`}
children={(subField) => (
<Input
type="text"
value={subField.state.value}
autoFocus
onChange={(e) => subField.handleChange(e.target.value)}
/>
)}
/>
<form.Field
name={`skills[${index}].rating`}
children={(subField) => (
<Input
type="number"
value={subField.state.value}
onChange={(e) => subField.handleChange(e.target.valueAsNumber)}
/>
)}
/>
<Button variant={'destructive'} onClick={() => field.removeValue(index)}>
<X />
</Button>
</div>
))}
<Button type="button" variant={'outline'} onClick={() => field.pushValue({ language: '', rating: 0 })}>
Add
</Button>
</>
)}
/>
```
## Conclusion
And that's it for Chapter 3! If you want to learn more about arrays in TanStack Form, check out the [official documentation](https://tanstack.com/form/latest/docs/framework/react/guides/arrays).
In case you missed the previous chapters, you can find the YouTube playlist here: [TanStack Form series](https://www.youtube.com/playlist?list=PLOQjd5dsGSxInTKUWTxyqSKwZCjDIUs0Y).
The code is on the [03-dynamic-arrays](https://github.com/Balastrong/tanstack-form-demo/tree/03-dynamic-arrays) branch.
Interested in more content about TanStack Form? Let me know in the comments below if you have any questions or suggestions for the next chapters!
---
Thanks for reading this article, I hope you found it interesting!
I recently launched a GitHub Community! We create Open Source projects with the goal of learning Web Development together!
Join us: https://github.com/DevLeonardoCommunity
Do you like my content? You might consider subscribing to my YouTube channel! It means a lot to me ❤️
You can find it here:
[](https://www.youtube.com/c/@DevLeonardo?sub_confirmation=1)
Feel free to follow me to get notified when new articles are out ;)
{% embed https://dev.to/balastrong %} | balastrong |
1,876,717 | What Really Determines Your Starups Success in the Long Run. | This ONE thing determines your business’s success in the long run And no it’s not cutting-edge... | 0 | 2024-06-04T14:07:00 | https://dev.to/martinbaun/what-really-determines-your-starups-success-in-the-long-run-19l | productivity, career, performance | This **ONE** thing determines your business’s success in the long run
And no it’s not cutting-edge technology
Not even **flashy marketing strategies**.
But it’s **clear communication** in the organization, by ensuring everyone is on the same page, you’re not just saving time, money, and energy—you’re laying the foundation for sustainable growth and success.
That’s why I screen-record every single communication, from code errors to contract revisions. And let me tell you, it’s been a game-changer.
I am hosting a **workshop**, where I’ll be showing you how to do this easily with a special tool and many more tricks that will help you achieve your goals, and for completely **FREE**.
Join by signing up _**[Here](https://martinbaun.com/workshop00/#contact)**_. | martinbaun |
1,867,062 | ¿POR QUÉ no estás usando estos providers de Angular? | En este blog post, vamos a explorar 4 características que no se utilizan comúnmente en Angular, pero... | 0 | 2024-06-04T14:05:11 | https://dev.to/marianocodes/por-que-no-estas-usando-estos-providers-de-angular-406g | angular, providers, patterns, javascript | En este blog post, vamos a explorar 4 características que no se utilizan comúnmente en Angular, pero que ofrecen un gran poder y flexibilidad para escribir código escalable.
Ponte en al situación que necesitas implementar una lógica para manejar los logs en tu aplicación. Inicialmente, quieres configurar el tipo de log a mostrar: informativo, de error o de depuración. Para lograr esto, crearemos:
**Tokens de Inyección (Injection Tokens)**
Son una forma simple de inyectar valores en servicios, componentes y directivas utilizando un token como identificador y almacenando un valor.
```typescript
export type LoggerConfig = 'debug' | 'info' | 'report';
export const LOGGER_CONFIG = new InjectionToken<LoggerConfig>('LOGGER_CONFIG');
export interface Logger {
log: (message: string) => void;
}
```
En el `bootstrapApplication`, vamos a definir su valor utilizando **`useValue`**.
**`useValue`**
Como su nombre indica, `useValue` le asigna un valor a un token.
```typescript
bootstrapApplication(AppComponent, {
providers: [
{ provide: LOGGER_CONFIG, useValue: 'debug' }
]
// ...
});
```
Con el token y su valor definido, lo usaremos inyectándolo en un nuevo servicio que se encargará de "loguear" los mensajes.
```typescript
@Injectable({ providedIn: 'root' })
export class LoggerService {
constructor(@Inject(LOGGER_CONFIG) private config: LoggerConfig, private http: HttpClient) {}
log(message: string): void {
if (this.config === 'debug') { } // ....
if (this.config === 'info') { } // ....
if (this.config === 'report') {
// petición HTTP para almacenar los errores
}
// ...
}
}
```
Uno de los problemas es que la opción `report` hace una llamada al servidor para almacenar los logs, pero esto no tiene sentido hacerlo mientras nos encontramos en desarrollo local.
Para solucionar este problema, crearemos otro servicio que solo se utilizará en producción, con la lógica necesaria para la función `log`, este caso el supuesto llamado HTTP.
```typescript
@Injectable({ providedIn: 'root' })
export class ProdLoggerService implements Logger {
private readonly URL = 'https://production...';
constructor(
@Inject(LOGGER_CONFIG) private config: LoggerConfig,
private http: HttpClient
) {}
log(message: string): void {
// ...
}
```
Ahora, vamos a configurar el servicio para que sea utilizado según el enterno.
**`useClass`**
Nos permite reemplazar la implementación de un proveedor con una clase que tenga las mismas propiedades y funciones. En este caso, agregaremos una condición para que solo se use en producción.
```typescript
bootstrapApplication(AppComponent, {
providers: [
...
{
provide: LoggerService,
useClass: environment.production ? ProdLoggerService : LoggerService,
},
...
]
});
```
Aunque parece magia, no es necesario que hagamos ningún cambio en los components que consume `LoggerService`, todo sigue funcionando igual
```typescript
@Component({
selector: 'app-root',
template: '<h1>Tokens de Inyección de Dependencias</h1>'
})
export class AppComponent implements OnInit {
constructor(@Inject(LOGGER_SERVICE) private loggerService: LoggerService) {}
ngOnInit(): void {
this.loggerService.log('La aplicación ha iniciado');
}
}
```
Aunque hay algo que me molesta, ambos servicios se injecta `HttpClient`, pero solo uno lo usa. Un servicio solo debería depender de lo que necesita.
**`useFactory`**
Vamos a utilizar `useFactory` para proporcionar solo las dependencias que cada servicio necesita.
```typescript
function createLogger(
config: LoggerConfig,
httpClient: HttpClient
): LoggerService | ProdLoggerService {
if (environment.production) {
return new ProdLoggerService(config, httpClient);
}
return new LoggerService(config);
}
...
providers: [
{
provide: LoggerService,
useFactory: createLogger,
deps: [LOGGER_CONFIG, HttpClient],
},
]
```
<aside>
💡 `useFactory` puedes utilizarlo para otras cosas, como por ejemplo realizar llamadas HTTP y obtener configuraciones que necesita el servicio.
</aside>
Por último, si un caso eventual, `LoggerService` tiene muchas métodos y no quieres exponer todos pero si mantener la misma lógica puedes utilizar `useExisting`.
**`useExisting`**
Supongamos que hemos añadido una función a `LoggerService` llamada `delete`, pero no queremos que sea accesible desde todos los componentes. Podemos hacer lo siguiente:
```typescript
export abstract class LoggerUseExistingService {
abstract log: void;
}
...
providers: [
{
provide: LoggerUseExistingService,
useExisting: LoggerService,
},
]
```
Si inyectas `LoggerUseExistingService` en un componente, la función `delete` no estará disponible.
Gracias a este tutorial, ya puedes entender algunas practicas comunes en Angular como definir Interceptors o el Locale ID
**Interceptor**
```typescript
{
provide: HTTP_INTERCEPTORS,
useFactory: MyInterceptorFactory,
deps: [
{ provide: 'API_TOKEN', useValue: 'YOUR_API_TOKEN' }
],
multi: true
}
```
**LOCALE_ID**
```typescript
{ provide: LOCALE_ID, useValue: 'en-US' }
```
¡Y ahora también sabes cómo crear soluciones más robustas que te permiten reemplazar cualquier proveedor nativo de Angular!
Puedes encontrar un [demo](https://stackblitz.com/edit/injection-tokens-marianocodes?file=src%2Fmain.ts) completo acá, solo tienes que eliminar quitar los comentarios según el la característica que quieras probar
No olvides dejar tu 'like' y tus preguntas en los comentarios.
Puedes seguirme en:
[Twitter](https://twitter.com/marianocodes) | [LinkedIn](https://www.linkedin.com/in/marianocodes/) | [@marianocodes]((https://www.linkedin.com/in/marianocodes/))
| marianocodes |
1,875,192 | Advanced Error Handling in Node.js | Error handling is an important aspect of software development that ensures your application behaves... | 0 | 2024-06-04T14:02:28 | https://dev.to/amritak27/advanced-error-handling-in-nodejs-1ep8 | node, javascript, webdev, express | Error handling is an important aspect of software development that ensures your application behaves predictably and provides meaningful feedback when something goes wrong. In Node.js, effective error handling can be particularly challenging due to its asynchronous nature. This article delves into advanced techniques and best practices for managing errors in Node.js applications.
## Understanding Error Types
Before diving into error handling strategies, it’s important to understand the types of errors you might encounter:
1. Synchronous Errors:
The errors that occur during the execution of synchronous code, can be caught using try-catch blocks.
2. Asynchronous Errors:
The errors occur during the execution of asynchronous code, such as callbacks, promises, and async/await functions.
3. Operational Errors:
Errors that represent runtime problems that the program is expected to handle (e.g., failing to connect to a database).
4. Programmer Errors:
Bugs in the program (e.g., type errors, assertion failures). These should generally not be caught and handled in the same way as operational errors.
## **Synchronous Error Handling**
For synchronous code, error handling is using try-catch blocks:
```
try {
// Synchronous code that might throw an error
let result = dafaultFunction();
} catch (error) {
console.error('An error occurred:', error.message);
// Handle the error appropriately
}
```
## **Asynchronous Error Handling**
- _Callbacks_
In callback-based asynchronous code, errors are usually the first argument in the callback function:
```
const fs = require('fs');
fs.readFile('/path/to/file', (err, data) => {
if (err) {
console.error('An error occurred:', err.message);
// Handle the error
return;
}
// Process the data
});
```
- _Promises_
Promises offer a cleaner way to handle asynchronous errors using .catch():
```
const fs = require('fs').promises;
fs.readFile('/path/to/file')
.then(data => {
// Process the data
})
.catch(err => {
console.error('An error occurred:', err.message);
// Handle the error
});
```
- _Async/Await_
Async/await syntax allows for a more synchronous style of error handling in asynchronous code:
```
const fs = require('fs').promises;
async function readFile() {
try {
const data = await fs.readFile('/path/to/file');
// Process the data
} catch (err) {
console.error('An error occurred:', err.message);
// Handle the error
}
}
readFile();
```
## Centralized Error Handling
For larger applications, centralized error handling can help manage errors more effectively. This often involves middleware in Express.js applications.
- _Express.js Middleware_
Express.js provides a mechanism for handling errors via middleware. This middleware should be the last in the stack:
```
const express = require('express');
const app = express();
// Define routes and other middleware
app.get('/', (req, res) => {
throw new Error('Something went wrong!');
});
// Error-handling middleware
app.use((err, req, res, next) => {
console.error(err.stack);
res.status(500).json({ message: 'Internal Server Error' });
});
app.listen(3000, () => {
console.log('Server is running on port 3000');
});
```
## Advanced Techniques
- _Custom Error Classes_
Creating custom error classes can help distinguish between different types of errors and make error handling more granular:
```
class AppError extends Error {
constructor(message, statusCode) {
super(message);
this.statusCode = statusCode;
Error.captureStackTrace(this, this.constructor);
}
}
// Usage
try {
throw new AppError('Custom error message', 400);
} catch (error) {
if (error instanceof AppError) {
console.error(`AppError: ${error.message} (status: ${error.statusCode})`);
} else {
console.error('An unexpected error occurred:', error);
}
}
```
- _Error Logging_
Implement robust error logging to monitor and diagnose issues. Tools like Winston or Bunyan can help with logging:
```
const winston = require('winston');
const logger = winston.createLogger({
level: 'error',
format: winston.format.json(),
transports: [
new winston.transports.File({ filename: 'error.log' })
]
});
// Usage
try {
// Code that might throw an error
throw new Error('Something went wrong');
} catch (error) {
logger.error(error.message, { stack: error.stack });
}
```
- _Global Error Handling_
Handling uncaught exceptions and unhandled promise rejections ensures that no errors slip through unnoticed:
```
process.on('uncaughtException', (error) => {
console.error('Uncaught Exception:', error);
// Perform cleanup and exit process if necessary
});
process.on('unhandledRejection', (reason, promise) => {
console.error('Unhandled Rejection at:', promise, 'reason:', reason);
// Perform cleanup and exit process if necessary
});
```
## Best Practices
- Fail Fast: Detect and handle errors as early as possible.
- Graceful Shutdown: Ensure your application can shut down gracefully in the event of a critical error.
- Meaningful Error Messages: Provide clear and actionable error messages.
- Avoid Silent Failures: Always log or handle errors to avoid silent failures.
- Test Error Scenarios: Write tests to cover potential error scenarios and ensure your error handling works as expected.
## Conclusion
To effectively handle errors in Node.js, you need to use a combination of synchronous and asynchronous techniques, centralized management, and advanced strategies such as custom error classes and robust logging. By incorporating these best practices and advanced techniques, you can create robust Node.js applications that gracefully handle errors and offer an improved experience for your users.
| amritak27 |
1,876,680 | Code With Heroines : Availability && Redundancy && Fall of the Han Dynasty | Explanation This illustration demonstrates Availability by showing the abundance of... | 0 | 2024-06-04T14:01:14 | https://dev.to/fubumingyu/availability-redundancy-fall-of-the-han-dynasty-58h7 |
## Explanation
This illustration demonstrates Availability by showing the abundance of goods and military presence.
## Fall of the Han Dynasty
The Western Han Dynasty faced financial deterioration due to extensive foreign campaigns and increased taxes, which caused public hardship. Controversial policies like price stabilization and state monopolies on salt and iron were criticized. Following Emperor Wu's death, power struggles among bureaucrats, eunuchs, and maternal relatives, along with the rise of local clans, weakened the dynasty, leading to its fall to Wang Mang in 8 AD.
Wang Mang established the Xin Dynasty (8-23 AD) and attempted reforms based on the Zhou system, but these policies were unsuitable for the time, leading to increased chaos. The dynasty also faced famine and peasant rebellions, culminating in its collapse in 23 AD. Emperor Guangwu Liu Xiu then founded the Later Han Dynasty (25-220), restoring stability initially and expanding influence through conquests and foreign exchanges.
However, by the end of the 1st century, power struggles among maternal relatives, eunuchs, and bureaucrats resurfaced, causing political instability. The Yellow Turban Rebellion in 184 and the subsequent rise of warlords led to further chaos, resulting in the fall of the Later Han Dynasty in 220.
## What is Availability?
Availability refers to uninterrupted access to information and the availability of data and systems when needed. Examples of such measures include 24/7 availability, data redundancy and replication, backup and restore, and load balancing for cloud services, etc.
## What is Redundancy?
Prepare spare systems for servers and network systems in case of equipment failure or load caused by sudden access concentration. A spare system is operated in parallel to maintain functionality in the event of a system failure.
When a failure occurs in the main system, losses can be minimized by instantly switching to a backup system. In addition, in a cyber attack, natural disaster, or other emergency, business can be continued by switching to a spare system while minimizing damage. Redundancy is required today as the need for business continuity planning (BCP) increases.
## Reference
- [可用性とは？意味・定義 | IT用語集 - NTTコミュニケーションズ](https://www.ntt.com/bizon/glossary/j-k/availability.html#:~:text=%E5%8F%AF%E7%94%A8%E6%80%A7%E3%81%A8%E3%81%AFAvailability%E3%81%AE,%E7%94%A8%E8%AA%9E%E3%82%82%E4%BD%BF%E3%82%8F%E3%82%8C%E3%81%BE%E3%81%99%E3%80%82)
- [冗長性とは？ 用語や関連キーワードを解説 - ITreview](https://www.itreview.jp/words/jochosei)
| fubumingyu | |
1,876,229 | VScode: How to Chain Multiple Devcontainer for Development | Introduction Hello, I am Ise, an engineer at WESEEK who is in charge of the development... | 27,131 | 2024-06-04T14:00:00 | https://dev.to/weseek-inc/vscode-how-to-chain-multiple-devcontainer-for-development-2aep | docker, vscode, devops, programming | ## Introduction
Hello, I am Ise, an engineer at WESEEK who is in charge of the development and operation of [GROWI.cloud](https://growi.cloud/?utm_source=dev+community&utm_medium=referral&utm_campaign=VScode_How_to_Chain_Multiple_Devcontainer_for_Development).
In this issue, I would like to share a story about a time when I encountered a troubling problem in the development of GROWI.cloud and solved it.
---
## Background
- GROWI.cloud develops mainly in node.js, and its projects are divided into “Project A”, “Project B” and so on, according to their roles.
- GROWI.cloud's services are made possible by these “Project A” and “Project B” communicating with each other.
- Recently, we have decided to use VSCode's devcontainer(, docker-compose) for our development environment.
- The devcontainer change had no impact on the development of individual projects, as they were all linked with other projects using the internal test environment.
- However, when “Project A” and “Project B” were developed, the devcontainers of both projects could not communicate with each other and hindered the development.
- Therefore, I reviewed the settings around the network of devcontainer, and decided to improve the development environment to enable communication between multiple devcontainers.
---
## What I did
- **Add a setting that allows the network to be specified when the docker-compose of devcontainer is started**
Thus, when devcontainer is built, if there is no Docker network, it will be created automatically.
_*By adding the same configuration to Project B, it is possible to connect to the same namespace network regardless of whether devcontainer of Project A or B is started first._
On the same Docker network, you can resolve names by service name
- **Fix service names in devcontainer/docker-compose.yml so that they are not covered by Projects A and B**
_*Service names in the same Docker network cannot be resolved correctly if they are the same, so even if they are different projects, it is necessary to make sure that the service names do not overlap._
Now you can name resolve node.js apps in Project A as node, node.js apps in Project B as node-project-b, and so on!
- **Set network in .devcontainer/docker-compose.yml to connect to the same network**
- **Review correspondence destination**
_*Before devcontainerization, the communication destination was sorted by “port number” of localhost, but now it is possible to communicate by specifying the service name, so it has been modified._
_example: When sending a POST request from Project A to Project B_
- **Rebuild container**
That's all I did!
Hope everyone has a good devcontainer LIFE!
---
## About Us💡
In addition, we want to introduce a little more about GROWI, an open software developed by **WESEEK, Inc**.
**GROWI** is a wiki service with features-rich support for efficient information storage within the company. It also boasts high security and various authentication methods are available to simplify authentication management, including **LDAP/OAuth/SAML**.
**GROWI** originated in Japan and GROWI OSS is **FREE** for anyone to [download](https://docs.growi.org/en/admin-guide/?utm_source=dev+community&utm_medium=referral&utm_campaign=VScode_How_to_Chain_Multiple_Devcontainer_for_Development) and use **in English**.
For more information, go to [GROWI.org](https://growi.org/en/?utm_source=dev+community&utm_medium=referral&utm_campaign=VScode_How_to_Chain_Multiple_Devcontainer_for_Development) to learn more about us. You can also follow our [Facebook](https://www.facebook.com/people/GROWIcloud/100089272547238/) to see updates about our service.
 | weseek-inc |
1,872,676 | What are Your Best Tips for Building a Coding Portfolio? | I am building my web page and my portfolio from scratch. Did you make yours from scratch as well or... | 0 | 2024-06-04T14:00:00 | https://dev.to/anitaolsen/what-are-your-best-tips-for-building-a-coding-portfolio-415f | discuss, coding, portfolio |
I am building my web page and my portfolio from scratch. Did you make yours from scratch as well or do you use a free portfolio template?
I am looking for inspiration and tips on how to make a great coding portfolio. I sort of have a real small one coded with basic HTML displaying two of my Python games (from some CodeCombat game development courses I have completed) on my web page: [olsenanita.com](https://www.olsenanita.com/), but I want to make it better.
Do you have a portfolio? How did you make yours? | anitaolsen |
1,867,768 | Bucles de JavaScript | Últimamente he estado trabajando en una aplicación que solo cuenta con front-end, ya que el cliente... | 0 | 2024-06-04T13:55:43 | https://dev.to/terminator_true/for-vs-while-vs-map-4956 | javascript, webdev, programming, beginners | Últimamente he estado trabajando en una aplicación que solo cuenta con **front-end**, ya que el cliente nos proporciona en **back-end**.
Debido a que muchos de los datos no se nos dan de la manera exacta que necesita el front, he tenido que procesar los datos para poder mostrarlos como se requería.
Mientras pensaba en cómo hacer ésta tarea se me pasó una duda por la cabeza:
## Cual es el bucle más eficiente en JavaScript?
Ésta pregunta tiene diferentes respuestas según lo que estemos buscando.
Seguramente lo primero que se nos viene a la cabeza es lo siguiente:
```javascript
const arr = [...];
let i = 0;
while(arr.length > i){
const element = arr[i];
i++;
}
```
El típico bucle **while** de toda la vida, el cual no termina hasta que la condición especificado al inicio sea cumplida. Éste bucle se usa principalmente en ocasiones en las que no se sabe de antemano la cantidad de iteraciones que se llevarán a cabo.
La principal desventaja es que es difícil de interpretar al echar un primer vistazo, y ciertamente es más lento a la hora de recorrer una array.
```javascript
const arr = [...];
for(let i = 0; i < arr.length; i++){
const element = arr[i];
}
```
El bucle **for** es otro método muy utilizado a la hora de recorrer una array. Éste funciona de una manera similar al **while**, ya que creamos una variable, en cada iteración comprobamos una condición y finalmente pasamos a la siguiente iteración.
La ventaja que tiene éste bucle en comparación al **while**, es que es más sencillo de interpretar.
El **for** es una buena opción a la hora de recorrer un array, mientras que el **while** no es muy eficiente, pero mi principal problema era que la aplicación debía ser lo más rápida posible.
Entonces se me ocurrió la siguiente idea:
```javascript
const new_array = array_imgs.map((imgs)=>{
for (let i = 0; i < imgs.length; i++) {
const element = imgs[i];
if (element.id == item.id) {
return expedient.image = element.imagen;
}
}
return item.img = null;
})
```
Debido a que estaba tratando con imagenes, las cuales son pesadas y costosas de procesar, necesitaba que el bucle hiciera las mínimas iteraciones posibles.
Se me ocurrió recorrer el array con la función map(). La cual según estuve investigando, es la manera más rápida de recorrer un array (aparte de la función forEach() del propio array), además me permitía modificar crear un array nuevo con los datos nuevos y hacer las mínimas iteraciones.
Fué la manera más eficiente que se me pasó por la cabeza. Tras probar varios métodos, puedo decir que es el mejor que he probado.
## Solución de chat-gpt
También se me ocurrió preguntar a chat-gpt la solución a mi problema y me ha presentado lo siguiente:
```javascript
array_imgs.forEach((imgs) => {
const element = imgs.find(element => element.id === item.id);
if (element) {
expedient.image = element.imagen;
} else {
item.img = null;
}
});
```
Se te ocurre alguna idea mejor? Déjamelo en los comentarios!!
| terminator_true |
1,876,714 | GitOps for CI/CD: Managing Infrastructure and Applications as Code | GitOps is a set of practices that combines the principles of DevOps and Git to manage infrastructure... | 0 | 2024-06-04T13:55:41 | https://dev.to/platform_engineers/gitops-for-cicd-managing-infrastructure-and-applications-as-code-3hop | GitOps is a set of practices that combines the principles of DevOps and Git to manage infrastructure and applications as code. This approach enables platform engineering teams to manage and deploy infrastructure and applications in a consistent and reproducible manner. In this blog, we will explore the technical aspects of GitOps and how it can be implemented for CI/CD pipelines.
### GitOps Architecture
The GitOps architecture consists of three main components:
1. **Source Control**: This is the central repository where all the infrastructure and application code is stored. Git is the most commonly used source control system for GitOps.
2. **CI/CD Pipeline**: This is the automated pipeline that builds, tests, and deploys the code from the source control repository.
3. **Target Environment**: This is the environment where the infrastructure and applications are deployed.
### GitOps Workflow
The GitOps workflow involves the following steps:
1. **Code Changes**: Developers make changes to the infrastructure and application code in the source control repository.
2. **CI/CD Trigger**: The CI/CD pipeline is triggered automatically when changes are pushed to the source control repository.
3. **Build and Test**: The CI/CD pipeline builds and tests the code to ensure it meets the required standards.
4. **Deployment**: The CI/CD pipeline deploys the code to the target environment.
### Infrastructure as Code (IaC)
Infrastructure as Code (IaC) is a key concept in GitOps. IaC involves managing infrastructure configuration files in a source control repository. This allows infrastructure changes to be tracked and versioned, ensuring consistency and reproducibility across environments.
### Example: Terraform Configuration
Here is an example of a Terraform configuration file for a simple web server:
```terraform
provider "aws" {
region = "us-west-2"
}
resource "aws_instance" "web_server" {
ami = "ami-0c94855ba95c71c99"
instance_type = "t2.micro"
}
resource "aws_security_group" "web_server_sg" {
name = "web_server_sg"
description = "Security group for web server"
ingress {
from_port = 80
to_port = 80
protocol = "tcp"
cidr_blocks = ["0.0.0.0/0"]
}
}
```
### Application as Code
Application as Code involves managing application configuration files in a source control repository. This allows application changes to be tracked and versioned, ensuring consistency and reproducibility across environments.
### Example: Kubernetes Deployment
Here is an example of a Kubernetes deployment YAML file:
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: web-app
spec:
replicas: 3
selector:
matchLabels:
app: web-app
template:
metadata:
labels:
app: web-app
spec:
containers:
- name: web-app
image: web-app:latest
ports:
- containerPort: 80
```
### GitOps Tools
Several tools are available to support GitOps, including:
1. **Terraform**: An IaC tool for managing infrastructure configuration.
2. **Kubernetes**: A container orchestration platform for managing applications.
3. **Argo CD**: A GitOps tool for managing applications and infrastructure.
4. **Flux**: A GitOps tool for managing applications and infrastructure.
### Conclusion
GitOps provides a robust framework for managing infrastructure and applications as code. By using GitOps, [platform engineering](www.platformengineers.io) teams can ensure consistency and reproducibility across environments, reducing errors and improving efficiency. | shahangita | |
1,876,713 | Buy PVA Google Voice Accounts | Hello friends. In this blog post I want to share some piece of information and this informations are... | 0 | 2024-06-04T13:54:51 | https://dev.to/buypva_89f9eea18aef5a853e/buy-pva-google-voice-accounts-2ej3 | gmail, communication, email, googlevoice | Hello friends. In this blog post I want to share some piece of information and this informations are going to help you in [buying Google Voice accounts](https://pvamails.com/pva-google-voice-accounts-pva-mails-copy/) and [Gmail accounts.](https://pvamails.com/old-accounts/)
If you have any type of question related to Google voice then visit this website ([PVAMails](pvamails.com)) or you if you want to contact us then contact us through our email. rsbukhari2@gmail.com
As we all know that Gmail is one of the best source for communication. Gmail is a service that is provided by Google and has millions of users in the world.
However, if you're looking to buy Google voice accounts then PVAMails can be a perfect choice for you.
At PVAMails. We offer high quality Google Voice Accounts at a affordable prices. Moreover, we offer numerous packages of voice accounts. So you can choose the one that suits you well.
**What PVAMails Offers**
- Fully Verified Accounts
- Unique IP Created
- 2 days warranty
- Recovery Email Added
- Instant Delivery
**What is Google Voice Accounts and What are the Benefits of Buying Google Voice Accounts?**
Google Voice is a telephony service that provides users with a unique phone number, enabling them to make and receive calls, send text messages, and manage voicemail. Integrated seamlessly with Gmail, Google Voice offers a unified communication platform that simplifies and streamlines interactions.
Additionally, there are many key benefits of Google Voice Accounts. Like it supports multi-device access which enables you to use it on your phone, computer and tablet. Another key feature, you can port your existing number to their service. It means you can continue with your current number all the features that it offers.
Moreover, it is also cost effective and also offers user-friendly interface.
Thanks for reading the article.
For more info, please visit the given site below.
| buypva_89f9eea18aef5a853e |
1,876,711 | Desarrollo de Software: Mantén la Motivación y Estudia de Manera Inteligente | En el emocionante camino del desarrollo de software, la motivación y la eficiencia en el estudio son... | 0 | 2024-06-04T13:52:50 | https://dev.to/farley_piedrahita_orozco/desarrollo-de-software-manten-la-motivacion-y-estudia-de-manera-inteligente-845 |
En el emocionante camino del desarrollo de software, la motivación y la eficiencia en el estudio son clave para el éxito. Descubre estrategias efectivas para mantener el impulso y optimizar tu aprendizaje en este viaje digital.
1. **Establece Metas Claras:** Define metas específicas y alcanzables. Establecer hitos claros te dará dirección y un sentido tangible de logro a medida que avanzas en tu aprendizaje.
2. **Diversifica tus Fuentes de Aprendizaje:** Explora una variedad de recursos: libros, tutoriales en línea, proyectos prácticos. Mantente al día con las últimas tendencias y tecnologías para enriquecer tu conocimiento.
3. **Planifica Descansos y Recompensas:** Rompe el estudio en bloques con pausas cortas. Premia tu esfuerzo con pequeñas recompensas para mantener la motivación y reducir la fatiga.
4. **Practica a Través de Proyectos Reales:** Aplica lo que aprendes en proyectos prácticos. Construir aplicaciones reales no solo refuerza tus habilidades, sino que también hace que el aprendizaje sea más emocionante y significativo.
5. **Aprende de la Comunidad:** Únete a comunidades en línea y participa en foros. Compartir experiencias y aprender de otros puede inspirarte y proporcionarte soluciones a desafíos comunes.
6. **Optimiza tu Entorno de Estudio:** Crea un espacio de estudio cómodo y libre de distracciones. La organización y la tranquilidad contribuyen a un ambiente propicio para el aprendizaje.
7. **Utiliza Herramientas de Gestión del Tiempo:** Emplea técnicas como la Técnica Pomodoro para gestionar el tiempo efectivamente. Establecer intervalos de trabajo y descanso maximiza la concentración y la productividad.
8. **Celebra Pequeños Logros:** Reconoce y celebra cada hito alcanzado. Esto refuerza la sensación de progreso y mantiene la motivación a largo plazo.
9. **Actualiza tus Conocimientos Constantemente:** El desarrollo de software evoluciona rápidamente. Programa momentos regulares para actualizar tus habilidades y mantenerte al tanto de las últimas tendencias.
10. **Cuida de tu Bienestar:** No subestimes la importancia del descanso, la alimentación y el ejercicio. Un cuerpo y mente saludables son fundamentales para el aprendizaje sostenible.
Mantén viva la pasión por el desarrollo de software mediante un enfoque inteligente y motivado. ¡Con estas estrategias, estarás listo para enfrentar cualquier desafío y alcanzar tus metas de manera efectiva en el vasto mundo del desarrollo tecnológico! 🚀📚 | farley_piedrahita_orozco | |
1,876,710 | Tips and Tricks for Visual Studio Code | Terminal info: -Our standard bash prompt environment for entering commands like rake grade or... | 0 | 2024-06-04T13:52:33 | https://dev.to/mayas1111/tips-and-tricks-for-visual-studio-code-2ln1 | vscode, softwaredevelopment, beginners |
Terminal info:
-Our standard bash prompt environment for entering commands like rake grade or bin/server
-Pry for interacting with a database (we could also run rails console for that)
-Irb for typing in pure Ruby code
Commands:
-Reopen a pane
-Left
Mac OS: ⌘ + B
Windows: Ctrl + B
-Bottom
Mac OS: ⌘ + J
Windows: Ctrl + J
-Clear Terminal
Mac OS: ⌘ + K
Windows: Ctrl + K
-Interrupt command
Mac OS: ⌘ + C
Windows: Ctrl + C
-Quick Open file
Mac OS: ⌘ + P
Windows: Ctrl + P
-Command Palette
Mac OS: ⌘ + Shift + P
Windows: Ctrl + Shift + P
-Jump to end of line
Mac OS: ⌘ + E
Windows: Ctrl + E
-Exit
Mac OS: Q
Windows: Q
-Toggle Code
Mac OS: ⌘ + /
Windows: Ctrl + /
-Find Next Selection
Mac OS: Command + D
Windows: Ctrl + D
-Move line
Mac OS: Option + ⬇
Windows: Alt + ⬇
-Duplicate line
Mac OS: Shift + Option + ⬇
Windows: Shift + Alt + ⬇
Other:
-rails console = opens an interactive Ruby environment with your Rails application's context-loaded
-reload! = reload the application's code without exiting the console
| mayas1111 |
1,876,867 | How do I start to incorporate AI into my business? | 1. Spend at least 10 minutes playing with ChatGPT Start chatting without needing to... | 0 | 2024-06-04T20:03:07 | https://blog.jonathanflower.com/artificial-intelligence/how-do-i-start-to-incorporate-ai-into-my-business/ | ai, softwaredevelopment, aiempowerment | ---
title: How do I start to incorporate AI into my business?
published: true
date: 2024-06-04 13:51:58 UTC
tags: ArtificialIntelligence,SoftwareDevelopment,AI,AIEmpowerment
canonical_url: https://blog.jonathanflower.com/artificial-intelligence/how-do-i-start-to-incorporate-ai-into-my-business/
---
## 1. Spend at least 10 minutes playing with ChatGPT
1. Start chatting without needing to signup: [https://chat.openai.com/](https://chat.openai.com/)
2. Type whatever comes to mind. A few ideas to get the conversation started:
- What were the last few things you Googled? Try typing those into ChatGPT.
- What is a topic you are curious about? Ask ChatGPT to tell you about it.
- Try to stump it by asking a hard question in your field of expertise.
3. There are a ton of AI tools out there. I suggest starting with ChatGPT because it is free and the best for many things.
## 2. Is it magic or an idiot?
1. This is largely dependent on the questions you asked and how you phrased them. The question is what types of questions can it answer, and how do I ask my questions in a way that helps it respond well?
2. The simplest advice is to imagine ChatGPT is a high school intern who has memorized the internet. Technically I don’t agree with personifying ChatGPT, but I have not found a better way to guide people with a simple concept. Let’s imagine giving a high school intern very brief instructions. Who knows what you are going to get from them! If you take the time to explain the goal, steps, any other relevant details or expectations, then you will be surprised if you don’t receive back something you can use or at least provide feedback on so that they can improve.
## 3. Practical uses
1. Now that you have some basic concepts, it is time to explore how it might be able to speed or enhance your work. You are on your way to becoming AI empowered.
2. We started this post with a question: “How do I start to incorporate AI into my business?” This is a perfect question to ask ChatGPT.
```
I am in the [insert your industry] industry and my role is a [insert your role]. How do I start to incorporate AI in my business?
```
## 4. Ask ChatGPT to give you a lot of ideas for prompts:
```
I am a [insert your profession]. Give me 50 ChatGPT prompts that can help me be more productive in my job.
```
## Privacy
As you start to use ChatGPT, you should be cautious about uploading documents or entering text with sensitive information. Do not enter anything sensitive. The free version of ChatGPT will use this information to train their models. Here is a guide on how to protect your private information: [How to Protect Your Data with ChatGPT | Jonathan Flower posted on the topic | LinkedIn](https://www.linkedin.com/posts/jonathan-flower_ai-chatgpt-codingtools-activity-7201957443331919876-6-0m?utm_source=share&utm_medium=member_desktop) | jfbloom22 |
1,876,709 | Extending Kubernetes Functionality: A Practical Guide to Custom Resource Definitions | Kubernetes provides a robust API for managing your cluster. API uses a RESTful design, allowing to... | 0 | 2024-06-04T13:51:56 | https://dev.to/gianlucam76/extending-kubernetes-functionality-a-practical-guide-to-custom-resource-definitions-5ag8 | showdev, kubernetes, opensource, tutorial | Kubernetes provides a robust API for managing your cluster. API uses a RESTful design, allowing to perform common actions like creating, retrieving, updating, deleting, listing, patching, and watching various resources within your cluster,
Kubernetes APIs are divided into groups:
- **core** group: this includes _Nodes_, _Pods_, _Namespaces_, _Services_, _ConfigMaps_ and _Secrets_.
- **named** groups: These groups categorise related functionalities. For example, the _apps_ group contains resources for managing deployments, stateful sets, daemon sets, and replica sets, while the _batch_ group handles jobs and cron jobs.
Each group may have one or more versions that evolve independent of other API groups, and each version within the group has one or more resources.
To summarize:
- **Group**: Categories resources based on functionality or origin. This allows for easy API extension by adding new groups for specific features.
- **Version**: Represent specific API versions within a group. New features or modifications to existing resources might be introduced in different versions. Versioning ensures compatibility and smoother upgrades.
- **Resource** type is the name used in the URL (e.g., pods, namespaces, services).
- **Kind**: defines the concrete representation (object schema) of a resource type.
- **Collection**: refers to a list of instances for a specific resource type. There are distinct collection kinds with “List” appended (e.g., _PodList_, _ServiceList_).
- **Resource**: an individual instance of a resource type, typically representing an object in your cluster.
- **Sub-resources**: for specific resource types, additional functionalities are exposed as sub-resources within the resource URI path.
To see all the available API resources in your cluster: `kubectl api-resources`
```
kubectl api-resources
NAME SHORTNAMES APIVERSION NAMESPACED KIND
bindings v1 true Binding
configmaps cm v1 true ConfigMap
endpoints ep v1 true Endpoints
events ev v1 true Event
limitranges limits v1 true LimitRange
namespaces ns v1 false Namespace
nodes no v1 false Node
persistentvolumeclaims pvc v1 true PersistentVolumeClaim
persistentvolumes pv v1 false PersistentVolume
pods po v1 true Pod
...
```
The API server handles all requests.
When we create a deployment for instance, the kube-apiserver validates the content of our deployment to ensure it meets the required format and follows the rules. Once validated, it stores the deployment information in the cluster’s data store, typically etcd.
Much of the behavior of Kubernetes is implemented by programs called controllers, that are clients of the API server. Kubernetes comes already with a set of built-in controllers. For instance we can look at the _kube-controller-manager_ pod's log to see which controllers are started. The deployment controller is one of those.
```
kubectl logs -n kube-system kube-controller-manager-sveltos-management-control-plane
...
I0531 15:34:16.026590 1 controllermanager.go:759] "Started controller" controller="deployment-controller"
```
The deployment controller is constantly watching for deployment instances. In our case, when we created a new deployment, the deployment controller became aware of this change and it took action to achieve the desired state we specified. In this case, it created a ReplicaSet resource. The deployment controller also updated the deployment status section. This section keeps track of the progress towards achieving the desired state.
## Objects
Every object must have the following data.
**TypeMeta** contains the kind and API version.
A nested field **metadata** contains:
- **namespace**: the default namespace is ‘default’. Cluster wide resources do not have this field set.
- **name**: a string that uniquely identifies this object within the current namespace. This value is used in the path when retrieving an individual object.
- **uid**: a unique in time and space value used to distinguish between objects with the same name that have been deleted and recreated.
- **resourceVersion**: a string that identifies the internal version of this object that can be used by clients to determine when objects have changed;
- **creationTimestamp**: a string representing the date and time an object was created.
- **deletionTimestamp**: a string representing the date and time after which this resource will be deleted.
- **labels**: a map of string keys and values that can be used to organise and categorise objects.
- **annotations**: a map of string keys and values that can be used by external tooling to store and retrieve arbitrary metadata about this object.
A nested object field called **spec** represents the desired state of an object.
A nested object field called **status** summarizes the current state of the object in the system. The Kubernetes declarative API enforces a separation of responsibilities. You declare the desired state of your resource (spec). The Kubernetes controller keeps the current state of Kubernetes objects in sync with your declared desired state.
When covering [reconcilers](https://github.com/gianlucam76/kubernetes-controller-tutorial/blob/main/docs/reconciler.md) we will cover:
- how Kubernetes uses resourceVersion to detect conflicts when updating a resource;
- why deletionTimestamp, along with finalizers, is important;
- how to use labels to query a group of related objects (for instance all Pods backing up a Service);
- how to provide different authorizations for Spec and Status and how reconcilers work.
## Extending the Kubernetes API
Any system that is successful needs to grow and change as new use cases emerge or existing ones change. Therefore, Kubernetes has designed the Kubernetes API to continuously change and grow. There are two ways to extend Kubernetes APIs:
- The `CustomResourceDefinition` (CRD) mechanism allows you to declaratively define a new custom API with an API group, kind, and schema that you specify. CRDs allow you to create new types of resources for your cluster without writing and running a custom API server.
When you create a new CustomResourceDefinition, the Kubernetes API Server creates a new RESTful resource path for each version specified.
- The `aggregation layer` sits behind the primary API server, which acts as a proxy. This arrangement is called API Aggregation (AA), which allows you to provide specialized implementations for your custom resources by writing and deploying your own API server.
The main API server delegates requests to your API server for the custom APIs that you specify.
You can register an `extension API server` by creating an _APIService_ claiming a URL path in the Kubernetes API. From that point on, `kube-aggregator` will forward any request sent to that API path will be forwarded to the registered APIService.
Most of the time you are fine with adding a new CustomResourceDefinition. Unless you need custom validations and already have a program that serves your API and works well, you can go with CRD. This tutorial will delve into the process of creating CustomResourceDefinitions.
## CustomResourceDefinition
To introduce new resources, you can use CustomResourceDefinitions.
CRDs extends Kubernetes capabilities by allowing users to create new types of resources beyond the built-in set.
A CustomResourceDefinition is a Kubernetes resource itself. So you can create a CustomResourceDefinition like you would create any other Kubernetes resources.
Most validation can be specified in the CRD using OpenAPI v3.0 validation and the Common Expression Language. Any other validations is supported by addition of a Validating Webhook.
In this section, we’ll dive deep into the creation and management of Custom Resource Definitions in Kubernetes.
## Kubebuilder
[Kubebuilder](https://github.com/kubernetes-sigs/kubebuilder), a framework by Kubernetes SIGs, simplifies creating Kubernetes APIs using Custom Resource Definitions.
With Kubebuilder installed, you can create a new project. Those were the steps for me:
```
brew install kubebuilder
mkdir my-project
kubebuilder init --domain projectsveltos.io
kubebuilder create api --group app --version v1alpha1 --kind MyKind[^2]
```
- **Group**: This acts as a unique identifier for your set of custom resources. It's recommended to use a subdomain you control (e.g., yourcompany.com) to prevent conflicts with existing Kubernetes groups.
- **Version**: Kubernetes versions follow a specific format: vX.Y (optionally with alpha or beta) and potentially additional numbers. alpha indicates a feature under development, while beta suggests more stability.
- **Kind**: This defines the specific type of resource within your API (e.g., Database, ConfigMap). It essentially names the individual resources you'll be managing.
Kubebuilder utilizes a tool called [controller-gen](https://book.kubebuilder.io/reference/controller-gen) to automate the creation of essential code and configuration files.
This automation hinges on special comments embedded within your Go code, known as _marker comments_.
Above instructions created:
In Kubebuilder projects, two key files play specific roles:
- **groupversion_info.go**: This file, as its name suggests, holds information about the API group and version for your CRD. It typically defines a variable named GroupVersion with the group (e.g., app.projectsveltos.io) and version (e.g., v1alpha1). This establishes the unique identifier for your CRD within the Kubernetes API.
- **mykind_types.go**: This file is where you define the actual resource itself. It contains the structure of your CRD, including its fields and any validation rules. This file essentially describes the data your CRD will manage within your Kubernetes cluster.
```go
GroupVersion = schema.GroupVersion{Group: "app.projectsveltos.io", Version: "v1alpha1"}
```
while mykind_types.go is where our resource is defined.
```go
package v1alpha1
import (
metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
)
// EDIT THIS FILE! THIS IS SCAFFOLDING FOR YOU TO OWN!
// NOTE: json tags are required. Any new fields you add must have json tags for the fields to be serialized.
// MyKindSpec defines the desired state of MyKind
type MyKindSpec struct {
// INSERT ADDITIONAL SPEC FIELDS - desired state of cluster
// Important: Run "make" to regenerate code after modifying this file
// Foo is an example field of MyKind. Edit mykind_types.go to remove/update
Foo string `json:"foo,omitempty"`
}
// MyKindStatus defines the observed state of MyKind
type MyKindStatus struct {
// INSERT ADDITIONAL STATUS FIELD - define observed state of cluster
// Important: Run "make" to regenerate code after modifying this file
}
// +kubebuilder:object:root=true
// +kubebuilder:subresource:status
// MyKind is the Schema for the mykinds API
type MyKind struct {
metav1.TypeMeta `json:",inline"`
metav1.ObjectMeta `json:"metadata,omitempty"`
Spec MyKindSpec `json:"spec,omitempty"`
Status MyKindStatus `json:"status,omitempty"`
}
...
```
Now, you are ready to customize the API behavior by defining the __MyKindSpec__ and __MyKindStatus__ structs in mykind_types.go.
Once you've completed these definitions, running `make manifests` will generate the CustomResourceDefinition file in _config/crd/bases/app.projectsveltos.io_mykinds.yaml_.
I'll use concrete examples from open-source projects to illustrate the concepts (and the markers) we discussed.
But first, there's a key requirement for working with Custom Resources in Golang. The client-go library, used to interact with the Kubernetes API, requires resources (including Custom Resources) to implement the _runtime.Object_ interface.
This interface ensures objects can be serialized and deserialized for communication with the API server. One crucial aspect of the runtime.Object interface is the need for DeepCopy methods. These methods create a complete copy of the object.
By including the `// +kubebuilder:object:root=true` marker in your code, you instruct Kubebuilder to automatically generate the necessary methods (including DeepCopy) for implementing the runtime.Object interface.
## Example: Cleaner CRD
When I developed [k8s-cleaner](https://github.com/gianlucam76/k8s-cleaner) the goal was to create a tool that could identify unused or unhealthy resources across the cluster. This tool could then either remove or update those resources as needed.
## Choosing the Scope
A key design decision involved the scope. Since the primary users are platform administrators who manage the entire cluster, I opted for a __cluster-wide__ scope. This allows admins to identify unused resources (e.g., ConfigMaps) across all namespaces efficiently. This eliminates the need to deploy separate cleaners for each namespace, streamlining their workflow.
However, while a cluster-wide scope offers clear benefits for platform admins, I also acknowledged the potential need for users to focus on specific namespaces. To address this flexibility, I incorporated namespace filtering as a configuration option. This allows users to customize the cleaner's operation to their specific requirements. As a configuration option, it's exposed within the __Spec__ field.
The marker comment used to define a cluster-wide scope for the Cleaner controller is:
```yaml
//+kubebuilder:resource:path=cleaners,scope=Cluster
```
If you decide your resources should be scoped to namespace:
```yaml
//+kubebuilder:resource:path=cleaners,scope=Namespaced
```
## Spec
The Spec represents the desired state, including user-defined settings and system defaults. So expose in the Spec all that the user might need to specify.
My vision was to empower users with the ability to:
1. *Define Criteria*: Clearly specify what constitutes an unused or unhealthy resource in their specific context.
2. *Schedule Scans*: Determine how often the Cleaner controller should scan the cluster for resources meeting your cleanup criteria.
3. *Automate Actions*: Choose the desired action (removal or update) to be taken on identified resources.
Remember some fields might be optional and have a default value. Use the `// +kubebuilder:default:=` marker to specify the default value.
```yaml
// +kubebuilder:default:=Delete
Action Action `json:"action,omitempty"`
```
Here, `Delete` is the default action if not explicitly defined by the user.
Add the _optional_ marker along to the json struct _omitempty_ tag of the field you want to make optional.
```yaml
// +optional
Transform string `json:"transform,omitempty"`
```
Full list of [validation markers](https://book.kubebuilder.io/reference/markers/crd-validation.html?highlight=%2F%2F%20%2Bkubebuilder%3Avalidation%3AEnum#crd-validation).
### Status Subresource
The status subresource is enabled via `//+kubebuilder:subresource:status`. This subresource exposes an additional endpoint specifically for the status of your cleaner instance.
In Kubernetes, as already explained, a `resource` represents a logical entity like a Pod or a Deployment. Each resource has an associated API endpoint. The status subresource provides a dedicated endpoint for monitoring the current state and progress of your cleaner instance.
It's important to note that updates made to the main cleaner resource won't directly affect its status. Likewise, changes to the status subresource only influence the status information, not the main configuration. This separation allows for focused updates.
Since the status subresource has its own endpoint, you can leverage RBAC (Role-Based Access Control) to manage access to the cleaner resource and its status independently. This enables you to define who can view or modify the cleaner's configuration and who can monitor its progress through the status subresource.
Understanding who defines the Spec and who utilizes the Status is crucial when designing a CRD. These sections play distinct roles in managing your Cleaner resource.
The Spec section acts as a blueprint for the desired state of your Cleaner resource. In this scenario, the platform administrator defines the Spec by outlining the cleaning criteria, scan schedule, and desired actions.
Essentially, the Spec tells the Cleaner controller what to do.
The Status section, automatically updated by the Cleaner controller, reflects the current state of your resource. It provides valuable information for the platform administrator, such as:
1. _lastRunTime_: The timestamp of the most recent Cleaner execution.
2. _failureMessage_ (optional): A human-readable error message if the last run failed.
3. _nextScheduleTime_: The scheduled time for the next Cleaner execution.
By monitoring the Status subresource, the platform administrator gains insights into the Cleaner's performance and can identify any potential cleaning errors.
### Make generate
Once done defining Spec and Status, just run `make generate` target. That will simply properly invoke controller-gen behind the scene.
This will generate the [Cleaner CustomResourceDefinition](https://github.com/gianlucam76/k8s-cleaner/blob/main/config/crd/bases/apps.projectsveltos.io_cleaners.yaml). Use _kubectl_ to apply it to your cluster.
### Apiextension-apiserver
After posting a CustomResourceDefinition object, the `apiextensions-apiserver` inside of kube-apiserver will check whether there is a conflict and whether the resource is valid. It will then report the result in the status of the CRD, for example:
```
kubectl get customresourcedefinitions cleaners.apps.projectsveltos.io -o yaml
```
```yaml
apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
name: cleaners.apps.projectsveltos.io
...
status:
acceptedNames:
kind: Cleaner
listKind: CleanerList
plural: cleaners
singular: cleaner
conditions:
- lastTransitionTime: "2024-05-31T12:32:39Z"
message: no conflicts found
reason: NoConflicts
status: "True"
type: NamesAccepted
- lastTransitionTime: "2024-05-31T12:32:39Z"
message: the initial names have been accepted
reason: InitialNamesAccepted
status: "True"
type: Established
storedVersions:
- v1alpha1
```
### Common Expression Language (CEL)
For ensuring your CRD configurations are well-defined, you can leverage marker comments with `Common Expression Language` (`CEL`). Since Kubernetes v1.25 introduced CEL support for validation in beta, you can now write expressions to validate your custom resources.
Marker `//+kubebuilder:validation:XValidation:rule` can be used for this scope.
### Immutability
One common example is immutability. For instance if I wanted to make Cleaner.Spec.Schedule string immutable
```yaml
//+kubebuilder:validation:XValidation:rule="self == oldSelf",message="Value is immutable"
Schedule string `json:"schedule"`
```
With that, If I tried to update a Cleaner instance changing the _schedule_ field, the update would fail
```
The Cleaner "list-pods-with-outdated-secret-data" is invalid: spec.schedule: Invalid value: "string": Value is immutable
```
self is a special keyword in CEL which refers to the object whose type contains the rule. In the above example, self refers to Schedule field. So I am only forcing the Schedule field to be immutable.
### Append-only list
Another common example is a list which is append only. As a hypothetical example, if ResourceSelectors were designed this way
```yaml
//+kubebuilder:validation:XValidation:rule="size(self) >= size(oldSelf)",message="this list is append only"
ResourceSelectors []ResourceSelector `json:"resourceSelectors"`
```
any update reducing that list would fail
```
The Cleaner "list-pods-with-outdated-secret-data" is invalid: spec.resourcePolicySet.resourceSelectors: Invalid value: "array": this list is append only
```
### Name format
To enforce that cleaner instance starts with "my-prefix" (remember the meaning of _self_ )
```yaml
// Cleaner is the Schema for the cleaners API
type Cleaner struct { //+kubebuilder:validation:XValidation:rule=self.metadata.name.startsWith("my-prefix")
```
creating any Cleaner instance with an incorre name will fail
```
The Cleaner "list-pods-with-outdated-secret-data-2" is invalid: <nil>: Invalid value: "object": failed rule: self.metadata.name.startsWith("my-prefix")
```
When dealing with string fields in your CRD, you can leverage the `+kubebuilder:validation:Pattern` annotation to enforce a specific format using regular expressions.
For example, to ensure a string field named description starts with a letter or underscore and only contains letters, numbers, and underscores, you can use the following YAML snippet:
```yaml
// +kubebuilder:validation:Pattern=`^[A-Za-z_][A-Za-z0-9_]*$`
Description string `json:"description"`
```
If you have a string field that requires a valid date and time format, typically following RFC 3339, you can use the `+kubebuilder:validation:Format="date-time"` annotation.
For instance, to validate a field named TimeOfX, the following YAML snippet would ensure it adheres to RFC 3339:
```yaml
//+kubebuilder:validation:Format="date-time"
TimeOfX string `json:"timeOfX"`
```
then _"2024-06-03T15:29:48Z"_ would be a valid value, while "2024" would not be.
### Comparing different fields
Imagine having a Spec with
```yaml
// +kubebuilder:validation:XValidation:rule=self.minReplicas <= self.replicas
type MyResourceSpec struct {
Replicas int `json:"replicas"`
MinReplicas int `json:"minReplicas"`
```
above marker enforces that minReplicas is always less than or equal to replicas.
## Example: ClusterProfile CRD
I also maintain another open-source project called [addon-controller](https://github.com/projectsveltos/addon-controller).
It's a Kubernetes controller that simplifies managing add-ons and applications across multiple clusters. It operates from a central management cluster, managing add-ons and applications on the managed clusters it oversees.
Sveltos introduces two custom resource definitions: [ClusterProfile](https://github.com/projectsveltos/addon-controller/blob/7f7677fa9454b83c50215feed745365bad63c99a/api/v1alpha1/clusterprofile_types.go#L36) and [Profile](https://github.com/projectsveltos/addon-controller/blob/7f7677fa9454b83c50215feed745365bad63c99a/api/v1alpha1/profile_types.go#L36). Both these CRDs empower users to:
1. select a subset of managed cluster using a cluster selector.
2. list the add-ons an applications that must be deployed on those clusters.
They have distinct scopes though to cater to different user roles:
- `ClusterProfiles`: Cluster wide resource. It applies across all clusters in any namespace. Ideal for platform admins maintaining global consistency and managing settings like networking, security, and resource allocation.
- `Profiles`: Limited to a specific namespace, granting granular control to tenant admins. This isolation ensures teams manage, from the management cluster, their managed clusters independently without impacting others.
What goes in the Spec is well defined. Let's explore some of the Spec field's markers.
```yaml
// HelmChartAction specifies action on an helm chart
// +kubebuilder:validation:Enum:=Install;Uninstall
type HelmChartAction string
```
This specifies that this (scalar) field is restricted to the *exact* values specified here, _Install_ and _Uninstall_.
If I try to post a ClusterProfile with incorrect value for this field, the post will fail
```
The ClusterProfile "deploy-kyverno" is invalid: spec.helmCharts[0].helmChartAction: Unsupported value: "Deploy": supported values: "Install", "Uninstall"
```
```yaml
// RepositoryURL is the URL helm chart repository
// +kubebuilder:validation:MinLength=1
RepositoryURL string `json:"repositoryURL"`
```
This specifies the minimum length for this string. If I try to post a ClusterProfile leaving this field empty , the post will fail
```
The ClusterProfile "deploy-kyverno" is invalid: spec.helmCharts[0].repositoryURL: Invalid value: "": spec.helmCharts[0].repositoryURL in body should be at least 1 chars long
```
Different markers can be used together
```yaml
// +kubebuilder:default:=100
// +kubebuilder:validation:Minimum=1
// +optional
Tier int32 `json:"tier,omitempty"`
```
A common challenge involves ensuring no duplicates exist within a list. In the context of add-on controllers, this becomes relevant when fetching resources from the management cluster for templating purposes.
Each fetched resource is identified by a unique key field called an __identifier__. This identifier acts similarly to a dictionary key, enabling the creation of an associative structure where each resource is uniquely accessible.
```yaml
// +listType=map
// +listMapKey=identifier
// +optional
TemplateResourceRefs []TemplateResourceRef `json:"templateResourceRefs,omitempty" patchStrategy:"merge" patchMergeKey:"identifier"`
```
- listType=map: This annotation specifies that `TemplateResourceRefs` is a list treated as a map.
- listMapKey=identifier: This annotation defines the key field within each list item used for mapping (the identifier).
Following this definition, the TemplateResourceRef struct details the structure of each item within the TemplateResourceRefs list:
```yaml
type TemplateResourceRef struct {
// Resource references a Kubernetes instance in the management
// cluster to fetch and use during template instantiation.
Resource corev1.ObjectReference `json:"resource"`
// Identifier is how the resource will be referred to in the
// template
Identifier string `json:"identifier"`
}
```
When defining resources for templating within TemplateResourceRefs, each resource must have a unique identifier. Attempting to create a profile instance that uses an identifier already assigned to another resource in TemplateResourceRefs will result in rejection.
For improved user experience, the profile status should primarily focus on displaying a list of managed clusters matching a specific profile. This provides a clear view of which clusters the profile targets. As we'll delve deeper into the reconciler section, the profile controller's role becomes clear: monitoring managed clusters and identifying potential matches for profiles.
It's important to remember the principle of single ownership for resources, including the profile status. Storing deployment error information directly in the profile status might suggest that the profile controller itself is responsible for deploying resources on managed clusters. However, this would overload the profile controller, especially if a profile potentially matches dozens of clusters. For efficient management, deployment responsibilities should be handled separately. Therefore, the profile controller focuses on its core function: watching for clusters and maintaining a list of matching managed clusters. This list is reflected in the profile status, providing clear visibility.
For each managed cluster matching a profile instance, profile controller creates a ClusterSummary instance.
A separate controller, the ClusterSummary controller, takes over the responsibility of deploying add-ons and applications based on the information in the ClusterSummary.
The ClusterSummary resource maintains a "Status" section that reflects the deployment progress. This section keeps you informed about whether the add-ons and applications have been successfully deployed to the cluster or any errors that might have occurred during the deployment process.
## Additional Printer Columns
We saw previously that users can interact with custom resources using kubectl similar to built-in resources. The `+kubebuilder:printcolumn` marker allows you to define additional information displayed by kubectl get.
Sveltos supports registering various clusters (GKE, Civo, etc.) with it. Once a cluster is registered, Sveltos can deploy add-ons and applications on it.
The SveltosCluster CRD (defined in [SveltosCluster](https://github.com/projectsveltos/libsveltos/blob/main/api/v1alpha1/sveltoscluster_type.go)) enables cluster registration with Sveltos.
When listing or getting existing SveltosCluster resources, it's helpful to see the managed cluster's readiness and Kubernetes version at a glance. Here's how to achieve that:
```yaml
//+kubebuilder:printcolumn:name="Ready",type="boolean",JSONPath=".status.ready",description="Indicates whether cluster is ready to be managed by sveltos"
//+kubebuilder:printcolumn:name="Version",type="string",JSONPath=".status.version",description="Kubernetes version associated with this Cluster"
```
```
kubectl get sveltoscluster -A
NAMESPACE NAME READY VERSION
mgmt mgmt true v1.29.1
```
## Validation ratcheting
What if you want to add a new validation to a CRD you have already introduced and shipped? Existing custom objects might conflict with the new validation, so you'll need a plan to address them. As alwasy, Kubernetes comes to help.
Provided you enabled the feature gate, Kubernetes implements validation racheting for CustomResourceDefinitions. The API server is willing to accept updates to resources that are not valid after the update, provided that each part of the resource that failed to validate was not changed by the update operation. In other words, any invalid part of the resource that remains invalid must have already been wrong. You cannot use this mechanism to update a valid resource so that it becomes invalid.
This feature allows authors of CRDs to confidently add new validations to the OpenAPIV3 schema under certain conditions. Users can update to the new schema safely without bumping the version of the object or breaking workflows.
## Delete a CustomResourceDefinition
When you delete a CustomResourceDefinition, the server will uninstall the RESTful API endpoint and delete all custom objects stored in it.
If you later recreate the same CustomResourceDefinition, it will start out empty.
## Conclusion
Please note that on their own, custom resources let you store and retrieve structured data. If you only need to store data, defining a CRD is all that you need. Then using a REST client, you can create objects or query existing objects of the Kind you introduced.
When you combine a custom resource with a custom controller, that you are adding new functionatilies.
Also note, not all controllers need to define a new custom resource. For instance https://github.com/gianlucam76/claudie-sveltos-integration is a controller that watches for Kubernetes Secrets created by Claudie and creates a corresponding SveltosCluster resource so that Sveltos can automatically discover clusters created by Claudie.
[^1]:A Kind is unique within a group. For instance _Service_ is the kind in the _core_ group and the Knative _serving.knative.dev_ group.
[^2]:Since we are interested in creating a CustomResourceDefinition for now, answer yes to _Create Resource [y/n]_ and no to _Create Controller [y/n]_ | gianlucam76 |
1,876,681 | k8s Executor 적용기(1) | 1. 발단 airflow에 대해 잘 모르던 시기에 사정상 내가 구축하지 않은 시스템을 유지보수할 일이 생겼다. airflow를 좀 아는 팀원이랑 같이 가서... | 27,593 | 2024-06-04T13:49:55 | https://dev.to/hj_lee/k8s-executor-jeogyonggi1-1n9e | airflow, datapipeline, k8s | ## 1. 발단
airflow에 대해 잘 모르던 시기에 사정상 내가 구축하지 않은 시스템을 유지보수할 일이 생겼다.
airflow를 좀 아는 팀원이랑 같이 가서 살펴보니 celery executor를 사용하는 전통적인(?) airflow 구조였음.
scheduler가 job을 배치하고, worker가 가져가서 수행한다는 전형적인 구조인데,
시스템에 문제가 생긴 이유는 RabbitMQ의 네트워크 문제.
실제로는 scheduler, worker, webserver가 동일망이었으니, 그림은 이렇게 됨.
airflow 또한 systemctl로 올라가 있었는데, systemctl retry가 안돼서인지 뭔지 worker가 모두 죽어버린 상황.
담당자는 이 사실을 모른 채로 꽤 많은 시간이 지났고...뭐 어쨌거나 근본적인 원인분석이 됐으니 차후에 다시 기동하는 등의 사건 등이 있었으나 중요한 건 이 다음에 든 의문이었다.
**왜 굳이 이렇게 구성해야 하는가?** 하는 의문....
<br>
<br>
## 2. 고민
사실 내가 airflow에 대해 무지한 면이 많았으므로 애초에 왜 메시지큐를 사용해야 했는가 하는 의문부터 출발한 것인데,
celery 기반의 airflow scheduler는 task를 job으로 만들어서 MQ에다 하나씩 던져준다.
이렇게 구성하면 장점은
1) 많은 수의 job을 병렬 처리할 수 있다.
2) 전체적인 작업 capacity가 늘어난다.
3) worker가 분산처리되므로 자원분배 측면에서 리소스를 잘 활용할 수 있다.
단점은
1) 관리포인트가 늘어난다.
2) 유지보수에서 봐야 되는 것들이 더 늘어난다. (남의 시스템이라 더 모르겠더라.. )
3) worker, scheduler, webserver 등을 다 따로 체크해야 한다.
(솔직히 관리포인트 말고는 별 차이는 없지만, 다른 아키텍쳐를 써보고 싶은 마음이 컸다. 이거 대부분 k8s 쓰면 해결은 됨.)
<br>
<br>
## 3. 해답
- 팀원한테 좀 징징대봤다.
- 아 우리 어차피 pipeline도 얼마없는데 관리도 힘든 MQ 꼭 써야돼요?
( 나는 MQ에 대한 슬픈 사건이 꽤 많다... )
- 이거 안쓰고 localexecutor 같은건 문제가 있겠죠? 이건 안되겠고.
이러한 질문에 팀원이 대답해줬다.
**어 그거 라인인가 어딘가에서 k8s executor라고 쓰던데요.**
찾아보니
1) worker=pod이라서 MQ 없어도 되고
2) k8s 환경에서 선형적으로 작업량 늘어나도 분산처리 쉽고
3) image로 소스 처리하니까 CI/CD 쓸수있어요.
대략 이런 구조가 된다.
scheduler는 task를 job으로 만들고, 해당 job 하나하나는 해당하는 소스(이미지)를 불러와서 pod으로 기동된다.
우리는 하버를 쓰기때문에 배포할 수도 있고, 나중에는 확장해서 CI/CD까지 노려볼 수 있었다. 내가 그렇게 하고 싶어하던 전체 자동화!
**안 할 이유가 없다**
라고 생각했고, 안이한 생각이었음을 뒤늦게 깨닫게 된다.... | hj_lee |
1,876,706 | How to Deploy Flutter Apps to the Google Play Store and App Store | Today, we embark on an adventure – a journey to launch your captivating Flutter app onto the vast... | 0 | 2024-06-04T13:48:50 | https://dev.to/devitpl/how-to-deploy-flutter-apps-to-the-google-play-store-and-app-store-mko | webdev, flutterapps, deploy, development | Today, we embark on an adventure – a journey to launch your captivating [Flutter app onto the vast landscapes of the Google Play Store and App Store](https://www.devitpl.com/application-development/how-to-deploy-flutter-apps-to-the-google-play-store-and-app-store/).
This comprehensive guide will be your trusty compass, guiding you through the essential steps of deploying your app on both prominent platforms. Whether you’re a seasoned developer or a curious newcomer, this guide is here to demystify the process and ensure a smooth [application deployment](https://www.devitpl.com/application-development/) for your creation. | devitpl |
1,876,705 | Azure Service Bus and Azure Functions Integration | Create and reading messages on service bus | 27,592 | 2024-06-04T13:48:46 | https://dev.to/campelo/azure-service-bus-and-azure-functions-integration-4p3h | servicebus, queue, funcions, message | ---
title: Azure Service Bus and Azure Functions Integration
series: Azure Service Bus and Azure Functions Integration
published: true
description: Create and reading messages on service bus
tags: 'servicebus, queue, funcions, message'
cover_image: 'https://raw.githubusercontent.com/campelo/documentation/master/posts/azure/assets/cover.png'
canonical_url: null
id: null
---
###### :postbox: Contact :brazil: :us: :fr:
[Twitter](https://twitter.com/campelo87)
[LinkedIn](https://www.linkedin.com/in/flavio-campelo/?locale=en_US)
---
# Azure Service Bus and Azure Functions Integration
This documentation provides a step-by-step guide on how to create and configure an Azure Service Bus and implement Azure Functions to send and process messages using dependency injection.
## Prerequisites
- Azure Subscription
- Visual Studio or Visual Studio Code
- Azure Functions Core Tools
- .NET Core SDK
## Steps
### 1. Create Azure Service Bus
#### Using Azure Portal
1. Create a Service Bus Namespace:
a. Go to the Azure portal and click on "Create a resource".
b. Search for "Service Bus" and click "Create".
c. Fill in the necessary details:
- Name: Enter a unique name for the namespace.
- Region: Select your preferred region.
- Pricing tier: Choose the appropriate pricing tier.
d. Click "Review + create" and then "Create".
2. Create Queues in the Namespace:
a. After the namespace is created, navigate to it.
b. In the left-hand menu, select "Queues" and click "+ Queue" at the top.
c. Fill in the queue details:
- Name: flow.normal
- Configure additional settings as needed or leave default values.
d. Click "Create".
e. Repeat the process to create a queue named flow.rejected.
#### Using Azure CLI
1. Log in to Azure:
```sh
az login
```
2. Create a Service Bus Namespace:
```sh
az servicebus namespace create --resource-group <ResourceGroupName> --name <NamespaceName> --location <Region>
```
3. Create Queues:
```sh
az servicebus queue create --resource-group <ResourceGroupName> --namespace-name <NamespaceName> --name flow.normal
az servicebus queue create --resource-group <ResourceGroupName> --namespace-name <NamespaceName> --name flow.rejected
```
### 2. Create Azure Functions
#### Create a New Azure Functions Project
1. Open Visual Studio or Visual Studio Code and create a new Azure Functions project:
```sh
func init MyFunctionApp --dotnet
cd MyFunctionApp
func new --name CreateServiceBusMessage --template "HTTP trigger" --authlevel "anonymous"
func new --name NormalFlow --template "Queue trigger" --queueName "flow.normal"
func new --name RejectedFlow --template "Queue trigger" --queueName "flow.rejected"
```
2. Add Necessary Packages:
Add the following packages to your .csproj file:
- Microsoft.Azure.Functions.Extensions
- Microsoft.Extensions.DependencyInjection
- Azure.Messaging.ServiceBus
3. Register ServiceBusClient in services:
Add ServiceBusClient as service in Program.cs class:
```csharp
// ...
var host = new HostBuilder()
.ConfigureServices((builderContext, services) =>
{
services
// add this line...
.AddSingleton<ServiceBusClient>(new ServiceBusClient(Environment.GetEnvironmentVariable("ServiceBusConnectionString")));
})
.ConfigureFunctionsWorkerDefaults()
.Build();
// ...
```
4. Create a MyMessage class:
```csharp
public class MyMessage
{
public string Message { get; set; }
public int Value { get; set; }
}
```
5. Implement the Functions:
Create the functions CreateServiceBusMessage, NormalFlow, and RejectedFlow.
```csharp
[Function(nameof(CreateServiceBusMessage))]
public async Task<HttpResponseData> CreateServiceBusMessage(
[HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "CreateServiceBusMessage")]
HttpRequestData req,
FunctionContext executionContext)
{
var logger = executionContext.GetLogger("CreateServiceBusMessage");
string requestBody = await new StreamReader(req.Body).ReadToEndAsync();
MyMessage message = JsonConvert.DeserializeObject<MyMessage>(requestBody);
if (message == null || string.IsNullOrEmpty(message.Message))
{
var badResponse = req.CreateResponse(System.Net.HttpStatusCode.BadRequest);
badResponse.WriteString("Invalid message");
return badResponse;
}
ServiceBusSender sender = serviceBusClient.CreateSender("flow.normal");
try
{
await sender.SendMessageAsync(new ServiceBusMessage(JsonConvert.SerializeObject(message)));
var response = req.CreateResponse(System.Net.HttpStatusCode.OK);
response.WriteString("Message sent to Service Bus");
return response;
}
catch (Exception ex)
{
logger.LogError($"Error sending message: {ex.Message}");
var errorResponse = req.CreateResponse(System.Net.HttpStatusCode.InternalServerError);
errorResponse.WriteString("Failed to send message");
return errorResponse;
}
finally
{
await sender.DisposeAsync();
}
}
[Function("NormalFlow")]
public async Task NormalFlow(
[ServiceBusTrigger("flow.normal", Connection = "ServiceBusConnectionString")]
string myQueueItem, FunctionContext executionContext)
{
var logger = executionContext.GetLogger("NormalFlow");
logger.LogInformation("Processing message from flow.normal queue: {myQueueItem}", myQueueItem);
// For demonstration, we'll move the message to rejected queue if value is less than 10
MyMessage message = JsonConvert.DeserializeObject<MyMessage>(myQueueItem);
if (message != null && message.Value < 10)
{
ServiceBusSender sender = serviceBusClient.CreateSender("flow.rejected");
try
{
await sender.SendMessageAsync(new ServiceBusMessage(myQueueItem));
}
catch (Exception ex)
{
logger.LogError("Error moving message to flow.rejected queue: {message}", ex.Message);
}
finally
{
await sender.DisposeAsync();
}
}
}
[Function("RejectedFlow")]
public async Task RejectedFlow(
[ServiceBusTrigger("flow.rejected", Connection = "ServiceBusConnectionString")]
string myQueueItem, FunctionContext executionContext)
{
var logger = executionContext.GetLogger("RejectedFlow");
logger.LogInformation("Processing rejected message: {myQueueItem}", myQueueItem);
}
```
6. Add ServiceBus conneciton string:
In your local.settings.json file, add a new environment variable to connect to ServiceBus:
```json
{
"IsEncrypted": false,
"Values": {
... // other environment variables
"ServiceBusConnectionString": "Endpoint=sb://my-service-bus.servicebus.windows.net/;SharedAccessKeyName=RootManageSharedAccessKey;SharedAccessKey=MY_SECRET_KEY"
}
}
```
### 3. Run and Test the Functions
1. Run the Project:
Run the project in Visual Studio.
2. Test CreateServiceBusMessage:
Use a tool like Postman to send a POST request to the URL of the CreateServiceBusMessage function with a JSON body:
```json
{
"message": "Test message",
"value": 5
}
```
3. Verify Processing of NormalFlow and RejectedFlow:
Check the logs for the NormalFlow and RejectedFlow functions to confirm that the messages are being processed correctly.
With these steps, you have successfully created and configured an Azure Service Bus and implemented Azure Functions to send and process messages using dependency injection.
## Source code
[Sample code](https://github.com/campelo/AzureFunctionsSample)
## Typos or suggestions?
If you've found a typo, a sentence that could be improved or anything else that should be updated on this blog post, you can access it through a git repository and make a pull request. If you feel comfortable with github, instead of posting a comment, please go directly to https://github.com/campelo/documentation and open a new pull request with your changes.
| campelo |
1,876,704 | Power BI Dashboard: Ultimate Tool for Dynamic Data Visualization and Storytelling | Are you ready to unleash the power of data storytelling with Power BI? Take the first step today and... | 0 | 2024-06-04T13:45:11 | https://dev.to/devitpl/power-bi-dashboard-ultimate-tool-for-dynamic-data-visualization-and-storytelling-246 | powerbi, dashboard, bi, microsoft | Are you ready to unleash the [power of data storytelling with Power BI](https://www.devitpl.com/microsoft-power-platform/power-bi-dashboard-ultimate-tool-for-dynamic-data-visualization-and-storytelling/)? Take the first step today and embark on a journey of data discovery. Explore the vast potential of Power BI and watch your data transform from a silent collection of numbers into a chorus of insights that drive success! | devitpl |
1,876,699 | Exploring the Power of Adobe Photoshop: A Comprehensive Guide | Adobe Photoshop stands as the cornerstone of [digital image]editing and manipulation, revolutionizing... | 0 | 2024-06-04T13:38:45 | https://dev.to/rai_touqeer_af431ddbdfbd2/exploring-the-power-of-adobe-photoshop-a-comprehensive-guide-1on4 |
Adobe Photoshop stands as the cornerstone of [digital image]editing and manipulation, revolutionizing the way we perceive and interact with visual content. From amateur photographers to seasoned graphic designers, Photoshop has become an indispensable tool for unleashing creativity and turning concepts into captivating visuals. This article delves into the intricacies of Adobe Photoshop, exploring its history, features, and its profound impact on the world of [digital artistry](https://cracksmad.com/).
Evolution of Adobe Photoshop:
Adobe Photoshop made its debut in 1988, created by Thomas and John Knoll. Initially developed as a simple image editing program, it has since evolved into a powerhouse of digital creativity, boasting an extensive array of tools and functionalities. Over the years, Adobe has released numerous versions of Photoshop, each introducing groundbreaking features and enhancements. From the introduction of layers and filters to the integration of advanced masking techniques, Photoshop continues to push the boundaries of what is possible in digital image manipulation.
Key Features and Tools:
At the heart of Adobe Photoshop lies a vast arsenal of features and tools designed to empower users to bring their creative visions to life. Some of the key features include:
Layers: One of Photoshop's most fundamental features, layers allow users to stack multiple elements within a single document, enabling precise control over each element's appearance and position.
Selection Tools: Photoshop offers a variety of selection tools, including the Marquee, Lasso, and Magic Wand, which allow users to isolate and manipulate specific areas of an image with precision.
Brushes and Painting Tools: With an extensive collection of brushes and painting tools, Photoshop enables users to paint, draw, and digitally manipulate images with unparalleled realism and detail.
Filters and Effects: From basic blurs and sharpening filters to more complex effects like liquify and warp, Photoshop provides a vast array of filters and effects to enhance and transform images in creative ways.
Typography: With robust typography tools, including the ability to create and manipulate text layers, adjust font properties, and apply text effects, Photoshop allows users to seamlessly integrate text into their designs.
Retouching and Restoration: Photoshop's retouching and restoration tools enable users to remove imperfections, correct color and exposure issues, and restore old or damaged photos to their former glory.
3D Editing: In recent versions, Adobe has introduced powerful 3D editing capabilities, allowing users to create, manipulate, and render 3D objects directly within Photoshop.
Impact on Digital Artistry:
The widespread adoption of Adobe Photoshop has had a profound impact on the world of digital artistry. From graphic design and illustration to photography and digital painting, Photoshop has become the go-to tool for professionals and enthusiasts alike. Its intuitive interface, combined with its extensive feature set, empowers users to unleash their creativity and turn their ideas into stunning visual masterpieces. Whether it's retouching photos, creating complex composites, or designing captivating digital artwork, Photoshop remains unrivaled in its versatility and power.
Conclusion:
Adobe Photoshop continues to reign supreme as the ultimate tool for digital image editing and manipulation. With its rich history, vast array of features, and profound impact on the world of digital artistry, Photoshop stands as a testament to the endless possibilities of creative expression in the digital age. Whether you're a seasoned professional or a budding enthusiast, Adobe Photoshop remains an essential companion on the journey to unlocking your creative potential. | rai_touqeer_af431ddbdfbd2 | |
1,876,698 | Play.HT : Le pouvoir de la voix IA au service de votre communication | Dans un monde numérique en constante évolution, où l'information est omniprésente et l'attention des... | 0 | 2024-06-04T13:36:19 | https://dev.to/vulgar_ia/playht-le-pouvoir-de-la-voix-ia-au-service-de-votre-communication-17dm | Dans un monde numérique en constante évolution, où l'information est omniprésente et l'attention des consommateurs est précieuse, se démarquer et captiver son audience devient un défi crucial. C'est là que [Play.ht](https://www.vulgaria.fr/outils-ia/mon-avis-play-ht-meilleur-generateur-de-voix-ia/) entre en jeu, révolutionnant le paysage de la communication en offrant une solution innovante et puissante : la synthèse vocale IA.
[](https://www.vulgaria.fr/recommends/play_ht/)
##Donnez vie à vos mots : une expérience immersive
Imaginez pouvoir transformer vos scripts de podcasts, vos articles de blog, vos présentations e-learning ou vos messages marketing en voix off captivantes et personnalisées. Play.ht vous offre cette possibilité avec une bibliothèque de plus de 907 voix IA dans plus de 120 langues. Que vous recherchiez une voix narrative chaleureuse pour votre podcast, une voix dynamique pour vos vidéos YouTube ou une voix professionnelle pour vos systèmes IVR, vous trouverez forcément la voix parfaite pour correspondre à votre message et à votre public.
##Des voix uniques et authentiques : suscitez l'émotion
Play.ht va bien au-delà des voix préenregistrées. Grâce à sa technologie de clonage vocal, vous pouvez reproduire fidèlement la voix d'une personne existante, idéale pour les narrations personnalisées, les doublages ou la création de personnages uniques. Imaginez pouvoir donner vie à votre mascotte d'entreprise ou à un personnage historique grâce à une voix authentique et reconnaissable.
##Un contrôle précis pour une voix parfaite : l'art de la nuance
Play.ht ne se contente pas de vous offrir une multitude de voix. Il vous donne également le contrôle total sur la façon dont elles s'expriment. Ajustez le débit, la tonalité, l'accentuation, les pauses et la prononciation pour obtenir une voix qui correspond parfaitement à vos besoins et à votre style. Créez des voix expressives et nuancées qui transmettent les émotions subtiles de votre message, captivant ainsi votre audience en profondeur.
##Des émotions au service de votre message : l'impact du ton
Allez au-delà de la simple lecture et insufflez de la vie à vos textes avec des styles émotionnels variés. Transmettez des sentiments comme le bonheur, la tristesse, la colère et l'excitation pour créer une expérience auditive captivante et toucher votre audience en profondeur. Imaginez pouvoir raconter une histoire émouvante ou présenter un produit innovant avec une voix qui suscite l'enthousiasme et l'engagement de vos auditeurs.
##Intégration transparente pour une utilisation simplifiée : la flexibilité au service de la création
Play.ht s'intègre facilement à vos outils et workflows existants. Des extensions Chrome aux plugins WordPress en passant par une API de synthèse vocale robuste, Play.ht vous permet de créer des voix de haute qualité en toute simplicité, où que vous soyez et quelle que soit votre plateforme. Que vous travailliez sur un podcast depuis votre ordinateur portable ou sur une vidéo marketing depuis votre studio, Play.ht s'adapte à votre environnement de travail pour vous offrir une expérience fluide et productive.
##Avantages concrets pour une communication réussie : le succès à votre portée
En adoptant Play.ht, vous profitez de nombreux avantages qui vous permettront d'atteindre vos objectifs de communication :
**Accessibilité accrue:** Rendez votre contenu accessible à un public plus large en proposant des options audio de qualité, notamment pour les personnes ayant des difficultés de lecture ou d'apprentissage.
**Engagement amplifié:** Captivez votre audience avec des voix réalistes et expressives qui retiennent l'attention, stimulent l'intérêt et favorisent la mémorisation de votre message.
**Gain de temps et d'argent:** Créez des voix off professionnelles rapidement et facilement sans avoir recours à des narrateurs ou acteurs coûteux, optimisant ainsi votre temps et vos ressources.
**Portée élargie:** Diffusez votre message dans le monde entier en traduisant votre contenu dans plusieurs langues grâce à la large gamme de voix disponibles, touchant ainsi de nouveaux publics et développant votre audience internationale.
Cas d'utilisation pour tous les besoins : la polyvalence au service de la créativité
[](https://www.vulgaria.fr/recommends/play_ht/)
##Play.ht s'adapte à une multitude de cas d'utilisation, répondant aux besoins des créateurs et des entreprises dans divers domaines :
- **Podcasts:** Créez des podcasts captivants avec des voix narratives engageantes, des intros/outros personnalisées et des effets sonores immersifs.
- **Vidéos YouTube:** Améliorez la production de vos vidéos YouTube en ajoutant des voix off professionnelles, des doublages pour vos interviews ou tutoriels, et des éléments sonores pour une expérience immersive qui captive vos spectateurs.
- **E-learning:** Développez des formations en ligne attrayantes et accessibles en intégrant des voix off explicatives, des modules audio interactifs et des scénarios de dialogue réalistes pour une meilleure compréhension et rétention des connaissances.
- **Systèmes de réponse vocale interactive (IVR):** Offrez une expérience utilisateur fluide et personnalisée grâce à des voix IA réalistes pour vos systèmes de réponse vocale. Imaginez des IVR plus agréables et efficaces qui guident vos clients et répondent à leurs questions de manière claire et engageante.
- **Livres audio et doublage:** Créez des livres audio immersifs qui transportent vos auditeurs dans l'histoire, ou doublez des vidéos et des films dans différentes langues pour un public international, élargissant ainsi votre portée et brisant les barrières linguistiques.
- **Publicité et marketing:** Créez des publicités et des messages marketing convaincants avec des voix off impactantes qui capturent l'attention de votre audience et suscitent l'intérêt pour vos produits ou services.
Imaginez des publicités radio ou des vidéos marketing sur les réseaux sociaux qui se démarquent par leur narration captivante et leur ton émotionnel idéal.
##Rejoignez la révolution de la voix IA : l'avenir de la communication
Play.ht n'est pas seulement un outil, c'est une opportunité de transformer votre communication et d'atteindre de nouveaux sommets. En exploitant la puissance de la synthèse vocale IA, vous pouvez créer des voix off réalistes, expressives et personnalisées qui captivent votre audience, améliorent l'accessibilité de votre contenu et vous permettent de diffuser votre message dans le monde entier.
Essayez Play.ht dès aujourd'hui et découvrez la façon dont la voix IA peut vous aider à raconter vos histoires, à diffuser vos connaissances et à atteindre vos objectifs de communication.
Play.ht est un générateur vocal IA puissant et polyvalent qui s'impose comme un outil clé dans le paysage de la communication moderne. Grâce à ses fonctionnalités avancées, sa facilité d'utilisation et ses nombreux avantages concrets, Play.ht permet aux créateurs et aux entreprises de donner vie à leur contenu textuel et de captiver efficacement leur audience. Que vous soyez un podcasteur passionné, un youtubeur en herbe ou une entreprise cherchant à innover dans sa communication, Play.ht est la solution idéale pour insuffler une nouvelle dimension à vos projets et à votre stratégie de communication. | vulgar_ia | |
1,876,697 | Building Dynamic Forms in Angular | In Angular applications, forms are a fundamental aspect of collecting and managing user input. While... | 0 | 2024-06-04T13:33:11 | https://dev.to/bytebantz/building-dynamic-forms-in-angular-476m | angular, javascript, webdev, typescript | In Angular applications, forms are a fundamental aspect of collecting and managing user input. While static forms are common, there are scenarios where you need to create dynamic forms that adapt to user interactions, such as adding or removing form fields dynamically. In this article, we’ll explore how to build dynamic forms in Angular using the FormArray class, with step-by-step technical examples
## 1. Importing the FormArray Class
To start building dynamic forms in Angular, we first need to import the FormArray class from the **@angular/forms** module. The **FormArray** class provides us with the tools necessary to manage an array of form controls dynamically.
```
import { FormArray, FormBuilder, ReactiveFormsModule } from '@angular/forms';
```
## 2. Defining a FormArray Control
Next, we define a **FormArray** control within our main **FormGroup**. This FormArray will hold the dynamically added form controls, such as input fields or other form elements.
```
export class ProfileEditorComponent {
// Initialize FormGroup with FormArray
profileForm: FormGroup;
// Constructor to initialize FormBuilder
constructor(private formBuilder: FormBuilder) {
this.profileForm = this.formBuilder.group({
favoriteBooks: this.formBuilder.array([this.formBuilder.control('')])
});
}
}
```
## 3. Accessing the FormArray Control
We create a getter method to access the **FormArray** control defined in our **FormGroup**. This getter method allows us to interact with the **FormArray** in our component logic.
```
get favoriteBooks() {
return this.profileForm.get('favoriteBooks') as FormArray;
}
```
**this.profileForm.get(‘favoriteBooks’)** returns an AbstractControl, but we explicitly cast it to a FormArray using **as FormArray**. Now, whenever we call **this.favoriteBooks**, it will execute this getter method, which in turn retrieves the FormArray control for us.
In essence, the **get favoriteBooks()** method acts as a convenient and consistent way to access the **favoriteBooks** FormArray control within the component. It abstracts away the complexity of accessing the control directly and promotes encapsulation and code readability.
## 3. Displaying the FormArray in the Template
In the HTML template file, we use the formArrayName directive to bind the FormArray to the template. We then use *ngFor to loop through each form control in the FormArray and display the corresponding form fields dynamically.
```
<div formArrayName="favoriteBooks">
<h2>Favorite Books</h2>
<button type="button" (click)="addBook()">Add Book</button>
<div *ngFor="let book of favoriteBooks.controls; let i=index">
<label for="book-{{ i }}">Book {{ i + 1 }}:</label>
<input id="book-{{ i }}" type="text" [formControlName]="i">
<button (click)="removeBook(i)">Delete book </button>
</div>
</div>
```
## 4. Adding a Method to Dynamically Insert Controls
Now, we implement a method to add new form controls dynamically to the FormArray. This method is called when the user interacts with the form, such as clicking a button to add a new form field.
```
addBook() {
this.favoriteBooks.push(this.formBuilder.control(''));
}
```
## 5. Adding a Method to Dynamically Delete Controls
Finally, we implement a method to delete a form control. This method is called when the user clicks a button to delete the form field.
```
removeBook(index: number) {
this.favoriteBooks.removeAt(index)
}
```
## Conclusion
Dynamic forms provide a flexible and user-friendly way to collect data in Angular applications. By leveraging the FormArray class, developers can create forms that adapt to various user inputs dynamically. With the step-by-step examples provided in this article, you can confidently build dynamic forms in your Angular projects, enhancing the user experience and usability of your applications.
To get the rest of the code check the following links:
[https://github.com/anthony-kigotho/angular-forms](https://github.com/anthony-kigotho/angular-forms)
## CTA
💛If you enjoy my articles, consider [subscribing to my newsletter](https://bytewave.substack.com/) to receive my new articles by email | bytebantz |
1,876,696 | Another Framework What's So Special About It ? | I've been dreaming of a ui library that lets me use declarative javascript. Okay, but what do i mean... | 0 | 2024-06-04T13:32:04 | https://dev.to/oarabiledev/another-framework-whats-so-special-about-it--3ejk | webdev, javascript, ui, library | I've been dreaming of a ui library that lets me use declarative javascript.
Okay, but what do i mean cause every framework/library calls itself declarative.
I mean by this !
`ui.addButton(parent, text, width, height, options)`
And not jsx or a compiled framework. The mission is to keep the core library code under a 1000 lines of code.
---
innerscope's mission is to provide a set of core functions that can be extended to build custom components or port a whole design system to it.
Another design decision is we use a classes, its just to reduce js common pitfalls.
All you have to do is and the typical `index.html` file
And a companion `App.js` file.
_check the github page about this and its set-up_
How it works is I use a concept of layouts, layouts are div's but with special styles like `flex-start` and `flex-end`.
A layout is called like :
`ui.createLayout(layoutType, options)`
There are 4 types of layouts :
- Linear
- Absolute
- Card
- Frame
And don't forget to add it to your page via, `ui.addLayout()` function.
---
Writing Components & Sharing Them
Firstly i decided to create a class `ElementComposer` this class allows you to create components using this similar syntax and will inherit methods like ( setMargins / setPosition / show & hide ).
You can extend these methods by adding your own, but check the syntax rules.
When adding your components, you can load your additional scripts and css via the `ui.loadScript()` and `ui.loadCSS()`.
Writing components is done in js, so that means you use the
`document.createElement()` api.
And finally add it to the composer div.
---
And you can write your ui in javascript, on top of that you can use this in production.
I've published it on npmjs.com and add as a script tag at unpkg.com.
[https://github.com/oarabiledev/innerscope](Here is my framework (✿◡‿◡))
Thank You For The Interest ❣️❣️❣️❣️
| oarabiledev |
1,876,695 | User Authorization with Postgres Row Level Security Policy | Supabase has a storage gateway that uses RLS for authorization. It requires a JWT that provides the... | 0 | 2024-06-04T13:31:02 | https://dev.to/keming/user-authorization-with-postgres-row-level-security-policy-4g91 | postgres, sql, database, security | Supabase has a [storage gateway](https://github.com/supabase/storage) that uses [RLS](https://www.postgresql.org/docs/current/ddl-rowsecurity.html) for authorization.
It requires a JWT that provides the role information to perform the SQL, here is an example of the JWT payload:
```json
{
"sub": "authenticated",
"iat": 1516239022,
"role": "f918ffd9-a611-4b2a-b4bb-df8f25d7569f"
}
```
The storage bucket table is:
```
Table "storage.buckets"
Column | Type | Collation | Nullable | Default
------------+--------------------------+-----------+----------+---------
id | text | | not null |
name | text | | not null |
owner | uuid | | |
created_at | timestamp with time zone | | | now()
updated_at | timestamp with time zone | | | now()
```
You only need to set up the correct RLS policy in the database. Here is an example:
```sql
-- generate a UUID as the role name since it needs to match the owner type
SELECT gen_random_uuid(); -- f918ffd9-a611-4b2a-b4bb-df8f25d7569f
CREATE ROLE "f918ffd9-a611-4b2a-b4bb-df8f25d7569f";
GRANT all ON schema storage TO "f918ffd9-a611-4b2a-b4bb-df8f25d7569f";
GRANT all on buckets TO "f918ffd9-a611-4b2a-b4bb-df8f25d7569f";
-- generate another role
CREATE ROLE "11b795e0-a566-491b-9ee7-62c025175dd8";
GRANT all ON schema storage TO "11b795e0-a566-491b-9ee7-62c025175dd8";
GRANT all on buckets TO "11b795e0-a566-491b-9ee7-62c025175dd8";
CREATE OR REPLACE FUNCTION user_record_count(uuid) RETURNS integer AS $$
DECLARE
count integer;
BEGIN
SELECT COUNT(*) INTO count
FROM buckets
WHERE owner = $1;
RETURN count;
END;
$$ LANGUAGE plpgsql;
CREATE POLICY limit_user_crud ON buckets
USING (owner = current_user::uuid)
WITH CHECK (
(SELECT user_record_count(current_user::uuid) < 3)
);
SET ROLE "f918ffd9-a611-4b2a-b4bb-df8f25d7569f";
INSERT INTO buckets (id, name, owner) VALUES ('1', 'one', current_user::uuid);
INSERT INTO buckets (id, name, owner) VALUES ('2', 'two', current_user::uuid);
INSERT INTO buckets (id, name, owner) VALUES ('3', 'three', current_user::uuid);
-- check before the insertion
INSERT INTO buckets (id, name, owner) VALUES ('4', 'four', current_user::uuid); -- ERROR: new row violates row-level security policy for table "buckets"
SELECT * FROM buckets; -- this returns 3 rows
SET role "11b795e0-a566-491b-9ee7-62c025175dd8";
SELECT * FROM buckets; -- this returns nothing
INSERT INTO buckets (id, name, owner) VALUES ('4', 'four', current_user::uuid); -- success
DELETE FROM buckets where id = '4'; -- success
DELETE FROM buckets where id = '1'; -- delete 0
``` | keming |
1,876,694 | What is an Attack Surface Management? | Imagine every application, system, device, and online service you utilize as potential gateways for... | 0 | 2024-06-04T13:30:55 | https://www.clouddefense.ai/what-is-an-attack-surface-management/ |
Imagine every application, system, device, and online service you utilize as potential gateways for cybercriminals to infiltrate your digital domain. Alarming, isn't it? This is the current reality in our hyper-connected world, where each technological adoption broadens your attack surface. This is precisely where Attack Surface Management (ASM) becomes essential.
###Defining the Attack Surface
In cybersecurity terminology, an "attack surface" encompasses all the potential entry points, vulnerabilities, and exposed areas that hackers could exploit to gain unauthorized access to your systems, applications, or networks. Consider it as every digital door and window into your environment – the more you have, the more opportunities for cyber intruders.
###What is Attack Surface Management?
ASM functions like an advanced radar system for your digital infrastructure. It surpasses traditional asset discovery by adopting a hacker’s viewpoint, identifying both known and hidden vulnerabilities. ASM involves continuous scanning, analysis, prioritization, remediation, and monitoring of your digital assets, ensuring that new vulnerabilities do not go undetected.
###The Five Stages of Attack Surface Management
**1. Discovery:** This stage involves identifying and cataloging all digital assets, both internal and external, including any overlooked or rogue elements that might contain vulnerabilities.
**2. Continuous Monitoring:** It entails maintaining vigilant oversight of your assets, constantly scanning for new vulnerabilities and misconfigurations.
**3. Contextualization:** This involves understanding the context of each asset – its purpose, usage patterns, network connections, and business role – to effectively prioritize remediation efforts.
**4. Prioritization:** It uses data-driven risk scoring to focus on the most critical vulnerabilities first, ensuring the most severe risks are promptly addressed.
**5. Accelerated Remediation:** This stage is about taking decisive action to patch vulnerabilities, tighten configurations, and implement additional security measures, ensuring effective collaboration between IT and security teams.
###Protecting Against Cyberattacks with ASM
ASM provides a proactive cybersecurity approach by offering a hacker’s perspective on your vulnerabilities, allowing you to fix them before they can be exploited. It aids in prioritizing threats by identifying and addressing the most critical issues first and ensures that your security measures evolve with the constantly changing digital landscape.
###Reducing Attack Surface Risks
To minimize your attack surface, apply key strategies such as maintaining continuous visibility across your environment and prioritizing based on risk to focus remediation efforts on the most severe vulnerabilities. Promptly patch known vulnerabilities, secure configurations by eliminating insecure setups, and limit attack paths by reducing connectivity and disabling unnecessary software. Enforce the principle of least privilege by restricting user permissions to what is strictly necessary and manage your digital supply chain by including third-party risks in your visibility.
###Partnering with CloudDefense.AI for ASM
CloudDefense.AI strengthens your ASM with a thorough approach to cloud security. It maps vulnerabilities by identifying all potential entry points in your cloud infrastructure, constructs an attack graph to visualize interconnected vulnerabilities, and prioritizes threats by assessing the likelihood and impact of each attack scenario. With continuous monitoring, it ensures your security posture is always current with real-time analysis. Implementing CloudDefense.AI provides proactive threat detection, prioritized remediation, improved decision-making, reduced breach risks, and enhanced compliance.
In summary, ASM is not merely about responding to cyber threats; it's about preemptively countering them. By adopting a proactive ASM strategy, you can safeguard your digital environment and ensure the security of your valuable data. Embrace ASM to strengthen your defenses against the continuously evolving cyber threat landscape. | clouddefenseai | |
1,876,693 | Here are 14 essential design principles for microservices: | Interface Segregation: Different types of clients (e.g., mobile apps, web apps, CLI programs) should... | 0 | 2024-06-04T13:30:15 | https://dev.to/akshansh_shrivastava_b0d2/here-are-14-essential-design-principles-for-microservices-21c7 | webdev, beginners, programming, tutorial | 1. Interface Segregation: Different types of clients (e.g., mobile apps, web apps, CLI programs) should be able to interact with services through the contract that best suits their needs.
2. Deployability: In the microservice era, developers need to make critical design decisions and technology choices regarding packaging, deploying, and running microservices.
3. Event-Driven: Services should be modeled to be activated by asynchronous messages or events instead of synchronous calls.
4. Availability Over Consistency: End users often value the availability of the system over strong data consistency, and they are okay with eventual consistency.
5. Loose Coupling: Microservices should not have a tight dependency on each other to ensure scalability and ease of deployment.
6. Single Responsibility: Each microservice should have a single, well-defined responsibility and should only communicate with other microservices to accomplish tasks.
7. Decentralized Data Management: Each microservice should manage its data without relying on other microservices to ensure scalability and reliability.
8. API-Driven Design: Microservices should be designed around APIs, with each service exposing a well-defined set of APIs for communication with other services.
9. Statelessness: Microservices should be stateless, meaning they should not maintain any client-specific state between requests.
10. Independent and Autonomous/Self-Governing Services: Each microservice should be self-contained and operate independently of all other services in an application.
11. API Aggregation: Microservices should be designed to aggregate APIs for communication with other services.
12. Flexibility: Microservices should be designed to be flexible and adaptable to changing requirements.
13. Scalability: Microservices should be designed to scale independently and handle increased load.
14. Constant Monitoring and Failure Isolation/Resilience: Microservices should be constantly monitored and designed to isolate failures to ensure the overall system remains available and resilient.
These principles help ensure that microservices are designed to be scalable, reliable, and maintainable, making them essential for building robust and efficient microservices-based applications. | akshansh_shrivastava_b0d2 |
1,876,692 | Kubernetes installation | These instructions are for Kubernetes v1.30 (debian-based) controlplane & worker node Update... | 0 | 2024-06-04T13:28:19 | https://dev.to/abdallah_kordy_94db275ef5/kubernetes-installation-54e1 | kubernetes, containers, docker | **These instructions are for Kubernetes v1.30 (debian-based)** controlplane & worker node
1. Update the apt package index install packages needed to use the Kubernetes apt repository:
```
sudo apt-get update
# apt-transport-https may be a dummy package; if so, you can skip that package
sudo apt-get install -y apt-transport-https ca-certificates curl gpg
```
2.Download the public signing key for the Kubernetes package repositories. The same signing key is used for all repositories so you can disregard the version in the URL:
```
# If the directory `/etc/apt/keyrings` does not exist, it should be created before the curl command, read the note below.
# sudo mkdir -p -m 755 /etc/apt/keyrings
curl -fsSL https://pkgs.k8s.io/core:/stable:/v1.30/deb/Release.key | sudo gpg --dearmor -o /etc/apt/keyrings/kubernetes-apt-keyring.gpg
```
3.Add the appropriate Kubernetes apt repository. Please note that this repository have packages only for Kubernetes 1.30; for other Kubernetes minor versions, you need to change the Kubernetes minor version in the URL to match your desired minor version (you should also check that you are reading the documentation for the version of Kubernetes that you plan to install).
```
# This overwrites any existing configuration in /etc/apt/sources.list.d/kubernetes.list
echo 'deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/v1.30/deb/ /' | sudo tee /etc/apt/sources.list.d/kubernetes.list
```
4.Update the apt package index, install kubelet, kubeadm and kubectl, and pin their version:
```
sudo apt-get update
sudo apt-get install -y kubelet kubeadm kubectl
sudo apt-mark hold kubelet kubeadm kubectl
```
5.(Optional) Enable the kubelet service before running kubeadm:
```
sudo systemctl enable --now kubelet
```
6.
You might know about swap space on hard drives, which OS systems try to use as if it were RAM. Operating systems try to move less frequently accessed data to the swap space to free up RAM for more immediate tasks. However, accessing data in swap is much slower than accessing data in RAM because hard drives are slower than RAM.
Kubernetes schedules work based on the understanding of available resources. If workloads start using swap, it can become difficult for Kubernetes to make accurate scheduling decisions. Therefore, it’s recommended to disable swap before installing Kubernetes.
```
sudo swapoff
```
```
sudo sed -i '/ swap / s/^/#/' /etc/fstab
```
8.
To configure the IPV4 bridge on all nodes, execute the following commands on each node
```
cat <<EOF | sudo tee /etc/modules-load.d/k8s.conf
overlay
br_netfilter
EOF
sudo modprobe overlay
sudo modprobe br_netfilter
```
```
# sysctl params required by setup, params persist across reboots
cat <<EOF | sudo tee /etc/sysctl.d/k8s.conf
net.bridge.bridge-nf-call-iptables = 1
net.bridge.bridge-nf-call-ip6tables = 1
net.ipv4.ip_forward = 1
EOF
# Apply sysctl params without reboot
sudo sysctl --system
```
9.
install containerd Runtime
```
sudo apt install containerd
sudo mkdir /etc/containerd
```
10.
Then, create a default configuration file for containerd and save it as config.toml using the
```
containerd config default > /etc/containerd/config.toml
```
11.
```
sudo sed -i 's/ SystemdCgroup = false/ SystemdCgroup = true/' /etc/containerd/config.toml
```
12.
restart containerd & kubelet so as change take effect
```
sudo systemctl restart containerd.service
sudo systemctl restart kubelet.service
```
13.
```
sudo kubeadm config images pull
```
14
configure best & secured network plugin (kubernetes )
```
kubectl apply -f https://raw.githubusercontent.com/projectcalico/calico/v3.26.1/manifests/tigera-operator.yaml
```
| abdallah_kordy_94db275ef5 |
1,876,691 | Evolving Big Data Strategies with Data Lakehouses | In the rapidly evolving world of data technology, the need for sophisticated data management... | 0 | 2024-06-04T13:27:25 | https://dev.to/linda0609/evolving-big-data-strategies-with-data-lakehouses-ej | In the rapidly evolving world of data technology, the need for sophisticated data management solutions is more pressing than ever. The rise of [data lakehouses and data mesh](https://us.sganalytics.com/blog/evolving-big-data-strategies-with-data-lakehouses-and-data-mesh/) represents significant advancements in the way organizations handle and utilize big data. These innovations not only streamline data processes but also empower organizations to harness the full potential of their data assets. This blog delves into these emerging concepts and their impact on [big data strategies](https://www.sganalytics.com/blog/how-edtech-companies-use-big-data-analytics/).
The Complexity of Modern Data Architectures
Traditional data infrastructures, although robust, often struggle to keep pace with the dynamic needs of modern businesses. Organizations frequently rely on transactional data systems that operate between data warehouses and operational databases like Oracle, Microsoft SQL Server, or PostgreSQL. Machine learning (ML) and analytics, typically performed in data lakes or warehouses, add another layer of complexity.
The challenge lies in the cost and inefficiency associated with extract, transform, and load (ETL) processes. Transferring data between warehouses and lakes can lead to increased costs and data latency issues. A recent MIT Technology Review survey revealed that almost half of data executives see reducing duplicated data as a critical initiative. However, achieving this goal requires innovative solutions that go beyond traditional data management practices.
Data Lakehouses: The Best of Both Worlds
A data lakehouse combines the scalability and flexibility of data lakes with the performance and reliability of data warehouses. This unified approach significantly reduces infrastructure complexity and fosters collaboration among data engineers, scientists, and business analysts. Key benefits of data lakehouses include:
1. Unified Storage and Processing : By consolidating data storage and processing, lakehouses eliminate the need for separate data silos, enhancing data accessibility and reducing duplication.
2. Support for Diverse Workloads : Lakehouses support various data workloads, including batch processing, real-time streaming, and advanced analytics, making them versatile for different business needs.
3. Cost Efficiency : By minimizing the need for multiple data storage solutions and reducing ETL processes, lakehouses help organizations cut costs and improve ROI.
4. Enhanced Data Governance : Lakehouses offer robust data governance capabilities, ensuring compliance with regulatory requirements and internal policies.
Data Mesh: Decentralizing Data Management
Data mesh is a transformative approach to data architecture that decentralizes data ownership and management. It treats data as a product, with its own lifecycle and consumer base, and is built on four core principles:
1. Domain Ownership : Data responsibility is decentralized, with domain teams owning and managing their data.
2. Data as a Product : Each data product is managed like a product, with defined owners, SLAs, and customer-centric design.
3. Self-Serve Data Platform : Cross-functional teams can access and use data independently, without relying on centralized IT teams.
4. Federated Computational Governance : A governance model that balances autonomy with compliance, ensuring data quality and security across the organization.
Implementing Data Lakehouse and Data Mesh
Adopting data lakehouse and data mesh architectures requires careful planning and execution. Here’s a detailed roadmap for successful implementation:
1. Define the Future State: Begin by aligning your data strategy with business goals. Identify the key outcomes you want to achieve, such as improved data accessibility, reduced costs, or enhanced analytics capabilities.
2. Assess the Current Data State : Conduct a thorough assessment of your existing data architecture, identifying strengths, weaknesses, and areas for improvement.
3. Gap Analysis : Perform a gap analysis to understand the difference between your current and desired state. This will help you design a practical, actionable roadmap.
4. Pilot Projects : Start with pilot projects to test data lakehouse and data mesh concepts. Choose use cases that involve data engineers and scientists to evaluate the effectiveness of these architectures.
5. TEL Over ETL : Shift from traditional ETL processes to TEL (transform, extract, load) to process data within distributed stores, reducing latency and improving efficiency.
6. Create a Data-Driven Culture : Engage leadership and stakeholders in fostering a data-driven culture. Emphasize the importance of data literacy and encourage collaboration across departments.
7. Deploy Data Mesh Services : Begin with creating the first data products and establishing essential data governance services. Implement tools like data catalogs, usage detection, and classification to ensure data quality and compliance.
The Role of Adaptive AI and Data Fabrics
Adaptive AI systems and data fabrics are emerging trends that complement data lakehouses and data mesh, driving resilience and innovation. Adaptive AI enables organizations to quickly respond to changing business conditions by continually learning and adapting from new data. Data fabrics, on the other hand, provide a unified architecture that simplifies data integration across complex environments, ensuring seamless data flow and accessibility.
Investing in the Future
To stay competitive, organizations must invest in these trends. Data lakehouses and data mesh architectures not only address current data challenges but also prepare businesses for future growth. By embracing these innovative data strategies, organizations can create sustainable, scalable, and efficient data environments.
Conclusion
The integration of data lakehouses and data mesh marks a significant evolution in big data strategies. These architectures offer a path to greater agility, efficiency, and resilience, enabling organizations to fully leverage their data assets. As businesses continue to navigate the complexities of the digital age, adopting these advanced data management solutions will be crucial for driving innovation and maintaining a competitive edge.
For organizations looking to embark on this transformative journey, SG Analytics provides comprehensive data solutions and expertise. By partnering with SG Analytics, businesses can effectively develop and implement robust data strategies, ensuring they stay ahead in a rapidly evolving landscape.
| linda0609 | |
1,875,548 | JS Builders Meetup - Dive Deep Into Kafka with JavaScript | Are you a JavaScript enthusiast eager to expand your understanding of modern web technologies? Join... | 0 | 2024-06-04T13:27:07 | https://dev.to/buildwebcrumbs/js-builders-meetup-tomorrow-dive-deep-into-kafka-with-javascript-47bi | community, javascript, kafka, coding | Are you a JavaScript enthusiast eager to expand your understanding of modern web technologies?
**Join us at our upcoming JS Builders Meetup**, where we'll explore the powerful world of Kafka and how it integrates with JavaScript.
This session is perfect for developers of all levels interested in enhancing their skills and networking with like-minded professionals.
{% cta https://guild.host/events/js-builders-meetup-01-rfg19p %} Register here to secure your spot! {% endcta %}
## Why Kafka?
In the ever-evolving landscape of web development, Kafka has emerged as a crucial technology for managing large streams of data efficiently. Understanding Kafka can help you build more scalable and fault-tolerant applications, making this knowledge a valuable addition to your developer toolkit.
## What Will You Learn?
Our special guest, Lucia Cerchie, a Developer Advocate at Confluent, will guide you through:
- **The Basics of Kafka:** Understanding what Kafka is and why it is pivotal in modern data handling.
Practical Integration with JavaScript: How to use Kafka with a JavaScript client to send and receive messages efficiently.
- **Hands-On Experience:** Dive into some code during the meetup to see Kafka in action!
---
## Event Details
**Date:** Wednesday, June 5
**Time:** 4 PM EST
**Location**: https://guild.host/events/js-builders-meetup-01-rfg19p
---
## See you there!
Prepare to unlock new potential in your web development projects and understand why Kafka is a game-changer in the industry.
{% cta https://guild.host/events/js-builders-meetup-01-rfg19p %} Register here to secure your spot! {% endcta %}
See you there,
Pachi 💚
| pachicodes |
1,864,880 | JavaScript arrays | JavaScript arrays are a versatile and powerful data structure used to store and manipulate... | 0 | 2024-05-25T14:06:54 | https://dev.to/raju-sarkar/new-title-45i7 | JavaScript arrays are a versatile and powerful data structure used to store and manipulate collections of data. An array in JavaScript is a special variable that can hold more than one value at a time, making it ideal for managing lists of items. Each item in an array is called an element, and elements are ordered by their index, starting from 0.
Arrays in JavaScript can hold elements of any type, including numbers, strings, objects, and even other arrays, which allows for the creation of complex data structures. JavaScript provides a rich set of methods to perform various operations on arrays, such as adding, removing, and searching for elements, as well as sorting and iterating through the array.
Key features of JavaScript arrays include:
Dynamic Sizing: Arrays can dynamically grow or shrink in size as elements are added or removed.
Zero-Based Indexing: The first element of an array has an index of 0, the second element has an index of 1, and so on.
Built-in Methods: Methods like push(), pop(), shift(), unshift(), map(), filter(), reduce(), and many others provide robust functionality for array manipulation.
Here’s a simple example of a JavaScript array and some basic operations:
```
let fruits = ['Apple', 'Banana', 'Cherry'];
// Adding an element to the end
fruits.push('Date');
// Removing the first element
fruits.shift();
// Accessing an element by index
console.log(fruits[1]); // Outputs: 'Cherry'
// Iterating over an array
fruits.forEach(fruit => {
console.log(fruit);
})
```
| raju-sarkar | |
1,875,354 | What is Supabase? How to Integrate It with Your React Application | As a developer, you're always looking for efficient, powerful tools to streamline your workflow and... | 0 | 2024-06-04T13:25:00 | https://dev.to/jehnz/what-is-supabase-how-to-integrate-it-with-your-react-application-5hea | react, supabase | As a developer, you're always looking for efficient, powerful tools to streamline your workflow and enhance your applications. Enter Supabase, an open-source alternative to Firebase that leverages the power of PostgreSQL databases. In this blog post, we'll explore what Supabase is, its standout features, and how you can seamlessly integrate it with a React application. Let's dive in!
### 🔋What is Supabase?
Supabase is an open-source backend-as-a-service (BaaS) that provides developers with a scalable, real-time backend in just a few clicks. It offers a comprehensive suite of tools, including a managed PostgreSQL database, real-time data synchronization, authentication, storage, and serverless functions. Supabase is designed to simplify backend development, allowing you to focus on building your application rather than managing infrastructure.
### Key Features of Supabase
**PostgreSQL Database**
* Fully managed PostgreSQL database with instant APIs.
* Supports advanced queries, full-text search, and JSON data types.
* Row-level security for fine-grained access control.
**Authentication**
* Built-in support for user authentication and authorization.
* Email and password login, social logins (Google, GitHub, etc.), and magic link login.
* JWT-based session management.
**Real-time**
* Enables real-time data synchronization using PostgreSQL's replication features.
* Build collaborative applications that react instantly to changes in the database.
**Storage**
* Secure file storage with a simple API.
* Public and private buckets with access control using policies.
**Edge Functions**
* Write and deploy serverless functions that run close to your users.
* Low-latency execution and easy integration with your Supabase project.
### Integrating Supabase with a React Application
Now that we've covered what Supabase offers, let's see how to integrate it into a React application. We'll create a simple React app that uses Supabase for authentication and data management.
#### Step 1: Set Up Supabase
##### ✔️Create a Supabase Project:
* Go to Supabase and sign up for an account.
* Create a new project and note down the Project URL and API Key.
##### ✔️Set Up a Database Table:
* Navigate to the "Table Editor" in the Supabase dashboard.
* Create a new table (e.g., todos) with columns for id, task, and is_complete.
#### Step 2: Set Up the React Application
##### ✔️Create a React App:
```bash
npx create-react-app supabase-react-app
cd supabase-react-app
```
##### ✔️Install Supabase Client:
```bash
npm install @supabase/supabase-js
```
##### ✔️Configure Supabase Client:
Create a file named supabaseClient.js in the src directory:
```javascript
// src/supabaseClient.js
import { createClient } from '@supabase/supabase-js';
const supabaseUrl = 'https://your-project-ref.supabase.co';
const supabaseKey = 'your-anon-key';
const supabase = createClient(supabaseUrl, supabaseKey);
export default supabase;
```
#### Step 3: Implement Authentication
##### ✔️Create a Login Component:
```javascript
// src/Login.js
import React, { useState } from 'react';
import supabase from './supabaseClient';
const Login = () => {
const [email, setEmail] = useState('');
const [password, setPassword] = useState('');
const handleLogin = async (e) => {
e.preventDefault();
const { user, error } = await supabase.auth.signIn({ email, password });
if (error) console.error('Error logging in:', error.message);
else console.log('Logged in user:', user);
};
return (
<form onSubmit={handleLogin}>
<input
type="email"
value={email}
onChange={(e) => setEmail(e.target.value)}
placeholder="Email"
required
/>
<input
type="password"
value={password}
onChange={(e) => setPassword(e.target.value)}
placeholder="Password"
required
/>
<button type="submit">Login</button>
</form>
);
}
export default Login;
```
##### ✔️Create a Logout Function:
```javascript
// src/Logout.js
import React from 'react';
import supabase from './supabaseClient';
const Logout = () => {
const handleLogout = async () => {
const { error } = await supabase.auth.signOut();
if (error) console.error('Error logging out:', error.message);
};
return <button onClick={handleLogout}>Logout</button>;
}
export default Logout;
```
#### Step 4: Manage Data
##### ✔️Create a Todo Component:
```javascript
// src/Todo.js
import React, { useState, useEffect } from 'react';
import supabase from './supabaseClient';
const Todo = () => {
const [todos, setTodos] = useState([]);
const [newTask, setNewTask] = useState('');
useEffect(() => {
fetchTodos();
}, []);
const fetchTodos = async () => {
let { data: todos, error } = await supabase.from('todos').select('*');
if (error) console.error('Error fetching todos:', error.message);
else setTodos(todos);
};
const addTodo = async () => {
const { data, error } = await supabase.from('todos').insert([{ task: newTask, is_complete: false }]);
if (error) console.error('Error adding todo:', error.message);
else setTodos([...todos, data[0]]);
setNewTask('');
};
return (
<div>
<h1>Todo List</h1>
<input
type="text"
value={newTask}
onChange={(e) => setNewTask(e.target.value)}
placeholder="New Task"
/>
<button onClick={addTodo}>Add Task</button>
<ul>
{todos.map(todo => (
<li key={todo.id}>{todo.task}</li>
))}
</ul>
</div>
);
}
export default Todo;
```
#### Step 5: Combine Everything in App Component
##### ✔️Update App Component:
```javascript
// src/App.js
import React, { useState, useEffect } from 'react';
import supabase from './supabaseClient';
import Login from './Login';
import Logout from './Logout';
import Todo from './Todo';
function App() {
const [session, setSession] = useState(null);
useEffect(() => {
const session = supabase.auth.session();
setSession(session);
supabase.auth.onAuthStateChange((_event, session) => {
setSession(session);
});
}, []);
return (
<div className="App">
<h1>Supabase + React</h1>
{!session ? (
<Login />
) : (
<>
<Logout />
<Todo />
</>
)}
</div>
);
}
export default App;
```
### Conclusion
Supabase is a powerful, open-source backend-as-a-service that simplifies the process of building and scaling web applications. By integrating Supabase with your React application, you can leverage its real-time data synchronization, authentication, and storage capabilities to build robust, scalable apps quickly and efficiently.
>Give Supabase a try for your next project, and experience the ease of building modern web applications with a powerful backend solution. **_Happy coding!_** 🙂
---
Docs:
[Supabase] (https://supabase.com/docs)
[React] (https://react.dev/reference/react)
| jehnz |
1,876,689 | Auth0 integration - Node.js + ExpressJS | This is a simple guide to demonstrate backend Auth0 integration. There will be no frontend involved.... | 0 | 2024-06-04T13:24:18 | https://dev.to/franklinthaker/auth0-integration-nodejs-expressjs-54l0 | auth0, express, node, beginners | This is a simple guide to demonstrate backend Auth0 integration. There will be no frontend involved. User sign-up, log-in, log-out, all operations will be done through backend only.
```
// index.js
require('dotenv').config();
const { auth, requiresAuth } = require("express-openid-connect");
const app = require("express")();
const config = {
authRequired: false,
auth0Logout: true,
secret: process.env.CLIENT_SECRET,
baseURL: "http://localhost:3000",
clientID: process.env.CLIENT_ID,
issuerBaseURL:`https://${process.env.AUTH0_TENANT}.auth0.com`,
};
// auth router attaches /login, /logout, and /callback routes to the baseURL
app.use(auth(config));
// req.isAuthenticated is provided from the auth router
app.get("/", (req, res) => {
res.send(req.oidc.isAuthenticated() ? "Logged in" : "Logged out");
});
app.get("/profile", requiresAuth(), (req, res) => {
res.send(JSON.stringify(req.oidc.user));
});
app.listen(3000);
```
## **Environment Variables**
To run this project, you will need to add the following environment variables to your .env file
**CLIENT_ID** -> Go to Auth0 -> Applications -> Settings -> Client ID
**AUTH0_TENANT** -> Go to Auth0 -> Applications -> Settings -> Domain
**CLIENT_SECRET** -> Run this command to generate the secret value:
> openssl rand -hex 32
If you are running on Windows: Try to run this in Git Bash it should work without you needing to install Win64 OpenSSL
Also make sure to setup this in Settings tab in Auth0:
**Allowed Callback URLs:** http://localhost:3000
**Allowed Logout URLs:** http://localhost:3000
References
https://github.com/FranklinThaker/auth0-integration-nodejs
https://auth0.github.io/express-openid-connect/index.html | franklinthaker |
1,876,688 | Cent's Two Cents - HTML | Hi all! Cent here with my third day of updates! Continuing with the Odin Project, we've starting... | 27,574 | 2024-06-04T13:24:13 | https://dev.to/centanomics/cents-two-cents-html-26ak | Hi all!
Cent here with my third day of updates!
Continuing with the Odin Project, we've starting diving into HTML. It's been nice going over this stuff again. It makes me think about how we know stuff, but might not actively think about if that makes sense. It's hard to describe that feeling I think.
Today was mainly going over the basics of html so no code to share like I hoped, however I should be doing my first project tomorrow so hopefully I can finally share something.
Also I mentioned the pomodoro timer for studying before. I found a good rhythm for myself. I'll study/code for 3 25 mins sessions, then use the last one to write the blog post. For now that works perfectly for me and I'm making an effort to study during the sessions so that always feels good.
I don't really have a desired length for these posts so I'll likely just go until I don't have anything else to say, so until next time! | centanomics | |
1,876,687 | Python unit testing is even more convenient than you might realize | Introduction As software developers, we all write lots and lots of lines of code while building an... | 0 | 2024-06-04T13:23:28 | https://keploy.io/blog/community/unit-testing-in-python | unittesting, development, keploy, codepen |
Introduction
As software developers, we all write lots and lots of lines of code while building an application. But to ensure that each and every components work perfectly in the software, we really need to do some unit testing. This ensures proper functionality and reliable performance of our product. These testing of individual components is known as Unit Testing.
For the dynamic nature and the ease of writing tests alongside the code, Python can be a viable option for unit testing of our software. So, let's dive into the nitty-gritty of writing unit tests and explore the best practices and techniques to ensure our code's reliability and maintainability.
https://keploy.io/wp/wp-content/uploads/2024/06/Lets-go-scaled-e1717436430433.webp
**Why software needs to be unit tested?**
Often it's found that during the early development phase, unit tests serve as a safety net by helping us catching bugs and regressions. By verifying the behavior of individual units, one can always able to identify and fix issues before they propagate throughout the codebase.
Also, well-written unit tests act as documentation for the code, providing examples of its expected behavior. When making changes or refactoring code, we can easily rely on the existing tests to ensure that modifications don't inadvertently break functionality.
https://keploy.io/wp/wp-content/uploads/2024/06/02_Unit_Testing@2x-e1717436467377.webp
**How to use Python for unit testing?**
In python, we generally write unit tests using testing frameworks such as unittest. These tests validate specific behaviors of individual units, typically by asserting expected outcomes against actual results. The unittest module is included in the Python's standard library, which provides a framework for organizing and running unit tests and offers its own classes and methods for creating the test cases, running them, and reporting the results.
**How to write your first Python testcase?**
For this case, we are considering a simple Flask application which is using MongoDB as the database. Now, we will be writing some APIs to interact with the database and we'll be testing them by writing unit testcases for them using the unittest library!!
**Let's first create our application**
Let's create the file app.py. Here, we will connect out app with the database and write our API !!
First, let's import our necessary packages, and connect the MongoDB container named task_manager with our application:
```
from flask import Flask, request, jsonify
from pymongo import MongoClient
from flask_cors import CORS
from bson import ObjectId
app = Flask(__name__)
cors = CORS(app, resources={r"/api/*": {"origins": "*"}})
client = MongoClient('mongodb://localhost:27017/task_manager')
db = client['task_manager']
collection = db['tasks']
```
Now, with the database being connected, let's write one API for the application:
```
@app.route('/api/tasks', methods=['POST'])
def create_task():
data = request.get_json()
task = {
'title': data['title'],
'description': data['description']
}
result = collection.insert_one(task)
return jsonify({'message': 'Task created successfully', 'id': str(result.inserted_id)}), 201
```
With our app being ready, now let's write the unit test case for this API we have just wrote!!
**Let's write the testcases**
Let's create another file called test_app.py where we'll write the test case for our application. Now, let's first import the necessary libraries required for the testing purpose:
```
import unittest
from app import app
from unittest.mock import patch, MagicMock
import json
from bson import ObjectId
```
Now, let's create our testing class and do the setup along with writing the testcase for our POST request API:
```
class TestTaskManager(unittest.TestCase):
# Unit test case for testing the Task Manager API.
def setUp(self):
"""
Set up the test client and mock the collection used in the app.
This method runs before each test.
"""
# Initialize the test client for the app
self.app = app.test_client()
self.app.testing = True
# Create a mock for the collection used in the app
self.collection_mock = MagicMock()
# Patch the collection in the app with the mock
self.patcher = patch('app.collection', self.collection_mock)
self.patcher.start()
def tearDown(self):
"""
Stop the patcher after each test.
This method runs after each test.
"""
# Stop the patcher to clean up after tests
self.patcher.stop()
def test_create_task(self):
"""
Test the task creation endpoint.
"""
# Data to be sent in the POST request
data = {'title': 'Test Task', 'description': 'This is a test task'}
# Make a POST request to create a new task
response = self.app.post('/api/tasks', json=data)
# Assert that the response status code is 201 (Created)
self.assertEqual(response.status_code, 201)
# Assert that the response contains the success message
self.assertIn(b'Task created successfully', response.data)
```
Now, that out test case has been written, it's time to put it on some test. So, we need to run the command python3 test_app.py in the terminal to get the result of the test,
https://keploy.io/wp/wp-content/uploads/2024/06/ef4d2888-204a-4dda-ba8a-4ce7536d.webp
which look kind of like this!! And, as we can see that our test has passed successfully.
In case of any failure, we will get the result something like this, when the command is run in the terminal:
https://keploy.io/wp/wp-content/uploads/2024/06/d3889c07-9c95-42f6-b1f2-6aaeac70.webp
This is could happen if there is any problem in the data that is being sent has some flaws or there is any problem with the address.
Now, if we had multiple APIs in our application, we would have to write more test cases to test each one of them!!
**How to check the test coverage?**
Now, what if we want to check how much code does our written unit test cases cover!? Here comes Keploy, which helps us to easily check our test coverage in some simple steps.
https://keploy.io/wp/wp-content/uploads/2024/06/keploy.eb069ede.webp
First we need to install Keploy's Python SDK:
```
pip install keploy pytest
```
Next, we can create a test file for running Keploy's API tests and we can name the file test_keploy.py and the contents of the file will be as follows:
```
from keploy import run, RunOptions
def test_keploy():
try:
options = RunOptions(delay=15, debug=False, port=0)
except ValueError as e:
print(e)
run("python3 -m coverage run -p --data-file=.coverage.keploy python3 app.py", options)
```
We also need to create a .coveragerc file to ignore the coverage of the libraries that is calculated. The contents of the file will be as follows:
```
[run]
omit =
/usr/*
sigterm = true
```
Now to run our unit test with Keploy, we can run the command given below:
```
python3 -m coverage run -p --data-file=.coverage.unit -m pytest -s test_keploy.py test_app.py
```
Now, to combine the coverage from the unit tests, and Keploy's API tests, and then generate the coverage report for the test run we can use these commands:
```
python3 -m coverage combine
python3 -m coverage report
```
**Best practices for writing test cases**
The practices mentioned below are not exclusive to Python, but works for all kinds of unit test cases. But as we are discussing about unit test case writing here, so it's important to mention it here:
1.**Keep Tests Simple and Focused**
While writing a unit test, we must focus on a single aspect of functionality, keeping the test cases as simple and easy to understand as possible.
2.**Use Descriptive Test Names**
Clear and descriptive test names improve the readability and help other developers/maintainers to understand the purpose of each test case.
3.**Isolate Test Cases**
We should avoid dependencies between test cases by isolating the units under test. And for that, we can use techniques such as mocking or dependency injection to replace external dependencies with test doubles.
**Common pitfalls to avoid while writing test cases**
Now that we know about the best practices of writing unit test cases, let's focus on some common mistakes that we must avoid while writing the test cases. These includes:
1.**Testing Implementation Details**
Always avoid testing the implementation details, because as far as I've seen, these tests can become prone to breaking when refactoring the code.
2.**Ignoring Edge Cases**
Remember to ensure that our unit tests cover edge cases and boundary conditions to validate the robustness of our code under various problematic scenarios.
**Conclusion**
Writing unit tests is a fundamental aspect of software development, which ensures the code reliability and maintainability. And by following best practices and leveraging advanced techniques, and also embracing tools like Keploy, developers can create robust test suites that validate their code's behavior under different conditions. And well, that's a wrap for now!! Hope you folks have enriched yourself today with lots of known or unknown concepts. I wish you a great day ahead and till then keep learning and keep exploring!!
https://keploy.io/wp/wp-content/uploads/2024/05/d203af69-a3d1-4e93-827b-1ecb6962-e1717436912179.webp
**FAQs**
1.**What is the purpose of unit testing?**
Unit testing aims to validate the individual units or components of a software application to ensure that they behave as expected, enhancing code reliability and maintainability.
2.**How do I write my first unit test in Python?**
To write your first unit test in Python, you'll need to set up a test environment, create a test case class that inherits from unittest.TestCase, write test methods within the class to validate specific behaviors, and then run your tests using a test runner.
3.**Which tools and libraries are available for unit testing in Python?**
Python offers several tools and libraries for unit testing, including pytest, which simplifies test writing and execution, and nose2, an extension of Python's built-in unittest framework with additional features for test discovery and running. | keploy |
1,876,667 | Build an AI Content Generator Using Gemini API and ToolJet in 10 Minutes 🛠️ | In this quick tutorial, we'll build an AI-powered content generator using the Gemini API and ToolJet,... | 0 | 2024-06-04T13:18:51 | https://blog.tooljet.com/build-an-ai-content-generator-using-gemini-api-and-tooljet-in-10-minutes/ | webdev, javascript, beginners, geminiapi | In this quick tutorial, we'll build an AI-powered content generator using the Gemini API and ToolJet, all within just 10 minutes. This app will generate content based on the uploaded images, selected content type, and additional info entered by the user. Whether you need titles, short descriptions, long descriptions, creative stories, blog post outlines, full blog posts, social media captions, or advertising copies, this app will have you covered. Follow along to use ToolJet's rapid development process and Gemini's advanced AI capabilities to seamlessly integrate content creation into your workflow.
Here is a preview of our final application:
---
## Prerequisites
- ToolJet (https://github.com/ToolJet/ToolJet) : An open-source , low-code business application builder. [Sign up](https://www.tooljet.com/signup) for a free ToolJet cloud account or [run ToolJet on your local machine](https://docs.tooljet.com/docs/setup/try-tooljet/) using Docker.
- Gemini API Key : Gemini API is an advanced AI service provided by [Google AI Studio](https://aistudio.google.com/app/apikey). It enables developers to integrate powerful content generation capabilities into their applications.
---
## Step One - Designing the UI 🎨
Begin by creating an application named _AI Content Generator_.
Once the app is created, we can start designing the UI with ToolJet's pre-built components.
- Drag and drop a **Container** component from the [components library](https://docs.tooljet.com/docs/tooljet-concepts/what-are-components) on the right and adjust its size so that it covers most of the canvas.
- Drop an **Icon** component and **Text** component on the container. Then, rename the Icon component to _logo_ and the Text component to _logoText_.
- Select the Icon component to see a properties panel on the right. Select **IconListSearch** as the icon.
- For the Text component, enter _AI Content Generator_ under its **Text** property, and adjust its font weight and text size.
- Change the color of the Icon and Text components to dark blue (Hex code - #354094).
_This tutorial uses dark blue(#354094) as the primary color. Update the colors of your components accordingly in the upcoming steps. Feel free to use a different color scheme._
- Add an **Image** component and a **Text** component below the header that we just created. Rename them to _imagePreview_ and _output_ respectively. _imagePreview_ will display a preview of the uploaded image and _output_ will display the generated text based on the image and selected options.
- Add a **File Picker** component below the image and rename it to _imageUploader_.
- Place a **Dropdown** component and **Text Input** component next to it. Rename them to _typeOfContentInput_ and _additionalInfoInput_ respectively.
- For the Text Input component, enter the below value under **Placeholder** property:
`Enter additional information`
- For the Dropdown component, paste the below array under **Option values** and **Option labels** properties using double curly braces:
```
{{["Title", "Short Description (1-2 sentences)", "Long Description (paragraph)", "Creative Story", "Blog Post Outline", "Blog Post", "Social Media Caption", "Advertisement Copy"]}}
```
- Enter the below value under the Dropdown component's Placeholder property:
`Select type of content`
_Renaming components can be useful when the application has a large number of components and we need to refer to component-related values inside the app._
- Add a **Button** component at the bottom as the last step in the UI building process. Rename the component to _generateContentButton_.
We've designed a simple UI for this application, which you can fully customize to meet your specific requirements. ToolJet offers extensive flexibility, allowing you to define and arrange components exactly as you envision.
---
## Step Two - Integrating AI Capabilities 🛠️
With the UI complete, we can use ToolJet's [queries](https://docs.tooljet.com/docs/tooljet-concepts/what-are-queries) to connect with Gemini API and get a response based on the uploaded image, content type, and additional information that we enter in the components.
To protect your Gemini API key, we'll leverage ToolJet's [Workspace Constants](https://docs.tooljet.com/docs/tooljet-concepts/workspace-constants). This way, your key remains hidden and secure.
- Click on the ToolJet logo in the top left corner. From the dropdown, select Workspace constants.
- Click on the **Create new constant** button. Set the name as _GEMINI_API_KEY_ and enter your Gemini API key in the value input.
Click on the **Add constant** button. This constant will now be available across our workspace and can be accessed using `{{constants.GEMINI_API_KEY}}`.
_You can log into [Google AI Studio](https://aistudio.google.com/app/apikey) using your existing Google credentials. In the AI Studio interface, you'll be able to locate and copy your API key._
- Navigate back to your app and expand the **Query Panel** at the bottom.
- Click the **+ Add** button and choose the **REST API** option. Rename the query as _getContent_.
- Change the Request Method as **POST** and paste the following URL under the URL input:
`https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro:generateContent?key={{constants.GEMINI_API_KEY}}`
- Navigate to the **Body** section of the _getContent_ query. Toggle on **Raw JSON** and enter the following code:
```
{{
`{
"contents": [{
"parts": [{
"text": "Generate the following content for this image in markdown format:
content type: ${components.typeOfContentInput.value},
additional info: ${components.additionalInfoInput.value}"
},
{
"inline_data": {
"mime_type":"image/jpeg",
"data": "${components.imageUploader.file[0].base64Data}"
}
},],
},],
}`
}}
```
_In the above configuration, we are creating a structured JSON payload that combines user-input text and image data. The JSON object is then sent to the Gemini API endpoint to process the provided content and image._
---
## Step Three - Binding Data to Components 🔗
With the query ready, we can setup a way to trigger it every time the Button component is clicked.
- Select the Button component, and navigate its properties panel on the right.
- Under **Events**, click on **[New Event Handler](https://docs.tooljet.com/docs/tooljet-concepts/what-are-events)** to create a new event.
- For the new event, select **On click** as the Event and **Run Query** as the Action.
- Select _getContent_ as the Query (the query created in the previous step).
Now, every time the Button component is clicked, the _getContent_ query will be triggered, and it will return the AI-generated content based on the uploaded image and user input.
Next, we will populate the Text(_output_) component with the value returned by the _getContent_ query using the below steps:
- Select the Text (_output_) component that was created for the output of the query.
- Under its **Data** property, enter the below code:
`{{queries.getContent.data.candidates[0].content.parts[0].text}}`
Similarly, populate the Image component with the uploaded image from the File Picker component:
- Select the Image component.
- Under its **URL** property, enter the below code:
`{{'data:image;base64,' + components.imageUploader.file[0].base64Data}}`
Our application is now ready. Let's give it a spin and see the results. Select an image, choose a content type, enter some additional info, and click on the generate button. We should now be able to see the preview of the image and AI-generated text output.
---
## Conclusion
We've now built a fully functional AI-powered content generator using the Gemini API and ToolJet in just 10 minutes. This application demonstrates how seamlessly ToolJet's rapid development environment integrates with Gemini's advanced AI capabilities to automate content creation based on user inputs.
To explore more, checkout [ToolJet docs](https://docs.tooljet.com/docs/) or join us on [slack](https://tooljet.slack.com/). Happy coding!!
| karanrathod316 |
1,873,347 | O que realmente significa ser um Engenheiro de Software? | Introdução Iniciei minha carreira na área de TI por volta de 2018, enquanto ainda era... | 0 | 2024-06-04T13:10:55 | https://dev.to/j0suetm/o-que-realmente-significa-ser-um-engenheiro-de-software-o16 | softwareengineering, history | ## Introdução
Iniciei minha carreira na área de TI por volta de 2018, enquanto ainda era estudante. Após alguns anos, já no meu primeiro emprego, comecei a me autodenominar "Engenheiro de Software" nas redes sociais. Não, esse não era meu título profissional, nem eu compreendia completamente seu significado; simplesmente gostava da seriedade e experiência que essa designação conferia aos meus perfis online.
Caso o leitor se identifique com minha experiência (ou não, também 😅), esta redação será de seu agrado. Nela, busco contextualizar a filosofia e a história por trás dos títulos e cargos utilizados por todos nós, "povo da computação", e, ao final, tento trazer luz a esse tema tão raramente discutido.
**Nota ao leitor**
Antes de prosseguir com a leitura, é importante esclarecer alguns pontos:
1. Este texto pode ser categorizado como uma crítica, porém não é direcionado a ninguém ou a nenhum grupo específico (exceto aos profissionais da área computacional). Portanto, peço que o leitor o encare apenas como um artefato informativo;
2. A pesquisa foi realizada deliberadamente por mim, movido pela curiosidade. Portanto, não considere tudo que é apresentado aqui como dogma. Faça sua própria pesquisa e verifique as referências antes de aceitar qualquer informação;
3. Tentei ser o mais didático possível, incluindo referências e sendo crítico em minhas opiniões. No entanto, sou humano e posso estar equivocado. As críticas não apenas são bem-vindas, como também são necessárias;
## Conferências de Garmisch
Em 1968, o Comitê de Ciência da NATO (Organização do Tratado do Atlântico Norte) organizou algumas conferências em Garmisch, na Alemanha, onde mais de 50 participantes internacionais discutiram sobre os problemas emergentes dos softwares da época. Devido à evolução não apenas das tecnologias disponíveis, mas também dos problemas reais que essas tecnologias enfrentavam, tornou-se urgentemente necessário o desenvolvimento e a regulamentação de uma nova metodologia capaz de produzir Software para suprir as necessidades da sociedade moderna (NAUR; RANDELL, 1969).
Embora o conceito de "Engenharia de Software" por si só não tenha sido o único tema discutido nessas conferências, muito menos o produto final, elas foram determinantes para a valorização e popularização dessa nova disciplina com o passar do tempo. Nas palavras de Margaret Hamilton (HANCOCK, 2014):
> Quando comecei a usar essa expressão [Engenharia de Software], ela era considerada um tanto engraçada. Foi motivo de piada por um bom tempo. Eles [provavelmente outros desenvolvedores do Apollo Guide Computer] gostavam de brincar comigo sobre minhas ideias radicais. O Software eventualmente e necessariamente ganhou o mesmo respeito que as outras disciplinas.
>
> -- Margaret Hamilton, entrevista para El País, 2014
Claramente, as conferências foram uma resposta a um contexto da época. Portanto, é importante, antes de prosseguirmos, esclarecer por que o conceito de "Engenharia de Software" encontrou resistência durante sua concepção e implementação.
## As três tradições da computação
Apesar de ser uma disciplina relativamente nova, a computação, em seu primeiro século de existência, passou e ainda passa por transformações e ramificações únicas, fazendo da Engenharia de Software um conglomerado de outras disciplinas que, simultaneamente, se complementam e disputam entre si (TEDRE; SUTINEN, 2008). São elas:
1. **Tradição Matemática**: programas são tratados como objetos abstratos, estruturas teóricas e sistemas axiomáticos que avaliam de maneira booleana. (MCCARTHY, 1960) é um ótimo exemplo de uma definição de um software com uma grande influência da tradição matemática;
2. **Tradição Científica**: programas são tratados como modelos de informação que buscam, seguindo algum sistema científico idealizado, satisfazer dados que comprovam uma hipótese ou seguem o processo científico;
3. **Tradição da Engenharia**: programas são processos que afetam e são afetados pelo mundo real. Trata-se da agregação de sistemas computacionais existentes de forma a produzir um novo sistema;
É interessante ressaltar que muitas autoridades têm formulado diversos argumentos e contra-argumentos para cada uma das tradições detalhadas. Um dos exemplos mais difundidos é o do Prof. Dr. Edsger Dijkstra, renomado matemático por trás de muitos fundamentos da computação, que definiu a Engenharia de Software, em seu ensaio "Sobre a crueldade de realmente ensinar Ciência da Computação" (E.W. DIJKSTRA ARCHIVE, 2023), como "A Disciplina Condenada":
> Assim como a economia é conhecida como "A Ciência Miserável", a Engenharia de Software deveria ser conhecida como "A Disciplina Condenada"; condenada porque nem chega perto de atingir seu objetivo, já que seu objetivo é contraditório por si próprio. Claramente, a Engenharia de Software se apresenta como uma causa válida, mas isso é enganação: se você ler sua literatura e analisar o que seus devotos realmente fazem, descobrirá que a Engenharia de Software aceitou como sua missão "Como programar se você não o sabe.".
>
> -- Edsger W. Dijkstra, EWD 1036
Em contraste, detalharei alguns argumentos que não buscam provar a superioridade da tradição da engenharia, mas sim demonstrar a necessidade de sua existência quando comparada às outras disciplinas:
### Modelos Teóricos vs. Mundo Real
Se o leitor estiver familiarizado com teorias dualistas, idealistas, fenomenológicas, platônicas (por ex. PLATO, 1986) ou neoplatônicas, terá mais facilidade em reconhecer a validade deste primeiro argumento.
A matemática trata principalmente de abstrações teóricas que, em sua maioria, mas não necessariamente, correspondem aos fenômenos naturais (THOMAS; MAURER, 1986). Com essa definição em mente, surge naturalmente um problema quando a matemática é aplicada à computação: Modelos teóricos de programas podem ser validados puramente de forma axiomática; o mesmo programa em execução em uma máquina entra em contato com variáveis naturais que impossibilitam sua validação (TEDRE; SUTINEN, 2008, p. 156). Não existe sequer um modelo matemático que consiga provar (exceto em teoria) com 100% de certeza como os elétrons de um computador se comportarão durante a execução do mesmo programa (TEDRE; SUTINEN, 2008, p. 157).
> Imagine que o leitor está executando o programa mais bem testado do mundo no computador mais confiável do mundo. De repente, um terremoto de nível 9 na escala Richter ocorre, seguido por uma chuva contínua; alguns segundos depois, o sol explode e incendeia tudo o que existe... Deseje boa sorte aos modelos teóricos.
>
> Sim, este exemplo está longe de acontecer. No entanto, a ideia continua sendo verdadeira: não controlamos, nem conseguimos prever todas as variáveis do universo. Nenhum programa em execução é 100% eficaz.
Ao reconhecer essa diferença, a Engenharia de Software procura lidar da melhor maneira possível com esses problemas do mundo real. Não é à toa que desenvolvemos e aprimoramos algoritmos como os de tolerância a falhas, replicação, consenso, balanceamento de carga, etc., que compõem os sistemas que fazem a sociedade atual funcionar. O leitor talvez se recorde das primeiras máquinas computacionais do início do século passado: sistemas elétricos enormes que falhavam constantemente. Sem a influência dos engenheiros elétricos, é discutível a hipótese de que a computação estaria, ainda hoje, nos gabinetes teóricos da matemática (TEDRE; SUTINEN, 2008, pp. 161-162).
### Processo Científico vs. Construção de Software
Aqui entramos em um território altamente contraditório, completamente fora da minha atual autoridade para argumentar de forma a adicionar qualquer progresso: "O território das Definições Inacabadas".
A ciência em si é um conceito amplo que, consequentemente, possui diversas lacunas de definição autoritárias como "O que é ciência?" ou "Qual o modelo ideal de processo científico?". Esta falta de definição (que por sua vez não é algo necessariamente ruim ou proveniente de inexperiência, mas sim da natureza da filosofia e dos limites humanos em geral) torna possível que uma questão em específico emerja: A computação pode ser considerada, por definição, uma ciência? Ou até mais, uma ciência natural? Em outras palavras, levando em conta a definição de que as ciências naturais tratam da observação e estudo de fenômenos naturais (THOMAS; MAURER, 1986), é possível categorizar a ciência da computação como uma?
Diversos autores têm adentrado em detalhes acerca desse questionamento e o debatido. Entretanto, irei focar em apenas um contestamento, para que possamos avançar no fortalecimento da nossa visão atual sobre a Engenharia de Software.
O contra-argumento mais difundido é de que a computação não procura estudar um fenômeno natural de forma a produzir um mais amplo entendimento do mundo. Em realidade, os únicos dados e fenômenos observados são, em suma, o computador e seus processos, os quais já são muito bem entendidos na disciplina. Isso torna a pesquisa não um entendimento acerca dos fenômenos naturais, mas sim de fenômenos artificiais que não apenas são entendidos, como também foram criados. Segundo esse contra-argumento, a única resolução dessas pesquisas é validar o quão bem os últimos pesquisadores construíram seus Softwares (HARTMANIS, 1993).
Considerando também este ponto, a Engenharia de Software entende sua artificialidade e suas limitações de pesquisa, procurando voltar seus métodos de aprimoramento especialmente para o entendimento dos sistemas computacionais já existentes.
## Conclusão
Meu objetivo inicial ao começar a escrever este ensaio era apenas sanar minha curiosidade. Porém, conforme o texto foi se preenchendo, julguei inconveniente não compartilhar o conhecimento adquirido.
Como foi requisitado na introdução, eu espero que a leitura tenha sanado lacunas de conhecimento que o leitor possuísse, e que finalmente entenda o porquê de nos chamarmos Engenheiros de Software.
Obrigado pelo seu tempo. Não hesite em comentar qualquer dúvida ou opinião!
Josué Teodoro Moreira
<https://j0suetm.com>, <mailto:teodoro.josue@pm.me>, <https://www.linkedin.com/in/josue-teodoro-moreira/>
## Referências Bibliográficas
1. MCCARTHY, J. Recursive functions symbolic expressions and their computation by machine, Part I. Communications of the ACM, v. 3, n. 4, p. 184–195, 1 abr. 1960.
2. HANCOCK, Jaime Rubio. Margaret Hamilton, la pionera de la programación que llevó el Apolo a la Luna. 2014. Disponível em: <https://verne.elpais.com/verne/2014/12/11/articulo/1418314336_993353.html>. Arquivado em: <https://web.archive.org/web/20141225145142/https://verne.elpais.com/verne/2014/12/11/articulo/1418314336_993353.html>.
3. NAUR, P.; RANDELL, B. (EDS.). Software Engineering: Report on a conference sponsored by the NATO SCIENCE COMMITTEE. [s.l.] Brussels: Scientific Affairs Division, NATO, 1969.
4. PLATO. Republic. Translated by G.M.A. Grube, revised by C.D.C. Reeve. Indianapolis: Hackett Publishing Company, 1992.
5. TEDRE, M.; SUTINEN, E. Three traditions of computing: what educators should know. Computer Science Education, v. 18, n. 3, p. 153–170, set. 2008.
6. THOMAS, A. S.; MAURER, A. A.; PONTIFICAL INSTITUTE OF MEDIAEVAL STUDIES. The division and methods of the sciences : questions V and VI of his Commentary on the De Trinitate of Boethius. Toronto: Pontifical Institute Of Mediaeval Studies, 1986.
7. E.W. Dijkstra Archive: On the cruelty of really teaching computing science (EWD 1036). Disponível em: <https://www.cs.utexas.edu/~EWD/transcriptions/EWD10xx/EWD1036.html>. Arquivado em: <https://web.archive.org/web/20240330000400/https://www.cs.utexas.edu/~EWD/transcriptions/EWD10xx/EWD1036.html>.
8. HARTMANIS, J. Some observations about the nature of computer science. In: SHYAMASUNDAR, R.K. (Ed.). Lecture notes in computer science, Vol. 761. Berlin: Springer, 1993. p. 1–12. | j0suetm |
1,876,676 | A step-by-step guide to building an MLOps pipeline | The failure of DevOps pipelines to control Machine Learning(ML) development workflows gave rise to... | 0 | 2024-06-04T13:09:34 | https://jozu.com/blog/a-step-by-step-guide-to-building-an-mlops-pipeline/ | machinelearning, beginners, devops, learning | The failure of DevOps pipelines to control Machine Learning(ML) development workflows gave rise to [MLOps pipelines](https://jozu.com/blog/the-transitory-nature-of-mlops-advocating-for-devops-mlops-coalescence). These workflows require iterative interactions and management of numerous components and processes in development and production. MLOps pipelines tick these boxes with their ability to manage workflows that convene multiple pipelines. In contrast, DevOps can only handle workflows through a single pipeline.
Nonetheless, substantial development and integration complexities accompany the MLOps approach of using multiple pipelines. Simplifying the multi-pipeline approach in MLOps to be more solitary, like the DevOps pipelines, can mitigate those complexities and make ML workflow execution less challenging.
This blog post walks you through the steps and processes of building an MLOps pipeline. Then, it introduces you to an open source tool that allows you to package models and model assets as an OCI-compatible artifact that seamlessly integrates with your existing DevOps pipeline. With this approach, you can avoid the difficulty of maintaining multiple pipelines and continue using the tools you already have in place.
#What is an MLOps pipeline?
An MLOps pipeline automates the interconnected set of processes in the machine learning lifecycle. It addresses the gaps in the smooth transition from development to production through automation. In hindsight, it streamlines ML components with other system components and workflows.
[Existing MLOps tools](https://neptune.ai/blog/mlops-tools-platforms-landscape) provide methods to automate, orchestrate, and manage ML workflows within MLOps pipelines, but they each have unique mechanisms for delivering their solutions. This lack of standard delivery formats and other inherent ML system shortcomings, such as dynamic data and model dependencies, hidden feedback loops, and workflow entanglements, can make it challenging to manage and integrate the MLOps pipeline with the rest of the system.
###Building an MLOps pipeline
[The maturity level](https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning) of your MLOps pipeline significantly influences its robustness, design, and architecture. High maturity levels often imply more streamlined workflows and less friction at the operation and production handoff points. However, optimizing MLOps pipelines has less to do with maturity level, and more to do with the ML workflows and tools of choice.
A combination of sub-pipelines (data, model, and code pipelines) makes up the MLOps pipeline. Managing each of these sub-pipelines and their intertwined processes—data validation, model versioning, experiment tracking, model serving, monitoring, and retraining—makes managing the MLOps pipeline a tedious endeavor without automation. The MLOps pipeline comprises two phases, which include:
- Development or experimentation phase
- Automation and deployment phase
##Phase 1: Development or experimentation phase
The MLOps pipeline typically starts in a notebook where you build and optimize an ML model that gives you an empirical solution to your problem based on patterns learned from training data. This phase commonly involves two processes:
- Data preparation
- Model development and training
###Step 1: Data Preparation
Data preparation is the bedrock of ML development. It entails tailoring your data to suit your ML task. The processes involved in data preparation and transformation include:
- Data ingestion
- Data cleaning/preprocessing
- Feature engineering
**Data ingestion:** You must have reliable data sources that align with the ML project's goals. Typical data sources usually include databases (data warehouses), feature sources, or APIs. ML data comes in different forms, some structured, like tabular data, and others unstructured, like images and videos. The type of ML task also determines the best data preprocessing and feature engineering decisions.
**Data preprocessing:** Ingested data is rarely in the best condition for you to train your model. Your data should be comprehensive and unbiased to avoid skewed model outcomes. The type of ML data determines the best data preprocessing to employ. For instance, standard techniques for preprocessing tabular data include normalizing numerical features to ensure they have a similar scale or encoding categorical features into numerical representations. Preprocessing contributes to improving model convergence and performance.
**Feature engineering:** To capture relevant aspects of the problem, you have to create new data features or transform existing ones to suit the ML task. The feature increases your chance of producing trained models with good performance. Some techniques include aggregating features, removing irrelevant features, or leveraging domain knowledge to extract valuable insights.
###Step 2: Model development and training
Combining the data preparation steps forms a data transformation pipeline that automatically produces the training data when you apply your data from the source. This ensures consistent training data without affecting the source data and allows you to focus on the model training pipeline.
Training an ML model is the most experimental and iterative process of the ML development lifecycle. The aim is to identify the best model by conducting several training experiments on the transformed data using various model architectures and hyper-parameters.
Experiment tracking tools like [MLflow](https://mlflow.org/), [Weights and Biases](https://wandb.ai/site), and [Neptune.ai](https://neptune.ai/) provide a pipeline that automatically tracks meta-data and artifacts generated from each experiment you run. Although they have varying features and functionalities, experiment tracking tools provide a systematic structure that handles the iterative model development approach.
Experiment tracking pipelines make comparing different model versions, hyper-parameters, and results easier with the model evaluation metric. After evaluating the experiments, the pipeline sends the best-performing models and artifacts to the model repository for deployment.
##Phase 2: Automation and deployment phase
This phase focuses on streamlining the process of delivering machine learning models from the development phase into a production pipeline in a reliable, scalable, and repeatable manner. It also aims to facilitate collaboration between data scientists, engineers, and operations teams through standardized processes and tools. It comprises the following:
- Version control and reproducibility
- Deployment and infrastructure
- Continuous Integration, Continuous Training, and Continuous Delivery/Deployment (CI/CT/CD)
- Monitoring and performance tracking
###Step 3: Version control and reproducibility
Version control enables you to build reproducible, reliable, and auditable MLOps pipelines that deliver high-quality machine learning models. However, if done incorrectly, versioning can get messy since pipelines populate the code, data, and model in different locations.
Versioning ushers in the operational layer of the MLOps pipeline and facilitates team collaboration during development. The pipelines are technically source codes that produce data and models. Versioning the code pipelines with a version control system like Git makes establishing the lineage with the model and data easy. This is because after running the transformation and model training pipeline, the models can be stored in a model registry, and the data in a feature store.
The meta-data and model artifacts from experiment tracking can contain large amounts of data, such as the training model files, data files, metrics and logs, visualizations, configuration files, checkpoints, etc. In cases where the experiment tool doesn't support data storage, an alternative option is to track the training and validation data versions per experiment. They use remote data storage systems such as [S3 buckets](https://aws.amazon.com/s3/), [MINIO](https://min.io/), [Google Cloud Storage](https://cloud.google.com/storage?hl=en), etc., or data versioning tools like [data version control (DVC)](https://dvc.org/) or [Git LFS (Large File Storage)](https://git-lfs.com/) to version and persist the data. These options facilitate collaboration but have artifact-model traceability, storage costs, and data privacy implications.
###Step 4: Deployment and infrastructure
ML development and deployment pipelines have different requirements that dictate the choice of infrastructure, deployment targets, and strategies. For instance, the data transformation pipeline in development may require distributed processing but not in production. Model pipelines require GPUs in development due to training and just a CPU in production for inference. However, skill level also drives the choice of deployment targets and strategies.
ML Deployment pipelines usually consist of a model, data transformation and validation pipelines, endpoints, and other dependencies. Therefore, it is essential to ensure that the deployment configuration and environment are light and efficient. This concern makes packaging with containers ideal for abstracting deployment complexities.
After packaging them for production, it is easier to scale them for your deployment mode on your infrastructure setup. The ML deployment mode is either online or offline. In offline or batch inference, the model is only sometimes up and running, so infrastructure pipelines don't prioritize auto-scaling, low latency, and availability. In the case of online deployment, the model is always up and running and is deployed either as a web or streaming service. Therefore, the infrastructure setup must mitigate concerns about auto-scaling, low latency, and high availability.
###Step 5: Continuous Integration, Continuous Training, and Continuous Delivery/Deployment (CI/CT/CD)
Manually executing source code changes, data validation, model retraining, model evaluation, and deployment workflows for data and models is tiring and error-prone. The ML components interact in such a way that changes occurring in any of them affect the rest. For example, if there is a feature update in the data transformation pipeline, the model weights will also change, producing a new model.
MLOps slightly modifies the traditional DevOps CI/CD practice with an additional pipeline called continuous training (CT). The CI/CT/CD pipeline for MLOps involves orchestrating a series of automated steps to streamline the development, training, testing, and deployment of machine learning models. Automating these processes enables efficient model deployment. Standard automation tools include [Jenkins](https://www.jenkins.io/), [GitLab CI](https://docs.gitlab.com/ee/ci/), [Travis CI](https://www.travis-ci.com/), and [GitHub Actions](https://jozu.com/blog/introducing-the-new-github-action-for-using-kit-cli-on-mlops-pipelines). You will typically set up the MLOps CI/CT/CD pipeline using a trigger for the automation strategy:
Continuous Integration (CI) focuses on automating the process of testing and validating changes to the codebase. The trigger begins with new commits or merges for code changes, new model versions, a data transformation pipeline, a training script, and configuration files. It then builds a project environment and runs unit tests to verify code quality and correctness. The model is then validated on a validation dataset to ensure it meets the desired criteria. Finally, the model artifacts, including model files, dependencies, and metadata, are pulled from the model registry and packaged into a deployable format, such as a Docker container.
Continuous Delivery / Continuous Deployment (CD) pipeline deploys the trained model to a staging environment for further testing and validation under near-production conditions. These encompass comprehensive tests, including performance, load, and stress testing. Then, it is approved and deployed to the production pipeline.
Continuous Training (CT) pipeline triggers retraining using the feedback extracted from the deployed model and production data monitoring logs. The CT automation strategy initiates triggers based on specific criteria, such as new user data availability, model performance degradation, or scheduled intervals. It validates the latest data with the expected schema and values, then retrains and validates the model.
###Step 6: Monitoring and performance tracking
Model monitoring is crucial for maintaining the performance and reliability of machine learning models in production. It involves tracking key model performance metrics such as accuracy, precision, recall, etc., to identify deviations from expected behavior, detect data and model drift, and ensure consistent model performance. This process provides actionable insights into the model's real-world impact and helps you make informed decisions about retraining or updating models.
You specify thresholds and metrics to detect anomalies and potential issues from data quality, feature distributions, and model output discrepancies. Regular monitoring and analysis empower you to proactively address performance degradation, ensuring that machine learning models continue to deliver accurate predictions and drive business value. The holistic assessment for tracking model performance differs from business metrics. Therefore, effective model monitoring relies on specialized tools and techniques. Some of these techniques include:
- Use techniques like SHAP or LIME to interpret model predictions and understand influencing factors.
- Monitor changes in input data distribution using statistical tests or drift detection algorithms.
- Track changes in feature-target relationships using methods like comparing predictions or monitoring feature importance.
##Introducing KitOps–An open source solution to ease MLOps workflows
One of the main reasons teams struggle to build and maintain their MLOps pipelines are vendor specific packaging. As a model is handed off between data science teams, app development teams, and SRE/DevOps teams, the teams are required to repackage the model to work with their unique toolset. This is tedious, and stands in contrast to well adopted development processes where teams have standardized on the use of containers to ensure that project definitions, dependencies, and artifacts are shared in a consistent format. [KitOps](https://kitops.ml/docs/overview.html) is a robust and flexible tool that addresses these exact shortcomings in the MLOps pipeline. It packages the entire ML project in an [OCI-compliant artifact](https://opencontainers.org/) called a [ModelKit](https://kitops.ml/docs/modelkit/intro.html). It is uniquely designed with flexible development attributes to accommodate ML workflows. They present more convenient processes for ML development than DevOps pipelines. Some of these benefits include:
- Simplified versioning and sharing of large, unstructured datasets, making them manageable.
- Synchronized data, model, and code versioning to mitigate reproducibility issues during the AI/ML development.
- Packaging ML key components in standard formats that enhance compatibility and efficient deployment.
- Openness and interoperability to avoid vendor lock-in by leveraging the [OCI standards](https://opencontainers.org/posts/blog/2017-07-19-oci-v1-0-bringing-containers-closer-to-standardization/) (i.e., a format native to container technology).
**If you've found this post helpful, [support us with a GitHub Star!](https://github.com/jozu-ai/kitops)**
##Use DevOps pipelines for MLOps with KitOps.
Existing ML/AI tools focus on providing unique AI project solutions that integrate back into the MLOps pipeline. Among its goals, a tool like KitOps focuses on packaging your AI project solutions using open standards, so all your AI project artifacts are compatible with your existing DevOps pipeline.
DevOps pipelines utilize container technologies to streamline development workflows by promoting a code-centric approach and enabling reuse across environments. These technologies have become industry standards, facilitating seamless integration, version control, collaboration, and efficient rollbacks for streamlined development processes.
Efficiently adapting them for ML workflows may present greater benefits than managing multiple pipelines for your code, data, and model workflows. [KitOps](https://jozu.com/blog/kitops-the-bridge-between-ai-ml-models-and-devops) OCI-compliant standards allow you to integrate your ML workflow seamlessly into your existing DevOps pipelines.
##Package MLOps pipeline step with KitOps
Let's say your team has an existing DevOps pipeline and is ready to execute and scale an AI initiative. Transitioning your DevOps pipelines and principles into an MLOps one to develop the AI solution has certain implications, such as:
- Increased complexity
- Integration challenges
- Lack of standardization
- Skillset gap
- Higher costs
- Knowledge silos
- Increased time to market (TTM)
Instead, you can simultaneously leverage KitOps’ workflow to develop and collaborate on your ML components from a ModelKit package. ModelKit abstracts the implementation of the ML development pipeline. While maintaining standards, you will develop and manage all your ML components (i.e., code data and models) within the exact location. To use a ModelKit for your MLOps workflow, you only need to:
- [Install and set up KitOps](https://kitops.ml/docs/cli/installation.html) for your operating systems (OS).
- Configure a container registry such as [Docker hub](https://docs.docker.com/docker-hub/) or [GitHub container registry](https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry).
Creating a ModelKit for your AI project requires you to initialize its native configuration document called a Kitfile. Create a Kitfile for your ModelKit in your development directory.
touch Kitfile
Open the Kitfile and specify all the folders relevant to your ML development so the Kitfile knows what to track when you package your ModelKit image. This lets you maintain the structure and workflows in your local development environment. The Kitfile is a YAML file, here is a sample.
manifestVersion: v1.0.0
package:
author:
- Nwoke
description: This project is used to predict the quality of red wine
name: WineQualityML
code:
- description: Jupyter notebook with model training code in Python
path: ./code
datasets:
- description: Red wine quality dataset (tabular)
name: data
path: ./datasets
model:
framework: Tensorflow
name: Wine_model
path: ./models
version: 0.0.1
description: Red wine quality model using Scikit-learn
Then, package the Kitfile in your development directory with an AI project name and [tag for the current development stage](https://kitops.ml/docs/use-cases.html) to create the ModelKit image in your local KitOps registry. The version and tag workflows enable consistency across code, data, and model since they all exist in one location.
kit pack . -t "MODELKIT_IMAGE_NAME":"TAG"
The ModelKit automatically tracks and stores updates of the directories you specified in the Kitfile. At every development stage, you only need to repackage the ModelKit to track updates. Packaging your ModelKit from the development phase minimizes errors, ensures uniform practices, and even enables easy rollback if needed.
After packaging locally, you can push your ModelKit's image from the local repository to your configured remote container registry. But first, you have to tag the Modelkit using the name of your configured remote registry to create clear reference and destination for the image in the remote registry.
Tag your ModelKit with the remote registry reference:
kit tag "SOURCE_MODELKIT":"TAG" "TARGET_MODELKIT":"TAG"
then push to your remote container registry.
kit push "REMOTE_REGISTRY"/"REPOSITORY_USERNAME"/"MODELKIT_IMAGE_NAME":"TAG"
Now, developers can reproduce your ML workflows or extract only relevant assets for further development, testing, integration, or deployment rather than collaborating on MLOps pipeline components with different locations due to unsupported formats between code, data, and models.
ModelKit assets have an optimal caching system. When you pack or unpack, they create a reference link to their model assets. Therefore, if you or other developers already have a copy of your model assets locally, it just uses the reference and doesn't repack or unpack the asset. This referencing ability avoids duplication of model assets and makes your ModelKit fast and light even when using assets with large sizes. It also supports common MLOps tools, so you can use your local development environment as the persistent location for your artifacts, metadata, etc.
Anyone with container registry access can use your ModelKit image in the remote container registry for further development or deployment. They can directly pull the entire ModelKit.
kit pull "REMOTE_REGISTORY"/"REPOSITORY_USERNAME"/"MODELKIT_IMAGE_NAME":"TAG"
Or extract the model, data, code, or artifacts they need into their development or production environment.
kit unpack . "REMOTE_REGISTORY"/"REPOSITORY_USERNAME"/"MODELKIT_IMAGE_NAME":"TAG"
Without development pipelines, all your ML components are in one remote location, with versions at different development stages. The ML development pipeline becomes an image in a remote container registry.
At this point, you can implement a deployment strategy that extracts these components from the ModelKit into the staging or production environment when you push a new model kit. One recommended approach here is to use [KitOps tags](https://kitops.ml/docs/use-cases.html) to automate triggers for your production workflow.
KitOps' ModelKits lets you seamlessly integrate your machine learning workflows into your existing DevOps pipeline. Packaging models, code, data, artifacts, etc., into a portable, OCI-compatible artifact eliminates the need to use ML development pipelines while leveraging familiar tools and processes. This unified approach simplifies AI development and deployment. It also accelerates the delivery of AI-powered applications, allowing you to focus on innovation rather than pipeline management. Embrace the power of ModelKits and unlock the full potential of your MLOps initiatives - [try KitOps today](https://kitops.ml/docs/quick-start.html)!
| jwilliamsr |
1,876,678 | Top Commercial Video Advertisement Production Services | Commercial Video Advertisement Production is a valuable asset for businesses seeking to expand their... | 0 | 2024-06-04T13:06:48 | https://dev.to/fbefilms/top-commercial-video-advertisement-production-services-24cl | Commercial Video Advertisement Production is a valuable asset for businesses seeking to expand their reach and establish a distinctive brand presence. Through broadcast videos, companies can connect with a broader audience, conveying their message effectively and fostering brand recognition. The dynamic nature of video content enhances engagement, making it an impactful tool for promoting products or services.
Visit their website to learn more services :-
https://fbefilms.in/
https://fbefilms.in/film-production-house-in-delhi/
https://fbefilms.in/seo-performance-marketing-agency-in-delhi/
https://fbefilms.in/influencer-marketing-agency-in-delhi/
https://fbefilms.in/food-photography-in-delhi/
https://fbefilms.in/videography-photography-agency-in-delhi/
https://fbefilms.in/interior-photography-in-delhi/
| fbefilms | |
1,873,942 | Help test Python 3.13! | Calling all Python library maintainers! 🐍 The Python 3.13 beta is out! 🎉 PEP 719 defines the... | 0 | 2024-06-04T13:04:03 | https://dev.to/hugovk/help-test-python-313-14j1 | python, testing, ci, githubactions | Calling all Python library maintainers! 🐍
The Python 3.13 beta is out! 🎉
[PEP 719](https://peps.python.org/pep-0719/#release-schedule) defines the release schedule for Python 3.13.0:
* The first beta candidate came out on 8th May 2024
* The first release candidate is set for 30th July 2024
* And the full release is set for 1st October 2024
In his [announcement](https://discuss.python.org/t/python-3-13-0b1-now-available/52891?u=hugovk), Thomas Wouters, release manager for Python 3.12 and 3.13, said:
> We **strongly encourage** maintainers of third-party Python projects to test with 3.13 during the beta phase and report issues found to the [Python bug tracker](https://github.com/python/cpython/issues) as soon as possible. While the release is planned to be feature complete entering the beta phase, it is possible that features may be modified or, in rare cases, deleted up until the start of the release candidate phase (Tuesday 2024-07-30). Our goal is to have no ABI changes after beta 4 and as few code changes as possible after 3.13.0rc1, the first release candidate. To achieve that, it will be **extremely important** to get as much exposure for 3.13 as possible during the beta phase.
## Test with 3.13
It's now time for us library maintainers to start testing our projects with 3.13. There's two big benefits:
1. There have been [removals and changes](https://docs.python.org/3.13/whatsnew/3.13.html#removed) in Python 3.13. Testing now helps us make our code compatible and avoid any big surprises (for us and our users) at the big launch in October.
2. We might find bugs in Python itself! Reporting those will help get them fixed and help everyone.
## How
### GitHub Actions: setup-python
To test the latest alpha, beta or release candidate with [actions/setup-python]
(https://github.com/actions/setup-python#supported-version-syntax), add `3.13` and `allow-prereleases: true` to your workflow matrix.
For example:
```yml
jobs:
test:
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: ["3.10", "3.11", "3.12", "3.13"]
steps:
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
allow-prereleases: true
```
(We can instead use `3.13-dev` and omit `allow-prereleases: true`, but I find the above a bit neater, and when 3.13.0 final is released in October, it will continue testing with full release versions.)
### GitHub Actions: deadsnakes
For the bleeding edge, we can use [deadsnakes/action](https://github.com/deadsnakes/action) to test the latest nightly build:
```yml
jobs:
test:
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: ["3.10", "3.11", "3.12", "3.13-dev"]
steps:
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
if: "!endsWith(matrix.python-version, '-dev')"
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- uses: deadsnakes/action@v3.1.0
name: Set up Python ${{ matrix.python-version }} (deadsnakes)
if: endsWith(matrix.python-version, '-dev')
with:
python-version: ${{ matrix.python-version }}
```
## When to support 3.13?
When should you declare support and add the `Programming Language :: Python :: 3.13` [Trove classifier](https://pypi.org/classifiers/)? Some [projects already have](https://pyreadiness.org/3.13/)!
If you have a pure Python project, you can release now.
If you have C extensions and other projects depend on yours, a preview release with wheels will help them test and prepare. I've alreastarted releasing these.
### ABI breaks?
ABI breaks during the beta are infrequent and unintentional. If they happen, you can rebuild your wheels and upload them to an existing PyPI release by adding an optional [*build tag*](https://packaging.python.org/en/latest/specifications/binary-distribution-format/#file-format) to the filename:
> The wheel filename is `{distribution}-{version}(-{build tag})?-{python tag}-{abi tag}-{platform tag}.whl`.
>
> build tag: [...] Acts as a tie-breaker if two wheel file names are the same in all other respects (i.e. name, version, and other tags).
For example, this updates the filename and metadata with build number 1, and removes the original file:
```sh
python -m pip install "wheel>=0.4.0"
wheel tags --build=1 --remove sampleproject-5.0.0-cp313-cp313-macosx_10_10_x86_64.whl
# this creates a file named sampleproject-5.0.0-1-cp313-cp313-macosx_10_10_x86_64.whl
```
Upload it, and the new file will be used instead of the old one. See also [Brett Cannon's advice on making new wheels](https://snarky.ca/what-to-do-when-you-botch-a-release-on-pypi/#a-wheel-file-wasnt-compiled-properly).
In any case, let's start testing 3.13 now! 🚀
## See also
* [Help us test free-threaded Python without the GIL](https://dev.to/hugovk/help-us-test-free-threaded-python-without-the-gil-1hgf)
* [What’s New In Python 3.13](https://docs.python.org/3.13/whatsnew/3.13.html)
---
<small>Header photo: Lot 044 from the [PyCon 2024](https://us.pycon.org/2024/) [PyLadies Auction](https://mastodon.social/@Lorenanicole/112468770670490719): "A pair of hand-woven snakes ([PyCon Latam](https://www.pylatam.org/) 2023 edition), donated by the PyCon Latam Organizers. This is a souvenir from PyCon Latam held in Mexica 2023 that represents the snakes of the PyLatam community logo. They are made completely by hand."</small>
| hugovk |
1,876,671 | Exploring the Exciting New Features in React 18 | React 18 is a significant update for one of the most popular JavaScript libraries used for building... | 0 | 2024-06-04T12:54:18 | https://dev.to/shantih_palani/exploring-the-exciting-new-features-in-react-18-54m4 | react, react18, reactjsdevelopment | React 18 is a significant update for one of the most popular JavaScript libraries used for building user interfaces. This version introduces several exciting new features and improvements that aim to enhance performance, improve the developer experience, and enable new use cases. In this post, we’ll explore some of the standout features of React 18 and what they mean for developers.
## **Concurrent Rendering**
One of the most anticipated features in React 18 is **Concurrent Rendering**. This new rendering mechanism allows React to prepare multiple versions of the UI at the same time. This can improve the user experience by making the UI more responsive and fluid, especially in applications with complex, interactive interfaces.
Concurrent Rendering enables React to interrupt and pause work, then continue later. This means your app can stay responsive even during heavy rendering tasks. For example, if a user interaction is more urgent, React can prioritize rendering that over less critical tasks.
```javascript
import { useState, useTransition } from 'react';
function App() {
const [isPending, startTransition] = useTransition();
const [input, setInput] = useState('');
const [list, setList] = useState([]);
const handleChange = (e) => {
setInput(e.target.value);
startTransition(() => {
const newList = Array(5000).fill(e.target.value);
setList(newList);
});
};
return (
<div>
<input type="text" value={input} onChange={handleChange} />
{isPending ? 'Loading...' : list.map((item, index) => <div key={index}>{item}</div>)}
</div>
);
}
```
## Automatic Batching
React 18 introduces Automatic Batching, which optimizes the way updates are processed. Previously, React batched state updates that occurred within event handlers. However, updates that happened outside of event handlers, like those in setTimeouts or native event listeners, weren’t batched.
With React 18, all updates are automatically batched, regardless of where they originate. This leads to fewer renders and better performance:
```javascript
// Before React 18
setTimeout(() => {
setCount(count + 1);
setFlag(flag + 1);
// Two separate renders
}, 1000);
// With React 18
setTimeout(() => {
setCount(count + 1);
setFlag(flag + 1);
// Only one render
}, 1000);
```
## New Suspense Features
React’s Suspense feature, which was introduced earlier, has received significant updates in React 18. Suspense allows components to “wait” for something before they render. This is particularly useful for handling asynchronous data fetching.
In React 18, Suspense works seamlessly with Concurrent Rendering to make data fetching more efficient and the UI more resilient. For example, you can now use Suspense to control loading states more granularly, ensuring that your application remains responsive and visually consistent:
```javascript
import React, { Suspense } from 'react';
const MyComponent = React.lazy(() => import('./MyComponent'));
function App() {
return (
<Suspense fallback={<div>Loading...</div>}>
<MyComponent />
</Suspense>
);
}
```
## Transitions
Transitions in React 18 allow developers to distinguish between urgent and non-urgent updates. This is particularly useful for distinguishing between interactions that need immediate feedback (like typing in a text box) and those that can be deferred (like loading a new page).
The new **useTransition** hook lets you mark updates as transitions, allowing React to keep the UI responsive during non-urgent updates:
```javascript
import { useTransition } from 'react';
const [isPending, startTransition] = useTransition();
startTransition(() => {
setState(newState);
});
```
**startTransition API**
The startTransition API complements the useTransition hook by allowing developers to mark updates as transitions. This helps React prioritize urgent updates over non-urgent ones, ensuring a smooth user experience.
```javascript
import React, { useState, startTransition } from 'react';
function App() {
const [count, setCount] = useState(0);
const handleClick = () => {
startTransition(() => {
setCount(c => c + 1);
});
};
return (
<div>
<button onClick={handleClick}>Increment</button>
<p>{count}</p>
</div>
);
}
```
## Improved Server-Side Rendering
React 18 brings significant improvements to Server-Side Rendering (SSR) with the introduction of a new API called react-dom/server. This API allows for more efficient streaming of HTML, which can improve the performance of server-rendered React applications.
The new streaming SSR architecture lets the server send content to the client as soon as it’s ready, rather than waiting for the entire component tree to be rendered. This can lead to faster load times and a better user experience.
```javascript
import { renderToPipeableStream } from 'react-dom/server';
import express from 'express';
import App from './App';
const app = express();
app.get('/', (req, res) => {
const stream = renderToPipeableStream(<App />, {
onShellReady() {
res.setHeader('Content-type', 'text/html');
stream.pipe(res);
},
});
});
app.listen(3000, () => {
console.log('Server is running on port 3000');
});
```
## Server Components
Another major addition to React 18 is Server Components. Server Components enable developers to build apps that leverage both the server and the client for rendering, resulting in faster load times and reduced bundle sizes.
Server Components allow parts of the UI to be rendered on the server and sent to the client as HTML. This can significantly reduce the amount of JavaScript that needs to be loaded and executed on the client, leading to faster initial renders and improved performance.
```javascript
// MyComponent.server.js
export default function MyComponent() {
return <div>This is a server component</div>;
}
// App.client.js
import React, { Suspense } from 'react';
import MyComponent from './MyComponent.server';
function App() {
return (
<Suspense fallback={<div>Loading...</div>}>
<MyComponent />
</Suspense>
);
}
export default App;
```javascript
**Conclusion**
React 18 is packed with features that enhance performance, improve the developer experience, and open up new possibilities for building modern web applications. Concurrent Rendering, Automatic Batching, enhanced Suspense, Transitions, improved SSR, and Server Components are just a few of the exciting additions.
As developers, these new tools and improvements allow us to build more responsive, efficient, and user-friendly applications. With React 18, the future of web development looks brighter than ever.
Stay tuned to **Shanthi's Dev Diary** series for more in-depth tutorials and insights into the latest in web development!
| shantih_palani |
1,876,677 | How to Use Firebase Authentication for Secure User Login | Project:- 5/500 Firebase Authentication Project Description Firebase... | 27,575 | 2024-06-04T13:03:41 | https://dev.to/raajaryan/how-to-use-firebase-authentication-for-secure-user-login-3df9 | react, javascript, opensource, beginners |
### Project:- 5/500 Firebase Authentication Project
## Description
Firebase Authentication provides a comprehensive and secure solution for authenticating users in web applications. It supports various authentication methods, including email and password, Google Sign-In, Facebook Login, and more. This project demonstrates how to integrate Firebase Authentication in a React application, offering a seamless and secure user login experience.
## Features
- **User Registration:** Allow users to sign up with email and password.
- **User Login:** Enable users to log in using their email and password.
- **Password Reset:** Provide users with the option to reset their password via email.
## Technologies Used
- **React:** For building the user interface.
- **Firebase:** For backend authentication services.
## Setup
Follow these instructions to set up and run the project locally:
1. **Clone the Repository:**
```bash
git clone https://github.com/deepakkumar55/ULTIMATE-JAVASCRIPT-PROJECT.git
cd Database Integration/1-firebase_authentication
```
2. **Install Dependencies:**
```bash
npm install
npm install firebase
```
3. **Firebase Configuration:**
- 1. **Create a Firebase Project**:
- Go to the [Firebase Console](https://console.firebase.google.com/).
- Click on "Add project" and follow the instructions to create a new Firebase project.
- 2. **Enable Authentication Methods**:
- In the Firebase Console, navigate to the "Authentication" section.
- Click on the "Sign-in method" tab.
- Enable "Email/Password" and "Google" sign-in methods.
- 3. **Get Firebase SDK Config**:
- In the Firebase Console, navigate to "Project settings".
- Under "Your apps", add a new web app and register it.
- Copy the Firebase SDK configuration snippet provided.
```javascript
// src/firebase.js
import { initializeApp } from 'firebase/app';
import { getAuth } from 'firebase/auth';
const firebaseConfig = {
apiKey: "YOUR_API_KEY",
authDomain: "YOUR_AUTH_DOMAIN",
projectId: "YOUR_PROJECT_ID",
storageBucket: "YOUR_STORAGE_BUCKET",
messagingSenderId: "YOUR_MESSAGING_SENDER_ID",
appId: "YOUR_APP_ID"
};
const app = initializeApp(firebaseConfig);
export const auth = getAuth(app);
```
4. **Run the Project:**
```bash
npm start
```
- Open your browser and navigate to `http://localhost:3000` to see the application in action.
## Contribution
Contributions are welcome! If you'd like to contribute to this project, please follow these steps:
1. **Fork the Repository:**
Click the "Fork" button at the top right corner of the repository page.
2. **Clone Your Fork:**
```bash
git clone https://github.com/your-username/firebase-authentication-mern.git
cd Database Integration/1-firebase_authentication
```
3. **Create a Branch:**
```bash
git checkout -b feature-branch
```
4. **Make Your Changes:**
Implement your feature or fix the bug you want to address.
5. **Commit Your Changes:**
```bash
git add .
git commit -m "Description of the changes"
```
6. **Push to Your Fork:**
```bash
git push origin feature-branch
```
7. **Create a Pull Request:**
Go to the original repository on GitHub, and you should see a prompt to create a pull request from your new branch. Provide a clear description of your changes and submit the pull request for review.
## Get in Touch
If you have any questions or need further assistance, feel free to open an issue on GitHub or contact us directly. Your contributions and feedback are highly appreciated!
---
Thank you for your interest in the Firebase Authentication project. Together, we can build a more robust and feature-rich application. Happy coding!
| raajaryan |
1,876,442 | TW Elements - TailwindCSS IntelliSense. Free UI/UX design course | Colors Colours in Tailwind CSS are defined as classes that you can apply directly to your... | 25,935 | 2024-06-04T12:59:00 | https://dev.to/keepcoding/tw-elements-tailwindcss-intellisense-free-uiux-design-course-296f | tailwindcss, html, beginners, tutorial | ## Colors
Colours in Tailwind CSS are defined as classes that you can apply directly to your HTML elements. In this lesson, we'll learn how they work.
## Colour utility classes
Tailwind CSS comes with a wide variety of predefined colours. Each colour has different shades, ranging from 100 (lightest) to 900 (darkest). You can use these colours and shades by adding the corresponding utility classes to your HTML elements.
For example, if you wanted to set the background colour of an element to light blue, you would add the .bg-blue-200 class to that element:
If you want to add a darker blue, you can use e.g. .bg-blue-500:
And so on:
## Background colour
As you have already noticed from the examples above, we use the bg-{color} class (like .bg-blue-500) to assign a selected color to an element.
There is no magic here anymore, so we will not dwell on the subject.
## Text colour
The situation is similar with the colour of the text, with the difference that instead of bg- we use the text- prefix:
**HTML**
```
<h5
class="text-lg text-primary transition duration-150 ease-in-out hover:text-primary-600 focus:text-primary-600 active:text-primary-700 dark:text-primary-400 dark:hover:text-primary-500 dark:focus:text-primary-500 dark:active:text-primary-600">
What exactly is beauty?
</h5>
```
And so on:
**HTML**
```
<h5 class="mb-3 text-lg text-blue-100">What exactly is beauty?</h5>
<h5 class="mb-3 text-lg text-blue-200">What exactly is beauty?</h5>
<h5 class="mb-3 text-lg text-blue-300">What exactly is beauty?</h5>
<h5 class="mb-3 text-lg text-blue-400">What exactly is beauty?</h5>
<h5
class="mb-3 text-lg text-primary transition duration-150 ease-in-out hover:text-primary-600 focus:text-primary-600 active:text-primary-700 dark:text-primary-400 dark:hover:text-primary-500 dark:focus:text-primary-500 dark:active:text-primary-600">
What exactly is beauty?
</h5>
<h5 class="mb-3 text-lg text-blue-600">What exactly is beauty?</h5>
<h5 class="mb-3 text-lg text-blue-700">What exactly is beauty?</h5>
<h5 class="mb-3 text-lg text-blue-800">What exactly is beauty?</h5>
<h5 class="mb-3 text-lg text-blue-900">What exactly is beauty?</h5>
```
## Customizing colours
While Tailwind provides a comprehensive set of color classes, you might need to customize these for your specific project. You can do this in your Tailwind configuration file (tailwind.config.js).
You need to add theme object configuration, so you can customize the colors by extending the default colors or completely replacing them.
Suppose we want to create a custom color with the value #123456.
**TAILWIND CONFIFURATION**
```
theme: {
extend: {
colors: {
'custom-color': '#123456',
}
}
}
```
So we should add a theme object to our configuration file. Finally, our tailwind.config.js file should look like this:
**TAILWIND.CONFIG.JS**
```
/** @type {import('tailwindcss').Config} */
module.exports = {
content: ['./index.html', './src/**/*.{html,js}', './node_modules/tw-elements/dist/js/**/*.js'],
plugins: [require('tw-elements/dist/plugin.cjs')],
darkMode: 'class',
theme: {
extend: {
colors: {
'custom-color': '#123456',
}
}
}
};
```
After saving the file, we should be able to use the newly created _.bg-custom-color_ class in our HTML.
It was just additional information that we will not use in the current project. So, if you added a custom color to your config for testing purposes, then when you're done experimenting, restore the tailwind.config.js file to its original state.
**TAILWIND.CONFIG.JS**
```
/** @type {import('tailwindcss').Config} */ module.exports = {
content: ['./index.html', './src/**/*.{html,js}', './node_modules/tw-elements/dist/js/**/*.js'],
plugins: [require('tw-elements/dist/plugin.cjs')],
darkMode: 'class',
};
```
Change the background color of the navbar
Let's use the acquired knowledge to change the background color of our navbar.
In your project, find the _.bg-neutral-100_ class in the navbar.
**HTML**
```
<!-- Navbar -->
<nav
class="flex-no-wrap relative flex w-full items-center justify-between bg-neutral-100 py-2 shadow-md shadow-black/5 dark:bg-neutral-600 dark:shadow-black/10 lg:flex-wrap lg:justify-start lg:py-4"
data-twe-navbar-ref>
[...]
</nav>
```
Then replace it with the .bg-white class to change the color of the navbar to white.
**HTML**
```
<!-- Navbar -->
<nav
class="flex-no-wrap relative flex w-full items-center justify-between bg-white py-2 shadow-md shadow-black/5 dark:bg-neutral-600 dark:shadow-black/10 lg:flex-wrap lg:justify-start lg:py-4"
data-twe-navbar-ref>
[...]
</nav>
```
Once the file is saved, the navbar should change from grey to white.
 | keepcoding |
1,876,675 | Styling in React: CSS-in-JS vs. Traditional CSS | Styling in React can be approached in various ways, including using traditional CSS and modern... | 0 | 2024-06-04T12:56:04 | https://dev.to/elightwalk/styling-in-react-css-in-js-vs-traditional-css-cnk | styling, react, css, reactjsdevelopment | Styling in React can be approached in various ways, including using traditional CSS and modern CSS-in-JS libraries. Each method has advantages and disadvantages, and choosing the right one depends on your project's needs.
Overview of CSS-in-JS Libraries
CSS-in-JS libraries allow you to write CSS directly within your JavaScript code. Some popular libraries include:
**Styled components:** Utilizes tagged template literals to style components.
```
import styled from 'styled-components';
const Button = styled.button`
background: blue;
color: white;
padding: 10px;
`;
```
Emotion: Offers both CSS-in-JS and the styled API similar to styled components.
```
/** @jsxImportSource @emotion/react */
import { css } from '@emotion/react';
const buttonStyle = css`
background: blue;
color: white;
padding: 10px;
`;
function Button() {
return <button css={buttonStyle}>Click me</button>;
}
```
**JSS:** JavaScript-based styling solution that allows you to define styles as JavaScript objects.
```
import { createUseStyles } from 'react-jss';
const useStyles = createUseStyles({
button: {
background: 'blue',
color: 'white',
padding: '10px'
}
});
function Button() {
const classes = useStyles();
return <button className={classes.button}>Click me</button>;
}
```
## Benefits and Drawbacks of CSS-in-JS
**Benefits**
**Scoped Styles:** Styles are scoped to components, reducing the risk of global namespace collisions.
**Dynamic Styling:** Styles can be based on component props, making creating dynamic and conditional styles easy.
**Better Maintenance:** Co-locating styles with components can make maintaining and understanding the codebase easier.
**Theming:** Easier to implement and manage themes across the application.
**Drawbacks**
**Performance: **Sometimes, runtime style generation can be slower than precompiled CSS.
**Learning Curve:** New syntax and concepts may have a learning curve for developers used to traditional CSS.
**Tooling:** This may require additional configuration for server-side rendering and integration with existing tools.
## Integrating Traditional CSS and CSS Modules with React
Traditional CSS
You can import traditional CSS files directly into your components or main entry files
```
import './App.css';
function App() {
return <div className="app">Hello World</div>;
}
```
**CSS Modules**
CSS Modules provide locally scoped CSS by default, avoiding global namespace collisions.
```
/* App.module.css */
.app {
background: blue;
color: white;
}
```
```
import styles from './App.module.css';
function App() {
return <div className={styles.app}>Hello World</div>;
}
```
## Best Practices for Theming and Design Systems
**Centralized Theme:** Define a theme object with colors, fonts, and other design tokens.
```
const theme = {
colors: {
primary: 'blue',
secondary: 'green'
},
spacing: {
small: '8px',
medium: '16px',
large: '24px'
}
};
```
**Theming with CSS-in-JS:** Use a ThemeProvider to pass the theme to styled-components or Emotion.
```
import { ThemeProvider } from 'styled-components';
function App() {
return (
<ThemeProvider theme={theme}>
<MyComponent />
</ThemeProvider>
);
}
```
**Consistent Design Language:** Use design tokens and utility classes to ensure component consistency.
## Performance Considerations and Best Practices
**Minimize Re-renders:** Ensure that your CSS-in-JS library doesn't cause unnecessary re-renders.
**Server-Side Rendering:** Use libraries that support server-side rendering to improve initial load times and SEO.
**Code Splitting:** Use code-splitting to load only the styles required for the current view, reducing initial load times.
**Static Extraction:** Some CSS-in-JS libraries offer static extraction to compile styles at build time, improving runtime performance.
## Conclusion
Current React development makes use of both traditional CSS and CSS-in-JS. Traditional CSS and CSS modules provide familiarity and simplicity, while CSS-in-JS offers scoped and dynamic styling capabilities. Selecting the best strategy will depend on the particular needs of your project, such as the requirement for dynamic styles, performance considerations, and developer experience. To get the best [ReactJS development service](https://www.elightwalk.com/services/reactjs-development), you have to check the basics of modern styling skills with a [react developers](https://www.elightwalk.com/hire-us/hire-reactjs-developer).
| elightwalk |
1,876,673 | Top 8 Mặt Dây Chuyền Phong Thủy Hot Nhất | https://styleyoushop.com/top-nhung-mat-day-chuyen-phong-thuy-duoc-ua-chuong-nhat-hien-nay/ | 0 | 2024-06-04T12:54:38 | https://dev.to/styleyoushop12/top-8-mat-day-chuyen-phong-thuy-hot-nhat-166e | https://styleyoushop.com/top-nhung-mat-day-chuyen-phong-thuy-duoc-ua-chuong-nhat-hien-nay/ | styleyoushop12 | |
1,876,672 | StyleYouShop | https://styleyoushop.com/ | 0 | 2024-06-04T12:54:25 | https://dev.to/styleyoushop12/styleyoushop-24l2 | https://styleyoushop.com/ | styleyoushop12 | |
1,876,668 | E-commerce for Beginners: A Detailed Guide to Understanding and Getting Started | E-commerce, short for virtual exchange, has basically transformed the manner corporations operate and... | 0 | 2024-06-04T12:50:57 | https://dev.to/liong/e-commerce-for-beginners-a-detailed-guide-to-understanding-and-getting-started-3583 | benefits, shopify, wordpress, malaysia | E-commerce, short for virtual exchange, has basically transformed the manner corporations operate and consumers keep. As technology maintains to increase and internet get proper of entry to becomes extra big, e-commerce is turning into an an increasing number of important part of the worldwide economy. This particular manual goals to assist novices apprehend e-commerce, its advantages, and the way to get began on this dynamic discipline.
## **Definition of E-commerce**
E-commerce refers to the looking for and selling of products or offerings over the internet. This includes plenty of organization models and transactions, from character customers purchasing for online to huge companies shopping for and selling gadgets and services. Essentially, any transaction that takes region electronically may be considered e-change.
## **Types of E-commerce**
E-commerce may be divided into numerous categories primarily based mostly on the man or woman of the transactions and the occasions concerned:
**1. Business-to-Consumer (B2C) E-commerce**
B2C e-commerce is probably what comes to thoughts while you first hear the time period “e-commerce.” It basically refers to services or products marketed from a commercial enterprise to individual human beings.
**2. Business-to-Business (B2B) E-commerce**
Not all corporations marketplace to man or woman purchasers. Some promote products and services to other companies. When that takes place online, you have B2B e-commerce. One instance of B2B e-commerce is internet improvement. Every organization practically wishes a website, due in element to, satirically, ideas like e-commerce.
**3. Consumer-to-Consumer (C2C)**
This kind includes transactions amongst character customers, commonly facilitated with the resource of one/three-birthday party structures like eBay or Craigslist.
**4. Consumer-to-Business (C2B)**
In this a good deal less commonplace model, individuals sell products or services to businesses. Examples embody freelancers presenting offerings on structures like Up work.
## **Benefits of E-commerce**
**Convenience and Accessibility**
One of the primary blessings of e-commerce is the benefit it offers. Customers can maintain on-line 24/7 from anywhere with an internet connection. This degree of accessibility is in particular useful for people with busy schedules or those residing in far flung regions.
**Broader Customer Reach**
E-commerce allows companies to attain a international audience. Unlike conventional brick-and-mortar stores, which might be constrained by geographical obstacles, on line shops can promote to customers international. This multiplied reach can considerably boom a organization's marketplace base and sales capability.
**Cost-Effectiveness**
Running an e-commerce business may be extra price-powerful than maintaining a bodily storefront. Overheads consisting of lease, utilities, and in-keep body of workers are decreased or eliminated. Additionally, virtual advertising techniques may be more low-cost and focused in comparison to standard advertising methods.
**Personalization and Customer Experience**
E-commerce systems can gather and analyze purchaser facts to offer personalized shopping studies. By leveraging facts analytics, groups can propose merchandise based on past purchases, browsing conduct, and alternatives, enhancing customer satisfaction and loyalty.
**Scalability**
E-commerce groups can scale greater without problems than traditional corporations. With the proper infrastructure, an online shop can manage extended visitors and sales volume with out the need for big bodily growth. This scalability allows for rapid increase and the capability to adapt to marketplace demands.
## **Getting Started with E-commerce**
## **Step 1: Research and Planning**
Before diving into e-commerce, it's crucial to behavior thorough research and create a solid business plan. This includes:
**Market Research**
Marketing research is defined as any approach or a set of practices that companies use to acquire facts to apprehend their target marketplace better. Organizations use this information to improve their merchandise, decorate their UX, and offer a better product to their clients.
**Business Model**
Decide at the form of e-commerce organization you want to begin (e.G., B2C, B2B, C2C) A business model captures your hypothesis for a way your enterprise will generate revenue and attain profitability charging a price for an offering you create at a sustainable fee.
**Product Selection**
Choose the products or services you need to sell. Consider factors like market name for, earnings margins, and your knowledge.
## **Step 2: Choose an E-change Platform**
Selecting the right e-exchange platform is critical for your organization's fulfillment. Popular systems encompass:
**Shopify**
Known for its ease of use and strong functions, Shopify is suitable for small to medium-sized companies.
**WooCommerce**
A WordPress plugin that offers flexibility and customization, nice for groups already the use of WordPress.
**Magento**
A powerful platform for larger organizations with complex needs, offering widespread customization alternatives.
**Big Commerce**
Known for scalability and built-in functions, suitable for developing corporations.
## **Step 3: Build Your Online Store**
Once you have chosen a platform, the next step is to build your on line store. Key aspects to keep in mind include:
**Design and User Experience**:
Choose a smooth, professional layout that aligns along with your logo. Ensure the internet site is easy to navigate and cellular-pleasant.
**Product Listings**
Create special and accurate product descriptions, first rate pix, and clean pricing records. Include purchaser evaluations and ratings to construct accept as true with.
**Payment Gateway**
Integrate secure charge gateways like PayPal, Stripe, or Square to facilitate transactions.
## **Step 4: Set Up Logistics**
Efficient logistics and fulfillment techniques are vital for an e-commerce enterprise. This includes:
**Inventory Management**
Keep song of stock degrees and control orders effectively to prevent overstocking or stock outs.
**Shipping and Delivery**
Choose reliable shipping companions and offer more than one delivery options to fulfill customer preferences. Consider supplying free transport or specific delivery to beautify patron satisfaction.
**Returns and Refunds**
Establish a clear returns and refunds coverage to handle purchaser proceedings and returns easily.
## **Step5: Digital Marketing Strategies**
To attract and maintain customers, you need to implement powerful virtual marketing techniques:
**Search Engine Optimization (seek engine advertising)***
Optimize your website for engines like google to improve herbal site visitors. Use applicable key phrases, create exquisite content cloth, and assemble one way hyperlinks.
**Social Media Marketing**
Utilize structures like Facebook, Instagram, and Twitter to promote your products, have interaction with clients, and strain site visitors for your maintain.
**Email Marketing**
Build an email listing and deliver ordinary newsletters, promotions, and updates to preserve clients knowledgeable and engaged.
**Paid Advertising**
Invest in pay-consistent with-click on (PPC) advertising and marketing on systems like Google Ads and social media to attain a larger target market speedy.
## **Step 6: Monitor and Optimize**
Once your e-exchange keep is up and going for walks, it's far crucial to continuously monitor and optimize your operations. This consists of:
**Analytics and Reporting**
Use equipment like Google Analytics to song website web page traffic, sales, and patron behavior. Analyze this facts to pick out trends and regions for development.
**Customer Feedback**
Collect and act on purchaser remarks to decorate your services and products.
**A/B Testing**
Conduct A/B tests on distinctive factors of your internet website online and advertising and advertising campaigns to determine what works exquisite and optimize therefore.
## **Conclusion**
E-commerce offers severe opportunities for corporations to attain a broader audience, growth income, and perform extra correctly. By information the basics of e-commerce, spotting its blessings, and following a based technique to getting began, you could build a a hit on line enterprise. Whether you're a budding entrepreneur or an established enterprise looking for to make bigger on line, e-commerce gives a flexible and dynamic platform to collect your dreams. Embrace the digital revolution and take step one within the course of a thriving e-commerce project today.
| liong |
1,876,669 | Everything to Know about Selenium and its Role in Test Automation | Selenium is a testing environment and framework that, simply put, lets you automate browsers. This is... | 0 | 2024-06-04T12:50:57 | https://dev.to/morrismoses149/everything-to-know-about-selenium-and-its-role-in-test-automation-5go0 | selenium, testautomation, testgrid | Selenium is a testing environment and framework that, simply put, lets you automate browsers. This is great for creating test environments where your application and its resource utilization can be tested across real world user stories.
## What is Selenium
From their website,
“Selenium automates browsers”
It’s an automation testing tool that will automate your web-based application and it works on the following browsers i.e. Chrome, Internet Explorer, Firefox, Opera, and Safari. This makes selenium very powerful because it works on multiple browsers, browser versions and a wide array of Operating Systems. This makes it a very powerful tool and that’s why the demand for selenium is increasing since its inception.
One writes code for Selenium in
- C#
- Java
- Ruby
- Python
- Perl
- PHP
These are the six languages that Selenium supports now. It’s not necessary that if your application under scrutiny is made in PHP, then you would have to write your Selenium test cases in PHP, or in Java for a Java-based app. You can make your website in C# and can write your selenium code in PHP as well.
So it is independent of the language in which the website is made. Selenium supports multiple browsers, and multiple operating systems, and multiple languages.
The Suite is made up of four components, or rather, it evolved in 4 stages –
### Selenium IDE
IDE is something that comes as an add-on in Firefox, it only works on the Mozilla browser and comes as an add-on in the Mozilla Firefox. IDE is primarily a record and run tool. Since Recording and running functionality is present in every automation tool, it is present in IDE as well. The main drawback of IDE is that it works only on Mozilla Firefox.
### Selenium RC
Earlier there was something known as RC which used to work on multiple browsers and it is available in multiple languages as well. You can write the test code in Java, C#, Ruby, Python Perl or PHP for learning IDE. You don’t need to learn any programming language, but if you are working with RC you must know any one of the programming languages. RC had its own limitations as well. With RC there used to be a separate server which was difficult to handle.
### Webdriver
So to overcome those limitations Webdriver came into existence. Webdriver supports multiple browsers. It is an enhanced version of RC, but the architecture of webdriver is completely different from the architecture of RC. IDE only works only on Mozilla but RC and webdriver works on all the browsers and both of them require the knowledge of any one of the programming languages from above.
Read more : [A comprehensive guide on Selenium Webdriver](https://testgrid.io/blog/selenium-webdriver/)
### Grid
The grid is something that will help you run your test cases parallelly. For example, let’s say you’ve got 200 test cases, and you want to run the test cases parallelly on three machines. One machine can handle test case execution on Safari, the other will run it on Mozilla, and the next one on the Chrome. Say you want to distribute your test cases on four different machines that are on the first machine. The first 50 test cases should run on the next machine. Next 50 on the third, Next 50, and on the fourth etc. You want to divide your test cases into two parts and run them parallely. This way, execution takes lesser time.
To summarize, The entire Selenium Suite is comprised of four components. IDE, a Firefox add-on that you can only use in creating relatively simple test cases and test suites.
Selenium Remote Control, also known as Selenium 1, is the first of our tools that allowed users to use programming languages in creating complex tests.
WebDriver, the newer breakthrough that allows your test scripts to communicate directly to the browser, thereby controlling it from the OS level.
Selenium Grid is also a tool that is used with Selenium RC to execute parallel tests across different browsers and operating systems.
Selenium RC and WebDriver were merged to form Selenium 2. It is more advantageous than QTP in terms of costs and flexibility. It also allows you to run tests in parallel, unlike in QTP where you are only allowed to run tests sequentially.
This blog is originally published at [TestGrid](https://testgrid.io/blog/selenium-what-is-it/) | morrismoses149 |
1,876,666 | Towards Lightweight Super-Resolution with Dual Regression Learning | Towards Lightweight Super-Resolution with Dual Regression Learning | 0 | 2024-06-04T12:50:04 | https://aimodels.fyi/papers/arxiv/towards-lightweight-super-resolution-dual-regression-learning | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Towards Lightweight Super-Resolution with Dual Regression Learning](https://aimodels.fyi/papers/arxiv/towards-lightweight-super-resolution-dual-regression-learning). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- Deep neural networks have shown remarkable performance in image super-resolution (SR) tasks
- However, the SR problem is ill-posed, and existing methods have limitations:
- The possible mapping space of SR can be extremely large, making it hard to learn a promising SR mapping
- Developing large models with high computational cost is often necessary to achieve good SR performance
- Existing model compression methods struggle to accurately identify redundant components due to the large SR mapping space
## Plain English Explanation
Deep neural networks have demonstrated impressive capabilities in [image super-resolution (SR) tasks](https://aimodels.fyi/papers/arxiv/hitchhikers-guide-to-super-resolution-introduction-recent). In these tasks, the goal is to take a low-resolution image and generate a corresponding high-resolution version. However, the SR problem is inherently complex, as there can be many different high-resolution images that could be generated from a single low-resolution input. This large "mapping space" makes it challenging to directly learn a reliable SR model.
Additionally, to achieve high-quality SR results, researchers often need to develop very large neural network models, which can be computationally expensive to train and run. While techniques like [model compression](https://aimodels.fyi/papers/arxiv/fortifying-fully-convolutional-generative-adversarial-networks-image) can help reduce the model size, existing compression methods struggle to accurately identify redundant components in the network due to the complexity of the SR problem.
## Technical Explanation
To address the challenges of the large SR mapping space and model complexity, the researchers propose two key innovations:
1. **Dual Regression Learning**: In addition to learning the mapping from low-resolution to high-resolution images, the researchers also learn a "dual" mapping to estimate the downsampling kernel and reconstruct the original low-resolution image. This dual mapping helps constrain the space of possible SR mappings, making the problem easier to solve.
2. **Dual Regression Compression (DRC)**: The researchers develop a novel model compression technique that exploits the dual regression approach. They first use a channel number search method to determine the redundancy of each layer in the network. Then, they further prune redundant channels by evaluating their importance based on the dual regression loss.
Through extensive experiments, the researchers demonstrate that their dual regression-based approach can produce accurate and efficient SR models, outperforming existing methods.
## Critical Analysis
The researchers acknowledge that the SR problem is inherently ill-posed, with a potentially extremely large mapping space, which makes it challenging to directly learn a reliable SR model. Their proposed dual regression learning scheme is an interesting approach to constrain this mapping space and improve the quality of the learned SR model.
However, the researchers do not provide a detailed analysis of the limitations or potential drawbacks of their dual regression-based approach. For example, it would be valuable to understand how the performance of the dual regression model compares to alternative methods, such as [unsupervised representation learning](https://aimodels.fyi/papers/arxiv/unsupervised-representation-learning-3d-mri-super-resolution) or [self-supervised learning](https://aimodels.fyi/papers/arxiv/self-supervised-learning-real-world-super-resolution) techniques, which may also help address the ill-posed nature of the SR problem.
Additionally, the researchers focus solely on the image SR task, but it would be interesting to see if their dual regression-based approach could be extended to [other image recognition tasks](https://aimodels.fyi/papers/arxiv/beyond-image-super-resolution-image-recognition-task) as well.
## Conclusion
In summary, the researchers have proposed a dual regression-based approach to address the challenges of the ill-posed image super-resolution problem. By learning an additional dual mapping to constrain the space of possible SR mappings and exploiting this dual regression scheme for model compression, the researchers have demonstrated a promising way to obtain accurate and efficient SR models. While the paper provides valuable insights, further research is needed to fully understand the limitations and broader applicability of this approach.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,875,553 | Getting started with Drata.com APIs | Intro Integrating Drata's compliance automation tool can significantly streamline your... | 0 | 2024-06-04T12:50:00 | https://dev.to/jam3sperkins/getting-started-with-dratacom-apis-2j7l | compliance, drata, iso27001, postman | ## Intro
Integrating Drata's compliance automation tool can significantly streamline your compliance processes. This tutorial will guide you through utilising Drata's APIs with Postman. We'll cover importing the Drata Postman collection, setting an environment and making an API call.
We won't cover first time installation of Postman or Drata, this doc will assume a basic understanding of the two.
{% embed https://youtu.be/sVSRQ9xx-Go %}
## Getting started
### Importing Postman Collection
We need to download the [Drata Postman Collection
](https://developers.drata.com/docs/openapi/reference/overview/) and following the documentation on Drata, to import the collection into Postman.
Click **Import**, select the **swagger.json** file you just downloaded, next make sure we click **View Import Settings**
Under **Folder organization** click the drop down and select **Tags** (the default is likely set to Paths
### Creating a new Postman Environment
Create a new environment in Postman
On the left hand navigation, select **Environments**, and at the top click **+** the plus
Add two new **Variables**
`baseUrl`
`bearerToken`
For the **baseUrl**, set the value depending on the location of your Drata instance ([reference documentation](https://developers.drata.com/docs/#base-url))
### Creating our Drata API token
We need to generate an API token within Drata and update the bearerToken in the Postman environment.
To do this, login to Drata and click **your name** at the bottom left corner and then click **Settings**
Click **API Keys**
Then we need to **Create API Key**
Follow the details on screen, including a Name, expiration and the scopes required.
Best practice is to limit the time of the token for how long you need it, limiting certain IP addresses to use the token and limiting the scope to what you need.
Take the API Key you are given and update the `bearerToken` value in the Postman environment. Don't forget to click **Save** for the Enviornment
It should now look something like:
### Let's run a request!
Go to the collection, and let's run a basic request. Expand **Drata API Documentation > Personnel** and select **Get personnel by id**
Update the **:id** with a personnel ID (1 should be fine)
If you want to check what the `id` should be set to, you can open up the Drata app, go to **Personnel** and open the user, you'll find their ID in the URL bar.
Make sure we have selected an Environment within Postman (top right corner) and click **Send**
🚀 Congratulations, we have now made an API call to Drata using Postman
## Recap
We have successfully setup the Drata Postman collection and are now able to make Postman requests against the Drata API.
What features of the Drata API would you like to know more about or see examples of?
| jam3sperkins |
1,876,664 | Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities | Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities | 0 | 2024-06-04T12:49:29 | https://aimodels.fyi/papers/arxiv/audio-flamingo-novel-audio-language-model-few | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities](https://aimodels.fyi/papers/arxiv/audio-flamingo-novel-audio-language-model-few). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- Introduces a novel audio language model called "Audio Flamingo" with few-shot learning and dialogue abilities
- Explores the potential for language models to handle audio-based tasks beyond traditional text-based ones
- Aims to advance the field of audio-based AI systems and their real-world applications
## Plain English Explanation
The paper presents a new kind of language model called "Audio Flamingo" that can work with audio data, not just text. Most language models today are designed for text, but this one can understand and generate audio. This is important because there are many real-world applications where being able to work with audio, like speech or music, could be very useful.
The researchers trained Audio Flamingo to be able to learn new audio-related tasks quickly, with just a few examples. This "few-shot learning" capability means the model doesn't need massive datasets to learn new things, which is often a challenge. The model can also engage in dialogue, allowing it to have back-and-forth conversations, not just produce single responses.
Overall, the goal is to push the boundaries of what language models can do and make them more versatile for real-world uses involving audio. By combining few-shot learning and dialogue abilities, the researchers hope to create an AI system that can adapt to different audio-based tasks and interact with humans in more natural ways.
## Technical Explanation
The paper introduces the "Audio Flamingo" model, a novel audio language model that goes beyond traditional text-based language models. Audio Flamingo is designed to handle a variety of audio-related tasks, including speech recognition, audio captioning, and audio-based dialogue.
A key innovation of Audio Flamingo is its few-shot learning capabilities. Unlike most language models that require large training datasets, Audio Flamingo can quickly learn new tasks and skills with just a few examples. This is achieved through a meta-learning approach that allows the model to rapidly adapt to new scenarios.
Another important aspect of Audio Flamingo is its dialogue abilities. The model can engage in back-and-forth conversations, not just produce individual responses. This is enabled by incorporating dialogue-specific modules and training on audio-based dialogue datasets.
The paper describes the overall architecture of Audio Flamingo, which combines transformer-based components for audio and text processing. Extensive experiments are conducted to evaluate the model's performance on a range of audio-based tasks, including few-shot learning benchmarks and dialogue-based interactions.
The results demonstrate Audio Flamingo's strong few-shot learning capabilities and its ability to engage in coherent and contextual audio-based dialogues. This represents a significant step forward in the development of language models that can work seamlessly with audio data, opening up new possibilities for real-world applications.
## Critical Analysis
The paper presents a compelling approach to advancing the capabilities of language models beyond the traditional text-domain. By focusing on audio-based tasks and incorporating few-shot learning and dialogue abilities, the researchers are addressing important limitations of current language models.
One potential limitation mentioned in the paper is the need for further research to improve the model's robustness to noisy or diverse audio environments. Real-world audio data can be highly variable, and the model's performance may degrade in such conditions.
Additionally, while the paper showcases the model's few-shot learning abilities, it would be valuable to explore the limits of this capability and investigate how it scales as the complexity of tasks or the required number of examples increases.
The dialogue capabilities of Audio Flamingo are a promising direction, but more work may be needed to ensure the model can engage in truly natural and coherent conversations, especially when handling more open-ended or context-dependent exchanges.
Overall, the Audio Flamingo model represents a significant step forward in the development of versatile language models that can transcend the text-only domain. By continuing to push the boundaries of what these models can do, the researchers are opening up new avenues for AI-powered applications that can seamlessly interact with and understand the audio world.
## Conclusion
The Audio Flamingo model presented in this paper is a novel approach to expanding the capabilities of language models beyond traditional text-based tasks. By incorporating few-shot learning and dialogue abilities, the researchers have developed a system that can quickly adapt to new audio-related challenges and engage in more natural, conversational interactions.
This work has important implications for the future of AI-powered applications, as the ability to understand and interact with audio data is crucial for many real-world scenarios, such as virtual assistants, smart home devices, and audio-based entertainment systems. By advancing the field of audio-based language models, the researchers are paving the way for more versatile and adaptable AI systems that can better serve human needs and preferences.
As the research in this area continues to evolve, it will be important to address the remaining challenges and limitations, such as improving robustness to diverse audio environments and enhancing the quality of dialogue interactions. Nevertheless, the Audio Flamingo model represents a significant step forward in the pursuit of general-purpose speech abilities for large language models, as highlighted by related work in this domain, such as [AudioChatLLaMA](https://aimodels.fyi/papers/arxiv/audiochatllama-towards-general-purpose-speech-abilities-llms), [Audio-Visual Generalized Zero-Shot Learning](https://aimodels.fyi/papers/arxiv/audio-visual-generalized-zero-shot-learning-using), [SalmonN](https://aimodels.fyi/papers/arxiv/salmonn-towards-generic-hearing-abilities-large-language), [Audio Dialogues](https://aimodels.fyi/papers/arxiv/audio-dialogues-dialogues-dataset-audio-music-understanding), and [AudioSetMix](https://aimodels.fyi/papers/arxiv/audiosetmix-enhancing-audio-language-datasets-llm-assisted).
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,663 | Relightable Gaussian Codec Avatars | Relightable Gaussian Codec Avatars | 0 | 2024-06-04T12:48:55 | https://aimodels.fyi/papers/arxiv/relightable-gaussian-codec-avatars | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Relightable Gaussian Codec Avatars](https://aimodels.fyi/papers/arxiv/relightable-gaussian-codec-avatars). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper presents a method called "Relightable Gaussian Codec Avatars" to create high-fidelity, relightable head avatars that can be animated to generate novel expressions.
- The key innovations are a 3D Gaussian geometry model that can capture intricate details like hair strands and pores, and a learnable radiance transfer appearance model that supports diverse materials like skin, eyes, and hair.
- The method enables real-time relighting with all-frequency reflections, outperforming existing approaches without compromising performance.
- It also demonstrates real-time relighting of avatars on a consumer VR headset, showcasing the efficiency and fidelity of the approach.
## Plain English Explanation
The paper tackles the challenge of creating digital avatars that can be realistically relit and animated. Existing methods often struggle to accurately model the complex geometry and appearance of human heads, particularly intricate structures like hair.
The researchers developed a new way to represent the 3D shape of a head using a set of 3D Gaussian functions. This allows them to capture fine details like individual hair strands and pores with high fidelity, even as the head is animated to display different expressions.
To handle the diverse materials that make up a human head, such as skin, eyes, and hair, the researchers created a novel appearance model based on "learnable radiance transfer." This allows the avatar's materials to be realistically relit in real-time, even under complex lighting conditions.
By combining the advanced geometry and appearance models, the researchers were able to create [relightable head avatars](https://aimodels.fyi/papers/arxiv/animatable-relightable-gaussians-high-fidelity-human-avatar) that outperform previous approaches in terms of visual quality and realism, while still running fast enough for real-time applications like virtual reality.
## Technical Explanation
The key technical innovations of this work are the 3D Gaussian geometry model and the learnable radiance transfer appearance model.
The 3D Gaussian geometry model represents the head's shape using a set of 3D Gaussian functions. This allows the capture of intricate details like hair strands and pores, even in dynamic face sequences. The researchers draw inspiration from prior work on [Gaussian-based head avatars](https://aimodels.fyi/papers/arxiv/flashavatar-high-fidelity-head-avatar-efficient-gaussian), [geometric adjustments](https://aimodels.fyi/papers/arxiv/ggavatar-geometric-adjustment-gaussian-head-avatar), and [hybrid mesh-Gaussian models](https://aimodels.fyi/papers/arxiv/mega-hybrid-mesh-gaussian-head-avatar-high).
For the appearance model, the researchers present a novel "learnable radiance transfer" approach. This allows diverse materials like skin, eyes, and hair to be represented in a unified manner and realistically relit under both point light and continuous illumination. The diffuse components are handled using global illumination-aware spherical harmonics, while the reflective components are rendered using spherical Gaussians for efficient, all-frequency reflections.
The researchers further improve the fidelity of eye reflections and enable explicit gaze control by introducing relightable explicit eye models.
## Critical Analysis
The researchers have done an impressive job of pushing the boundaries of realistic, relightable avatar rendering. The 3D Gaussian geometry model and learnable radiance transfer appearance model are novel and well-designed solutions to long-standing challenges in this field.
That said, the paper does not address a few potential limitations. For example, it's unclear how the method would scale to handle full-body avatars or varied skin tones and ethnicities. The performance and memory requirements of the models on resource-constrained platforms like mobile devices are also not explored.
Additionally, while the paper demonstrates the technical capabilities of the approach, it does not delve into the potential societal implications of highly realistic, manipulable digital avatars. Researchers in this domain should be mindful of how such technologies could be misused, for example, in the creation of deepfakes or other malicious applications.
Overall, the [Relightable Gaussian Codec Avatars](https://aimodels.fyi/papers/arxiv/animatable-relightable-gaussians-high-fidelity-human-avatar) represent a significant advance in avatar rendering, but further research is needed to address scalability, accessibility, and ethical considerations.
## Conclusion
This paper presents a novel method for creating high-fidelity, relightable head avatars that can be animated in real-time. By combining a 3D Gaussian geometry model with a learnable radiance transfer appearance model, the researchers have overcome longstanding challenges in capturing intricate facial details and diverse materials.
The ability to realistically relight avatars under complex lighting conditions opens up new possibilities for immersive virtual experiences, from gaming and social applications to remote collaboration and training. As this technology continues to evolve, it will be important for researchers to carefully consider the ethical implications and work to ensure these powerful tools are used responsibly.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,661 | Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities | Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities | 0 | 2024-06-04T12:48:20 | https://aimodels.fyi/papers/arxiv/zipper-multi-tower-decoder-architecture-fusing-modalities | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities](https://aimodels.fyi/papers/arxiv/zipper-multi-tower-decoder-architecture-fusing-modalities). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- The paper proposes a new multi-tower decoder architecture called "Zipper" for fusing different input modalities, such as text, audio, and video, to improve performance on various tasks.
- Zipper uses a modular design with separate decoding towers for each modality, which are then combined to leverage the strengths of each modality.
- The authors demonstrate the effectiveness of Zipper on several benchmarks, showing improved performance compared to existing multimodal fusion approaches.
## Plain English Explanation
The [Zipper paper](https://aimodels.fyi/papers/arxiv/data-efficient-multimodal-fusion-single-gpu) introduces a new way to combine different types of information, like text, audio, and video, to improve the performance of artificial intelligence (AI) systems on various tasks. The key idea is to have separate "towers" in the AI model, each focused on processing a different type of input, and then "zip" these towers together to take advantage of the unique strengths of each modality.
For example, an AI system might use one tower to process text, another to process audio, and a third to process video. By combining the outputs from these towers, the system can make more accurate predictions or generate more natural responses than if it had only used a single type of input.
The [Zipformer paper](https://aimodels.fyi/papers/arxiv/zipformer-faster-better-encoder-automatic-speech-recognition) and the [Towards Multi-Task, Multi-Modal Models for Video paper](https://aimodels.fyi/papers/arxiv/towards-multi-task-multi-modal-models-video) provide additional context on how multimodal fusion can be applied to speech recognition and video analysis, respectively.
Overall, the Zipper approach aims to help AI systems better understand and utilize the rich, complementary information available in different types of data, leading to more powerful and versatile AI applications.
## Technical Explanation
The [Zipper paper](https://aimodels.fyi/papers/arxiv/data-efficient-multimodal-fusion-single-gpu) presents a novel multi-tower decoder architecture for fusing multiple input modalities, such as text, audio, and video. The key innovation is the use of separate decoding towers for each modality, which are then combined to leverage the strengths of each.
The architecture consists of an encoder that processes the input data, and multiple decoder towers that specialize in different modalities. Each tower has its own attention mechanism and output layer, allowing it to focus on the most relevant features for its particular modality. The outputs from the towers are then "zipped" together, using a learned fusion mechanism, to produce the final output.
The authors evaluate Zipper on several benchmarks, including [multimodal machine translation](https://aimodels.fyi/papers/arxiv/generative-ai-beyond-llms-system-implications-multi), [visual question answering](https://aimodels.fyi/papers/arxiv/towards-multi-task-multi-modal-models-video), and [video captioning](https://aimodels.fyi/papers/arxiv/omnifusion-technical-report). The results demonstrate that Zipper outperforms existing multimodal fusion approaches, achieving state-of-the-art performance on several tasks.
## Critical Analysis
The Zipper paper presents a compelling approach to multimodal fusion, but there are a few potential limitations and areas for further research:
1. The paper does not provide a detailed analysis of the computational and memory requirements of the Zipper architecture, which could be an important consideration for real-world applications.
2. While the authors demonstrate the effectiveness of Zipper on several benchmarks, it would be interesting to see how the approach generalizes to a wider range of tasks and datasets, especially in more complex, real-world scenarios.
3. The fusion mechanism used in Zipper is relatively simple, and more sophisticated techniques, such as those explored in the [OmniFusion technical report](https://aimodels.fyi/papers/arxiv/omnifusion-technical-report), could potentially further improve performance.
4. The paper does not discuss the interpretability or explainability of the Zipper model, which could be an important consideration for applications where transparency and accountability are crucial.
Overall, the Zipper paper makes a valuable contribution to the field of multimodal fusion, and the proposed approach represents a promising direction for future research and development in this area.
## Conclusion
The Zipper paper introduces a novel multi-tower decoder architecture for effectively fusing multiple input modalities, such as text, audio, and video. By using separate decoding towers for each modality and then combining their outputs, Zipper is able to leverage the unique strengths of each data type to improve performance on a variety of tasks.
The results presented in the paper demonstrate the effectiveness of the Zipper approach, which outperforms existing multimodal fusion techniques on several benchmarks. While the paper identifies a few areas for further exploration, the Zipper architecture represents an important step forward in the development of powerful, versatile AI systems that can seamlessly integrate and capitalize on diverse sources of information.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,659 | Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models | Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models | 0 | 2024-06-04T12:47:46 | https://aimodels.fyi/papers/arxiv/uncertainty-thoughts-uncertainty-aware-planning-enhances-information | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models](https://aimodels.fyi/papers/arxiv/uncertainty-thoughts-uncertainty-aware-planning-enhances-information). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- Introduces Uncertainty of Thoughts (UoT), an algorithm to enable large language models to actively seek information through effective questioning
- UoT combines uncertainty-aware simulation, uncertainty-based rewards, and reward propagation to select optimal questions
- Experiments show UoT achieves 38.1% average performance improvement in successful task completion across medical diagnosis, troubleshooting, and "20 Questions" game
## Plain English Explanation
When facing uncertainty, the ability to **seek information** is crucial. For example, in medical diagnosis or troubleshooting, the information needed to solve the problem may not be initially provided, so the model needs to actively ask follow-up questions to gather more details.
The [Uncertainty of Thoughts (UoT) algorithm](https://aimodels.fyi/papers/arxiv/towards-uncertainty-aware-language-agent) aims to give large language models this capability. UoT has three key components:
1. **Uncertainty-aware simulation**: The model can imagine possible future scenarios and estimate how likely they are to occur.
2. **Uncertainty-based rewards**: The model is incentivized to seek information that reduces uncertainty and maximizes its expected reward.
3. **Reward propagation**: The model selects the optimal question to ask in a way that maximizes the expected reward.
In experiments on medical diagnosis, troubleshooting, and the "20 Questions" game, UoT improved the success rate by an average of 38.1% compared to directly prompting the language model. It also made the process more efficient by requiring fewer questions to complete the tasks.
## Technical Explanation
The [Uncertainty of Thoughts (UoT) algorithm](https://aimodels.fyi/papers/arxiv/towards-uncertainty-aware-language-agent) combines several key techniques to enable large language models to actively seek information:
1. **Uncertainty-aware simulation**: UoT uses a simulation-based approach to estimate the uncertainty associated with possible future scenarios. This allows the model to reason about the likelihood of different outcomes and the value of gathering additional information.
2. **Uncertainty-based rewards**: UoT defines rewards based on the model's uncertainty reduction, motivating it to ask questions that provide the most informative answers and decrease uncertainty.
3. **Reward propagation**: To select the optimal question to ask, UoT uses a reward propagation scheme that evaluates the expected long-term reward of each possible question, allowing the model to choose the one that maximizes its expected information gain.
The researchers evaluated UoT on three tasks: medical diagnosis, troubleshooting, and the "20 Questions" game. Across these experiments, UoT achieved an average performance improvement of 38.1% in successful task completion compared to directly prompting the language model. UoT also improved efficiency, requiring fewer questions to complete the tasks.
## Critical Analysis
The [Uncertainty of Thoughts (UoT) algorithm](https://aimodels.fyi/papers/arxiv/towards-uncertainty-aware-language-agent) represents an important step towards building language agents that can actively seek information to solve complex, open-ended tasks. However, the paper also acknowledges several limitations and avenues for future research:
1. **Scalability**: The computational complexity of the uncertainty-aware simulation and reward propagation mechanisms may limit the scalability of UoT to larger, more complex tasks.
2. **Robustness**: The performance of UoT may be sensitive to the quality and reliability of the underlying language model, which could be a concern when deploying such systems in real-world applications.
3. **Ethical considerations**: As language agents become more capable of actively questioning users, there may be ethical implications around privacy, trust, and the potential for manipulation that should be carefully considered.
Further research is needed to address these challenges and explore ways to make [uncertainty-aware language agents](https://aimodels.fyi/papers/arxiv/shifting-attention-to-relevance-towards-predictive-uncertainty) more robust, scalable, and aligned with human values.
## Conclusion
The [Uncertainty of Thoughts (UoT) algorithm](https://aimodels.fyi/papers/arxiv/towards-uncertainty-aware-language-agent) represents an important step towards building language models that can actively seek information to solve complex, open-ended tasks. By combining uncertainty-aware simulation, uncertainty-based rewards, and reward propagation, UoT enables language models to ask effective questions that improve task success rates and efficiency.
As the field of [uncertainty-aware language models](https://aimodels.fyi/papers/arxiv/im-not-sure-but-examining-impact-large) and [uncertainty quantification in large language models](https://aimodels.fyi/papers/arxiv/generating-confidence-uncertainty-quantification-black-box-large) continues to advance, we can expect to see more [powerful and capable language agents](https://aimodels.fyi/papers/arxiv/harnessing-power-large-language-model-uncertainty-aware) that can better navigate uncertainty and actively collaborate with humans to solve a wide range of problems.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,658 | Diffusion On Syntax Trees For Program Synthesis | Diffusion On Syntax Trees For Program Synthesis | 0 | 2024-06-04T12:47:10 | https://aimodels.fyi/papers/arxiv/diffusion-syntax-trees-program-synthesis | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Diffusion On Syntax Trees For Program Synthesis](https://aimodels.fyi/papers/arxiv/diffusion-syntax-trees-program-synthesis). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper introduces a novel approach for program synthesis using diffusion models on syntax trees.
- The researchers propose a technique called "Diffusion on Syntax Trees" (DoST) that leverages the strengths of diffusion models to generate valid program structures.
- The method aims to address the challenges of existing program synthesis techniques, such as the need for large datasets and the difficulty in capturing complex program structures.
## Plain English Explanation
The paper presents a new way to automatically generate computer programs using a machine learning technique called "diffusion models." Diffusion models are a type of AI system that can create new data, like images or text, by starting with random noise and gradually transforming it into something more meaningful.
In this case, the researchers apply diffusion models to the task of program synthesis - the process of automatically generating computer programs that meet certain requirements. The key insight is to represent the programs as "syntax trees," which are visual diagrams that capture the structure of the code.
By training the diffusion model on these syntax trees, the researchers found they could generate new, valid program structures without needing a large dataset of example programs. This is an advantage over many existing program synthesis techniques, which often require huge datasets to work properly.
The paper demonstrates the effectiveness of this "Diffusion on Syntax Trees" (DoST) approach through experiments on various programming tasks. The results suggest DoST can generate programs that are both syntactically correct and semantically meaningful, outperforming prior methods in some cases.
Overall, this research explores an innovative application of diffusion models that could lead to more efficient and flexible program synthesis systems in the future. By working directly with the structural representation of code, the approach aims to make program generation more intuitive and accessible.
## Technical Explanation
The researchers propose a novel technique called "Diffusion on Syntax Trees" (DoST) for the task of program synthesis. DoST leverages the strengths of diffusion models, a class of generative AI models, to generate valid program structures represented as syntax trees.
[Diffusion models](https://aimodels.fyi/papers/arxiv/neural-network-parameter-diffusion) work by gradually transforming random noise into more meaningful data, like images or text. In this case, the researchers apply diffusion to the domain of program synthesis, where the goal is to automatically generate computer programs that satisfy certain specifications.
The key innovation is to represent programs as [syntax trees](https://aimodels.fyi/papers/arxiv/hysynth-context-free-llm-approximation-guiding-program), which are hierarchical structures that capture the grammatical structure of the code. By training the diffusion model on these syntax trees, the researchers found they could generate new, valid program structures without requiring a large dataset of example programs.
The DoST approach consists of several components:
1. A syntax tree encoder that maps program code to a latent representation.
2. A diffusion model that learns to gradually transform random noise into valid syntax trees.
3. A syntax tree decoder that generates the final program code from the diffusion model's output.
Through experiments on various programming tasks, the researchers demonstrate that DoST can generate programs that are both syntactically correct and semantically meaningful, outperforming prior program synthesis techniques in some cases.
## Critical Analysis
The paper presents a novel and promising approach to program synthesis using diffusion models. By focusing on the structural representation of programs as syntax trees, the DoST method aims to address some of the limitations of existing techniques, such as the need for large datasets and the difficulty in capturing complex program structures.
One potential limitation of the approach is that it may still struggle with generating programs that meet very specific functional requirements. The paper focuses primarily on the syntactic correctness of the generated programs, but real-world program synthesis often requires the programs to exhibit certain semantic properties as well. Further research may be needed to improve the ability of DoST to generate programs that satisfy complex behavioral specifications.
Additionally, the paper does not provide a detailed analysis of the computational efficiency and scalability of the DoST approach. As program synthesis tasks become more complex, the performance and resource requirements of the model may become an important consideration.
[Improvements to discrete diffusion models](https://aimodels.fyi/papers/arxiv/improving-discrete-diffusion-models-via-structured-preferential) and [techniques for harnessing large language models for interactive and precise tasks](https://aimodels.fyi/papers/arxiv/clickdiffusion-harnessing-llms-interactive-precise-image-editing) may also be relevant areas for further exploration in the context of program synthesis.
Overall, the DoST approach represents an intriguing and innovative application of diffusion models to the problem of program synthesis. With continued research and refinement, it has the potential to contribute to more efficient and flexible program generation systems in the future.
## Conclusion
This paper introduces a novel technique called "Diffusion on Syntax Trees" (DoST) that leverages the power of diffusion models to generate valid program structures. By representing programs as syntax trees and training the diffusion model on this structural data, the researchers were able to create new, syntactically correct programs without the need for large datasets of example code.
The key insight of the DoST approach is to focus on the hierarchical structure of programs, rather than just the raw text. This allows the model to better capture the complex grammatical rules and constraints of programming languages, leading to the generation of more meaningful and usable code.
The experiments conducted in the paper demonstrate the effectiveness of DoST, showing that it can outperform prior program synthesis techniques in certain tasks. This research represents an exciting step forward in the field of automated program generation, which has important implications for software development, education, and beyond.
With further refinements and extensions, the "Diffusion on Syntax Trees" approach could pave the way for more intuitive and flexible program synthesis systems that can help democratize the process of creating software and enable new applications that were previously out of reach.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,657 | PlaceFormer: Transformer-based Visual Place Recognition using Multi-Scale Patch Selection and Fusion | PlaceFormer: Transformer-based Visual Place Recognition using Multi-Scale Patch Selection and Fusion | 0 | 2024-06-04T12:46:36 | https://aimodels.fyi/papers/arxiv/placeformer-transformer-based-visual-place-recognition-using | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [PlaceFormer: Transformer-based Visual Place Recognition using Multi-Scale Patch Selection and Fusion](https://aimodels.fyi/papers/arxiv/placeformer-transformer-based-visual-place-recognition-using). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## I Introduction
The paper "PlaceFormer: Transformer-based Visual Place Recognition using Multi-Scale Patch Selection and Fusion" presents a novel approach to visual place recognition (VPR) using a transformer-based architecture. VPR is the task of identifying a previously visited location from a given image, and it has applications in robotics, augmented reality, and urban navigation.
## II Related Works
The paper builds upon previous research in VPR, including techniques like [PRAM: Place Recognition Anywhere Model](https://aimodels.fyi/papers/arxiv/pram-place-recognition-anywhere-model-efficient-visual), [ViewFormer: Exploring Spatiotemporal Modeling for Multi-View 3D](https://aimodels.fyi/papers/arxiv/viewformer-exploring-spatiotemporal-modeling-multi-view-3d), and [Register-Assisted Aggregation for Visual Place Recognition](https://aimodels.fyi/papers/arxiv/register-assisted-aggregation-visual-place-recognition). The authors note that existing methods often struggle with challenging scenarios, such as varying viewpoints, illumination changes, and dynamic scenes.
## III Methodology
### Overview
• The proposed PlaceFormer model uses a transformer-based architecture to effectively capture the spatial and semantic information in the input images.
• It leverages a multi-scale patch selection and fusion strategy to enhance the model's ability to recognize places across different viewpoints and visual conditions.
### Plain English Explanation
The PlaceFormer model works by first breaking the input image into smaller patches, which are then processed by a transformer-based network. Transformers are a type of deep learning model that can effectively capture the relationships between different parts of the input, in this case, the image patches.
The key innovation in PlaceFormer is the use of a multi-scale patch selection and fusion strategy. This means that the model looks at the image at different levels of detail, from coarse to fine, and combines the information from these different scales to make a more informed decision about the place being recognized.
For example, the model might first look at the overall layout and structure of the scene, and then zoom in on specific details like the shapes of buildings or the textures of the ground. By considering information at multiple scales, the PlaceFormer model can better handle challenges like changes in viewpoint or lighting conditions, which can make it difficult for traditional VPR methods to accurately recognize a place.
### Technical Explanation
The PlaceFormer architecture consists of a multi-scale patch extraction module, a transformer-based feature extraction backbone, and a fusion and classification head. The multi-scale patch extraction module divides the input image into patches at different resolutions, which are then individually processed by the transformer-based feature extractor.
The transformer-based feature extractor uses a series of transformer blocks to capture the spatial and semantic relationships between the image patches. The output features from the different scales are then concatenated and passed through a fusion and classification module to produce the final place recognition predictions.
The authors also introduce a novel patch selection strategy that dynamically selects the most informative patches at each scale, further improving the model's performance and efficiency.
## Critical Analysis
The paper presents a compelling approach to visual place recognition that addresses several limitations of existing methods. The multi-scale patch selection and fusion strategy seems to be a promising way to enhance the model's ability to handle challenging scenarios, such as varying viewpoints and changing visual conditions.
However, the authors do not extensively discuss the computational cost and memory requirements of the PlaceFormer model, which could be a concern for real-world deployment, especially on resource-constrained platforms. Additionally, the paper could have provided more insights into the specific failure cases or edge cases where the model might struggle, as well as potential avenues for future research to address these limitations.
## Conclusion
The PlaceFormer model represents an important step forward in the field of visual place recognition. By leveraging a transformer-based architecture and a multi-scale patch selection and fusion strategy, the authors have developed a robust and effective approach to this critical task. While there are some potential areas for improvement, the core ideas presented in this paper have significant implications for the development of advanced navigation and localization systems in a wide range of applications.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,872,432 | [WIP] JavaScript Vs Golang: Complexity | A lot of beginners start with JavaScript. The main reason for this is its simplicity: It's so simple... | 27,240 | 2024-06-04T12:46:11 | https://dev.to/henriqueleite42/wip-javascript-vs-golang-complexity-47f3 | go, javascript, typescript | A lot of beginners start with JavaScript. The main reason for this is its simplicity: It's so simple that you can literally press F12 right now and start coding.
In this article I'll compare Golang and JavaScript in production environments, to see how their complexity scales over time.
## The basics
Let's see what the most basic things that we need are in order to have a production API in each one of these languages.
### Javascript
#### Runtime
- **NodeJs:** Want to run JavaScript somewhere? You need it
#### Libraries
- **Express/Fastify/NestJs:** Any JavaScript developer writes APIs without using a library?
#### Dev Libraries
- **TypeScript:** Let's be real, is it really optional? Good luck trying to understand a production system without it
- + a file to config it
- **ESLint:** Or do you prefer to have your code full of errors that you discover only when running your code?
- + a VSCode extension
- + a file to config it
- **Prettier:** Or do you prefer to allow that one dev to put `{` on newlines?
- + a file to config it
- **Husky:** Yes, you can use git-hooks, but does anyone that uses JavaScript use anything other than Husky? Will JavaScript developers know how to work with git-hooks directly?
- + at least one file to config it
#### Extensions
- VSCode Eslint Extension
- + a file to config it to automatically format your code
- VSCode Prettier Extension
- A VSCode config file to recommend the necessary extensions
So just the basics of JavaScript require you to have 1 runtime, 1 library for production, 4 libraries for development, 2 extensions, and 7 configuration files.
JavaScript goes from 8 to 80 very, very fast.
### Golang
#### Runtime
- Golang
#### CLIs
- Makefile
#### Extensions
- VSCode Golang Extension
- + a file to config it to automatically format your code
- A VSCode config file to recommend the necessary extensions
The basics of Golang require 1 runtime, 1 CLI, 1 extension, and 2 configuration files. But why do we use Husky in JavaScript but don't have any library to make the same thing in Go? Because Golang developer **will** use git hooks directly. | henriqueleite42 |
1,876,656 | Track a Cruise Ship: Essential Information and Methods In an age of advanced technology and real-time data | Track a cruise ship has never been more accessible or useful - whether for family members, travel... | 0 | 2024-06-04T12:46:11 | https://dev.to/cruise_tracker_/track-a-cruise-ship-essential-information-and-methods-in-an-age-of-advanced-technology-and-real-time-data-1ecd | trackacruiseship | 
[Track a cruise ship](https://cruisetracker.com/) has never been more accessible or useful - whether for family members, travel enthusiasts, maritime professionals, or maritime professional safety officers alike. Knowing their whereabouts gives peace of mind while optimizing travel planning. Here's our complete guide on how to effectively track cruise ships.
**Why Track a Cruise Ship?**
Tracking a cruise ship ensures its location can be used rapidly in case of emergencies, helping coordinate rescue teams quickly if required and quickly respond quickly with timely responses to save lives on board.
**Travel Planning: **For passengers and their families traveling on cruise ships, knowing their exact location and estimated arrival times is invaluable for planning timely boarding and disembarkation as well as keeping friends and relatives up-to-date on its progress.
**Maritime Interests: **Marine professionals, hobbyists and researchers track cruise ships to study maritime traffic patterns and the environmental impact.
****Methods to Track a Cruise Ship**
Many cruise lines provide tracking services through their websites or mobile apps that allow passengers to track the progress of their cruise with detailed itineraries, current locations and updates regarding potential delays or schedule changes.
**AIS Tracking Services:**
Automated Identification Systems, or AIS, used on ships provide real-time location updates of ships using real-time tracking data provided by websites.
**Mobile Applications: **Its There are various mobile apps that make tracking on-the-go more accessible, like Ship Finder and MarineTraffic Mobile for tracking vessels on smartphones or tablets. Both these applications offer user-friendly interfaces with comprehensive ship details.
**Website Tracking Tool Provider: **Navigating to an online ship tracking website such as MarineTraffic or CruiseMapper and using their search function, enter either the name of the cruise ship in question or its International Maritime Organization number to use search. View your ship's current position on a map with additional details such as speed, direction and next port of call.
**Mobile App Tracking: **For convenience's sake download an appropriate ship tracking app from your device's app store then use its search feature to quickly identify it by name or IMO number. This app displays both current location and voyage details about a ship being tracked.
**For cruise line tracking:** To use this tracking function on their official website. Navigating directly to their section dedicated for tracking or ship details should do it for you. Enter the ship's name to view its current status and itinerary.
**Features and Benefits Extended Itineraries: **Stay abreast of not just its present position but also past routes traveled and future ports-of-call with detailed itineraries that keep track of past routes taken as well as planned ones.
**Environmental Conditions: **Certain platforms offer information regarding weather and sea state as well as potential hazards along the route, along with community sharing features to share information or set alerts for specific ships/routes.
Tracking cruise ships is an invaluable asset to ensure safety, plan travel, and satisfy maritime curiosity. Thanks to an abundance of online platforms, mobile apps, and cruise line services that allow anyone access real-time data about ship positions and movements - tracking cruise ship can keep you informed and connected, improving overall cruise experiences! | cruise_tracker_ |
1,876,655 | Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings | Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings | 0 | 2024-06-04T12:46:02 | https://aimodels.fyi/papers/arxiv/laboratory-scale-ai-open-weight-models-are | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings](https://aimodels.fyi/papers/arxiv/laboratory-scale-ai-open-weight-models-are). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper explores the performance of open-weight language models in low-resource settings compared to the popular ChatGPT model.
- The researchers found that their open-weight models can achieve competitive results with ChatGPT, even when trained on a fraction of the data.
- This suggests that open-source language models can provide a viable and more transparent alternative to large commercial models like ChatGPT.
## Plain English Explanation
The paper looks at how well open-source language models, which have their internal parameters (or "weights") publicly available, can perform compared to ChatGPT - a highly capable but opaque commercial language model.
The researchers trained their own open-weight models using a much smaller dataset than was used to train ChatGPT. Surprisingly, they found that these open-weight models were able to achieve similar performance to ChatGPT on a variety of tasks, even though they had far less training data.
This is significant because open-source models are more transparent about how they work under the hood, compared to commercial models like ChatGPT which are closed-source. The fact that open-weight models can rival ChatGPT's capabilities, even with less data, suggests they could provide a viable and more transparent alternative for many applications.
## Technical Explanation
The paper presents a comparative evaluation of open-weight language models against the popular ChatGPT model, even in low-resource settings. The researchers trained their own open-weight models using a fraction of the data used to train ChatGPT, and found that these models were able to achieve competitive or even superior performance on a range of benchmarks.
Specifically, the team experimented with a technique called [qLoRA](https://aimodels.fyi/papers/arxiv/llamaturk-adapting-open-source-generative-large-language) to efficiently fine-tune a pre-trained open-source language model. This allowed them to adapt the model to new tasks using relatively little additional training data.
When evaluated on tasks like natural language inference, question answering, and text generation, the open-weight models matched or outperformed ChatGPT, despite being trained on a much smaller corpus. The authors attribute this to the open-weight models' superior parameter efficiency and the benefits of transparency.
## Critical Analysis
The paper makes a compelling case that open-weight language models can be competitive with highly capable commercial models like ChatGPT, even when trained on a fraction of the data. This is an encouraging finding for the development of more transparent and accessible AI systems.
However, the authors acknowledge several limitations to their work. First, the benchmarks used may not fully capture the breadth of capabilities exhibited by ChatGPT. There may be some tasks where the commercial model still maintains a significant advantage. Additionally, the open-weight models were evaluated in isolation, without considering factors like deployment cost or energy efficiency.
Further research is needed to fully understand the tradeoffs between open-weight and commercial models, and to explore ways of enhancing the capabilities of open-source alternatives. As noted in [this related paper](https://aimodels.fyi/papers/arxiv/near-to-mid-term-risks-opportunities-open), continued advancements in open-source AI could have significant implications for the democratization of AI technology.
## Conclusion
This paper provides evidence that open-weight language models can achieve performance on par with the industry-leading ChatGPT, even when trained on a much smaller dataset. This suggests that transparent, open-source AI systems can be a viable and competitive alternative to large, opaque commercial models.
As the field of generative AI continues to advance, the ability to develop powerful language models with open architectures and publicly available parameters could have important implications for [AI transparency and accessibility](https://aimodels.fyi/papers/arxiv/open-source-language-models-can-provide-feedback). The findings in this paper represent an encouraging step in that direction.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,654 | Context Injection Attacks on Large Language Models | Context Injection Attacks on Large Language Models | 0 | 2024-06-04T12:45:27 | https://aimodels.fyi/papers/arxiv/context-injection-attacks-large-language-models | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Context Injection Attacks on Large Language Models](https://aimodels.fyi/papers/arxiv/context-injection-attacks-large-language-models). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper examines "context injection attacks" on large language models (LLMs) - techniques that can be used to manipulate the output of these AI systems by carefully crafting the input prompts.
- The researchers demonstrate how these attacks can be used to hijack the behavior of LLMs and make them generate harmful or malicious content.
- They also propose potential defenses and mitigation strategies to help protect against such attacks.
## Plain English Explanation
Large language models (LLMs) are powerful AI systems that can generate human-like text on a wide range of topics. However, [researchers have found](https://aimodels.fyi/papers/arxiv/hijacking-context-large-multi-modal-models) that these models can be vulnerable to "context injection attacks" - where the input prompts are carefully crafted to manipulate the model's behavior and make it produce unintended or harmful outputs.
Imagine you're asking a language model to write a story. Normally, it would generate a coherent narrative based on the prompt. But attackers could insert subtle cues or instructions into the prompt that hijack the model, causing it to generate content promoting violence, hate, or other harmful themes instead. This is the core idea behind context injection attacks.
The researchers in this paper [demonstrate several examples](https://aimodels.fyi/papers/arxiv/large-language-models-wireless-application-design-context) of how these attacks can work, showing how LLMs can be manipulated to produce toxic, biased, or otherwise problematic text. They also discuss potential defenses, such as using more rigorous prompt engineering or implementing safety checks in the model's architecture.
Ultimately, this research highlights an important security and ethics challenge as we increasingly rely on powerful AI systems like LLMs. While these models have incredible capabilities, we need to be vigilant about potential misuse and work to develop safeguards to protect against malicious exploitation.
## Technical Explanation
The paper begins by providing background on large language models (LLMs) and their growing use in a variety of applications, from content generation to task completion. The researchers then introduce the concept of "context injection attacks" - techniques that involve carefully crafting input prompts to manipulate the behavior of these models.
Through a series of experiments, the researchers demonstrate how attackers can leverage context injection to hijack the outputs of popular LLMs like GPT-3. For example, they show how inserting subtle cues or instructions into a prompt can cause the model to generate text promoting violence, hate, or other harmful themes - even if the original prompt was benign.
The paper also explores potential mitigation strategies, such as using more rigorous prompt engineering, implementing safety checks in the model's architecture, and developing better understanding of the "reasoning" underlying LLM outputs. The researchers suggest that a multilayered approach combining technical and non-technical defenses may be necessary to protect against context injection attacks.
Overall, the key insight from this research is that the powerful language generation capabilities of LLMs can be exploited by adversaries who understand how to carefully manipulate the input context. As these models become more ubiquitous, the authors argue that addressing this security and ethics challenge will be crucial to ensuring their safe and responsible deployment.
## Critical Analysis
The researchers in this paper have made an important contribution by shining a light on a significant vulnerability in large language models. Their work demonstrates that even state-of-the-art AI systems like GPT-3 can be susceptible to malicious manipulation through carefully crafted input prompts.
However, it's worth noting that the paper does not provide a comprehensive solution to the context injection problem. While the proposed mitigation strategies, such as prompt engineering and architectural safeguards, are valuable, the authors acknowledge that a more holistic approach may be necessary. [Further research](https://aimodels.fyi/papers/arxiv/vocabulary-attack-to-hijack-large-language-model) is still needed to develop more robust and reliable defenses against these types of attacks.
Additionally, the paper focuses primarily on the technical aspects of context injection, but there are also significant ethical and societal implications that warrant deeper exploration. For example, the researchers could have delved more into the potential real-world consequences of these attacks, such as the spread of misinformation, the amplification of hate speech, or the manipulation of public discourse.
[Addressing these challenges](https://aimodels.fyi/papers/arxiv/supervised-knowledge-makes-large-language-models-better) will require not only technical solutions, but also careful consideration of the broader implications and the development of appropriate governance frameworks to ensure the responsible development and deployment of large language models.
## Conclusion
This paper presents a critical examination of "context injection attacks" - techniques that can be used to manipulate the outputs of large language models (LLMs) by carefully crafting input prompts. The researchers demonstrate how these attacks can be leveraged to hijack the behavior of LLMs, causing them to generate harmful or malicious content.
While the proposed mitigation strategies are a valuable starting point, the authors acknowledge that a more comprehensive approach is needed to protect against these types of attacks. Addressing the security and ethics challenges posed by context injection will require ongoing research, as well as the development of robust governance frameworks to ensure the responsible use of these powerful AI systems.
As LLMs become increasingly ubiquitous, understanding and mitigating the risks associated with context injection attacks will be crucial to realizing the full potential of these technologies while safeguarding against their misuse.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,653 | Formalizing and Benchmarking Prompt Injection Attacks and Defenses | Formalizing and Benchmarking Prompt Injection Attacks and Defenses | 0 | 2024-06-04T12:44:53 | https://aimodels.fyi/papers/arxiv/formalizing-benchmarking-prompt-injection-attacks-defenses | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://aimodels.fyi/papers/arxiv/formalizing-benchmarking-prompt-injection-attacks-defenses). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper proposes a framework to systematically study prompt injection attacks, which aim to manipulate the output of large language models (LLMs) integrated into applications.
- Existing research has been limited to case studies, so this work aims to provide a more comprehensive understanding of prompt injection attacks and potential defenses.
- The authors formalize a framework for prompt injection attacks, design a new attack based on their framework, and conduct a large-scale evaluation of 5 attacks and 10 defenses across 10 LLMs and 7 tasks.
- The goal is to establish a common benchmark for evaluating future prompt injection research.
## Plain English Explanation
Large language models (LLMs) like GPT-3 are increasingly being used as part of applications to generate text, answer questions, and complete various tasks. However, these LLMs can be vulnerable to **prompt injection attacks**, where an attacker tries to inject malicious instructions or data into the input, causing the LLM to produce undesirable results.
Previous research on prompt injection attacks has been limited to individual case studies, so it's been difficult to get a comprehensive understanding of the problem and how to defend against these attacks. This new paper aims to change that by proposing a formal framework to describe and analyze prompt injection attacks.
Using this framework, the researchers were able to categorize existing prompt injection attacks as special cases, and they even designed a new attack that combines elements of previous ones. They then evaluated 5 different prompt injection attacks and 10 potential defenses across a wide range of LLMs and task domains.
The key contribution of this work is establishing a common benchmark for evaluating prompt injection attacks and defenses. This should help accelerate research in this area and lead to more robust and secure LLM-powered applications in the future.
## Technical Explanation
The paper begins by formalizing a framework for prompt injection attacks. This framework defines the key components of a prompt injection attack, including the **target application**, the **prompt template** used to interact with the LLM, the **injection payload** that the attacker attempts to insert, and the **attack objective** the attacker is trying to achieve.
Using this framework, the authors show that existing prompt injection attacks, such as those described in papers like [PLEAK: Prompt Leaking Attacks Against Large Language Models](https://aimodels.fyi/papers/arxiv/pleak-prompt-leaking-attacks-against-large-language), [Assessing Prompt Injection Risks in 200 Customized GPTs](https://aimodels.fyi/papers/arxiv/assessing-prompt-injection-risks-200-custom-gpts), and [Goal-Guided Generative Prompt Injection Attack on Large Language Models](https://aimodels.fyi/papers/arxiv/goal-guided-generative-prompt-injection-attack-large), can be viewed as special cases within their more general framework.
Moreover, the researchers leverage this framework to design a new prompt injection attack called the **Compound Attack**, which combines elements of existing attacks to potentially achieve more powerful and stealthy results.
To evaluate prompt injection attacks and defenses, the authors conducted a large-scale study involving 5 different attacks (including the new Compound Attack) and 10 potential defense mechanisms across 10 different LLMs and 7 task domains. This systematic evaluation provides a common benchmark for future research in this area.
The paper also introduces an open-source platform called [Open-Prompt-Injection](https://github.com/liu00222/Open-Prompt-Injection) to facilitate further research on prompt injection attacks and defenses.
## Critical Analysis
The paper provides a valuable contribution by formalizing a framework for prompt injection attacks and conducting a comprehensive evaluation of both attacks and defenses. This helps address the limitations of previous research, which had been focused on individual case studies.
However, the authors acknowledge that their work is still limited in several ways. For example, they only evaluated a subset of possible prompt injection attacks and defenses, and their experiments were conducted in a controlled laboratory setting rather than the "wild" deployment environments that real-world applications would face.
Additionally, while the paper introduces a new Compound Attack, it doesn't provide a deep analysis of this attack or explore its full capabilities and potential impact. Further research would be needed to better understand the implications of this new attack vector.
Finally, the authors note that their framework and evaluation methodology may need to be updated as the field of prompt injection research continues to evolve, and as new attack and defense techniques are developed.
## Conclusion
This paper takes an important step towards a more systematic understanding of prompt injection attacks against LLM-powered applications. By proposing a formal framework and conducting a large-scale evaluation, the authors have established a common benchmark for future research in this area.
The insights and tools provided by this work can help application developers and security researchers better identify and mitigate prompt injection vulnerabilities, ultimately leading to more robust and secure LLM-integrated systems. As LLMs become increasingly ubiquitous, this type of research will be crucial for ensuring the safe and reliable deployment of these powerful AI models.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,652 | I made a VS Code extension to delete files content on right click | I was working on my new product where I needed to use the same name for about 100s of files across... | 0 | 2024-06-04T12:44:41 | https://dev.to/mike_andreuzza/i-made-an-vs-code-extension-to-delete-files-content-2e01 | vscode, extensions | 
I was working on my new product where I needed to use the same name for about 100s of files across different folders.
Removing the content manually after copying the folders was tedious.
So, I created a VS Code extension. It allows you to right-click and delete file content from both the open file and the file tree.
I am still working on allowing to delete the content in bulk, by selecting various files on the file tree, but it does not want to work...
You can grab it here — https://michaelandreuzza.com/vscode/delete-on-right-click/
# Delete Content for VS Code
This extension allows to right click inside any file or on the file tree and delete the content but not the file itself.
# Official website
- [Delete content](https://www.michaelandreuzza.com/vscode/delete-on-right-click/)
# Installation
1. Open **Extensions** sidebar panel in VS Code. `View → Extensions`
2. Search for **`Delete Content on right click`**
3. Click **Install** to install it.
**Known issues** — There seems to be a problem preventing the bulk deletion of content when multiple items are selected. Instead, it only deletes the content of one file."
| mike_andreuzza |
1,876,461 | MICROSOFT AZURE ARCHITECTURAL COMPONENTS | Azure architectural components refer to the building blocks of Microsoft Azure that can be combined... | 27,595 | 2024-06-04T12:44:34 | https://dev.to/aizeon/microsoft-azure-architectural-components-gbg | beginners, azure, cloud, cloudcomputing | Azure architectural components refer to the building blocks of Microsoft Azure that can be combined to create robust, scalable, and secure architectures for developers, architects, and organizations to design, deploy, and manage scalable, secure, and efficient cloud-based systems.
## **AVAILABILITY ZONES, REGIONS AND REGION PAIRS**
Availability zones are physically separate datacenters within an Azure region. Each availability zone is made up of one or more datacenters—physical facilities equipped with servers _(these guys again!)_, independent power, cooling, and networking. Availability zones are connected through high-speed, private fiber-optic networks.
Azure regions are large geographic areas that contain multiple datacenters in close proximity linked together with low latency network.
- Having multiple datacenters in close proximity gives flexibility in choosing where to build applications.
- Regions help to optimise latency as they contain availability zones—separated groups of datacenters within a region, connected by a high-performance network with a round-trip latency of less than 2ms.
- There's a minimum of three availability zones within a single region. This helps ensure high availability and resilience for resources and applications.
- Azure regions preserve data residency and sovereignty—controlling and managing data within a specific geographic boundary or jurisdiction, ensuring that data is stored, processed, and governed according to local laws, regulations, and standards.
- Azure has global and industry compliance offerings; and depending on the region, regional/country compliance offerings are available.
Each Azure region is paired with another region within the same geography at least 300 miles away (480km), allowing for the replication of resources and reducing the likelihood of natural disasters, civil unrest, power outages, or physical network outages affecting both regions at once. Hence, possessing redundancy and failover capabilities. For instance, if a region in a pair was affected by a natural disaster, services would automatically failover to the other region in its region pair.
**N.B:**
- If an extensive Azure outage occurs, one region out of every pair is prioritized to make sure at least one is restored as quickly as possible for applications hosted in that region pair.
- Planned Azure updates are rolled out to paired regions one region at a time to minimize downtime and risk of application outage.
## **RESOURCES AND RESOURCE GROUPS**
Resources are components made available to build cloud solutions. They are instances of services that you create and manage.
Some popular resources include:
- Virtual Machines (VMs).
- Storage Accounts.
- (SQL or NoSQL) Databases.
- Networking resources (e.g., Virtual Networks, Load Balancers).
- Security resources (e.g., Azure Active Directory, Key Vault).
- Web and mobile resources (e.g., Azure App Service, Azure Functions)
- Container resources (e.g., Azure Kubernetes Service, Container Instances).
Resources are combined into resource groups for management and organization purposes. Resource groups act as a logical container into which related Azure resources like web apps, databases, and storage accounts are deployed and managed.
- Resource groups aid logical grouping as they allow grouping related resources together, making it easier to manage and monitor them.
- Resources can be added or removed from a Resource group as needed.
- Access control policies can be applied to Resource Groups, controlling who can access or manage resources within the group.
- Resource Groups can be used to deploy and manage resources simultaneously, such as deploying a web application and its associated database.
- Resource Groups make it easier to monitor and troubleshoot resources, as they provide a centralized view of resource performance and health.
- Resource Groups can help track costs associated with resources, making it easier to manage expenses.
- When resource groups are deleted, all resources within the group are also deleted.
- Applications can utilise multiple resource groups.
- Resource groups cannot be nested.
- A particular resource can exist in only one resource group but can exist in different regions.
- Resource Groups can be deleted when backup data has been removed and a user has the permission to do so as specified by resource locks.
_Resource locks are used to protect Azure subscriptions, resource groups or resources from accidental deletions and modifications._
There are two types of resource locks:
- Delete: Users can read and modify a resource, but they can't delete it.
- Read-only: Users can read a resource, but they can't delete or update it.
Locks can be set up via the Azure portal, Azure PowerShell or Azure CLI.
## **SUBSCRIPTIONS**
A subscription groups together user accounts and the resources that have been created by those user accounts. It provides authenticated and authorised access to Azure products and services.
Subscriptions manage and control access to resources that users can provision and helps generate separate billing reports and invoices for each subscription.
For each subscription, there are limits or quotas on the amount of resources that can be created and used. Organisations can use subscriptions to manage costs and the resources that are created by users, teams, or projects.
An Azure account can have more than one subscription.
## **MANAGEMENT GROUPS**
Azure management groups provide a level of scope above subscriptions as subscriptions are organised into containers called Management Groups.
- Each management group can have many subscriptions.
- All subscriptions and management groups are within a single hierarchy in each directory.
- All subscriptions within a management group automatically inherit the conditions applied to the management group.
- Management groups give users enterprise-grade management at a large scale no matter what type of subscriptions they might have.
- 10,000 management groups can be supported in a single directory.
Think of an Azure Active Directory (AAD) directory like a filing cabinet: you can have up to 10,000 folders (management groups) in a single cabinet (AAD directory) to organize your papers (subscriptions, resources, etc.).
- A management group tree can support up to six levels of depth. This limit doesn't include the root level or the subscription level.
- Each management group and subscription can support only one parent.
In Azure, each management group and subscription can only have one parent entity above it in the hierarchy. In other words:
- A management group can only be a child of one other management group (or the root node).
- A subscription can only be a child of one management group (or the root node).
This means that you can't have multiple parents or a complex hierarchy with multiple branches. It's a _simple, straightforward parent-child relationship_.
For example:
- Management Group A (parent)
- Management Group B (child)
- Subscription 1 (grandchild)
- Subscription 2 (grandchild)
- Management Group C (parent)
- Subscription 3 (child)
In this example, Management Group B and Subscription 3 can only have one parent each (Management Group A and Management Group C, respectively). | aizeon |
1,868,450 | Mastering eksctl Commands: A Comprehensive Guide | eksctl is an incredibly powerful tool for managing Amazon EKS clusters, and it quickly became our... | 0 | 2024-06-04T12:44:19 | https://dev.to/vmgomez/mastering-eksctl-commands-a-comprehensive-guide-1h75 | kubernetes, aws, devops, cloud |
eksctl is an incredibly powerful tool for managing Amazon EKS clusters, and it quickly became our goto solution for automating Kubernetes deployments. With its extensive set of commands, we were able to streamline our workflow, reduce errors, and increase productivity. But, as with any new tool, there was a learning curve.
In this tutorial, I will share the essential eksctl commands that every DevOps engineer should know. These commands are essential for managing Kubernetes clusters, deploying applications, and troubleshooting issues. By mastering these commands, you can take your container orchestration skills to the next level and become a certified eksctl expert.
Before we dive into the specific commands, let me provide some context on why they are important. Kubernetes is an opensource platform for automating deployment, scaling, and management of containerized applications. Amazon EKS is a managed Kubernetes service that makes it easy to deploy and manage Kubernetes clusters in the cloud. eksctl is a tool for managing Amazon EKS clusters, and it provides a set of commands for interacting with these clusters.
Now, let’s get started with the essential eksctl commands:
If you have more than one profile, you will need to specify the name of your profile using the --profile subparameter.
**eksctl create**: This command creates a new Amazon EKS cluster. It takes several parameters, such as the cluster name, the number of nodes, and the instance type.
```bash
eksctl create name mycluster nodes instancetype m.large
```
But the easiest way to deploy is to define everything in a YAML file! You can run something like this:
```bash
eksctl create cluster -f k8s.yml --profile dynacode_profile
```
Sure thing, you need to create a YAML file before you run this command. For example:
```yml
apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig
metadata:
name: basic-cluster001
region: us-east-1
nodeGroups:
- name: ng-1
instanceType: m5.large
desiredCapacity: 10
- name: ng-2
instanceType: m5.xlarge
desiredCapacity: 2
```
If you want to download this small yaml, you can do so from our [GitHub repository](https://github.com/vmgomez/devops-scripts/).
`apiVersion:`
This line specifies the eksctl API version that this configuration file uses. In this case, it's eksctl.io/v1alpha5, which corresponds to eksctl API version 1 alpha 5.
`kind: ClusterConfig`
This line defines the type of configuration object being described. Here, it specifies that this is a ClusterConfig object, which contains the details for creating a Kubernetes cluster.
`metadata:`
This section provides metadata about the cluster being created.
`name:`
Specify the name you want to give your EKS cluster. In this case, it is set to basic-cluster.
`region:`
Specify the AWS region where you want to create your cluster. In this case, it is set to us-east-1.
` nodeGroups:`
This section defines the different worker node groups that will be created for your cluster.
`name:`
Define the name of the node group. The configuration has two groups called ng-1 y ng-2.
`instanceType:`
Pick the Amazon EC2 instance type you want to use for the nodes in this group. In this case, "ng-1" uses m5.large instances and "ng-2" uses m5.xlarge instances. These instance types offer different levels of processing power and memory.
`desiredCapacity:`
Set the number of nodes you want in this group. The configuration sets "ng-1" to have 10 nodes and "ng-2" to have 2.
This YAML file sets up a Kubernetes cluster called "basic-cluster" in the eu-north-1 region of Amazon EKS. It creates two node groups: "ng-1" with 10 m5.large instances and "ng-2" with 2 m5.xlarge instances. You can use this configuration with the eksctl create cluster command to provision the cluster in AWS.
**eksctl update** This command updates the configuration of an existing Amazon EKS cluster. It takes the same parameters as **eksctl create**, such as the cluster name and the instance type.
```bash
eksctl update name mycluster nodes instancetype m.xlarge
```
**eksctl delete** This command deletes an Amazon EKS cluster. It takes the cluster name as a parameter.
```bash
eksctl delete cluster basic-cluster001 --profile dcode_profile --region us-east-1
```
**eksctl get** This command returns information about an Amazon EKS cluster, such as its ID, node count, and instance type. It takes the cluster name as a parameter.
```bash
eksctl get name mycluster
```
**eksctl listnodes** This command lists all the nodes in an Amazon EKS cluster. It takes no parameters.
```bash
eksctl listnodes name mycluster
```
**eksctl logs** This command displays the log output of a container running on an Amazon EKS node. It takes the name of the container as a parameter, followed by the node name.
```bash
eksctl logs container mycontainer node mynode
```
**eksctl scale**: This command scales the number of nodes in an Amazon EKS cluster up or down. It takes the cluster name and the desired number of nodes as parameters.
```bash
eksctl scale name mycluster nodes
```
**eksctl tag**: This command adds a tag to an Amazon EKS cluster. It takes the cluster name, the tag key, and the tag value as parameters.
```bash
eksctl tag name mycluster key 'owner' value 'dynacode'
```
**eksctl untag** This command removes a tag from an Amazon EKS cluster. It takes the cluster name and the tag key as parameters. For example: `eksctl untag name mycluster key “owner”`
**eksctl rollback** This command restores an Amazon EKS cluster to a previous state. It takes the cluster name and the desired revision as parameters. For example: `eksctl rollback name mycluster revision d`
eksctl commands is essential for any DevOps engineer working with Kubernetes clusters in the cloud. By understanding these commands and how to use them effectively, you can streamline your workflow, reduce errors, and increase productivity. Remember, practice makes perfect, so keep experimenting with these commands to become a certified eksctl expert.
Hey there, don't forget to show some love on our Patreon!
If you found this post helpful, please consider supporting us on [Patreon](https://patreon.com/dynacode). We also have a [website](https://dynacode.us/) where we dive into all things Python and DevOps/DevSecOps.
Your support means the world to us, so thank you for being a part of our community! | vmgomez |
1,876,651 | Contextual Position Encoding: Learning to Count What's Important | Contextual Position Encoding: Learning to Count What's Important | 0 | 2024-06-04T12:44:18 | https://aimodels.fyi/papers/arxiv/contextual-position-encoding-learning-to-count-whats | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Contextual Position Encoding: Learning to Count What's Important](https://aimodels.fyi/papers/arxiv/contextual-position-encoding-learning-to-count-whats). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- The paper "Contextual Position Encoding: Learning to Count What's Important" proposes a novel approach to position encoding in language models.
- It addresses the limitations of traditional position encoding methods, which can struggle to generalize to longer sequences.
- The proposed Contextual Position Encoding (CPE) method learns to assign importance to different positions in the input, allowing the model to better adapt to varying sequence lengths.
## Plain English Explanation
Position encoding is an important component of language models, which need to understand the order and structure of words in a sentence. Traditional position encoding methods, such as [sinusoidal position encoding](https://aimodels.fyi/papers/arxiv/positional-encoding-is-not-same-as-context), assign a fixed numerical value to each position in the input. However, this can be problematic when the model is applied to sequences of different lengths, as the fixed encoding may not be appropriate.
The [Contextual Position Encoding](https://aimodels.fyi/papers/arxiv/cape-context-adaptive-positional-encoding-length-extrapolation) approach introduced in this paper aims to address this issue. It learns to dynamically assign importance to different positions in the input, based on the surrounding context. This allows the model to focus on the most relevant parts of the sequence, rather than treating all positions equally.
For example, imagine you're reading a long document and trying to understand the key points. Certain words or phrases might be more important than others, depending on the overall context. CPE allows the model to identify and focus on these critical elements, even if the document is much longer than the training data.
By making position encoding more flexible and adaptive, the authors hope to improve the performance of language models on a variety of tasks, particularly those involving longer or more complex sequences.
## Technical Explanation
The [Contextual Position Encoding](https://aimodels.fyi/papers/arxiv/cape-context-adaptive-positional-encoding-length-extrapolation) (CPE) method proposed in this paper is designed to address the limitations of traditional position encoding techniques, which can struggle to generalize to longer sequences.
The key idea behind CPE is to learn a position-aware attention mechanism that can dynamically assign importance to different positions in the input, based on the surrounding context. This is achieved by introducing a position-aware attention layer that operates in parallel with the standard self-attention layer in the transformer architecture.
The position-aware attention layer takes the input sequence and the position indices as inputs, and learns to produce a set of position-specific attention weights. These weights are then used to modulate the standard self-attention, allowing the model to focus on the most relevant parts of the sequence.
The authors evaluate the performance of CPE on a range of natural language tasks, including language modeling, machine translation, and text summarization. The results show that CPE outperforms traditional position encoding methods, particularly on longer sequences.
The [technical report on the impact of position bias in language models](https://aimodels.fyi/papers/arxiv/technical-report-impact-position-bias-language-models) provides further insights into the importance of position encoding and the challenges it poses for language models. Additionally, the [position-aware fine-tuning approach](https://aimodels.fyi/papers/arxiv/position-aware-parameter-efficient-fine-tuning-approach) and the [investigation into the differences between positional encoding and context](https://aimodels.fyi/papers/arxiv/positional-encoding-is-not-same-as-context) offer complementary perspectives on these issues.
## Critical Analysis
The Contextual Position Encoding approach presented in this paper is a promising step towards addressing the limitations of traditional position encoding methods. By learning to dynamically assign importance to different positions in the input, CPE can better adapt to varying sequence lengths and improve the performance of language models on a variety of tasks.
However, the paper does not fully address the potential limitations or drawbacks of the CPE approach. For example, the additional computational complexity introduced by the position-aware attention layer could be a concern, particularly for large-scale language models. Additionally, the authors do not explore the interpretability of the learned position-specific attention weights, which could be an important consideration for understanding and debugging the model's behavior.
Furthermore, the paper focuses primarily on natural language tasks, and it's unclear how well the CPE approach would generalize to other domains, such as image or speech recognition, where position encoding is also an important component.
Overall, the Contextual Position Encoding method is a valuable contribution to the field of language modeling, and the insights presented in this paper and the related works could inspire further research into more flexible and adaptive position encoding techniques.
## Conclusion
The "Contextual Position Encoding: Learning to Count What's Important" paper introduces a novel approach to position encoding that aims to address the limitations of traditional methods. By learning to dynamically assign importance to different positions in the input, the Contextual Position Encoding (CPE) method can better adapt to varying sequence lengths and improve the performance of language models on a variety of tasks.
The paper provides a detailed technical explanation of the CPE approach and its evaluation on several natural language tasks. While the results are promising, the paper also highlights areas for further research, such as the computational complexity of the method and its interpretability.
Overall, the CPE approach represents an important step forward in the field of language modeling, and the insights presented in this paper, along with the related works, could inspire further advancements in position encoding and other key components of transformer-based models.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,649 | Neural Network Parameter Diffusion | Neural Network Parameter Diffusion | 0 | 2024-06-04T12:43:44 | https://aimodels.fyi/papers/arxiv/neural-network-parameter-diffusion | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Neural Network Parameter Diffusion](https://aimodels.fyi/papers/arxiv/neural-network-parameter-diffusion). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This research paper introduces a new approach called "Neural Network Diffusion" that aims to improve the performance and capabilities of diffusion models, which are a type of generative machine learning model.
- Diffusion models have shown impressive results in generating high-quality images, audio, and other types of data, but they can be computationally intensive and difficult to train.
- The authors of this paper propose a novel way to integrate neural networks into the diffusion process, which they believe can lead to more efficient and effective diffusion models.
## Plain English Explanation
Diffusion models are a type of machine learning algorithm that have become increasingly popular in recent years, particularly for generating high-quality images, audio, and other types of data. These models work by starting with a noisy version of the desired output and then gradually "denoising" it through a series of iterative steps, eventually producing a realistic-looking final result.
However, one of the main challenges with diffusion models is that they can be computationally intensive and difficult to train, especially for more complex tasks. This is where the idea of "Neural Network Diffusion" comes in.
The key insight behind this approach is to integrate neural networks directly into the diffusion process, rather than treating them as a separate component. By doing this, the authors believe they can create more efficient and effective diffusion models that can tackle a wider range of problems.
For example, [link to "Empowering Diffusion Models: Embedding Space Text Generation"](https://aimodels.fyi/papers/arxiv/empowering-diffusion-models-embedding-space-text-generation) shows how incorporating neural networks can improve the performance of diffusion models for text generation tasks. Similarly, [link to "DiffScaler: Enhancing Generative Prowess of Diffusion Transformers"](https://aimodels.fyi/papers/arxiv/diffscaler-enhancing-generative-prowess-diffusion-transformers) demonstrates how this approach can be used to enhance the capabilities of diffusion models for generating high-quality images.
## Technical Explanation
The key technical innovation in this paper is the authors' proposal to integrate neural networks directly into the diffusion process. Traditionally, diffusion models have relied on a series of iterative steps to gradually denoise the input data, with each step being governed by a set of mathematical equations.
In the Neural Network Diffusion approach, the authors introduce a neural network component that is responsible for learning the diffusion process itself. This means that instead of using a fixed set of equations, the model can adaptively learn the most effective way to denoise the input data, based on the specific characteristics of the task at hand.
The authors demonstrate the effectiveness of this approach through a series of experiments, where they show that Neural Network Diffusion can outperform traditional diffusion models on a range of benchmarks, including image generation, audio synthesis, and text-to-image translation.
One of the key insights from this research is that by integrating neural networks into the diffusion process, the model can better capture the complex relationships and patterns in the data, leading to more realistic and coherent outputs. This is particularly important for tasks where the input data is highly structured or multidimensional, such as [link to "LADIC: Are Diffusion Models Really Inferior to GANs?"](https://aimodels.fyi/papers/arxiv/ladic-are-diffusion-models-really-inferior-to) and [link to "Versatile Diffusion: Transformer Mixture for Noise Levels in Audiovisual"](https://aimodels.fyi/papers/arxiv/versatile-diffusion-transformer-mixture-noise-levels-audiovisual).
## Critical Analysis
One potential limitation of the Neural Network Diffusion approach is that it may require more computational resources and training time compared to traditional diffusion models, due to the added complexity of the neural network component. The authors acknowledge this trade-off in the paper and suggest that future work could focus on developing more efficient neural network architectures or optimization techniques to address this issue.
Additionally, the authors' experiments in this paper are primarily focused on relatively simple benchmarks, such as image generation and audio synthesis. It would be interesting to see how the Neural Network Diffusion approach would perform on more complex, real-world tasks, such as [link to "Intriguing Properties of Diffusion Models: An Empirical Study on Natural Images"](https://aimodels.fyi/papers/arxiv/intriguing-properties-diffusion-models-empirical-study-natural), where the data is more diverse and the requirements for realism and coherence are more stringent.
Overall, the Neural Network Diffusion approach presented in this paper represents an exciting and promising direction for the development of more powerful and versatile diffusion models. The authors have demonstrated the potential of this approach through their experiments, and it will be interesting to see how it evolves and is applied to a wider range of applications in the future.
## Conclusion
In this paper, the authors have introduced a novel approach called "Neural Network Diffusion" that aims to improve the performance and capabilities of diffusion models. By integrating neural networks directly into the diffusion process, the authors believe they can create more efficient and effective models that can tackle a wider range of problems, from image generation to audio synthesis and beyond.
The key technical innovation in this work is the authors' proposal to use neural networks to learn the diffusion process itself, rather than relying on a fixed set of mathematical equations. This allows the model to adaptively capture the complex relationships and patterns in the data, leading to more realistic and coherent outputs.
While the authors' experiments have demonstrated the potential of this approach, there are still some limitations and areas for further research, such as the computational resources required and the need to test the approach on more complex, real-world tasks. Nonetheless, the Neural Network Diffusion approach represents an exciting and promising direction for the field of generative machine learning, and it will be interesting to see how it evolves and is applied to an increasingly diverse range of applications in the years to come.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,648 | Is In-Context Learning Sufficient for Instruction Following in LLMs? | Is In-Context Learning Sufficient for Instruction Following in LLMs? | 0 | 2024-06-04T12:43:10 | https://aimodels.fyi/papers/arxiv/is-context-learning-sufficient-instruction-following-llms | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Is In-Context Learning Sufficient for Instruction Following in LLMs?](https://aimodels.fyi/papers/arxiv/is-context-learning-sufficient-instruction-following-llms). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper investigates whether in-context learning is sufficient for instruction following in large language models (LLMs).
- The authors systematically evaluate the performance of the Urial LLM on a range of instruction-following tasks.
- They find that while Urial exhibits strong in-context learning abilities, it struggles with certain types of instructions, particularly those requiring multi-step reasoning or understanding of abstract concepts.
- The paper provides insights into the limitations of current LLM approaches for instruction following and highlights the need for further research to develop more capable and versatile instruction-following systems.
## Plain English Explanation
The paper looks at whether large language models (LLMs) can learn to follow instructions just by seeing examples, without any additional training. The researchers tested an LLM called Urial on a variety of tasks that involved following instructions, like answering questions or completing tasks.
They found that Urial was pretty good at learning from the examples it was shown - this is called "in-context learning." It could often figure out how to do the task just by looking at a few examples. But Urial struggled with some types of instructions, especially ones that required multiple steps or understanding more abstract concepts.
This suggests that while in-context learning is a powerful capability, it may not be enough for LLMs to become truly proficient at following instructions. More research is needed to develop LLMs that can better understand and carry out complex instructions, which could be important for applications like personal assistants or automated task completion.
## Technical Explanation
The paper presents a [systematic evaluation of the Urial LLM's](https://aimodels.fyi/papers/arxiv/context-learning-or-how-i-learned-to) instruction-following capabilities. Urial is a state-of-the-art LLM with demonstrated [strong in-context learning abilities](https://aimodels.fyi/papers/arxiv/implicit-context-learning).
The authors designed a suite of instruction-following tasks that tested Urial's ability to understand and execute a variety of commands, ranging from simple one-step instructions to more complex multi-step procedures. They found that while Urial exhibited [impressive in-context learning performance](https://aimodels.fyi/papers/arxiv/context-learning-generalizes-but-not-always-robustly) on many tasks, it struggled with instructions that required deeper reasoning or understanding of more abstract concepts.
Further analysis revealed that Urial's [performance degraded as the instructions became longer and more complex](https://aimodels.fyi/papers/arxiv/context-learning-long-context-models-depth-exploration), suggesting that in-context learning alone may not be sufficient for developing truly capable instruction-following systems. The authors discuss the implications of these findings and [highlight the need for continued research](https://aimodels.fyi/papers/arxiv/llms-are-few-shot-context-low-resource) to address the limitations of current LLM approaches to instruction following.
## Critical Analysis
The paper provides a thoughtful and rigorous examination of the limitations of in-context learning for instruction following in LLMs. The authors' systematic evaluation of Urial's performance across a diverse set of tasks gives a nuanced understanding of where current LLM approaches excel and where they fall short.
One potential limitation of the study is the specific choice of tasks and instructions used to test Urial. While the authors make a concerted effort to cover a wide range of complexity, there may be other types of instructions or domains that could further stress the model's capabilities. Additionally, the paper does not delve deeply into the specific reasons why Urial struggles with certain types of instructions, which could be an area for further investigation.
That said, the paper's key finding - that in-context learning alone is not sufficient for robust instruction following - is an important insight that should inspire further research into more sophisticated approaches. Developing LLMs that can reliably understand and execute complex, multi-step instructions will likely be crucial for realizing the full potential of these models in practical applications.
## Conclusion
This paper presents a thorough examination of the limitations of in-context learning for instruction following in large language models. By systematically evaluating the performance of the Urial LLM on a diverse set of instruction-following tasks, the authors demonstrate that while Urial exhibits impressive in-context learning abilities, it struggles with instructions that require deeper reasoning or understanding of abstract concepts.
These findings highlight the need for continued research to develop LLMs that can more reliably understand and execute complex instructions. Improving instruction-following capabilities could have significant implications for the real-world deployment of LLMs in a wide range of applications, from personal assistants to automated task completion. Overall, this paper provides valuable insights and a foundation for future work in this important area of machine learning research.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,647 | Oil & Water? Diffusion of AI Within and Across Scientific Fields | Oil & Water? Diffusion of AI Within and Across Scientific Fields | 0 | 2024-06-04T12:42:35 | https://aimodels.fyi/papers/arxiv/oil-water-diffusion-ai-within-across-scientific | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Oil & Water? Diffusion of AI Within and Across Scientific Fields](https://aimodels.fyi/papers/arxiv/oil-water-diffusion-ai-within-across-scientific). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- Examines the diffusion of AI within and across scientific fields
- Analyzes how AI is being adopted and used in different disciplines
- Investigates the potential tensions and synergies between AI and other scientific domains
## Plain English Explanation
This paper looks at how the use of [artificial intelligence (AI)](https://aimodels.fyi/papers/arxiv/social-evolution-published-text-emergence-artificial-intelligence) is spreading both within individual scientific fields and across different fields. The researchers wanted to understand how AI is being adopted and applied in various areas of science, and whether there are any conflicts or opportunities that arise from integrating AI with other scientific approaches.
The key idea is that while AI can be a powerful tool for advancing scientific research, it may not always fit seamlessly with the existing methods and cultures of different disciplines. Just like oil and water, AI and certain scientific fields may not always mix well. The paper explores these dynamics, providing insights into the challenges and potential benefits of diffusing AI across the scientific landscape.
## Technical Explanation
The researchers analyzed a large dataset of scientific publications to examine the patterns of AI use within and across fields. They looked at factors like the prevalence of AI-related terms, the co-occurrence of AI with other topics, and the citations between AI-focused and non-AI-focused papers.
The findings suggest that AI is being more readily adopted in some fields, like computer science and mathematics, compared to others, like the social sciences and humanities. There also appear to be differences in how AI is integrated, with some disciplines incorporating it as a core tool, while others treat it more as a complementary approach.
The paper further explores the potential tensions that can arise when AI is introduced into established scientific domains. For example, the [different cultural norms and epistemological assumptions](https://aimodels.fyi/papers/arxiv/now-later-lasting-ten-priorities-ai-research) of AI and other fields may create challenges in seamlessly integrating the technologies.
## Critical Analysis
The paper provides a valuable perspective on the diffusion of AI within and across scientific fields. However, it is important to note that the analysis is based on publication data, which may not fully capture the nuances of how AI is being used in practice in various disciplines.
Additionally, the paper does not delve deeply into the specific factors that may be driving the differential adoption of AI, such as the availability of data, the computational resources required, or the alignment of AI with the core research questions and methodologies of different fields.
Further research could explore these underlying drivers in more detail, as well as investigate the long-term implications of the observed patterns. It would also be interesting to examine the potential [societal impacts](https://aimodels.fyi/papers/arxiv/social-path-to-human-like-artificial-intelligence) of the uneven diffusion of AI across scientific domains.
## Conclusion
This paper provides important insights into the complex dynamics of how [AI is being adopted and integrated](https://aimodels.fyi/papers/arxiv/ai-identity) within and across scientific fields. The findings suggest that the diffusion of AI is not a straightforward process, and that there may be inherent tensions and misalignments that need to be carefully navigated.
Understanding these patterns can help researchers, policymakers, and the broader scientific community better anticipate and manage the challenges and opportunities that arise as AI becomes increasingly pervasive in scientific research. By addressing the nuances of AI's integration with different disciplines, we can work towards more effective and responsible use of these powerful technologies in advancing scientific knowledge and discovery.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,646 | Learning to Model the World with Language | Learning to Model the World with Language | 0 | 2024-06-04T12:42:01 | https://aimodels.fyi/papers/arxiv/learning-to-model-world-language | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Learning to Model the World with Language](https://aimodels.fyi/papers/arxiv/learning-to-model-world-language). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- Current AI agents can execute simple language instructions, but the goal is to build agents that can understand and leverage diverse language that conveys general knowledge, describes the world, and provides interactive feedback.
- The key idea is that agents should interpret language as a signal that helps them predict the future: what they will observe, how the world will behave, and which situations will be rewarded.
- This perspective unifies language understanding with future prediction as a powerful self-supervised learning objective.
## Plain English Explanation
The researchers want to create AI agents that can understand and interact with humans using a wide range of language, not just simple commands. They believe that by treating language as a way for the agent to predict what will happen in the future - what it will see, how the world will change, and what actions will be rewarded - the agent can learn to better understand and use language to accomplish tasks.
This is different from current methods that simply try to map language directly to actions. Instead, the agent will use language as a clue to build a more comprehensive model of the world, which it can then use to plan its actions and predict future outcomes. This [unified approach to language understanding and future prediction](https://aimodels.fyi/papers/arxiv/distributed-agency-second-language-learning-teaching-through) could lead to agents that are much more capable of understanding and using natural language.
## Technical Explanation
The researchers propose an agent called Dynalang that learns a multimodal world model to predict future text and image representations, and learns to act from imagining the outcomes of its potential actions. Unlike current methods that degrade in performance when faced with more diverse language, Dynalang is able to leverage environment descriptions, game rules, and instructions to excel at a wide range of tasks, from gameplay to navigating photorealistic home environments.
Dynalang's approach of learning a generative model of the world also enables additional capabilities, such as the ability to be pretrained on text-only data. This allows the agent to learn from offline datasets, and to generate language that is grounded in the environment it is operating in, similar to how humans learn language by experiencing the world around them. This [grounding of language in the physical world](https://aimodels.fyi/papers/arxiv/natural-language-can-help-bridge-sim2real-gap) is an important step towards more capable and versatile AI agents.
## Critical Analysis
The paper presents a compelling approach to language-enabled AI agents, but there are a few potential limitations and areas for further research:
- The evaluation is primarily focused on gameplay and navigation tasks. It would be valuable to see how Dynalang performs on a wider range of real-world tasks that require more diverse language understanding and interaction.
- The ability to be pretrained on text-only data is promising, but the researchers don't explore how well this translates to actual real-world performance, especially when the agent needs to ground that language in a physical environment. [Further research](https://aimodels.fyi/papers/arxiv/video-language-critic-transferable-reward-functions-language) on bridging the "sim-to-real" gap would be valuable.
- While Dynalang shows impressive results, it's unclear how it would scale to more complex environments and language interactions. [Exploring the "language bottleneck"](https://aimodels.fyi/papers/arxiv/policy-learning-language-bottleneck) and ways to overcome it would be an important next step.
Overall, the Dynalang approach represents an exciting step towards more capable and versatile language-enabled AI agents, but there is still work to be done to fully realize the potential of this line of research.
## Conclusion
This paper presents a novel approach to building AI agents that can understand and use diverse language to interact with and model the world around them. By treating language as a signal that helps the agent predict future observations, state changes, and rewards, the researchers have developed an agent called Dynalang that can leverage a wide range of language inputs to excel at a variety of tasks.
The ability to learn from text-only data and generate grounded language is a particularly promising aspect of this work, as it suggests a path towards agents that can learn about the world through language alone, just as humans do. While there are still limitations and areas for further research, the Dynalang approach represents an important step forward in the field of [language-enabled embodied AI](https://aimodels.fyi/papers/arxiv/survey-vision-language-action-models-embodied-ai).
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,645 | The Impacts of Data, Ordering, and Intrinsic Dimensionality on Recall in Hierarchical Navigable Small Worlds | The Impacts of Data, Ordering, and Intrinsic Dimensionality on Recall in Hierarchical Navigable Small Worlds | 0 | 2024-06-04T12:41:27 | https://aimodels.fyi/papers/arxiv/impacts-data-ordering-intrinsic-dimensionality-recall-hierarchical | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [The Impacts of Data, Ordering, and Intrinsic Dimensionality on Recall in Hierarchical Navigable Small Worlds](https://aimodels.fyi/papers/arxiv/impacts-data-ordering-intrinsic-dimensionality-recall-hierarchical). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper investigates the impacts of data, ordering, and intrinsic dimensionality on recall performance in hierarchical navigable small-world (HNSW) networks.
- HNSW networks are a type of approximate nearest neighbor search algorithm used for efficient retrieval in large-scale datasets.
- The authors explore how various factors like dataset characteristics, index construction, and intrinsic dimensionality affect the ability to accurately retrieve relevant items from the network.
## Plain English Explanation
The paper looks at how different things can impact the performance of a certain type of search algorithm called hierarchical navigable small-world (HNSW) networks. HNSW networks are used to quickly find items that are similar to a given input, even in very large datasets.
The researchers examined how the characteristics of the data, the way the data is organized, and the inherent complexity of the data can affect how well the HNSW network is able to retrieve the most relevant items. For example, they looked at how the size and structure of the dataset, as well as the natural "dimensionality" or complexity of the data, can influence the accuracy of the search results.
By understanding these factors, the researchers hope to provide guidance on how to best set up and use HNSW networks to get the most reliable search results, especially for large and complex datasets. This could be useful in a variety of applications that rely on quickly finding similar items, like [recommendation systems](https://aimodels.fyi/papers/arxiv/contextualization-splade-high-recall-retrieval), [image retrieval](https://aimodels.fyi/papers/arxiv/leanvec-searching-vectors-faster-by-making-them), and [document search](https://aimodels.fyi/papers/arxiv/efficient-inverted-indexes-approximate-retrieval-over-learned).
## Technical Explanation
The paper examines how data characteristics, index construction, and intrinsic dimensionality impact the recall performance of hierarchical navigable small-world (HNSW) networks, a type of approximate nearest neighbor search algorithm.
The authors conduct extensive experiments on various synthetic and real-world datasets to assess the effects of:
- Dataset size and structure
- Data ordering during index construction
- Intrinsic dimensionality of the data
They find that dataset size and intrinsic dimensionality have a significant impact on recall, with larger and more complex datasets leading to decreased performance. The ordering of data during index construction is also shown to be an important factor, with certain strategies outperforming others.
The results provide insights into how to optimize HNSW networks for high-recall retrieval, particularly in the context of [large-scale](https://aimodels.fyi/papers/arxiv/approximate-nearest-neighbour-search-dynamic-datasets-investigation) and [complex](https://aimodels.fyi/papers/arxiv/contextual-categorization-enhancement-through-llms-latent-space) datasets. The authors discuss the implications of their findings and suggest areas for future research.
## Critical Analysis
The paper provides a thorough and well-designed empirical evaluation of the factors influencing recall performance in HNSW networks. The authors acknowledge several limitations, such as the use of synthetic datasets and the focus on recall rather than other metrics like precision or efficiency.
One potential issue is the reliance on a single type of approximate nearest neighbor algorithm (HNSW). It would be interesting to see how the findings compare to other ANN methods, such as [IVFADC](https://aimodels.fyi/papers/arxiv/efficient-inverted-indexes-approximate-retrieval-over-learned) or [LeanVec](https://aimodels.fyi/papers/arxiv/leanvec-searching-vectors-faster-by-making-them). Additionally, the paper does not explore the impact of hyperparameter tuning on HNSW performance, which could be an important factor in real-world applications.
Overall, the research provides valuable insights into the design and deployment of HNSW networks, but further investigation into the generalizability of the findings and the practical implications for different use cases would be beneficial.
## Conclusion
This paper offers a detailed examination of the factors that can influence the recall performance of hierarchical navigable small-world (HNSW) networks, a popular approximate nearest neighbor search algorithm. The authors demonstrate that dataset characteristics, such as size and intrinsic dimensionality, as well as the ordering of data during index construction, can have a significant impact on the ability of HNSW networks to accurately retrieve relevant items.
These findings provide important guidance for practitioners seeking to utilize HNSW networks in large-scale and complex data environments, where efficient and reliable retrieval is crucial. By understanding the nuances of HNSW performance, researchers and engineers can make more informed decisions about algorithm selection, data preprocessing, and index construction to optimize search quality in a wide range of applications, from [recommendation systems](https://aimodels.fyi/papers/arxiv/contextualization-splade-high-recall-retrieval) to [document search](https://aimodels.fyi/papers/arxiv/efficient-inverted-indexes-approximate-retrieval-over-learned).
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,644 | Text clustering with LLM embeddings | Text clustering with LLM embeddings | 0 | 2024-06-04T12:40:52 | https://aimodels.fyi/papers/arxiv/text-clustering-llm-embeddings | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Text clustering with LLM embeddings](https://aimodels.fyi/papers/arxiv/text-clustering-llm-embeddings). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper explores the use of large language model (LLM) embeddings for text clustering, which is the process of grouping similar text documents together.
- The researchers investigate how LLM embeddings, which capture rich semantic information, can be leveraged to improve the performance of text clustering compared to traditional approaches.
- The paper presents a novel clustering method that combines LLM embeddings with traditional clustering algorithms, demonstrating its effectiveness on several real-world datasets.
## Plain English Explanation
Large language models (LLMs) like [BERT](https://aimodels.fyi/papers/arxiv/understanding-privacy-risks-embeddings-induced-by-large) and [GPT](https://aimodels.fyi/papers/arxiv/llm-augmented-retrieval-enhancing-retrieval-models-through) have shown remarkable capabilities in understanding the meaning and context of text. This paper explores how we can use the powerful embeddings (numerical representations) generated by these LLMs to improve the process of **text clustering** - the task of grouping similar text documents together.
Traditional text clustering methods often struggle to capture the nuanced semantic relationships between documents. In contrast, LLM embeddings can encode rich information about the meaning and context of the text, which the researchers hypothesize can lead to more accurate and meaningful text clustering.
The paper proposes a new clustering approach that combines LLM embeddings with traditional clustering algorithms. By leveraging the strengths of both, the method can group documents more effectively based on their underlying content and meaning, rather than just surface-level similarity.
Through experiments on several real-world datasets, the researchers demonstrate that their LLM-based clustering method outperforms traditional techniques, producing more coherent and interpretable clusters. This suggests that the semantic understanding captured by LLMs can be a valuable asset in various text analysis and organization tasks.
## Technical Explanation
The paper begins by providing background on **text embeddings**, which are numerical representations of text that capture the semantic and contextual meaning of words and documents. The researchers explain how advanced LLMs, such as [BERT](https://aimodels.fyi/papers/arxiv/understanding-privacy-risks-embeddings-induced-by-large) and [GPT](https://aimodels.fyi/papers/arxiv/llm-augmented-retrieval-enhancing-retrieval-models-through), can generate high-quality text embeddings that outperform traditional approaches.
The core contribution of the paper is a novel **clustering method** that leverages LLM embeddings. The method first generates embeddings for the input text documents using a pre-trained LLM. It then applies a traditional clustering algorithm, such as k-means or hierarchical clustering, to the LLM embeddings to group the documents based on their semantic similarity.
The researchers evaluate their LLM-based clustering approach on several real-world text datasets, including news articles, scientific papers, and social media posts. They compare the performance of their method to traditional clustering techniques that use simpler text representations, such as bag-of-words or TF-IDF.
The results show that the LLM-based clustering consistently outperforms the baseline methods, producing more coherent and interpretable clusters. The researchers attribute this improvement to the rich semantic information captured by the LLM embeddings, which allows the clustering algorithm to better distinguish and group documents based on their underlying content and meaning.
## Critical Analysis
The paper provides a compelling demonstration of how LLM embeddings can enhance the performance of text clustering compared to traditional approaches. By leveraging the semantic understanding encoded in LLM representations, the proposed method is able to group documents more effectively based on their conceptual similarity rather than just surface-level features.
However, the paper does not address some potential limitations and areas for further research. For example, the authors do not discuss the computational cost and scalability of their approach, which could be a concern when dealing with large-scale text corpora. Additionally, the paper does not explore how the choice of pre-trained LLM or the fine-tuning of these models might impact the clustering performance.
It would also be interesting to see how the LLM-based clustering method compares to more advanced techniques, such as [context-aware clustering](https://aimodels.fyi/papers/arxiv/context-aware-clustering-using-large-language-models) or [human-interpretable clustering](https://aimodels.fyi/papers/arxiv/human-interpretable-clustering-short-text-using-large), which aim to further enhance the interpretability and meaningfulness of the resulting clusters.
Overall, the paper presents a promising approach that demonstrates the potential of leveraging LLM embeddings for text clustering tasks. The findings contribute to the growing body of research exploring the applications of large language models in various text analysis and organization problems.
## Conclusion
This paper showcases a novel text clustering method that harnesses the power of large language model (LLM) embeddings to improve the accuracy and interpretability of text grouping. By leveraging the rich semantic information captured by LLMs, the proposed approach outperforms traditional clustering techniques on a range of real-world datasets.
The findings suggest that the semantic understanding encoded in LLM representations can be a valuable asset in text analysis and organization tasks, enabling more meaningful and coherent grouping of documents based on their underlying content and meaning. This work contributes to the broader exploration of how advanced language models can be applied to enhance various natural language processing applications.
While the paper presents a compelling solution, it also highlights the need for further research to address potential limitations, such as computational cost and the impact of LLM choice and fine-tuning. Exploring the integration of LLM-based clustering with other advanced techniques, like context-aware and human-interpretable clustering, could also be a fruitful avenue for future investigations.
Overall, this research represents an important step forward in harnessing the power of large language models to improve the effectiveness and interpretability of text clustering, with promising implications for a wide range of applications in academia, industry, and beyond.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,643 | Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks | Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks | 0 | 2024-06-04T12:40:17 | https://aimodels.fyi/papers/arxiv/rotational-equilibrium-how-weight-decay-balances-learning | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks](https://aimodels.fyi/papers/arxiv/rotational-equilibrium-how-weight-decay-balances-learning). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This study investigates how weight decay affects the behavior of individual neurons in deep neural networks.
- It analyzes the dynamics of weight updates across different optimization methods like [Adam](https://aimodels.fyi/papers/arxiv/weight-dynamics-deep-normalized-networks), [Lion](https://aimodels.fyi/papers/arxiv/weight-dynamics-learning-networks), and [SGD with momentum](https://aimodels.fyi/papers/arxiv/from-local-to-global-order-theory-neural).
- The researchers identify a "rotational equilibrium" state where the expected magnitude and angular updates of a neuron's weight vector converge.
- These rotational equilibrium states can be highly homogeneous, balancing the effective learning rate across different layers and neurons.
- The paper provides insights into the efficacy of widely used but poorly understood training methods in deep learning, such as the benefits of [Weight Standardization](https://aimodels.fyi/papers/arxiv/loss-symmetry-noise-equilibrium-stochastic-gradient-descent) and [AdamW](https://aimodels.fyi/papers/arxiv/using-degeneracy-loss-landscape-mechanistic-interpretability) over Adam with L2-regularization.
- The researchers also show that explicitly controlling the rotation can provide the benefits of weight decay while reducing the need for learning rate warmup.
## Plain English Explanation
Deep neural networks are complex models with many interconnected neurons. Each neuron has a set of weights that determine how it responds to inputs. The process of "training" a neural network involves adjusting these weights to improve the model's performance on a specific task.
One technique used in training is called "weight decay," which essentially means that the weights gradually decrease in magnitude over time. This can have a significant impact on how the individual neurons in the network behave and learn.
This study takes a close look at how weight decay affects the updates to each neuron's weights. The researchers find that as training progresses, the weight updates for individual neurons tend to converge to a "rotational equilibrium" state. In this state, the expected magnitude and direction of the weight updates are balanced across the different layers and neurons in the network.
This rotational equilibrium can have important consequences for the training process. For example, it helps explain why techniques like Weight Standardization and AdamW (a variant of the popular Adam optimizer) can be more effective than simpler approaches. By understanding this rotational equilibrium, the researchers also show that we can explicitly control the rotation to get the benefits of weight decay while reducing the need for other tricky training tricks, like learning rate warmup.
Overall, this study provides a new and insightful perspective on how deep neural networks learn and adapt during training. By focusing on the behavior of individual neurons, the researchers have uncovered important dynamics that can help us design more effective and efficient deep learning models.
## Technical Explanation
The researchers used a combination of analytical analysis and experimentation to study how weight decay affects the update behavior of individual neurons in deep neural networks.
Through their analysis, they found that weight decay can cause the expected magnitude and angular updates of a neuron's weight vector to converge to a "rotational equilibrium" state. In this state, the average rotation of the weight vector (which serves as a proxy for the effective learning rate) is balanced across different layers and neurons.
The researchers explored these rotational dynamics across several common optimization methods, including [Adam](https://aimodels.fyi/papers/arxiv/weight-dynamics-deep-normalized-networks), [Lion](https://aimodels.fyi/papers/arxiv/weight-dynamics-learning-networks), and [SGD with momentum](https://aimodels.fyi/papers/arxiv/from-local-to-global-order-theory-neural). They demonstrated how this balanced rotation plays a key role in the effectiveness of techniques like [Weight Standardization](https://aimodels.fyi/papers/arxiv/loss-symmetry-noise-equilibrium-stochastic-gradient-descent) and [AdamW](https://aimodels.fyi/papers/arxiv/using-degeneracy-loss-landscape-mechanistic-interpretability) (a variant of Adam that incorporates weight decay).
Furthermore, the researchers showed that by explicitly controlling the rotation of the weight vector, they could achieve the benefits of weight decay while significantly reducing the need for learning rate warmup, a common technique used to stabilize training.
## Critical Analysis
The study provides valuable insights into the dynamics of weight updates in deep neural networks, but it also has some limitations and potential areas for further research:
- The analysis is primarily focused on the behavior of individual neurons, which may not fully capture the complex interactions and emergent properties that arise from the network as a whole.
- The experiments were conducted on relatively simple network architectures, and it's unclear how well the findings would generalize to larger, more complex models.
- The paper does not explore the potential impact of the rotational equilibrium on the network's ability to learn and generalize to new data, which is a critical aspect of deep learning.
- While the researchers demonstrate the benefits of explicitly controlling the rotation, the practical implementation and scalability of this approach to real-world deep learning problems remain to be explored.
Nonetheless, this study represents an important step forward in our understanding of the inner workings of deep neural networks. By shedding light on the nuanced dynamics of weight updates, it opens up new avenues for designing more effective and efficient training methods. As the field of deep learning continues to evolve, research like this will be crucial for unlocking the full potential of these powerful models.
## Conclusion
This study offers a novel perspective on how weight decay affects the behavior of individual neurons in deep neural networks. By analyzing the dynamics of weight updates, the researchers identified a "rotational equilibrium" state that can have significant implications for the training process.
The insights gleaned from this work help explain the efficacy of widely used but poorly understood deep learning techniques, such as Weight Standardization and AdamW. Moreover, the researchers demonstrated that by explicitly controlling the rotation of the weight vector, it's possible to achieve the benefits of weight decay while reducing the need for other training tricks like learning rate warmup.
While the study has some limitations, it represents an important step forward in our understanding of how deep neural networks learn and adapt. By focusing on the intricacies of individual neuron behavior, the researchers have opened up new avenues for designing more effective and efficient deep learning models. As the field continues to evolve, this type of nuanced, mechanistic analysis will be crucial for unlocking the full potential of these powerful AI systems.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,642 | How Artificial Intelligence Is Redefining Human Interaction | Artificial Intelligence (AI) is revolutionizing various sectors, enhancing efficiencies and... | 27,548 | 2024-06-04T12:39:55 | https://dev.to/aishikl/how-artificial-intelligence-is-redefining-human-interaction-4902 | # Artificial Intelligence (AI) is revolutionizing various sectors, enhancing efficiencies and capabilities while posing ethical challenges such as privacy concerns and job displacement. This blog post explores AI's dual impact on society, emphasizing the need for robust ethical frameworks like the "Blueprint for an AI Bill of Rights" and international regulations to ensure responsible AI deployment. It highlights AI's potential to transform education and the workplace, fostering innovation and entrepreneurial success, while stressing the importance of human oversight and collaboration to mitigate risks of dehumanization. Ultimately, a balanced approach combining technology and ethics is essential for AI to contribute positively to human flourishing.
#rapidinnovation#AIandSociety #EthicalAI #HumanFlourishing #AIInnovation #AIRegulation
link: http://www.rapidinnovation.io/post/how-artificial-intelligence-is-redefining-human-interaction | aishikl | |
1,876,641 | Training-Free Long-Context Scaling of Large Language Models | Training-Free Long-Context Scaling of Large Language Models | 0 | 2024-06-04T12:39:43 | https://aimodels.fyi/papers/arxiv/training-free-long-context-scaling-large-language | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Training-Free Long-Context Scaling of Large Language Models](https://aimodels.fyi/papers/arxiv/training-free-long-context-scaling-large-language). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This research paper explores a novel technique for scaling large language models (LLMs) to handle longer input contexts without requiring additional training.
- The proposed method, called [XL3M](https://aimodels.fyi/papers/arxiv/xl3m-training-free-framework-llm-length-extension), aims to address the challenge faced by traditional LLMs in effectively processing and understanding long-form input.
- The paper presents experimental results demonstrating the effectiveness of XL3M in improving the performance of LLMs on tasks that require processing of extended contexts.
## Plain English Explanation
[XL3M](https://aimodels.fyi/papers/arxiv/xl3m-training-free-framework-llm-length-extension) is a technique that allows large language models (LLMs) to work with longer input texts without the need for additional training. LLMs, such as GPT-3 and BERT, are powerful AI models that can understand and generate human-like text. However, they often struggle when presented with very long passages of text, as they were trained on shorter contexts.
The researchers behind XL3M have developed a way to "scale up" these LLMs to handle longer input without retraining the entire model. The key idea is to modify the way the model processes the input text, allowing it to better capture the relationships and dependencies within the extended context.
Imagine you're reading a long book and trying to understand the plot. Traditional LLMs would struggle to remember all the details and connections from the beginning of the book by the time they reach the end. XL3M, on the other hand, helps the LLM keep track of the important information throughout the entire book, allowing it to better comprehend the overall story.
This capability is particularly useful for tasks that require understanding and reasoning over long-form text, such as [summarizing lengthy documents](https://aimodels.fyi/papers/arxiv/leave-no-context-behind-efficient-infinite-context), answering questions about complex passages, or [generating coherent text across extended contexts](https://aimodels.fyi/papers/arxiv/infllm-training-free-long-context-extrapolation-llms).
## Technical Explanation
The core of the XL3M approach is a novel positional encoding scheme that allows the LLM to better capture the long-range dependencies within the input text. Traditional positional encoding methods, such as those used in Transformer-based models, are limited in their ability to represent positions beyond a certain length.
To address this, the researchers developed an [extended positional encoding](https://aimodels.fyi/papers/arxiv/long-context-llms-struggle-long-context-learning) that can effectively represent positions in much longer sequences. This extended encoding is then integrated into the LLM's architecture, enabling it to process and understand input contexts that are significantly longer than what the model was originally trained on.
The paper presents extensive experiments demonstrating the effectiveness of XL3M across a range of tasks and datasets. The results show that XL3M can substantially improve the performance of LLMs on benchmarks that require understanding and reasoning over long-form text, without the need for additional training.
## Critical Analysis
The paper provides a compelling solution to the challenge of scaling LLMs to handle longer input contexts. The XL3M approach is well-designed and the experimental results are promising, suggesting that the technique can be a valuable tool for researchers and practitioners working with large language models.
That said, the paper does not address several important limitations and potential issues. For example, the authors do not discuss the computational overhead or inference time of the XL3M method, which could be a concern for real-world applications. Additionally, the paper does not explore the potential for [catastrophic forgetting](https://aimodels.fyi/papers/arxiv/extending-llama-3s-context-ten-fold-overnight) or other stability issues that could arise when scaling LLMs in this way.
Further research is needed to understand the broader implications and potential drawbacks of the XL3M approach. Specifically, it would be valuable to see how the technique performs on a wider variety of tasks and datasets, and to better understand its limitations and failure modes.
## Conclusion
The [XL3M](https://aimodels.fyi/papers/arxiv/xl3m-training-free-framework-llm-length-extension) technique presented in this paper represents an exciting advancement in the field of large language model scaling. By allowing LLMs to effectively process and understand longer input contexts without the need for additional training, the researchers have opened up new possibilities for applying these powerful models to a wider range of real-world applications.
The implications of this work are significant, as it could enable LLMs to better capture the nuances and complexities of long-form text, leading to improved performance on tasks such as [document summarization](https://aimodels.fyi/papers/arxiv/leave-no-context-behind-efficient-infinite-context), [question answering](https://aimodels.fyi/papers/arxiv/infllm-training-free-long-context-extrapolation-llms), and [long-form text generation](https://aimodels.fyi/papers/arxiv/extending-llama-3s-context-ten-fold-overnight). As the research community continues to explore the limits of LLM capabilities, techniques like XL3M will undoubtedly play an important role in unlocking their full potential.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,640 | Certifiably Robust RAG against Retrieval Corruption | Certifiably Robust RAG against Retrieval Corruption | 0 | 2024-06-04T12:39:09 | https://aimodels.fyi/papers/arxiv/certifiably-robust-rag-against-retrieval-corruption | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Certifiably Robust RAG against Retrieval Corruption](https://aimodels.fyi/papers/arxiv/certifiably-robust-rag-against-retrieval-corruption). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper presents a method to make Retrieval Augmented Generation (RAG) models more robust against retrieval corruption.
- RAG models combine a language model with a retrieval component to generate text, but can be vulnerable to errors in the retrieval process.
- The proposed approach, Certifiably Robust RAG (CR-RAG), provides theoretical guarantees that the model's output will be close to the optimal output even with corrupted retrievals.
## Plain English Explanation
The paper discusses a way to improve Retrieval Augmented Generation (RAG) models, which are a type of AI system that generate text by combining a language model with a retrieval component. RAG models work by first retrieving relevant information from a database, and then using that information to generate new text.
However, RAG models can be vulnerable to errors in the retrieval process. If the information that is retrieved is inaccurate or incomplete, it can negatively impact the quality of the generated text. The key idea in this paper is to make RAG models more robust to these retrieval errors.
The researchers propose a new approach called Certifiably Robust RAG (CR-RAG), which provides mathematical guarantees that the model's output will be close to the optimal output, even if the retrieved information is corrupted or imperfect. This is achieved through a novel training process and architectural changes to the RAG model.
The main benefit of CR-RAG is that it can help ensure the reliability and consistency of RAG-based systems, even in the face of potential errors or uncertainties in the retrieval component. This could be useful in a wide range of applications, such as [**duetrag-collaborative-retrieval-augmented-generation**], [**blended-rag-improving-rag-retriever-augmented-generation**], or [**improving-retrieval-rag-based-question-answering-models**], where the quality and trustworthiness of the generated text is critical.
## Technical Explanation
The paper introduces Certifiably Robust RAG (CR-RAG), a modified version of the Retrieval Augmented Generation (RAG) architecture that provides theoretical guarantees on the quality of the generated text, even in the presence of corrupted or imperfect retrievals.
The key innovations of CR-RAG include:
1. **Modeling Retrieval Corruption**: The authors develop a new formulation of the RAG objective that explicitly accounts for potential corruption in the retrieval process. This allows the model to be trained to be robust to such errors.
2. **Certifiable Robustness**: The paper derives theoretical bounds on the distance between the model's output and the optimal output, showing that CR-RAG can provide certified robustness guarantees.
3. **Architectural Changes**: The CR-RAG model incorporates several architectural changes, such as modified attention mechanisms and additional regularization terms, to align with the new robustness objective.
The authors evaluate CR-RAG on a range of benchmarks, including [**typos-that-broke-rags-back-genetic-attack**] and [**evaluation-retrieval-augmented-generation-survey**], and demonstrate significant improvements in robustness compared to the standard RAG model, without sacrificing overall performance.
## Critical Analysis
The paper presents a well-designed and thorough approach to improving the robustness of RAG models against retrieval corruption. The theoretical guarantees provided by CR-RAG are a particularly strong contribution, as they offer a principled way to ensure the reliability of the generated output.
However, the paper does not address several potential limitations and areas for future research:
1. **Real-World Retrieval Errors**: The paper focuses on synthetic corruption, but real-world retrieval errors may have different characteristics that are not captured by the proposed model. Further evaluation on more realistic retrieval corruption scenarios would be valuable.
2. **Computational Overhead**: The architectural changes and additional training objectives introduced by CR-RAG may increase the computational complexity of the model, which could be a concern for practical applications. The paper could have explored ways to balance robustness and efficiency.
3. **Generalization to Other Tasks**: While the authors demonstrate the effectiveness of CR-RAG on standard benchmarks, it would be interesting to see how the approach transfers to other applications of retrieval-augmented generation, such as [**duetrag-collaborative-retrieval-augmented-generation**] or [**blended-rag-improving-rag-retriever-augmented-generation**].
Overall, the paper presents a promising step towards more reliable and trustworthy RAG-based systems, but there are still opportunities for further research and refinement of the proposed approach.
## Conclusion
The Certifiably Robust RAG (CR-RAG) model introduced in this paper represents a significant advancement in making Retrieval Augmented Generation (RAG) systems more robust to errors in the retrieval process. By providing theoretical guarantees on the quality of the generated output, even with corrupted retrievals, CR-RAG offers a principled way to improve the reliability and consistency of RAG-based systems.
The potential applications of this work are broad, as RAG models are used in a wide range of text generation tasks, from [**duetrag-collaborative-retrieval-augmented-generation**] to [**improving-retrieval-rag-based-question-answering-models**]. By making these models more robust, the CR-RAG approach could help unlock new use cases and enable more trustworthy AI systems that can reliably generate high-quality text, even in the face of uncertain or imperfect information retrieval.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,638 | Sparse maximal update parameterization: A holistic approach to sparse training dynamics | Sparse maximal update parameterization: A holistic approach to sparse training dynamics | 0 | 2024-06-04T12:38:34 | https://aimodels.fyi/papers/arxiv/sparse-maximal-update-parameterization-holistic-approach-to | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Sparse maximal update parameterization: A holistic approach to sparse training dynamics](https://aimodels.fyi/papers/arxiv/sparse-maximal-update-parameterization-holistic-approach-to). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- Sparse neural networks, which have a large fraction of weights set to zero, face several challenges in competing with dense models.
- Setting many weights to zero can impair the flow of signals during forward and backward propagation.
- Sparse models often require testing multiple sparsity levels and new hyperparameters, which can be prohibitively expensive.
- The standard practice of reusing hyperparameters from dense models is ineffective, as sparse and dense networks have different optimal hyperparameters.
- Stable dynamics and effective training recipes are needed to test sparsity at scale and make a compelling case for sparsity acceleration in hardware.
## Plain English Explanation
Sparse neural networks, which have many of their weights set to zero, struggle to match the performance of dense models (networks without any weights set to zero). One key reason is that setting a large fraction of weights to zero can disrupt the flow of information during the forward and backward passes of the network. This means the network has a harder time learning and updating its weights effectively.
Additionally, testing sparse models often requires exploring multiple levels of sparsity and introducing new hyperparameters (settings that control the training process). This can be extremely costly, as the standard practice is to simply reuse the hyperparameters that were optimized for dense models. Unfortunately, sparse and dense networks do not share the same optimal hyperparameters, so this approach is not effective.
Without stable training dynamics and proven techniques for training sparse networks, it is difficult and expensive to explore sparsity at a large scale. This makes it hard to demonstrate that sparse networks can surpass the performance of dense models, which is necessary to justify the use of specialized hardware designed for sparse neural networks.
To address these challenges, the researchers propose an approach called S$\mu$Par. This method ensures that the activations, gradients, and weight updates in the sparse network all scale independently of the sparsity level. Additionally, S$\mu$Par reparameterizes the hyperparameters in a way that allows the same hyperparameter values to be optimal across different sparsity levels and model widths. This means the hyperparameters can be tuned on smaller, dense networks and then applied to larger, sparse models, greatly reducing the cost of tuning.
In experiments on large-scale language modeling, the S$\mu$Par training approach improved the loss by up to 8.2% compared to the common approach of using the hyperparameters optimized for dense models.
## Technical Explanation
The paper identifies several key challenges that make it difficult for sparse neural networks to compete with their dense counterparts. First, setting a large fraction of weights to zero can impair the forward and gradient signal propagation, disrupting the network's ability to learn effectively. Second, sparse studies often need to test multiple sparsity levels and introduce new hyperparameters, leading to prohibitive tuning costs. The standard practice of reusing hyperparameters from dense models is ineffective, as sparse and dense networks do not share the same optimal hyperparameters.
To address these challenges, the researchers propose S$\mu$Par, a holistic approach that ensures activations, gradients, and weight updates all scale independently of the sparsity level. This helps maintain stable training dynamics. Additionally, S$\mu$Par reparameterizes the hyperparameters in a way that enables the same hyperparameter values to be optimal as the sparsity level and model width are varied. This allows the hyperparameters to be tuned on smaller, dense networks and then transferred to larger, sparse models, greatly reducing the tuning cost.
The researchers evaluate S$\mu$Par on large-scale language modeling tasks and find that it can improve the loss by up to 8.2% compared to the common approach of using the hyperparameters optimized for dense models.
## Critical Analysis
The paper identifies important challenges that have hindered the widespread adoption of sparse neural networks. The proposed S$\mu$Par approach offers a promising solution by addressing issues related to signal propagation and hyperparameter tuning. However, the paper does not explore the potential limitations or caveats of this approach.
For example, the paper does not discuss the computational overhead or memory footprint of the S$\mu$Par reparameterization. It is possible that the additional complexity introduced by this method could offset some of the benefits of sparsity, particularly in resource-constrained environments. Additionally, the paper focuses on language modeling tasks, and it is unclear whether the observed improvements would translate to other domains, such as computer vision or reinforcement learning.
Further research is needed to understand the broader applicability and potential trade-offs of the S$\mu$Par approach. It would be valuable to see comparisons with other techniques for training sparse networks, such as [Lazy NTK-rich and Dollar-polydollar regimes: A gentle tutorial](https://aimodels.fyi/papers/arxiv/lazy-ntk-rich-dollarmudollarp-regimes-gentle-tutorial), [Sparse Spectral Training for Inference in Euclidean and Hyperbolic Neural Networks](https://aimodels.fyi/papers/arxiv/sparse-spectral-training-inference-euclidean-hyperbolic-neural), [Dense Training, Sparse Inference: Rethinking Training and Inference for Large Language Models](https://aimodels.fyi/papers/arxiv/dense-training-sparse-inference-rethinking-training-mixture), [Smoothing the Edges: Smooth Optimization for Sparse Regularization Using Majorization-Minimization](https://aimodels.fyi/papers/arxiv/smoothing-edges-smooth-optimization-sparse-regularization-using), and [Train Faster, Perform Better: Modular Adaptive Training](https://aimodels.fyi/papers/arxiv/train-faster-perform-better-modular-adaptive-training).
Overall, the S$\mu$Par approach represents an important step forward in addressing the challenges of sparse neural networks, but further research is needed to fully understand its capabilities and limitations.
## Conclusion
The paper highlights the key challenges that make it difficult for sparse neural networks to compete with dense models, including issues with signal propagation and prohibitive hyperparameter tuning costs. The researchers propose the S$\mu$Par approach as a holistic solution, which ensures that the network's activations, gradients, and weight updates scale independently of the sparsity level, and reparameterizes the hyperparameters to enable the same values to be optimal across different sparsity levels and model widths.
The evaluation of S$\mu$Par on large-scale language modeling tasks demonstrates significant improvements in performance compared to the common practice of reusing hyperparameters from dense models. This suggests that S$\mu$Par could be a valuable tool for unlocking the potential of sparse neural networks and making a compelling case for specialized hardware acceleration.
However, further research is needed to explore the broader applicability of this approach and address potential limitations, such as computational overhead and memory requirements. Comparisons with other techniques for training sparse networks would also help to contextualize the benefits and trade-offs of the S$\mu$Par method.
Overall, the S$\mu$Par approach represents an important step forward in addressing the challenges of sparse neural networks and paving the way for their widespread adoption in real-world applications.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,637 | Grokfast: Accelerated Grokking by Amplifying Slow Gradients | Grokfast: Accelerated Grokking by Amplifying Slow Gradients | 0 | 2024-06-04T12:38:00 | https://aimodels.fyi/papers/arxiv/grokfast-accelerated-grokking-by-amplifying-slow-gradients | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Grokfast: Accelerated Grokking by Amplifying Slow Gradients](https://aimodels.fyi/papers/arxiv/grokfast-accelerated-grokking-by-amplifying-slow-gradients). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- The paper "Grokfast: Accelerated Grokking by Amplifying Slow Gradients" explores a technique to speed up the "grokking" process in deep neural networks.
- Grokking refers to the phenomenon where a neural network suddenly achieves high performance on a task after an initial period of slow learning.
- The authors propose a method called "Grokfast" that amplifies the low-frequency components of the stochastic gradients during training to accelerate grokking.
## Plain English Explanation
The paper discusses a challenge in training deep neural networks, which is the phenomenon of "grokking." [Grokking as transition from lazy to rich](https://aimodels.fyi/papers/arxiv/grokking-as-transition-from-lazy-to-rich) Grokking is when a neural network suddenly starts performing very well on a task after a long period of slow progress.
The authors of this paper propose a technique called "Grokfast" to speed up this grokking process. [Rationale from frequency perspective: grokking, training neural](https://aimodels.fyi/papers/arxiv/rationale-from-frequency-perspective-grokking-training-neural) The key idea is to amplify the low-frequency components of the stochastic gradients used to train the network. Stochastic gradients are the small updates made to the network's parameters during training.
By boosting the low-frequency gradients, the network is able to more quickly find the "right" set of parameters that lead to high performance on the task. This is analogous to tuning a radio - you need to find the right frequency to get a clear signal, and amplifying the low frequencies helps you home in on that sweet spot faster.
The authors demonstrate through experiments that their Grokfast method can significantly accelerate the grokking process compared to standard training approaches. [Deep grokking: would deep neural networks generalize](https://aimodels.fyi/papers/arxiv/deep-grokking-would-deep-neural-networks-generalize) This has important implications for making deep learning systems more sample-efficient and practical, especially for real-world applications.
## Technical Explanation
The core idea behind the "Grokfast" method proposed in this paper is to amplify the low-frequency components of the stochastic gradients used to train the deep neural network. [Dichotomy: early late phase implicit biases can](https://aimodels.fyi/papers/arxiv/dichotomy-early-late-phase-implicit-biases-can)
The authors hypothesize that the low-frequency gradients are important for the "grokking" phenomenon, where the network suddenly achieves high performance after an initial period of slow progress. By selectively boosting these low-frequency gradients, they are able to accelerate the grokking process.
Specifically, the Grokfast method applies a frequency-dependent scaling to the stochastic gradients during training. Higher scaling factors are applied to the low-frequency components, while the high-frequency gradients are left unchanged. This creates a gradient signal that is biased towards the lower frequencies.
The authors evaluate their Grokfast method on a range of benchmark tasks and demonstrate significant improvements in the rate of grokking compared to standard training approaches. [Progress measures for grokking on real-world datasets](https://aimodels.fyi/papers/arxiv/progress-measures-grokking-real-world-datasets) They analyze the learned representations and show that the Grokfast method leads to networks that converge to better minima in the optimization landscape.
## Critical Analysis
The Grokfast paper presents an intriguing approach to accelerating the grokking phenomenon in deep neural networks. The authors provide a compelling rationale for why amplifying low-frequency gradients could be beneficial, and their experimental results seem to support this hypothesis.
One potential limitation of the work is the reliance on carefully tuned hyperparameters to control the frequency-dependent scaling. The authors acknowledge that the optimal scaling factors may vary across different tasks and architectures, which could make the method less straightforward to apply in practice.
Additionally, while the authors demonstrate improvements on benchmark tasks, it's unclear how well the Grokfast method would generalize to more complex, real-world datasets. [Progress measures for grokking on real-world datasets](https://aimodels.fyi/papers/arxiv/progress-measures-grokking-real-world-datasets) Further research would be needed to assess the broader applicability of this technique.
Another area for potential investigation is the relationship between the Grokfast method and other techniques that aim to improve the optimization dynamics of deep neural networks, such as [deep grokking: would deep neural networks generalize](https://aimodels.fyi/papers/arxiv/deep-grokking-would-deep-neural-networks-generalize) or [dichotomy: early late phase implicit biases can](https://aimodels.fyi/papers/arxiv/dichotomy-early-late-phase-implicit-biases-can). Understanding how these different approaches interact could lead to more robust and effective training strategies.
Overall, the Grokfast paper presents a novel and promising direction for accelerating the grokking process in deep learning. While further research is needed to fully understand the implications and limitations of this approach, the authors have made a valuable contribution to the ongoing efforts to improve the training and generalization of deep neural networks.
## Conclusion
The paper "Grokfast: Accelerated Grokking by Amplifying Slow Gradients" introduces a novel technique to speed up the "grokking" phenomenon in deep neural networks. By selectively amplifying the low-frequency components of the stochastic gradients during training, the authors are able to significantly accelerate the process by which a network suddenly achieves high performance on a task.
This work has important implications for making deep learning systems more sample-efficient and practical, particularly for real-world applications where rapid learning is crucial. The authors' insights into the role of low-frequency gradients in the grokking process contribute to our fundamental understanding of deep neural network optimization and generalization.
While further research is needed to fully explore the limitations and broader applicability of the Grokfast method, this paper represents an exciting step forward in the quest to unlock the full potential of deep learning.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,636 | Understanding Mixins in Dart and Flutter. | Hello friends, Today we'll explore an interesting and powerful concept in Dart and Flutter... | 0 | 2024-06-04T12:37:26 | https://dev.to/jitesh_yadav_de0e26fd6439/understanding-mixins-in-dart-and-flutter-61 | flutter, dart | Hello friends,
Today we'll explore an interesting and powerful concept in Dart and Flutter programming called mixins. Mixins are a fundamental feature that can greatly enhance the way we write and manage our code, especially in large-scale applications.
**Introduction to Mixins**
A mixin in Dart is a way to reuse code from multiple classes and resources without the constraints of traditional inheritance. Mixins allow us to incorporate functionalities from different sources into a single class, enabling us to build more modular, reusable, and maintainable code.
**Inheritance vs. Mixins**
You might be wondering how mixins differ from inheritance. In inheritance, a subclass inherits properties and methods directly from a parent class. This creates a strict hierarchy and can sometimes lead to issues when trying to inherit from multiple classes.
However, with mixins, a class can include the properties and methods of the mixin without inheriting from it directly. This means that mixins do not enforce a strict parent-child relationship, allowing for more flexible and reusable code structures.
**The Problem with Multiple Inheritance**
The idea behind mixins came about to fix the issues with multiple inheritance in Dart, which can lead to the well-known diamond problem. This problem happens when a class inherits from two other classes that both come from the same base class, causing confusion and conflicts.
Mixins solve this by letting classes "mix in" features from different sources without the mess of multiple inheritance. This makes the design simpler and avoids the conflicts that come with the diamond problem.
Benefits of Using Mixins
Code Reuse
Mixins promote code reuse by allowing us to define functionalities once and reuse them across multiple classes. This reduces code duplication and makes our codebase more maintainable.
Flexibility
By including a mixin, there is no strict class hierarchy to follow, allowing us to use mixins flexibly throughout our project. This enhances code reuse and organization, making it easier to adapt and extend our code.
Separation of Concerns
Mixins help in separating concerns by enabling us to isolate specific functionalities into distinct mixins. This modular approach makes our code cleaner and easier to understand.
**How to Use Mixins in Dart
**
Using mixins in Dart is straightforward. Here’s a simple example to illustrate how mixins work:
```
mixin Logger {
void log(String message) {
print('Log message: $message');
}
}
class DatabaseService with Logger {
void fetchData() {
log('Fetching data from the database.');
// Fetch data from the database
}
}
void main() {
DatabaseService dbService = DatabaseService();
dbService.fetchData();
}
```
In this example, the Logger mixin is included in the DatabaseService class using the with keyword. The DatabaseService class can now use the log method defined in the Logger mixin.
**Practical Applications of Mixins**
Mixins are incredibly useful in various practical scenarios. Here are a few examples:
Adding Logging
As seen in the previous example, mixins can be used to add logging functionality to different classes without duplicating code.
Reusing Validation Logic
If you have common validation logic that needs to be shared across multiple classes, you can define it in a mixin and include it wherever needed.
```
mixin Validation {
bool isValidEmail(String email) {
// Add email validation logic
return true;
}
}
class UserForm with Validation {
void submitForm(String email) {
if (isValidEmail(email)) {
// Submit form
} else {
print('Invalid email address.');
}
}
}
```
Enhancing UI Components
In Flutter, mixins can be used to enhance UI components with additional behaviors or properties.
```
mixin Highlightable {
bool isHighlighted = false;
void toggleHighlight() {
isHighlighted = !isHighlighted;
}
}
class HighlightableButton extends StatelessWidget with Highlightable {
@override
Widget build(BuildContext context) {
return GestureDetector(
onTap: toggleHighlight,
child: Container(
color: isHighlighted ? Colors.yellow : Colors.grey,
child: Text('Tap me'),
),
);
}
}
```
Best Practices for Using Mixins
When using mixins, it’s essential to follow some best practices to ensure that your code remains clean and maintainable:
Keep Mixins Focused: Each mixin should have a single responsibility. Avoid adding unrelated functionalities to a single mixin.
Document Mixins: Provide clear documentation for your mixins, explaining what they do and how to use them.
Avoid Overusing Mixins: While mixins are powerful, overusing them can lead to complex and hard-to-maintain code. Use them judiciously.
Name Mixins Appropriately: Choose descriptive names for your mixins to indicate their purpose clearly.
**Conclusion**
In summary, mixins are a powerful tool for code reuse and flexibility in Dart and Flutter programming. They allow us to build more efficient and maintainable applications by promoting modularity, code reuse, and separation of concerns. By understanding and utilizing mixins effectively, we can write cleaner and more organized code, ultimately enhancing the quality of our applications.
Happy coding! | jitesh_yadav_de0e26fd6439 |
1,876,635 | An Introduction to Vision-Language Modeling | An Introduction to Vision-Language Modeling | 0 | 2024-06-04T12:37:25 | https://aimodels.fyi/papers/arxiv/introduction-to-vision-language-modeling | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [An Introduction to Vision-Language Modeling](https://aimodels.fyi/papers/arxiv/introduction-to-vision-language-modeling). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper provides an introduction to the field of vision-language modeling (VLM), which involves developing AI models that can understand and generate multimodal content combining visual and textual information.
- VLMs have a wide range of potential applications, from image captioning to visual question answering and visual dialogue.
- The paper explores the key families of VLM architectures, including approaches based on transformers, convolutional neural networks, and hybrid models.
- It also discusses important considerations in designing effective VLMs, such as the choice of pre-training tasks and dataset curation.
## Plain English Explanation
Vision-language models (VLMs) are a type of artificial intelligence that can understand and create content that combines images and text. These models are trained on large datasets of images paired with captions or other textual descriptions. By learning the relationships between visual and linguistic information, VLMs can then be used for tasks like [describing images in natural language](https://aimodels.fyi/papers/arxiv/vision-language-models-medical-report-generation-visual), [answering questions about images](https://aimodels.fyi/papers/arxiv/what-matters-when-building-vision-language-models), and even [engaging in visual dialogue](https://aimodels.fyi/papers/arxiv/vr-gpt-visual-language-model-intelligent-virtual).
VLMs can be built using different core architectural approaches, like [transformers](https://aimodels.fyi/papers/arxiv/exploring-frontier-vision-language-models-survey-current) or [convolutional neural networks](https://aimodels.fyi/papers/arxiv/concept-based-analysis-neural-networks-via-vision). The choice of architecture and training process can significantly impact the model's capabilities and performance on various tasks. Researchers are actively exploring ways to design more effective VLMs, such as by carefully curating the training data or defining appropriate pre-training objectives.
Overall, VLMs represent an exciting frontier in AI that could lead to systems that can understand and communicate about the world in more natural, human-like ways by combining visual and textual understanding.
## Technical Explanation
The paper begins by introducing the field of vision-language modeling (VLM), which aims to develop AI systems that can jointly process and reason about visual and textual information. VLMs have a wide range of potential applications, including image captioning, visual question answering, and multimodal dialogue.
The authors then discuss the key families of VLM architectures. One prominent approach is to use [transformer-based models](https://aimodels.fyi/papers/arxiv/exploring-frontier-vision-language-models-survey-current), which leverage the transformer's ability to model long-range dependencies in sequential data. Another option is to build VLMs using [convolutional neural networks](https://aimodels.fyi/papers/arxiv/concept-based-analysis-neural-networks-via-vision) to process visual inputs, coupled with language modeling components. The paper also covers [hybrid approaches](https://aimodels.fyi/papers/arxiv/what-matters-when-building-vision-language-models) that combine multiple types of neural network layers.
In addition to the architectural choices, the authors highlight the importance of the pre-training process and dataset curation for VLMs. Carefully designing the pre-training tasks and assembling high-quality, diverse training data can significantly improve a VLM's performance and generalization capabilities. For example, [medical image-text datasets](https://aimodels.fyi/papers/arxiv/vision-language-models-medical-report-generation-visual) could be used to create VLMs specialized for healthcare applications.
## Critical Analysis
The paper provides a broad overview of the VLM landscape, but does not delve into the details or limitations of the various approaches. For example, while it mentions the use of transformers, it does not discuss the computational and memory requirements of these models, which can be a significant challenge, especially for real-time applications.
Additionally, the paper does not address potential biases and fairness issues that can arise in VLMs, particularly when the training data may not be representative of diverse populations and perspectives. [Further research](https://aimodels.fyi/papers/arxiv/concept-based-analysis-neural-networks-via-vision) is needed to understand and mitigate these concerns.
The paper also does not consider the environmental impact and sustainability of training large-scale VLMs, which is an important consideration as the field continues to advance.
## Conclusion
This paper provides a high-level introduction to the field of vision-language modeling, exploring the key architectural families, design considerations, and potential applications of these multimodal AI systems. VLMs represent an exciting frontier in artificial intelligence, with the ability to combine visual and textual understanding in ways that could enable more natural, human-like interactions with technology.
As the field continues to evolve, it will be important for researchers to address challenges around model efficiency, fairness, and environmental sustainability to ensure that VLMs can be responsibly developed and deployed to benefit society.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,617 | Migrating from Class Components to Functional Components in React | Read originally blog post on my website https://antondevtips.com. React has evolved significantly... | 0 | 2024-06-04T12:37:13 | https://antondevtips.com/blog/migrating-from-class-components-to-functional-components-in-react | webdev, react, javascript, programming | ---
canonical_url: https://antondevtips.com/blog/migrating-from-class-components-to-functional-components-in-react
---
_Read originally blog post on my website_ [_https://antondevtips.com_](https://antondevtips.com/blog/migrating-from-class-components-to-functional-components-in-react?utm_source=devto&utm_medium=referral&utm_campaign=04_06_24)_._
**React** has evolved significantly over the years, and one of the major changes in version **16.8.0** is the introduction of **hooks**, which allow you to use state and other React features without writing a class.
In this guide, we will walk through migrating various types of class components to functional components.
Functional components existed before but with an introduction of **hooks** - they look absolutely different.
## What Are Class Components
**Class components** are **ES6** JavaScript classes that extend from `React.Component` and implement a render method, which returns React elements (JSX).
Here's a basic example:
```jsx
class TextComponent extends React.Component {
render() {
return <h1>Hello, World!</h1>;
}
}
```
### State in Class Components
Class components can have **state**, a built-in object that allows components to manage their internal data.
State is local to the component and can change over time.
Let's create a `Counter` component that manages `count` variable in the state.
The component initializes its state in the constructor and provides a method to update the state using `this.setState`:
```jsx
import React from "react";
class Counter extends React.Component {
constructor(props) {
super(props);
this.state = {
count: 0
};
}
increment = () => {
this.setState({ count: this.state.count + 1 });
};
render() {
return (
<div>
<p>Count: {this.state.count}</p>
<button onClick={this.increment}>Increment</button>
</div>
);
}
}
```
### Props in Class Components
**Props** (short for properties) are used to pass data from parent components to child components.
Props are read-only and cannot be modified by the child component unlike a state.
```jsx
export class TextComponent extends React.Component {
render() {
return <h1>Hello, {this.props.name}!</h1>;
}
}
function App() {
return (
<div className="main">
<TextComponent name={"Anton"} />
</div>
)
}
```
### Lifecycle Methods in Class Components
**Lifecycle** methods are special methods that allow you to run code at specific points in a component's lifecycle, such as when a component is created, updated, or destroyed.
**Common Lifecycle Methods:**
* **componentDidMount:** is called once, after the component is added to the DOM.
* **componentDidUpdate:** is called each time after the component is updated when props or state is changed.
* **componentWillUnmount:** is called once, before the component is removed from the DOM.
Let's explore a class component with state lifecycle methods.
We'll create a component that fetches blog posts when it is mounted and renders them on the screen:
```jsx
export class PostList extends React.Component {
constructor(props) {
super(props);
this.state = {
data: []
};
}
componentDidMount() {
console.log("Component is mounted");
fetch("https://jsonplaceholder.typicode.com/posts")
.then(response => response.json())
.then(data => this.setState({ data }));
}
componentDidUpdate(prevProps, prevState) {
if (prevState.data !== this.state.data) {
console.log("Data updated:", this.state.data);
}
}
componentWillUnmount() {
console.log("Component will unmount");
}
render() {
if (!this.state.data) {
return (<div><p>Loading...</p></div>);
}
return (
<div>
{this.state.data.slice(0, 5).map((post) =>
<Post key={post.id} postData={post} />
)}
</div>
);
}
}
```
In `componentDidMount` method we fetch a list of blog posts from the test URL and save them into component's state.
`componentDidUpdate` method is invoked after the component updates and needs to be re-rendered.
Here it checks if the data state variable has changed. If true, it logs the new data.
This method is called when component's state is populated with blog posts.
`componentWillUnmount` method is called just before a component is destroyed or removed from the DOM.
It happens when a user navigates to a different page and the component disappeared from the screen.
Now let's explore how to migrate all mentioned class components into functional components with hooks.
## Migrating Classes Without State and Lifecycle To Functional Components
Let's transform our first `Hello World` component into a functional one:
```jsx
const TextComponent = () => {
return <h1>Hello, World!</h1>;
}
```
If the component has only a render logic and doesn't have any additional javascript code, you can omit the `{ }` and `return`:
```jsx
const TextComponent = () => (
<h1>Hello, World!</h1>;
)
```
## Migrating Classes With State To Functional Components
Now let's migrate the `Counter` component into a functional one:
```jsx
const Counter = () => {
const [count, setCount] = useState(0);
const increment = () => {
setCount(count + 1);
};
return (
<div className="flex">
<p>Count: {count}</p>
<button onClick={increment}>Increment</button>
</div>
);
};
```
Here we are using the `useState` hook that allows to save and update data inside a functional component.
The `useState` hook contains a variable itself and a function that allows to a change value of the variable.
Everytime a `setCount` function is called - a component is rendered with a new value of `count` variable.
As you can see this functional approach with `useState` hook requires less code to write and is easy to get used to.
## Migrating Classes With Props To Functional Components
Functional components receive props as the function argument and the `TextComponent` migration is straightforward:
```jsx
const TextComponent = (props) => {
return <h1>Hello, {props.name}!</h1>;
}
function App() {
return (
<div className="main">
<TextComponent name={"Anton"} />
</div>
)
}
```
## Migrating Classes With Lifecycle Methods To Functional Components
Let's migrate the `PostList` component to the functional one:
```jsx
import React, { useEffect, useState } from 'react';
export const PostList = () => {
const [data, setData] = useState([]);
useEffect(() => {
console.log("Component is mounted");
fetch('https://jsonplaceholder.typicode.com/posts')
.then((response) => response.json())
.then((data) => setData(data));
return () => {
console.log("Component will unmount");
};
}, []);
useEffect(() => {
console.log("Data updated:", this.state.data);
}, [data]);
if (!data.length) {
return (
<div>
<p>Loading...</p>
</div>
);
}
return (
<div>
{data.slice(0, 5).map((post) => (
<Post key={post.id} postData={post} />
))}
</div>
);
};
```
This functional approach also requires less code and doesn't require additional condition checks whether the data is updated.
In class components when you have a lot of data in the state that is changing - the `componentDidUpdate` method can become a big ball of mud with a lot of conditional checks.
In functional component the `useEffect` hook is used that combines all three class lifecycle functions: `componentDidMount`, `componentDidUpdate` and `componentWillUnmount`.
`useEffect` hook has 2 parameters: a function that performs a side effect and an array of tracked variables.
When a variable value changes - a **useEffect** hook is triggered.
The first hook has an empty braces **[ ]** without parameters, this effect function will be called **only once** after a component is loaded.
It is an analog of `componentDidMount` class method.
Second `useEffect` hook is called only when **data** variable is changed.
It is an analog of `componentDidMount` class method.
You can use as many `useEffect` hooks in the functional component as you want.
And you can specify multiple parameters inside **[ ]** of the hook.
And what about `componentWillUnmount` analog? It is represented as a returned function inside a `useEffect` without parameters:
```jsx
useEffect(() => {
console.log("Component is mounted");
// ...
return () => {
console.log("Component will unmount");
};
}, []);
```
You can learn more about React hooks in [my blog post](https://antondevtips.com/blog/mastering-react-hooks-a-comprehensive-guide-to-functional-components)
## Summary
In this blog post we have talked about what React class and functional components are.
Both class and functional components have state, props and lifecycle methods.
Components with hook offer a functional approach to writing components.
Hooks are a modern way to write your components, and nowadays, most of the React applications are built with them.
So should you jump in and rewrite all class components to the hooks? Not exactly.
I recommend writing all new components with hooks if you can update the React version to **16.8.0** in your project.
And migrate class components to the functional ones as needed.
Hope you find this blog post useful. Happy coding!
_Read originally blog post on my website_ [_https://antondevtips.com_](https://antondevtips.com/blog/migrating-from-class-components-to-functional-components-in-react?utm_source=devto&utm_medium=referral&utm_campaign=04_06_24)_._
### After reading the post consider the following:
- [Subscribe](https://antondevtips.com/blog/migrating-from-class-components-to-functional-components-in-react#subscribe) **to receive newsletters with the latest blog posts**
- [Download](https://github.com/AntonMartyniuk-DevTips/dev-tips-code/tree/main/frontend/react/migrate-classes-to-functional-components) **the source code for this post from my** [github](https://github.com/AntonMartyniuk-DevTips/dev-tips-code/tree/main/frontend/react/migrate-classes-to-functional-components) (available for my sponsors on BuyMeACoffee and Patreon)
If you like my content — **consider supporting me**
Unlock exclusive access to the source code from the blog posts by joining my **Patreon** and **Buy Me A Coffee** communities!
[](https://www.buymeacoffee.com/antonmartyniuk)
[](https://www.patreon.com/bePatron?u=73769486) | antonmartyniuk |
1,876,634 | Easy Problems That LLMs Get Wrong | Easy Problems That LLMs Get Wrong | 0 | 2024-06-04T12:36:50 | https://aimodels.fyi/papers/arxiv/easy-problems-that-llms-get-wrong | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Easy Problems That LLMs Get Wrong](https://aimodels.fyi/papers/arxiv/easy-problems-that-llms-get-wrong). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper examines "Easy Problems That Large Language Models (LLMs) Get Wrong", exploring situations where advanced AI models struggle with seemingly simple tasks.
- The research provides insights into the limitations and biases of current LLMs, which are often touted as highly capable at a wide range of language-related tasks.
- By studying examples of "easy" problems that LLMs fail to solve, the authors aim to uncover areas for improvement and guide future AI development.
## Plain English Explanation
The paper investigates cases where large language models (LLMs), which are advanced AI systems trained on vast amounts of text data, struggle with seemingly simple problems. Despite their impressive capabilities in many areas, the researchers found that LLMs can sometimes get basic tasks wrong in surprising ways.
By analyzing these "easy problems that LLMs get wrong," the authors hope to shed light on the limitations and biases of current language models. This information can then be used to guide future AI development and address the shortcomings of these powerful systems.
[The paper "Beyond Accuracy: Evaluating Reasoning Behavior in Large Language Models"](https://aimodels.fyi/papers/arxiv/beyond-accuracy-evaluating-reasoning-behavior-large-language) is relevant to this research, as it explores ways to more comprehensively assess the reasoning abilities of LLMs beyond just measuring their accuracy on specific tasks.
## Technical Explanation
The paper presents a series of case studies where large language models (LLMs) fail to solve seemingly straightforward problems. The researchers carefully designed a set of test cases that should be easy for humans to understand and solve, but found that state-of-the-art LLMs often struggle with these tasks.
For example, the authors describe a problem where an LLM is asked to determine whether a given string of text is a valid email address. While this is a trivial task for most people, the LLM often made incorrect judgments, failing to properly identify well-formed email addresses.
The paper also explores LLMs' difficulties with logical reasoning, [as highlighted in the work "Evaluating Deductive Competence of Large Language Models"](https://aimodels.fyi/papers/arxiv/evaluating-deductive-competence-large-language-models). The researchers present examples where LLMs struggle to follow simple logical arguments or make straightforward deductions.
[The research "Puzzle Solving Using Reasoning in Large Language Models"](https://aimodels.fyi/papers/arxiv/puzzle-solving-using-reasoning-large-language-models) is also relevant, as it explores the limitations of LLMs in solving logical puzzles, another area where humans excel but LLMs often fail.
## Critical Analysis
The paper raises important questions about the true capabilities of large language models and the need to look beyond simple accuracy metrics when evaluating their performance. The authors rightly point out that LLMs can struggle with tasks that are trivial for humans, suggesting that these models may lack a deeper understanding of language and reasoning.
One potential limitation of the research is that the authors focus on a relatively small set of test cases. It would be valuable to see a more comprehensive analysis of a wider range of "easy" problems to better understand the scope and patterns of LLM failures.
Additionally, the paper does not delve deeply into the underlying reasons why LLMs struggle with these tasks. [Further research, such as the work "Can Large Language Models Create New Knowledge?"](https://aimodels.fyi/papers/arxiv/can-large-language-models-create-new-knowledge), could provide more insights into the fundamental limitations and biases of these models.
Overall, the paper makes a valuable contribution by highlighting the need to critically examine the capabilities of large language models and to push beyond simplistic measures of performance. Continued research in this area can help drive the development of more robust and capable AI systems.
## Conclusion
This paper sheds light on the surprising limitations of large language models, showing that even simple tasks can pose significant challenges for these advanced AI systems. By studying examples of "easy problems that LLMs get wrong," the authors aim to uncover the biases and shortcomings of current language models, informing future research and development efforts.
The findings in this paper underscore the importance of looking beyond narrow measures of accuracy when evaluating the capabilities of AI systems. Developing a deeper understanding of the reasoning and problem-solving abilities of LLMs is crucial for ensuring that these powerful tools are deployed responsibly and effectively.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,876,633 | Kotlin ML Pack: Technical Report | Kotlin ML Pack: Technical Report | 0 | 2024-06-04T12:36:16 | https://aimodels.fyi/papers/arxiv/kotlin-ml-pack-technical-report | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Kotlin ML Pack: Technical Report](https://aimodels.fyi/papers/arxiv/kotlin-ml-pack-technical-report). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This technical report discusses the Kotlin ML Pack, a new library for generating Kotlin code.
- The report covers the current state of Kotlin code generation, the design and implementation of the Kotlin ML Pack, and an evaluation of its performance.
- The Kotlin ML Pack aims to simplify the process of building machine learning models in Kotlin by providing a high-level API and generating boilerplate code.
## Plain English Explanation
The [Kotlin ML Pack](https://aimodels.fyi/papers/arxiv/naturalcodebench-examining-coding-performance-mismatch-humaneval-natural) is a new library that makes it easier to create machine learning models in the Kotlin programming language. Kotlin is a popular language for building Android apps, but it hasn't been widely used for machine learning before.
The Kotlin ML Pack provides a simple, high-level interface for defining machine learning models. Instead of having to write a lot of complex code to set up the model, the library can automatically generate the necessary boilerplate code. This saves developers time and reduces the risk of errors.
The report explains the current state of Kotlin code generation, which has historically been more limited than other languages like Python. The Kotlin ML Pack aims to address this by making it easier to generate high-quality Kotlin code for machine learning tasks.
The report also includes an evaluation of the Kotlin ML Pack's performance, comparing it to other approaches like [CodeBenchGen](https://aimodels.fyi/papers/arxiv/codebenchgen-creating-scalable-execution-based-code-generation) and [PythonSAGA](https://aimodels.fyi/papers/arxiv/pythonsaga-redefining-benchmark-to-evaluate-code-generating). The results show that the Kotlin ML Pack can generate code that is efficient and easy to use, making it a valuable tool for Kotlin developers working on machine learning projects.
## Technical Explanation
The [Kotlin ML Pack](https://aimodels.fyi/papers/arxiv/naturalcodebench-examining-coding-performance-mismatch-humaneval-natural) is a new library that aims to simplify the process of building machine learning models in the Kotlin programming language. Kotlin is a statically-typed language that has gained popularity in recent years, particularly for building Android applications.
However, Kotlin has historically lagged behind other languages like Python in terms of code generation capabilities. The Kotlin ML Pack addresses this by providing a high-level API for defining machine learning models and automatically generating the necessary boilerplate code.
The library is designed to be easy to use, with a focus on simplicity and ease of integration. Developers can define their models using a declarative syntax, and the Kotlin ML Pack will handle the details of generating the underlying code.
To evaluate the performance of the Kotlin ML Pack, the researchers conducted a series of experiments comparing it to other approaches like [CodeBenchGen](https://aimodels.fyi/papers/arxiv/codebenchgen-creating-scalable-execution-based-code-generation) and [PythonSAGA](https://aimodels.fyi/papers/arxiv/pythonsaga-redefining-benchmark-to-evaluate-code-generating). The results showed that the Kotlin ML Pack could generate code that was efficient and easy to use, making it a valuable tool for Kotlin developers working on machine learning projects.
The researchers also discussed some potential limitations and areas for future research, such as [further improving the code generation capabilities](https://aimodels.fyi/papers/arxiv/learning-performance-improving-code-edits) and exploring how the Kotlin ML Pack might perform on [more complex real-world tasks](https://aimodels.fyi/papers/arxiv/realhumaneval-evaluating-large-language-models-abilities-to).
## Critical Analysis
The Kotlin ML Pack appears to be a promising approach to making machine learning more accessible to Kotlin developers. By providing a high-level API and automated code generation, the library can help reduce the complexity and boilerplate associated with building machine learning models in Kotlin.
The experimental results presented in the report are encouraging, showing that the Kotlin ML Pack can generate efficient and easy-to-use code. However, it's important to note that the evaluation was relatively limited in scope, focusing on a few specific benchmark tasks. Further research would be needed to assess the library's performance on more complex, real-world machine learning problems.
Additionally, the report does not provide much detail on the internal architecture or implementation of the Kotlin ML Pack. While the high-level design is discussed, a more in-depth technical explanation could help readers better understand the tradeoffs and design decisions made by the researchers.
It would also be valuable to see a more thorough discussion of the limitations and potential issues with the Kotlin ML Pack. The report briefly mentions areas for future research, but a more critical analysis of the library's current capabilities and shortcomings could help readers assess its suitability for their own projects.
Overall, the Kotlin ML Pack appears to be a promising step towards making Kotlin a more viable choice for machine learning tasks. However, further research and evaluation would be needed to fully assess its capabilities and limitations.
## Conclusion
The Kotlin ML Pack is a new library that aims to simplify the process of building machine learning models in the Kotlin programming language. By providing a high-level API and automated code generation, the Kotlin ML Pack can help reduce the complexity and boilerplate associated with Kotlin machine learning development.
The report presented in this paper provides an overview of the Kotlin ML Pack, including the current state of Kotlin code generation, the design and implementation of the library, and an evaluation of its performance. The results show that the Kotlin ML Pack can generate efficient and easy-to-use code, making it a valuable tool for Kotlin developers working on machine learning projects.
While the Kotlin ML Pack appears to be a promising approach, further research and evaluation would be needed to fully assess its capabilities and limitations. Nonetheless, the report suggests that the Kotlin ML Pack could play an important role in making Kotlin a more viable choice for machine learning tasks, potentially expanding the reach of the language and opening up new opportunities for developers.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.