A single line of code could allow hackers to breach MITRE and Splunk

This GitHub trick could let attackers steal secrets from major projects, and no one’s paying attention

AI.gov site was discovered by researchers

Trump administration AI plans apparently leaked on GitHub

GitHub's Copilot agent works autonomously in the background

Github's new Copilot AI wants to help you code and cut down on your tech debt

Just as OpenAI was going all in on the developer ecosystem, with the acquisition of Windsurf and the launch of Codex, Microsoft has entered the chat too. The tech giant announced that it is going to make GitHub Copilot in VS Code open-source.

Microsoft Just Showed Us Why VS Code Still Rules

Users can test drive the model for free for a limited time.

Grok 3 Makes Its Way to GitHub and Azure AI Foundry

The team behind Visual Studio Code shared a major update: VS Code is officially becoming an open-source AI editor. Microsoft is making the GitHub Copilot Chat extension open source and bringing AI features directly into the core editor, all under the MIT license. In an announcement on X, the VS Code team wrote: "Today, we're

Microsoft makes Visual Studio Code (VS Code) an open source AI editor, ushering in a new era of developer tools

The GitHub Copilot for artificial intelligence programming will be more visible in GitHub. It will accept assignments just like any other coder on the team.

Microsoft introduces GitHub AI agent that can code for you

GitHub is launching a new AI coding agent that can fix bugs, add features, and improve documentation on a developer’s behalf.

GitHub’s new AI coding agent can fix bugs for you

GitHub adds agentic capabilities to its Copilot coding assistant, competing with other more asynchronous coding platforms.

GitHub Copilot evolves into autonomous agent with asynchronous code testing

Microsoft just gave GitHub Copilot the power to create, modify, and submit code independently. Here's what this approach means for developers, workflows, and the future of programming careers.

Copilot's Coding Agent brings automation deeper into GitHub workflows

GitHub

github

What Happened: GitHub’s Cloud-Based Agent Drafts PRs Autonomously GitHub announced a major new capability for Copilot: GitHub has unveiled a significant upgrade to Copilot during Microsoft Build 2025: a cloud-based coding agent capable of drafting and iterating on pull requests directly within GitHub. While previous Copilot experiences focused on in-editor assistance via VSCode, this new agent runs asynchronously in the cloud, leveraging an infrastructure similar to GitHub Actions. Microsoft Build 2025 Developers can now assign tasks directly via GitHub or VSCode: > @github Open a pull request to refactor this query generator into its own class > @github Open a pull request to refactor this query generator into its own class The Copilot agent handles tasks autonomously: creating branches, iterating on PRs based on code review comments, and updating commits until the work is accepted - all without touching protected branches. Crucially, existing CI pipelines, branch protections, and review workflows remain intact, ensuring “trust by design.” With the addition of MCP, developers can even grant the agent access to external tools and data by configuring servers directly in the repository settings. This abstracts us from the actual implementation and allows us to focus on describing the tasks. This aligns with Codex Agents and others providing compute for the agents to run and do the coding. It also resonated with the broader movement toward headless agents - those that run independently, complete tasks, and report back when work is ready for review. Community Reactions: Awe, Caution, Fears, and Questions. Personally, using GitHub Copilot Agent was an enjoyable experience when asking to perform low-level tasks. I had a ‘wow’ moment when I performed typical GitHub actions directly inside the PR (or via VSCode’s chat bar). It was impressively fast, and the developer experience felt spot on. When digging into the community’s reaction, we found Copilot’s coding agent showing a mix of excitement, curiosity, and healthy skepticism. “Time-saving” was the common praise. Users liked that the agent’s draft PR and log let them see exactly what was happening, and that they retained the choice of when/if to merge. Similar to my experience, users shared they found Copilot to perform well with low-level tasks. To which GitHub’s Product Lead for Copilot coding agent replied: A portion of the community also shared a fear that by offloading coding to AI, human work might devolve into janitorial oversight: filing detailed JIRA tickets all day and rubber-stamping AI-generated code. Also, users expressed concern about what this means for junior devs. The GitHub CEO’s take on this is: take In an age where some claim that more AI means fewer opportunities for entry level devs, I believe the opposite is true. There has never been a more exciting time to join our industry. In an age where some claim that more AI means fewer opportunities for entry level devs, I believe the opposite is true. There has never been a more exciting time to join our industry. In an age where some claim that more AI means fewer opportunities for entry level devs, I believe the opposite is true. There has never been a more exciting time to join our industry. Building on that concern, security and policy questions were also raised. For example, could the agent inadvertently expose sensitive information, such as including a secret key in a PR? Adding to the complexity, some developers pointed out that the benefits of using AI agents don’t come for free. You only truly reap the rewards if you invest effort upfront; e.g, writing a good issue description with clear acceptance criteria. Enjoy being informed about the AI Native Development space (AIND)? I’m helping build the AIND community, where we cover the most relevant dev-focused news. For the busy folks, we send out a crisp one-pager newsletter read by 7,000+ AI tinkerers every week - free and straight to your inbox! AIND community, a crisp one-pager newsletter by 7,000+ AI tinkerers every week The AIND Take: Abstracting Grunt Work and Developing New Habits GitHub is evolving into an AI-enhanced development platform. Much like the historic growth of Actions Marketplace, we can expect a spike in tools within Copilot Extensions, making Copilot more powerful and covering more aspects of the development workflow. Actions Marketplace Copilot Extensions Drawing an analogy from history, this feels akin to the DevOps revolution or the shift to cloud/serverless computing: mundane infrastructure work is abstracted away, enabling developers to focus on higher-level logic. Similarly, the Copilot agent can abstract away a chunk of grunt work. It’s also a new skill: prompting and supervising AI in coding. Just as CI/CD and cloud led to new roles, AI agents could lead to new roles like “AI orchestration engineer”. I suspect we’ll see AI-native startups experimenting more and more with having multiple agents collaborate. One use case we believe in is the use of task-dependent agents: one agent could generate code while another reviews it, or one agent specializes in front-end and another in back-end, coordinating through issues and PRs. GitHub’s infrastructure could support this kind of multi-agent workflow, especially as MCP allows chaining different AI services. From a developer’s perspective, to prepare for this AI-centric future, there are a few concrete steps we’d recommend when playing with Copilot’s Agent. First, writing clear, detailed task/issue/PR descriptions with acceptance criteria will become a valuable skill. It’s a good practice even for human collaborators, but for AI, it’s very relevant. This helps build the muscle of communicating with machines about code intent. very intent Second, invest in tests. The prospect of an AI agent that relies on tests to know it didn’t break things is a strong incentive. High test coverage can turn the agent into a reliable contributor, while low coverage turns it into a serious risk. Strengthening your automated tests and CI pipelines can help position your projects to benefit more fully from AI involvement. The end-game is to become comfortable letting the AI draft something, and you guiding/refining it. In this new era, the developers who thrive will be those who embrace specifications-centric development, guiding AI with precision, shaping code, and validating it with tests.

At Build 2025, GitHub Showcased Its Copilot Coding Agent 

What Happened - GitHub Released Its Model Context Protocol Server GitHub has released a new open-source Model Context Protocol (MCP) server as part of its latest GitHub Copilot update. Announced in April 2025, the release marks GitHub’s first implementation of the MCP standard developed by Anthropic. The new server is a complete rewrite in Go, preserving “100% of the old server’s functionality” while adding improvements like customizable tool descriptions, integrated code scanning, and a new get_me function for natural language queries, e.g., “show me my private repos”. By releasing its own MCP server, GitHub provides an official gateway for agents to interact with GitHub features (repos, PRs, issues, etc.). Developers can thus automate GitHub workflows and processes, extract and analyze data from repositories, and build AI-powered tools and apps that interact with GH’s ecosystem. Notably, Visual Studio Code now has native support for MCP in GitHub Copilot. Community’s Reactions - Let’s Build! With close to 14,000 GitHub stars and over 150 PRs, the new Copilot agent + MCP combo is described as a kind of “awesome sauce” for agentic workflow. Copilot is no longer limited to suggesting code; it can actually take actions and fetch information on a developer’s behalf. In day-to-day workflows, developers can now ask Copilot to perform tasks that span beyond the editor. For instance, one could prompt a simple task like: “Find any markdown files in my project missing an author footer, and create a GitHub issue to track adding those”. This evolution has developers excited, not just because Copilot feels more capable, but because they can run more productive workflows and delegate real tasks, unlike ever before. Do you enjoy being informed about the AI Native Development space (AIND)? I’m helping build the AIND community, where we cover the most relevant dev-focused news. For the busy folks, we send out a crisp one-pager every week, free and straight to your inbox. Our AIND Take - GitHub’s MCP: Context and Workflows Reach New Heights Large language models only perform well when they have access to correct context, both of which need to be timely and accurate. GitHub’s MCP server addresses this core limitation by providing a structured way to integrate directly with public and private repos. This enables AI tools to operate with a real-time development context, accessing the latest repository data, issue updates, and pull request activity as it evolves. The GitHub MCP server also introduces a workflow that makes it easier for developers to move from identifying issues to generating PRs and conducting code reviews. This is the central value piece in this release: keep chatting, and the MCP takes care of both code-gen and workflow actions. This end-to-end flow brings a higher level of automation and structure to what are traditionally manual steps in the development lifecycle. A promising trend is thus the emergence of complementary MCP servers that extend support across more parts of the development workflow. For example, GitHub MCP can handle repository tasks while Context7 serves as a dependency management layer, working together to execute broader SDLC actions. When used together, these servers enable development tools to become "workflow-aware" and collaborative in nature. This will also raise key questions on accuracy and reliability: how do we ensure the MCP server’s actions, like creating issues or modifying repos, are accurate and aligned with developer intent? In an increasingly crowded landscape of MCP server implementations, we believe GitHub’s official MCP server will stand out as one of the most well-known and practically useful. It is also a sign that incumbents are taking MCP seriously. With both OpenAI and Anthropic now able to interact with GitHub, developers are gaining the ability to bring their repository context with them across different models (and platforms). Finally, bundling this with Microsoft now open-sourcing Copilot for Visual Studio Code, developers gain further control and customization in their agile workflows. What’s exciting is that AI is no longer limited to code generation via prompts - it’s now extending across the entire software development lifecycle, including GitHub repository management. With standardized protocols like MCP playing a part in the journey where builders’ intent is translated into workflow, GitHub’s release marks an important milestone. What Happened - GitHub Released Its Model Context Protocol Server What Happened - GitHub Released Its Model Context Protocol Server GitHub has released a new open-source Model Context Protocol (MCP) server as part of its latest GitHub Copilot update. Announced in April 2025, the release marks GitHub’s first implementation of the MCP standard developed by Anthropic. The new server is a complete rewrite in Go, preserving “100% of the old server’s functionality” while adding improvements like customizable tool descriptions, integrated code scanning, and a new get_me function for natural language queries, e.g., “show me my private repos”. open-source By releasing its own MCP server, GitHub provides an official gateway for agents to interact with GitHub features (repos, PRs, issues, etc.). Developers can thus automate GitHub workflows and processes, extract and analyze data from repositories, and build AI-powered tools and apps that interact with GH’s ecosystem. Notably, Visual Studio Code now has native support for MCP in GitHub Copilot. Community’s Reactions - Let’s Build! Community’s Reactions - Let’s Build! With close to 14,000 GitHub stars and over 150 PRs, the new Copilot agent + MCP combo is described as a kind of “awesome sauce” for agentic workflow. Copilot is no longer limited to suggesting code; it can actually take actions and fetch information on a developer’s behalf. “awesome sauce” In day-to-day workflows, developers can now ask Copilot to perform tasks that span beyond the editor. For instance, one could prompt a simple task like: “Find any markdown files in my project missing an author footer, and create a GitHub issue to track adding those” . This evolution has developers excited, not just because Copilot feels more capable, but because they can run more productive workflows and delegate real tasks, unlike ever before. “Find any markdown files in my project missing an author footer, and create a GitHub issue to track adding those” Do you enjoy being informed about the AI Native Development space (AIND)? I’m helping build the AIND community, where we cover the most relevant dev-focused news. For the busy folks, we send out a crisp one-pager every week, free and straight to your inbox. AIND community, AIND community, a crisp one-pager a crisp one-pager Our AIND Take - GitHub’s MCP: Context and Workflows Reach New Heights Our AIND Take - GitHub’s MCP: Context and Workflows Reach New Heights Large language models only perform well when they have access to correct context, both of which need to be timely and accurate. GitHub’s MCP server addresses this core limitation by providing a structured way to integrate directly with public and private repos. This enables AI tools to operate with a real-time development context, accessing the latest repository data, issue updates, and pull request activity as it evolves. The GitHub MCP server also introduces a workflow that makes it easier for developers to move from identifying issues to generating PRs and conducting code reviews. This is the central value piece in this release: keep chatting, and the MCP takes care of both code-gen and workflow actions. This end-to-end flow brings a higher level of automation and structure to what are traditionally manual steps in the development lifecycle. A promising trend is thus the emergence of complementary MCP servers that extend support across more parts of the development workflow. For example, GitHub MCP can handle repository tasks while Context7 serves as a dependency management layer, working together to execute broader SDLC actions. Context7 When used together, these servers enable development tools to become "workflow-aware" and collaborative in nature. This will also raise key questions on accuracy and reliability: how do we ensure the MCP server’s actions, like creating issues or modifying repos, are accurate and aligned with developer intent? In an increasingly crowded landscape of MCP server implementations, we believe GitHub’s official MCP server will stand out as one of the most well-known and practically useful. It is also a sign that incumbents are taking MCP seriously. With both OpenAI and Anthropic now able to interact with GitHub, developers are gaining the ability to bring their repository context with them across different models (and platforms). Finally, bundling this with Microsoft now open-sourcing Copilot for Visual Studio Code , developers gain further control and customization in their agile workflows. open-sourcing Copilot for Visual Studio Code What’s exciting is that AI is no longer limited to code generation via prompts - it’s now extending across the entire software development lifecycle, including GitHub repository management. With standardized protocols like MCP playing a part in the journey where builders’ intent is translated into workflow, GitHub’s release marks an important milestone.

You Can Now Talk To Your Repos Thanks to GitHub's MCP Server

Сега можете да говорите с почивките си благодарение на MCP сървъра на GitHub

Ahora puedes hablar con tus repuestos gracias al servidor MCP de GitHub

Voit nyt puhua lepoillesi GitHubin MCP-palvelimen ansiosta

Ora puoi parlare con i tuoi riposi grazie al server MCP di GitHub

あなたは今、GitHubのMCPサーバのおかげであなたのリポーズと話すことができます

이제 GitHub의 MCP 서버 덕분에 휴식자와 이야기 할 수 있습니다.

Dabar galite kalbėtis su savo poilsiu dėka "GitHub" MCP serverio

Tagad jūs varat runāt ar savu atpūtu, pateicoties GitHub MCP serverim

'गोबी' एक मङ्गोलियन शब्द हो, जसको अर्थ पनि नभएको क्षेत्र हो।

You Can Now Talk to Your Repos Thanks to GitHub's MCP Server - இப்போது நீங்கள் உங்கள் Repos-க்கு பேச முடியும்

آپ اب GitHub کے MCP سرور کا شکریہ اپنے Repos سے بات کر سکتے ہیں

GitHub’s MCP Server o‘z sizga sizga sizga sizga sizga.

Releasing Utilities Package to GitHub Packages: A Guide

While developers, DevOps, and DevSecOps teams work tirelessly to ship secure code, the steps that enable collaboration can lead to security risks. In the “old days,” programmers wrote code locally on a desktop or laptop. To gain access to source code, malicious actors needed to be physically present to compromise a device. Today, all they need is access to the internet and a valid credential. Even riskier, developers often share code snippets in public repositories which can expose secrets in plaintext, hardcoded into the source code. With nearly every organization building its own applications, DevSecOps and security teams face unique challenges trying to mitigate risks, especially when trying to mitigate risks outside their control. Dynamic application security testing (DAST) and static application security testing (SAST) tools identify vulnerabilities in source code, but they may not always identify all potentially compromised secrets that sit in the team’s tools. Increasingly, security teams need insights into the risks outside the traditional network perimeter to protect their applications. Secrets: What they are and why they get shared In IT, secrets are typically sensitive data that help provide access to or secure an application or an environment. Some examples of secrets may include: Authentication tokens
API keys
Usernames
Passwords
Encryption keys
Security certificates Authentication tokens API keys Usernames Passwords Encryption keys Security certificates When developers are pushing to ship code faster, data leaks are commonly caused by hardcoded secrets in the source code. Some reasons that programmers may leave these secrets in the source code include: Simplify workflows: Hardcoding secrets in code can allow for faster code sharing and testing since they don’t need to manually input credentials each time.
Training and awareness: Programmers may assume that the internal repository is secure enough to protect the source code containing secrets.
Complex CI/CD pipelines: Hardcoding secrets streamlines the multiple stages of changing keys and secure secrets storage during the phases of development, testing, and deployment, which push code live on tight deadlines. Simplify workflows: Hardcoding secrets in code can allow for faster code sharing and testing since they don’t need to manually input credentials each time. Simplify workflows Training and awareness: Programmers may assume that the internal repository is secure enough to protect the source code containing secrets. Training and awareness Complex CI/CD pipelines: Hardcoding secrets streamlines the multiple stages of changing keys and secure secrets storage during the phases of development, testing, and deployment, which push code live on tight deadlines. Complex CI/CD pipelines When developers hardcode secrets, they create security risks. Secrets stored in plaintext can be vulnerable to unauthorized access. As threat actors scan public GitHub repositories, they can find and exploit these accidentally leaked or hardcoded credentials. Once they have this information, they can compromise the service that the secret is authenticating to. Finally, developers’ failure to remove the hardcoded secrets means this information can spread during the typical development processes of cloning, forking, or checking out source code. Finding a hardcoded needle in a GitHub haystack Security teams are already overwhelmed trying to keep the organization’s IT resources secure. While DAST and SAST tools should help identify hardcoded secrets, many companies use a combination of automated and manual processes which can lead to undetectable human error when trying to review thousands of lines of code. Additionally, these tools only identify hardcoded secrets in locations that the organization knows or can control. According to a 2025 report, 39 million secrets were leaked across GitHub in 2024. In this report, GitHub explains that secret leaks remain one of the most common—and preventable—causes of security incidents. In a similar 2024 report, 3,066,304 GitHub repositories, accounting for only 4.6% of active repositories, leaked a secret in 2023. The same report noted that more than 1 in 10 commit authors leaked a secret, essentially 1,749,398 distinct contributing authors. The sheer volume and speed at which programmers develop code makes identifying all potentially leaked secrets a challenge. 2025 report 2025 report 2024 report 2024 report Often, AppSec and security teams need to review multiple locations for hardcoded secrets, including: Source code: Large projects can have hundreds — or even thousands — of lines of code that need scanning.
Configuration files: File paths uploaded to repositories can expose secrets when they trace back to resources.
Environment variables: Values affecting how processes run and behave can have hardcoded secrets that pass to the function code at runtime, exposing the secrets.
Container images: Accidentally copying sensitive files with hardcoded secrets can expose them during the image build.
Developers’ personal repositories: Team members may accidentally push code to their personal repositories and default settings are public. Source code: Large projects can have hundreds — or even thousands — of lines of code that need scanning. Source code Configuration files: File paths uploaded to repositories can expose secrets when they trace back to resources. Configuration files Environment variables: Values affecting how processes run and behave can have hardcoded secrets that pass to the function code at runtime, exposing the secrets. Environment variables Container images: Accidentally copying sensitive files with hardcoded secrets can expose them during the image build. Container images Developers’ personal repositories: Team members may accidentally push code to their personal repositories and default settings are public. Developers’ personal repositories Taking off the blinders with data leak monitoring Security teams work ceaselessly to monitor the organization’s enterprise IT environment yet still face challenges trying to protect development and testing environments. Monitoring repositories and identifying leaked information is critical but time-consuming. Security teams should incorporate auditing their code repositories in their data leak monitoring. While typical data loss prevention (DLP) tools are a good addition to DAST/SAST, these traditional enterprise environment tools focus more on monitoring sensitive files so they often fail to account for external risks like public code. To gain full insight into leaked hardcoded credentials, organizations need solutions to augment their DLP solutions. Data leaks can easily turn into data breaches, and organizations' security teams need to mitigate these risks. Source code leaks and hardcoded secrets are often overlooked because teams face time and staffing limitations. However, automated threat intelligence solutions that scan the clear, dark, and deep web as well as illicit channels on the messaging app Telegram where malicious actors sell and share stolen data can provide the additional coverage necessary. These solutions can help security teams identify data leaks and remediate risks by monitoring outside the network to detect potential exposures, like: Compromised credentials that attackers could use to gain access to the repository itself.
Infected devices with stealer malware collecting sensitive information, including secrets and credentials.
Leaked source code containing hardcoded secrets.
Accidental commits from full-time employees or contractors that contain hardcoded secrets. Compromised credentials that attackers could use to gain access to the repository itself. Infected devices with stealer malware collecting sensitive information, including secrets and credentials. Leaked source code containing hardcoded secrets. Accidental commits from full-time employees or contractors that contain hardcoded secrets. As security teams work to mitigate risk across the ever-expanding attack surface, they need to monitor more than just internally controlled resources, like the enterprise IT environment. They need to expand their reach to understand the external threat exposures that exist outside the traditional network perimeter, like GitHub. By augmenting traditional DLP, DAST, and SAST tools with clear, deep, and dark web monitoring, as well as monitoring illicit Telegram channels, security teams gain full visibility into risk across internal and external locations. Beyond the perimeter: Strengthening security against hardcoded secrets Hardcoded secrets in public code repositories remain a persistent security risk, exposing organizations to potential breaches and unauthorized access. While traditional security tools like DAST, SAST, and DLP provide a strong foundation, they often fail to capture the full scope of external threats. To stay ahead, security teams must expand their monitoring beyond internal environments, leveraging automated threat intelligence solutions that scan public GitHub repositories, the dark web, and illicit Telegram channels. By taking a proactive, data-driven approach, organizations can mitigate the risks of leaked secrets and safeguard their applications from evolving cyber threats.

From Repos to Risks: How Hardcoded Secrets in GitHub Source Code Create Security Risks

Let’s be real, tech moves fast. New frameworks, tools, and best practices seem to pop up every other week. It’s exciting, but also a bit overwhelming. If you’re trying to build and ship projects while keeping up with everything, it can feel like a lot. That’s where CI/CD, short for Continuous Integration and Continuous Deployment, can help. It takes care of the repetitive stuff like testing and deploying, so you can focus on writing actual features. I’ve personally found GitHub Actions to be a solid choice for setting up CI/CD workflows right inside a GitHub repo. CI/CD In this article, I’ll walk you through how to set up a simple CI/CD pipeline using GitHub Actions. The goal is to reduce manual errors, make our code easier to deploy, and save some time and stress in the process. Let’s get into it! Before we start, here are a few things you’ll want to have or know, just so you don’t get stuck halfway through. Prerequisites Prerequisites Have a GitHub account
Basic understanding of YAML syntax and GitHub Have a GitHub account Basic understanding of YAML syntax and GitHub Creating Your First CI/CD Pipeline You can choose to create your YAML file in VS Code or directly on GitHub. In this guide, we’ll create it directly on GitHub to keep things simple and easy to follow. Go to your GitHub repository (or open your code editor) and create a new folder named .github Inside that, create another folder called workflows, and then add a new file named pylinter.yml You can name your YAML file whatever you like. .github pylinter.yml Next, we need to define our YAML file, which is a linter workflow that checks for docstrings in our codebase. Start your YAML file by giving your pipeline a name and setting up the Git trigger — in other words, defining when you want the pipeline to run. In this guide, we’ll set it up to run whenever there’s a push to the main branch or when a pull request is opened against it. main name: Python Linting

on:
  push:
    branches: main
  pull_request:
    branches: main name: Python Linting

on:
  push:
    branches: main
  pull_request:
    branches: main Next, we need to tell the pipeline what to do when a push or pull request happens. In this case, we want it to review our code by setting up Python and installing the pylint package so it can run a linting check on our repository. pylint jobs:
  lint:
    name: Run PyLint
    runs-on: ubuntu-latest
    
    steps:
    - name: Check out repository
      uses: actions/checkout@v3

    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: '3.11'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install pylint jobs:
  lint:
    name: Run PyLint
    runs-on: ubuntu-latest
    
    steps:
    - name: Check out repository
      uses: actions/checkout@v3

    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: '3.11'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install pylint After setting up Python and installing the pylint package, the next step is to define the configuration that pylint will use during the pipeline run. pylint We’ll start by disabling all of pylint’s default checks using --disable=all, which gives us a clean slate. Then, we’ll enable just two specific checks: C0116 (which flags missing function docstrings) and W0311 (which highlights bad indentation). We’ll also set --max-line-length=100 to allow for longer lines of code while still keeping things readable. pylint --disable=all C0116 W0311 --max-line-length=100 You can check out the full list of pylint codes and what they do here. pylint Your pylint config should look like this: pylint - name: Run PyLint
      run: |
        pylint --disable=all --enable=C0116,W0311 --max-line-length=100 $(git ls-files '*.py') - name: Run PyLint
      run: |
        pylint --disable=all --enable=C0116,W0311 --max-line-length=100 $(git ls-files '*.py') And that’s it — you’ve just created your first pipeline workflow! Make sure your project structure matches what we’ve covered in this guide, as any differences might prevent the pipeline from running correctly. Once everything is set up, commit your changes and push them to the main branch. main Once you have committed your workflow, you should see a yellow dot next to your commit message. This signifies that your pipeline is up and running. If the pipeline fails, you’ll see a red dot next to the workflow. If it passes, it turns green. If the pipeline fails, you’ll see a red dot next to the workflow. If it passes, it turns green. Next, let's add a Python file to test the workflow and check if it correctly flags missing docstrings. Click on 'Add file,' then 'Create new file,' and name it main.py. Once you've added your code, commit the file and push it to the main branch. main.py main def some_function():
    """
    Description of what this function does.
    
    Returns:
        Type: Description of what is returned
    """
    pass def some_function():
    """
    Description of what this function does.
    
    Returns:
        Type: Description of what is returned
    """
    pass Testing the Pipeline To test the pipeline we just created, click on GitHub actions. You should see all your workflows. Click on the workflow to see more details. Now, let's break down how the workflow was triggered and what happened next. Click on Run PyLint to view the detailed results. The pipeline works by setting up a job that first checks out your repository, then sets up Python 3.11 (as specified in our YAML file). It installs the necessary dependencies and runs the pylint configuration script. Once the script passes, the pipeline sets up Python again and checks the repository one more time to make sure everything is working correctly before completing the job. pylint In summary, when you create a pipeline, GitHub will automatically run your configured checks (in this case, pylint) on your code whenever you commit to the main branch or make a pull request. This helps catch errors early in the development process. Wrapping Up As we wrap up this guide on GitHub Actions, it’s clear that using this practice can seriously supercharge your development process. By automating checks to make sure your code is running smoothly, you’ll save tons of time and avoid bugs when pushing code to production. That’s the real power of GitHub Actions — its flexibility. It lets you build workflows that are tailored to your project’s needs. Whether you're flying solo as an indie developer or working within a team, CI/CD helps you ship features faster and build a better product. So, go ahead and convince your team lead to integrate CI/CD into your company’s workflow. Watch your team start shipping better code, faster!

How to Setup a CI/CD Pipeline with GitHub Actions

GitHub is more than just a platform for hosting repositories—it’s a highly scalable distributed system that processes millions of transactions daily. From handling git push requests to computing file differences efficiently, GitHub relies on robust algorithms and architecture to ensure high performance and reliability. This article explores how GitHub processes vast amounts of data, scales to handle millions of transactions, and employs diff algorithms to track file changes efficiently. The article also includes detailed JavaScript implementations of core algorithms used in version control systems. 1. The Challenges of Large-Scale Version Control Systems. Modern version control systems must deal with several key challenges: Processing millions of transactions per day, including commits, merges, and pull requests.
Efficiently computing differences between files, which is critical for version tracking and merging.
Scaling storage and processing power, ensuring fast response times for developers worldwide. These principles are not exclusive to GitHub. Similar architectures and algorithms are used in GitLab, Bitbucket, and other platforms that deal with version control at scale. 2. How GitHub Computes File Differences (Diff Algorithm Implementation in JavaScript) When tracking changes in a file, GitHub (and Git itself) uses diff algorithms to compute the minimum number of edits needed to transform one version of a file into another. A widely used algorithm for this is Myers’ Diff Algorithm. 2.1. How Myers’ Algorithm Works. Myers’ algorithm finds the shortest sequence of insertions and deletions required to convert one file into another. It operates by iterating through possible edit distances (d) and computing possible transformations along “diagonals” in the edit graph. 2.2. JavaScript Implementation of Myers’ Algorithm. /**
* Computes the minimum edit distance between two arrays using Myers' Diff Algorithm.
* @param {Array} a - The original array (e.g., characters of a file)
* @param {Array} b - The modified array
* @returns {number} The minimum number of edit operations required
*/

function myersDiff(a, b) {
    const N = a.length;
    const M = b.length;
    const maxD = N + M;

    let v = {
        1: 0
    };

    for (let d = 0; d <= maxD; d++) {
        for (let k = -d; k <= d; k += 2) {
            let x;
          
            if (k === -d || (k !== d && (v[k - 1] || 0) < (v[k + 1] || 0))) {
                x = v[k + 1] || 0;
            } else {
                x = (v[k - 1] || 0) + 1;
            }

            let y = x - k;
            while (x < N && y < M && a[x] === b[y]) {
                x++;
                y++;
            }

            v[k] = x;
            if (x >= N && y >= M) {
                return d;
            }
        }
    }
    return maxD;
}

// Example usage:
const oldVersion = Array.from("Hello World");
const newVersion = Array.from("Hello GitHub World");
const operations = myersDiff(oldVersion, newVersion);

console.log(`Minimum number of edits: ${operations}`); Breakdown of the code: Initialization: The algorithm initializes an array v to store the maximum x values for each diagonal in the edit graph.


Loop through possible edit distances (d): Iterates through each possible number of edits needed.


Computing the optimal path: Determines whether to insert or delete based on v[k] values.


“Greedy match” step: Moves diagonally as long as characters match, minimizing unnecessary operations. 3. GitHub’s Transaction Processing Architecture. To handle millions of transactions, GitHub employs a multi-layered architecture. Here’s how a typical transaction flows: Receiving the request: API and Webhooks receive transactions (git push, git pull, etc.).
Queueing the request: Transactions are placed in a distributed queue (Redis/Kafka) for parallel processing.
Processing in microservices: Dedicated services handle indexing, diff computation, and statistics updates.
Updating storage: Results are committed to a database (SQL/NoSQL) and cached for quick access. This architecture allows GitHub to scale efficiently, ensuring that no single component becomes a bottleneck 4. JavaScript Implementation of GitHub-Like Transaction Processing. GitHub processes transactions asynchronously to handle high traffic. The following JavaScript code simulates parallel processing of transactions using Promises. /**
* Simulates a transaction in a version control system.
*/

class Transaction {
    constructor(id, action, payload) {
        this.id = id;
        this.action = action;
        this.payload = payload;
    }
}

/**

* Simulates processing a transaction step-by-step.
* @param {Transaction} tx - The transaction to process
* @returns {Promise<string>} The result of processing
*/

function processTransaction(tx) {

    return new Promise((resolve) => {
        console.log(`Processing transaction ${tx.id}: ${tx.action}`);
        setTimeout(() => {
            console.log(`Indexing ${tx.id}...`);
            setTimeout(() => {
                console.log(`Computing diff for ${tx.id}...`);
                setTimeout(() => {
                    console.log(`Updating database for ${tx.id}...`);
                    resolve("success");
                }, 100);
            }, 50);
        }, 100);
    });
}

/**
* Simulates processing multiple transactions in parallel.
*/
async function processTransactions() {
    const transactions = [
        new Transaction("tx001", "commit", "Modified file A"),
        new Transaction("tx002", "commit", "Fixed bug in file B"),
        new Transaction("tx003", "merge", "Merged branches"),
    ];

    const promises = transactions.map(async (tx) => {
        const result = await processTransaction(tx);
        console.log(`Transaction ${tx.id} result: ${result}`);
    });

    await Promise.all(promises);
    console.log("All transactions processed.");
}

// Run transaction processing

processTransactions(); Key takeaways from this code: Asynchronous processing: Transactions run in parallel using Promise.all().
Step-by-step simulation: Each transaction follows the same processing flow—indexing, computing diffs, and updating storage.
Scalability: Similar principles apply in real-world systems like GitHub, where Redis/Kafka queues help distribute workloads across microservices. 5. Conclusion: GitHub’s Scalable Approach to Version Control. GitHub’s ability to process millions of transactions per day relies on a combination of: Optimized diff algorithms, like Myers’ Algorithm, to efficiently compute file changes.
Scalable distributed architecture, using microservices and message queues to balance workload.
Parallel transaction processing, ensuring fast responses even under heavy loads. These techniques aren’t exclusive to GitHub—they are widely used in GitLab, Bitbucket, and large-scale data processing systems. Understanding these principles helps developers build efficient, scalable applications for handling high volumes of transactions. GitHub is more than just a platform for hosting repositories—it’s a highly scalable distributed system that processes millions of transactions daily. From handling git push requests to computing file differences efficiently, GitHub relies on robust algorithms and architecture to ensure high performance and reliability. This article explores how GitHub processes vast amounts of data, scales to handle millions of transactions, and employs diff algorithms to track file changes efficiently. The article also includes detailed JavaScript implementations of core algorithms used in version control systems. 1. The Challenges of Large-Scale Version Control Systems. Modern version control systems must deal with several key challenges: Processing millions of transactions per day, including commits, merges, and pull requests. Efficiently computing differences between files, which is critical for version tracking and merging. Scaling storage and processing power, ensuring fast response times for developers worldwide. Processing millions of transactions per day, including commits, merges, and pull requests. Efficiently computing differences between files, which is critical for version tracking and merging. Scaling storage and processing power, ensuring fast response times for developers worldwide. These principles are not exclusive to GitHub. Similar architectures and algorithms are used in GitLab, Bitbucket, and other platforms that deal with version control at scale. 2. How GitHub Computes File Differences (Diff Algorithm Implementation in JavaScript) 2. How GitHub Computes File Differences (Diff Algorithm Implementation in JavaScript) When tracking changes in a file, GitHub (and Git itself) uses diff algorithms to compute the minimum number of edits needed to transform one version of a file into another. A widely used algorithm for this is Myers’ Diff Algorithm. 2.1. How Myers’ Algorithm Works. Myers’ algorithm finds the shortest sequence of insertions and deletions required to convert one file into another. It operates by iterating through possible edit distances (d) and computing possible transformations along “diagonals” in the edit graph. 2.2. JavaScript Implementation of Myers’ Algorithm. 2.2. JavaScript Implementation of Myers’ Algorithm. /**
* Computes the minimum edit distance between two arrays using Myers' Diff Algorithm.
* @param {Array} a - The original array (e.g., characters of a file)
* @param {Array} b - The modified array
* @returns {number} The minimum number of edit operations required
*/

function myersDiff(a, b) {
    const N = a.length;
    const M = b.length;
    const maxD = N + M;

    let v = {
        1: 0
    };

    for (let d = 0; d <= maxD; d++) {
        for (let k = -d; k <= d; k += 2) {
            let x;
          
            if (k === -d || (k !== d && (v[k - 1] || 0) < (v[k + 1] || 0))) {
                x = v[k + 1] || 0;
            } else {
                x = (v[k - 1] || 0) + 1;
            }

            let y = x - k;
            while (x < N && y < M && a[x] === b[y]) {
                x++;
                y++;
            }

            v[k] = x;
            if (x >= N && y >= M) {
                return d;
            }
        }
    }
    return maxD;
}

// Example usage:
const oldVersion = Array.from("Hello World");
const newVersion = Array.from("Hello GitHub World");
const operations = myersDiff(oldVersion, newVersion);

console.log(`Minimum number of edits: ${operations}`); /**
* Computes the minimum edit distance between two arrays using Myers' Diff Algorithm.
* @param {Array} a - The original array (e.g., characters of a file)
* @param {Array} b - The modified array
* @returns {number} The minimum number of edit operations required
*/

function myersDiff(a, b) {
    const N = a.length;
    const M = b.length;
    const maxD = N + M;

    let v = {
        1: 0
    };

    for (let d = 0; d <= maxD; d++) {
        for (let k = -d; k <= d; k += 2) {
            let x;
          
            if (k === -d || (k !== d && (v[k - 1] || 0) < (v[k + 1] || 0))) {
                x = v[k + 1] || 0;
            } else {
                x = (v[k - 1] || 0) + 1;
            }

            let y = x - k;
            while (x < N && y < M && a[x] === b[y]) {
                x++;
                y++;
            }

            v[k] = x;
            if (x >= N && y >= M) {
                return d;
            }
        }
    }
    return maxD;
}

// Example usage:
const oldVersion = Array.from("Hello World");
const newVersion = Array.from("Hello GitHub World");
const operations = myersDiff(oldVersion, newVersion);

console.log(`Minimum number of edits: ${operations}`); Breakdown of the code: Initialization: The algorithm initializes an array v to store the maximum x values for each diagonal in the edit graph. Loop through possible edit distances (d): Iterates through each possible number of edits needed. Computing the optimal path: Determines whether to insert or delete based on v[k] values. “Greedy match” step: Moves diagonally as long as characters match, minimizing unnecessary operations. Initialization: The algorithm initializes an array v to store the maximum x values for each diagonal in the edit graph. Initialization: The algorithm initializes an array v to store the maximum x values for each diagonal in the edit graph. Loop through possible edit distances (d): Iterates through each possible number of edits needed. Loop through possible edit distances (d): Iterates through each possible number of edits needed. Computing the optimal path: Determines whether to insert or delete based on v[k] values. Computing the optimal path: Determines whether to insert or delete based on v[k] values. “Greedy match” step: Moves diagonally as long as characters match, minimizing unnecessary operations. “Greedy match” step: Moves diagonally as long as characters match, minimizing unnecessary operations. 3. GitHub’s Transaction Processing Architecture. To handle millions of transactions, GitHub employs a multi-layered architecture. Here’s how a typical transaction flows: Receiving the request: API and Webhooks receive transactions (git push, git pull, etc.). Queueing the request: Transactions are placed in a distributed queue (Redis/Kafka) for parallel processing. Processing in microservices: Dedicated services handle indexing, diff computation, and statistics updates. Updating storage: Results are committed to a database (SQL/NoSQL) and cached for quick access. Receiving the request: API and Webhooks receive transactions (git push, git pull, etc.). Queueing the request: Transactions are placed in a distributed queue (Redis/Kafka) for parallel processing. Processing in microservices: Dedicated services handle indexing, diff computation, and statistics updates. Updating storage: Results are committed to a database (SQL/NoSQL) and cached for quick access. This architecture allows GitHub to scale efficiently, ensuring that no single component becomes a bottleneck 4. JavaScript Implementation of GitHub-Like Transaction Processing. GitHub processes transactions asynchronously to handle high traffic. The following JavaScript code simulates parallel processing of transactions using Promises. /**
* Simulates a transaction in a version control system.
*/

class Transaction {
    constructor(id, action, payload) {
        this.id = id;
        this.action = action;
        this.payload = payload;
    }
}

/**

* Simulates processing a transaction step-by-step.
* @param {Transaction} tx - The transaction to process
* @returns {Promise<string>} The result of processing
*/

function processTransaction(tx) {

    return new Promise((resolve) => {
        console.log(`Processing transaction ${tx.id}: ${tx.action}`);
        setTimeout(() => {
            console.log(`Indexing ${tx.id}...`);
            setTimeout(() => {
                console.log(`Computing diff for ${tx.id}...`);
                setTimeout(() => {
                    console.log(`Updating database for ${tx.id}...`);
                    resolve("success");
                }, 100);
            }, 50);
        }, 100);
    });
}

/**
* Simulates processing multiple transactions in parallel.
*/
async function processTransactions() {
    const transactions = [
        new Transaction("tx001", "commit", "Modified file A"),
        new Transaction("tx002", "commit", "Fixed bug in file B"),
        new Transaction("tx003", "merge", "Merged branches"),
    ];

    const promises = transactions.map(async (tx) => {
        const result = await processTransaction(tx);
        console.log(`Transaction ${tx.id} result: ${result}`);
    });

    await Promise.all(promises);
    console.log("All transactions processed.");
}

// Run transaction processing

processTransactions(); /**
* Simulates a transaction in a version control system.
*/

class Transaction {
    constructor(id, action, payload) {
        this.id = id;
        this.action = action;
        this.payload = payload;
    }
}

/**

* Simulates processing a transaction step-by-step.
* @param {Transaction} tx - The transaction to process
* @returns {Promise<string>} The result of processing
*/

function processTransaction(tx) {

    return new Promise((resolve) => {
        console.log(`Processing transaction ${tx.id}: ${tx.action}`);
        setTimeout(() => {
            console.log(`Indexing ${tx.id}...`);
            setTimeout(() => {
                console.log(`Computing diff for ${tx.id}...`);
                setTimeout(() => {
                    console.log(`Updating database for ${tx.id}...`);
                    resolve("success");
                }, 100);
            }, 50);
        }, 100);
    });
}

/**
* Simulates processing multiple transactions in parallel.
*/
async function processTransactions() {
    const transactions = [
        new Transaction("tx001", "commit", "Modified file A"),
        new Transaction("tx002", "commit", "Fixed bug in file B"),
        new Transaction("tx003", "merge", "Merged branches"),
    ];

    const promises = transactions.map(async (tx) => {
        const result = await processTransaction(tx);
        console.log(`Transaction ${tx.id} result: ${result}`);
    });

    await Promise.all(promises);
    console.log("All transactions processed.");
}

// Run transaction processing

processTransactions(); Key takeaways from this code: Asynchronous processing: Transactions run in parallel using Promise.all(). Step-by-step simulation: Each transaction follows the same processing flow—indexing, computing diffs, and updating storage. Scalability: Similar principles apply in real-world systems like GitHub, where Redis/Kafka queues help distribute workloads across microservices. Asynchronous processing: Transactions run in parallel using Promise.all(). Step-by-step simulation: Each transaction follows the same processing flow—indexing, computing diffs, and updating storage. Scalability: Similar principles apply in real-world systems like GitHub, where Redis/Kafka queues help distribute workloads across microservices. 5. Conclusion: GitHub’s Scalable Approach to Version Control. GitHub’s ability to process millions of transactions per day relies on a combination of: Optimized diff algorithms, like Myers’ Algorithm, to efficiently compute file changes. Scalable distributed architecture, using microservices and message queues to balance workload. Parallel transaction processing, ensuring fast responses even under heavy loads. Optimized diff algorithms, like Myers’ Algorithm, to efficiently compute file changes. Scalable distributed architecture, using microservices and message queues to balance workload. Parallel transaction processing, ensuring fast responses even under heavy loads. These techniques aren’t exclusive to GitHub—they are widely used in GitLab, Bitbucket, and large-scale data processing systems. Understanding these principles helps developers build efficient, scalable applications for handling high volumes of transactions.

Unveiling GitHub's Secret Blueprint—How to Handle Millions of Transactions Daily

የGitHubን ሚስጥራዊ ንድፍ ማውጣት—በቀን በሚሊዮን የሚቆጠሩ ግብይቶችን እንዴት ማስተናገድ እንደሚቻል

Desvelando el plan secreto de GitHub: cómo gestionar millones de transacciones diarias

Dévoilement du plan secret de GitHub : comment gérer des millions de transactions quotidiennes

A GitHub titkos tervének bemutatása – Hogyan kezeljük a tranzakciók millióit naponta

GitHub の秘密の青写真を公開 - 毎日何百万ものトランザクションを処理する方法

GitHub의 비밀 청사진 공개 - 매일 수백만 건의 거래를 처리하는 방법

„GitHub“ slapto plano atskleidimas – kaip kasdien atlikti milijonus operacijų

Go Utolla Blueprint ya Sephiri ya GitHub—Kamoo o ka Swarago Dimilione tša Dikgwebišano Letšatši le Letšatši

Odsłaniamy tajny plan GitHub — jak obsługiwać miliony transakcji dziennie

GitHubpa pakasqa planonta riqsichiy —Imaynata sapa p'unchaw millonnintin ruwaykunata ruwayta

GitHub-yň gizlin meýilnamasyny açmak - Her gün millionlarça amallary nädip çözmeli

Pagbubunyag ng Lihim na Blueprint ng GitHub—Paano Pangasiwaan ang Milyun-milyong Transaksyon Araw-araw

The History of GitHub Awesome-Lists

Гісторыя GitHub Awesome-Lists

Historien om GitHub Awesome-lister

La historia de las Awesome-Lists de GitHub

ההיסטוריה של GitHub Awesome-Lists

GitHub Awesome-Lists の歴史

GitHub Awesome-Lists의 역사

GitHub Awesome тизмелеринин тарыхы

Ny tantaran'ny GitHub Awesome-Lists

Histori ya GitHub Awesome-Lists

Nhoroondo yeGitHub Awesome-Lists

GitHub Awesome-List'lerin Tarihi

Matimu ya GitHub Awesome-Lists

Fetch Commits Like a Pro With This Rust Meets GitHub Guide

Hello JavaScript Enthusiasts! Welcome to another edition of "This Week in JavaScript"! Today, we’ll be looking at the State of JavaScript 2024 report, unpacking Tailwind CSS 4 updates that’ll make your styles fly, and serving up fresh tools to streamline your dev grind. Oh, and did we mention that GitHub Copilot is now free? Buckle up! The Rise of TypeScript The State of JavaScript 2024 survey results are in, and TypeScript is officially dominating the JavaScript landscape. 67% of developers now use TypeScript more than traditional JavaScript. Type safety, better tooling, and cleaner code are driving its widespread adoption.
Vite is leading the pack when it comes to build tools, with developers loving its simplicity and speed over older tools like webpack. It’s clear that while JavaScript itself remains stable, TypeScript and modern build tools like Vite are shaping the future of how we build apps. GitHub Copilot is now Free GitHub has made a major move by offering its Copilot AI for free—well, with a few limits. If you have a GitHub account, you can now access 2,000 code completions and 50 chat messages per month, all within Visual Studio Code and GitHub. Here's how you can take advantage of it: Speed up your coding: Copilot offers AI-driven code completion that saves time and effort.
Multi-file edits: Easily make edits across multiple files.
Model options: Choose from models like Anthropic’s Claude 3.5 Sonnet for tailored results.
Copilot Chat: Ask questions, generate code snippets, and get real-time help with your codebase. This new offering is amazing for streamlining your workflow and boosting productivity. New Tailwind CSS 4 Developments We’ve already talked about the Tailwind CSS 4 Beta Release before, but you need to know about these features! Lightning CSS: Tailwind has moved to Lightning CSS, which speeds up builds and reduces the need for PostCSS configurations.
CSS-first configuration: With native CSS variables for theming, styling has become more streamlined, although you might miss the type safety from the JavaScript-based config.
Descendant Variants: A new double-star feature lets you style elements deep in the hierarchy. However, while this might help with CMS content, it could bring back maintainability challenges.
Radial and Conic Gradients: The new gradients are great but come with complex syntax, better suited for design systems than HTML.
Class Name Changes: Class names like grow (instead of flex-grow) are making tools like Tailwind Merge less efficient. While there’s a lot to love about the new update, be mindful of potential pitfalls with maintainability! Tools & Releases YOU Should Know About Now, let’s take a quick look at the latest tools and updates that can boost your workflow: JerryScript 3.0: A super lightweight JavaScript engine for IoT with 84% Test262 compliance. It introduces ES2020 features like optional chaining and dynamic imports—great for embedded systems.
Transformers.js v3.2: Bringing machine learning to the browser! Features include real-time speech recognition with Moonshine, image analysis with Phi-3.5 Vision, and bilingual text generation with EXAONE. Perfect for integrating AI directly into web projects.
Bun 1.1.39 & 1.1.40: With bun.lock for better tooling compatibility, 30% faster cached installations, and WebSocket improvements, Bun keeps optimizing the JavaScript ecosystem.
pnpm Update: The pnpm link command now adds dependency overrides to package.json, simplifying global and workspace linking. SHA256 hashing has been introduced, improving security.
ESLint v9.17.0: A major update! The no-unused-vars rule now auto-fixes unused variables, and crashes from nullish values are resolved. This update will keep your codebase cleaner and more efficient. If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers, and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious! Hello JavaScript Enthusiasts! Welcome to another edition of " This Week in JavaScript "! This Week in JavaScript Today, we’ll be looking at the State of JavaScript 2024 report, unpacking Tailwind CSS 4 updates that’ll make your styles fly, and serving up fresh tools to streamline your dev grind. Oh, and did we mention that GitHub Copilot is now free? Buckle up! The Rise of TypeScript The Rise of TypeScript The State of JavaScript 2024 survey results are in, and TypeScript is officially dominating the JavaScript landscape. The State of JavaScript 2024 TypeScript 67% of developers now use TypeScript more than traditional JavaScript. Type safety, better tooling, and cleaner code are driving its widespread adoption. Vite is leading the pack when it comes to build tools, with developers loving its simplicity and speed over older tools like webpack. 67% of developers now use TypeScript more than traditional JavaScript. Type safety, better tooling, and cleaner code are driving its widespread adoption. 67% of developers Vite is leading the pack when it comes to build tools, with developers loving its simplicity and speed over older tools like webpack. Vite It’s clear that while JavaScript itself remains stable, TypeScript and modern build tools like Vite are shaping the future of how we build apps. GitHub Copilot is now Free GitHub Copilot is now Free GitHub has made a major move by offering its Copilot AI for free —well, with a few limits. If you have a GitHub account, you can now access 2,000 code completions and 50 chat messages per month, all within Visual Studio Code and GitHub. GitHub Copilot AI free Here's how you can take advantage of it: Speed up your coding: Copilot offers AI-driven code completion that saves time and effort. Multi-file edits: Easily make edits across multiple files. Model options: Choose from models like Anthropic’s Claude 3.5 Sonnet for tailored results. Copilot Chat: Ask questions, generate code snippets, and get real-time help with your codebase. Speed up your coding: Copilot offers AI-driven code completion that saves time and effort. Speed up your coding: Multi-file edits: Easily make edits across multiple files. Multi-file edits: Model options: Choose from models like Anthropic’s Claude 3.5 Sonnet for tailored results. Model options: Copilot Chat: Ask questions, generate code snippets, and get real-time help with your codebase. Copilot Chat: This new offering is amazing for streamlining your workflow and boosting productivity. New Tailwind CSS 4 Developments New Tailwind CSS 4 Developments We’ve already talked about the Tailwind CSS 4 Beta Release before, but you need to know about these features! Tailwind CSS 4 Beta Release Lightning CSS: Tailwind has moved to Lightning CSS, which speeds up builds and reduces the need for PostCSS configurations. CSS-first configuration: With native CSS variables for theming, styling has become more streamlined, although you might miss the type safety from the JavaScript-based config. Descendant Variants: A new double-star feature lets you style elements deep in the hierarchy. However, while this might help with CMS content, it could bring back maintainability challenges. Radial and Conic Gradients: The new gradients are great but come with complex syntax, better suited for design systems than HTML. Class Name Changes: Class names like grow (instead of flex-grow) are making tools like Tailwind Merge less efficient. Lightning CSS: Tailwind has moved to Lightning CSS, which speeds up builds and reduces the need for PostCSS configurations. Lightning CSS: CSS-first configuration: With native CSS variables for theming, styling has become more streamlined, although you might miss the type safety from the JavaScript-based config. CSS-first configuration: Descendant Variants: A new double-star feature lets you style elements deep in the hierarchy. However, while this might help with CMS content, it could bring back maintainability challenges. Descendant Variants: Radial and Conic Gradients: The new gradients are great but come with complex syntax, better suited for design systems than HTML. Radial and Conic Gradients: Class Name Changes: Class names like grow (instead of flex-grow) are making tools like Tailwind Merge less efficient. Class Name Changes: While there’s a lot to love about the new update, be mindful of potential pitfalls with maintainability! Tools & Releases YOU Should Know About Tools & Releases YOU Should Know About Now, let’s take a quick look at the latest tools and updates that can boost your workflow: JerryScript 3.0: A super lightweight JavaScript engine for IoT with 84% Test262 compliance. It introduces ES2020 features like optional chaining and dynamic imports—great for embedded systems. Transformers.js v3.2: Bringing machine learning to the browser! Features include real-time speech recognition with Moonshine, image analysis with Phi-3.5 Vision, and bilingual text generation with EXAONE. Perfect for integrating AI directly into web projects. Bun 1.1.39 & 1.1.40: With bun.lock for better tooling compatibility, 30% faster cached installations, and WebSocket improvements, Bun keeps optimizing the JavaScript ecosystem. pnpm Update: The pnpm link command now adds dependency overrides to package.json, simplifying global and workspace linking. SHA256 hashing has been introduced, improving security. ESLint v9.17.0: A major update! The no-unused-vars rule now auto-fixes unused variables, and crashes from nullish values are resolved. This update will keep your codebase cleaner and more efficient. JerryScript 3.0: A super lightweight JavaScript engine for IoT with 84% Test262 compliance. It introduces ES2020 features like optional chaining and dynamic imports—great for embedded systems. JerryScript 3.0: Transformers.js v3.2: Bringing machine learning to the browser! Features include real-time speech recognition with Moonshine, image analysis with Phi-3.5 Vision, and bilingual text generation with EXAONE. Perfect for integrating AI directly into web projects. Transformers.js v3.2: Bun 1.1.39 & 1.1.40: With bun.lock for better tooling compatibility, 30% faster cached installations, and WebSocket improvements, Bun keeps optimizing the JavaScript ecosystem. Bun 1.1.39 & 1.1.40: pnpm Update: The pnpm link command now adds dependency overrides to package.json, simplifying global and workspace linking. SHA256 hashing has been introduced, improving security. pnpm Update: ESLint v9.17.0: A major update! The no-unused-vars rule now auto-fixes unused variables, and crashes from nullish values are resolved. This update will keep your codebase cleaner and more efficient. ESLint v9.17.0: If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers, and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious!

December Brings Free GitHub Copilot

How to Automatically Publish Your NPM Package Using GitHub Actions

Hoe om u NPM-pakket outomaties met GitHub-aksies te publiseer

Jak automaticky publikovat svůj balíček NPM pomocí akcí GitHub

Cómo publicar automáticamente su paquete NPM mediante GitHub Actions

Como publicar automaticamente o teu paquete NPM usando accións de GitHub

Ինչպես ավտոմատ կերպով հրապարակել ձեր NPM փաթեթը՝ օգտագործելով GitHub գործողությունները

Come pubblicare automaticamente il tuo pacchetto NPM utilizzando le azioni GitHub

כיצד לפרסם באופן אוטומטי את חבילת ה-NPM שלך באמצעות פעולות GitHub

GitHub Actions を使用して NPM パッケージを自動的に公開する方法

Cara Menerbitkan Pakej NPM Anda Secara Automatik Menggunakan Tindakan GitHub

Como publicar automaticamente seu pacote NPM usando ações do GitHub

Ako automaticky publikovať svoj balík NPM pomocou akcií GitHub

Hur du automatiskt publicerar ditt NPM-paket med hjälp av GitHub-åtgärder

Stories, News & Mentions about GITHUB

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Stories, News & Mentions about GITHUB

Github’s Stories on HackerNoon

Github's News & Mentions

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