Author name: ITMAITY

In 2025, software development crossed a quiet threshold. In our latest Octoverse report, we found that the fastest-growing languages, tools, and open source projects on GitHub are no longer about shipping more code. Instead, they’re about reducing friction in a world where AI is helping developers build more, faster. By looking at some of the areas of fastest growth over the past year, we can see how developers are adapting through:  The programming languages that are growing most in AI-assisted development workflows. The tools that win when speed and reproducibility matter. The areas where new contributors are showing up (and what helps them stick). Rather than catalog trends, we want to focus on what those signals mean for how software is being built today and what choices you might consider heading into 2026.  The elephant in the room: Typescript is the new #1 In August 2025, TypeScript became the most-used language on GitHub, overtaking Python and JavaScript for the first time. Over the past year, TypeScript added more than one million contributors, which was the largest absolute growth of any language on GitHub.  Python also continued to grow rapidly, adding roughly 850,000 contributors (+48.78% YoY), while JavaScript grew more slowly (+24.79%, ~427,000 contributors). Together, TypeScript and Python both significantly outpaced JavaScript in both total and percentage growth.  This shift signals more than a preference change. Typed languages are increasingly becoming the default for new development, particularly as AI-assisted coding becomes routine. Why is that? In practice, a significant portion of the failures teams encounter with AI-generated code surface as type mismatches, broken contracts, or incorrect assumptions between components. Stronger type systems act as early guardrails: they can help catch errors sooner, reduce review churn, and make AI-generated changes easier to reason about before code reaches production.  If you’re going to be using AI in your software design, which more and more developers are doing on a daily basis, strongly typed languages are your friend. Here’s what this means in practice:  If you’re starting a new project today, TypeScript is increasingly becoming the default (especially for teams using AI in daily development). If you’re introducing AI-assisted workflows into an existing JavaScript codebase, adding types may reduce friction more than switching models or tools. Python is key for AI Contributor counts show who is using a language. Repository data shows what that language is being used to build.  When we look specifically at AI-focused repositories, Python stands apart. As of August 2025, nearly half of all new AI projects on GitHub were built primarily in Python.  This matters because AI projects now account for a disproportionate share of open source momentum. Six of the ten fastest-growing open source projects by contributors in 2025 were directly focused on AI infrastructure or tooling. Python’s role here isn’t new, but it is evolving. The data suggests a shift from experimentation toward production-ready AI systems, with Python increasingly anchoring packaging, orchestration, and deployment rather than living only in notebooks.  Moreover, Python is likely to continue to grow in 2026, as AI continues to gain support and additional projects. Here’s what this means in practice: Python remains the backbone of applied AI work from training and inference to orchestration. Production-focused Python skills such as packaging, typing, CI, and containerization are becoming more important than exploratory scripting alone.  A deeper look at the top open source projects Looking across the fastest-growing projects, a clear pattern emerges: developers are optimizing for speed, control, and predictable outcomes.  Many of the fastest-growing tools emphasize performance and minimalism. Projects like astral-sh/uv, a package and project manager, focus on dramatically faster Python package management. This reflects a growing intolerance for slow feedback loops and non-deterministic environments.  Having just one of these projects could be an anomaly, but having multiple indicates a clear trend. This trend aligns closely with AI-assisted workflows where iteration speed and reproducibility directly impact developer productivity.  Here’s what this means in practice:  Fast installs and deterministic builds increasingly matter as much as feature depth. Tools that reduce “works on my machine” moments are winning developer mindshare. Where first-time open source contributors are showing up As the developer population grows, understanding where first-time contributors show up (and why) becomes increasingly important.  Projects like VS Code and First Contributions continued to top the list over the last year, reflecting both the scale of widely used tools and the persistent need for low-friction entry points into open source (notably, we define contributions as any content-generating activity on GitHub). Despite this growth, basic project governance remains uneven across the ecosystem. README files are common, but contributor guides and codes of conduct are still relatively rare even as first-time contributions increase. This gap represents one of the highest-leverage improvements maintainers and open source communities can make. The fact that most of the projects on this list have detailed documentation on what the project is and how to contribute shows the importance of this guidance. Here’s what this means in practice:  Clear documentation lowers the cost of contribution more than new features. Contributor guides and codes of conduct can help convert curiosity into sustained participation. Improving project hygiene is often the fastest way to grow a contributor base. Putting it all together Taken together, these trends point to a shift in what developers value and how they choose tools.  AI is no longer a separate category of development. It’s shaping the languages teams use, which tools gain traction, and which projects attract contributors.  Typed languages like TypeScript are becoming the default for reliability at scale, while Python remains central to AI-driven systems as they move from prototypes into production.  Across the ecosystem, developers are rewarding tools that minimize friction with faster feedback loops, reproducible environments, and clearer contribution paths. Developers and teams that optimize for speed, clarity, and reliability are shaping how software is being built. As a reminder, you can check out the full 2025 Octoverse report for more information and make your own conclusions. There’s a lot of good data in there, and

In January, we experienced two incidents that resulted in degraded performance across GitHub services. January 13 09:38 UTC (lasting 46 minutes) On January 13, 2026, from 09:25 to 10:11 UTC, GitHub Copilot experienced a service outage with error rates averaging 18% and peaking at 100%. This impacted chat features across Copilot Chat, VS Code, JetBrains IDEs, and other dependent products. The incident was triggered by a configuration error introduced during a model update and was initially mitigated by rolling back the change. A secondary recovery phase extended until 10:46 UTC due to upstream provider Open AI experiencing degraded availability for GPT‑4.1 model. We have completed a detailed root‑cause review and are implementing stronger monitors, improved test environments, and tighter configuration safeguards to prevent recurrence and accelerate detection and mitigation of future issues. January 15 16:56 UTC (lasting 1 hour and 40 minutes) On January 15, 2026, between 16:40 UTC and 18:20 UTC, we observed increased latency and timeouts across issues, pull requests, notifications, actions, repositories, API, account login, and an internal service, Alive, that powers live updates on GitHub. An average 1.8% of combined web and API requests saw failure, peaking briefly at 10% early on. The majority of impact was observed for unauthenticated users, but authenticated users were impacted as well. This was caused by an infrastructure update to some of our data stores. Upgrading this infrastructure to a new major version resulted in unexpected resource contention, leading to distributed impact in the form of slow queries and increased timeouts across services that depend on these datasets. We mitigated this by rolling back to the previous stable version. We are working to improve our validation process for these types of upgrades to catch issues that only occur under high load before full release, improve detection time, and reduce mitigation times in the future. Looking ahead  Please note that the incidents that occurred on February 9, 2026, will be included in next month’s February Availability Report. In the meantime, you can refer to incident report on the GitHub Status site for more details. Follow our status page for real-time updates on status changes and post-incident recaps. To learn more about what we’re working on, check out the engineering section on the GitHub Blog. The post GitHub availability report: January 2026 appeared first on The GitHub Blog. ​ Company news, News & insights, GitHub Availability Report The GitHub Blog

Software engineering has always included work that’s repetitive, necessary, and historically difficult to automate. This isn’t because it lacks values, but because it resists deterministic rules.  Continuous integration (CI) solved part of this by handling tests, builds, formatting, and static analysis—anything that can be described with deterministic rules. CI excels when correctness can be expressed unambiguously: a test passes or fails, a build succeeds or doesn’t, a rule is violated or isn’t.  But CI is intentionally limited to problems that can be reduced to heuristics and rules.  For most teams, the hardest work isn’t writing code. It’s everything that requires judgment around that code: reviewing changes, keeping documentation accurate, managing dependencies, tracking regressions, maintaining tests, monitoring quality, and responding to issues that only surface after code ships.  But a lot of engineering work goes into work that requires interpretation, synthesis, and context, rather than deterministic validation. And an increasing share of engineering tasks fall into a category CI was never designed to handle: work that depends on understanding intent.  “Any task that requires judgment goes beyond heuristics,” says Idan Gazit, head of GitHub Next, which works on research and development initiatives. Any time something can’t be expressed as a rule or a flow chart is a place where AI becomes incredibly helpful. Idan Gazit, head of GitHub Next This is why GitHub Next has been exploring a new pattern: Continuous AI, or background agents that operate in your repository the way CI jobs do, but only for tasks that require reasoning instead of rules. Why CI isn’t enough anymore CI isn’t failing. It’s doing exactly what it was designed to do.  CI is designed for binary outcomes. Tests pass or fail. Builds succeed or don’t. Linters flag well-defined violations. That works well for rule-based automation. But many of the hardest and most time-consuming parts of engineering are judgment-heavy and context-dependent.  Consider these scenarios:  A docstring says one thing, but the implementation says another. Text passes accessibility linting but is still confusing to users. A dependency adds a new flag, altering behavior without a major version bump. A regex is compiled inside a loop, tanking performance in subtle ways. UI behavior changes are only visible when interacting with the product. These problems are about whether intent still holds.  “The first era of AI for code was about code generation,” Idan explains. “The second era involves cognition and tackling the cognitively heavy chores off of developers.” This is the gap Continuous AI fills: not more automation, but a different class of automation. CI handles deterministic work. Continuous AI applies where correctness depends on reasoning, interpretation, and intent.  What Continuous AI actually means Continuous AI is not a new product or CI replacement. Traditional CI remains essential.  Continuous AI is a pattern: Continuous AI = natural-language rules + agentic reasoning, executed continuously inside your repository. In practice, Continuous AI means expressing in plain language what should be true about your code, especially when that expectation cannot be reduced to rules or heuristics. An agent then evaluates the repository and produces artifacts a developer can review: suggested patches, issues, discussions, or insights. Developers rarely author agentic workflows in a single pass. In practice, they collaborate with an agent to refine intent, add constraints, and define acceptable outputs. The workflow emerges through iteration, not a single sentence.  For example:  “Check whether documented behavior matches implementation, explain any mismatches, and propose a concrete fix.” “Generate a weekly report summarizing project activity, emerging bug trends, and areas of increased churn.” “Flag performance regressions in critical paths.” “Detect semantic regressions in user flows.” These workflows are not defined by brevity. They combine intent, constraints, and permitted outputs to express expectations that would be awkward or impossible to encode as deterministic rules.  “In the future, it’s not about agents running in your repositories,” Idan says. “It’s about being able to presume you can cheaply define agents for anything you want off your plate permanently.” Think about what your work looks like when you can delegate more of it to AI, and what parts of your work you want to retain: your judgment, your taste. Idan Gazit, head of GitHub Next Guardrails by design: Permissions and Safe Outputs In our work, we define agentic workflows with safety as a first principle. By default, agents operate with read-only access to repositories. They cannot create issues, open pull requests, or modify content unless explicitly permitted.  We call this Safe Outputs, which provides a deterministic contract for what an agent is allowed to do. When defining a workflow, developers specify exactly which artifacts an agent may produce, such as opening a pull request or filing an issue, and under what constraints.  Anything outside those boundaries is forbidden.  This model assumes agents can fail or behave unexpectedly. Outputs are sanitized, permissions are explicit, and all activity is logged and auditable. The blast radius is deterministic.  This isn’t “AI taking over software development.” It’s AI operating within guardrails developers explicitly define.  Why natural language complements YAML As we’ve developed this, we’ve heard a common question: why not just extend CI with more rules?  When a problem can be expressed deterministically, extending CI is exactly the right approach. YAML, schemas, and heuristics remain the correct tools for those jobs.  But many expectations cannot be reduced to rules without losing meaning.  Idan puts it simply: “There’s a larger class of chores and tasks we can’t express in heuristics.” A rule like “whenever documentation and code diverge, identify and fix it” cannot be expressed in a regex or schema. It requires understanding semantics and intent. A natural-language instruction can express that expectation clearly enough for an agent to reason over it.  Natural language doesn’t replace YAML, but instead complements it. CI remains the foundation. Continuous AI expands automation into commands CI was never designed to cover.  Developers stay in the loop, by design Agentic workflows don’t make autonomous commits. Instead, they can create the same kinds of artifacts developers would (pull requests, issues, comments, or discussions) depending

Open collaboration runs on trust. For a long time, that trust was protected by a natural, if imperfect filter: friction. If you were on Usenet in 1993, you’ll remember that every September a flood of new university students would arrive online, unfamiliar with the norms, and the community would patiently onboard them. Then mainstream dial-up ISPs became popular and a continuous influx of new users came online. It became the September that never ended. Today, open source is experiencing its own Eternal September. This time, it’s not just new users. It’s the sheer volume of contributions. When the cost to contribute drops In the era of mailing lists contributing to open source required real effort. You had to subscribe, lurk, understand the culture, format a patch correctly, and explain why it mattered. The effort didn’t guarantee quality, but it filtered for engagement. Most contributions came from someone who had genuinely engaged with the project. It also excluded people. The barrier to entry was high. Many projects worked hard to lower it in order to make open source more welcoming. A major shift came with the pull request. Hosting projects on GitHub, using pull requests, and labeling “Good First Issues” reduced the friction needed to contribute. Communities grew and contributions became more accessible. That was a good thing. But friction is a balancing act. Too much keeps people and their ideas out, too little friction can strain the trust open source depends on. Today, a pull request can be generated in seconds. Generative AI makes it easy for people to produce code, issues, or security reports at scale. The cost to create has dropped but the cost to review has not. It’s worth saying: most contributors are acting in good faith. Many want to help projects they care about. Others are motivated by learning, visibility, or the career benefits of contributing to widely used open source. Those incentives aren’t new and they aren’t wrong. The challenge is what happens when low-quality contributions arrive at scale. When volume accelerates faster than review capacity, even well-intentioned submissions can overwhelm maintainers. And when that happens, trust, the foundation of open collaboration, starts to strain. The new scale of noise It is tempting to frame “low-quality contributions” or “AI slop” contributions as a unique recent phenomenon. It isn’t. Maintainers have always dealt with noisy inbound. The Linux kernel operates under a “web of trust” philosophy and formalized its SubmittingPatches guide and introduced the Developer Certificate of Origin (DCO) in 2004 for a reason. Mozilla and GNOME built formal triage systems around the reality that most incoming bug reports needed filtering before maintainers invested deeper time. Automated scanners: Long before GenAI, maintainers dealt with waves of automated security and code quality reports from commercial and open source scanning tools. The question from maintainers has often been the same: “Are you really trying to help me, or just help yourself?“ Just because a tool—whether a static analyzer or an LLM—makes it easy to generate a report or a fix, it doesn’t mean that contribution is valuable to the project. The ease of creation often adds a burden to the maintainer because there is an imbalance of benefit. The contributor maybe gets the credit (or the CVE, or the visibility), while the maintainer gets the maintenance burden. Maintainers are feeling that directly. For example: curl ended its bug bounty program after AI-generated security reports exploded, each taking hours to validate. Projects like Ghostty are moving to invitation-only contribution models, requiring discussion before accepting code contributions. Multiple projects are adopting explicit rules about AI-generated contributions. These are rational responses to an imbalance. What we’re doing at GitHub At GitHub, we aren’t just watching this happen. Maintainer sustainability is foundational to open source, and foundational to us. As the home of open source, we have a responsibility to help you manage what comes through the door. We are approaching this from multiple angles: shipping immediate relief now, while building toward longer-term, systemic improvements. Some of this is about tooling. Some is about creating clearer signals so maintainers can decide where to spend their limited time. Features we’ve already shipped Pinned comments on issues: You can now pin a comment to the top of an issue from the comment menu. Banners to reduce comment noise: Experience fewer unnecessary notifications with a banner that encourages people to react or subscribe instead of leaving noise like “+1” or “same here.” Pull request performance improvements: Pull request diffs have been optimized for greater responsiveness and large pull requests in the new files changed experience respond up to 67% faster. Faster issue navigation: Easier bug triage thanks to significantly improved speeds when browsing and navigating issues as a maintainer. Temporary interaction limits: You can temporarily enforce a period of limited activity for certain users on a public repository. These improvements focus on reducing review overhead. Features we’ll be shipping soon Repo-level pull request controls: Gives maintainers the option to limit pull request creation to collaborators or disable pull requests entirely. While the introduction of the pull request was fundamental to the growth of open source, maintainers should have the tools they need to manage their projects. Pull request deletion from the UI: Remove spam or abusive pull requests so repositories can stay more manageable. Exploring next steps We know that walls don’t build communities. As we explore next steps, our focus is on giving maintainers more control while helping protect what makes open source communities work. Some of the directions we’re exploring in consultation with maintainers include: Criteria-based gating: Requiring a linked issue before a pull request can be opened, or defining rules that contributions must meet before submission. Improved triage tools: Potentially leveraging automated triage to evaluate contributions against a project’s own guidelines (like CONTRIBUTING.md) and surface which pull requests should get your attention first. These tools are meant to support decision-making, not replace it. Maintainers should always remain in control. We are also aware of tradeoffs. Restrictions can disproportionately affect first-time contributors

Imagine visiting your repository in the morning and feeling calm because you see: Issues triaged and labelled CI failures investigated with proposed fixes Documentation has been updated to reflect recent code changes. Two new pull requests that improve testing await your review. All of it visible, inspectable, and operating within the boundaries you’ve defined. That’s the future powered by GitHub Agentic Workflows: automated, intent-driven repository workflows that run in GitHub Actions, authored in plain Markdown and executed with coding agents. They’re designed for people working in GitHub, from individuals automating a single repo to teams operating at enterprise or open-source scale. At GitHub Next, we began GitHub Agentic Workflows as an investigation into a simple question: what does repository automation with strong guardrails look like in the era of AI coding agents? A natural place to start was GitHub Actions, the heart of scalable repository automation on GitHub. By bringing automated coding agents into actions, we can enable their use across millions of repositories, while keeping decisions about when and where to use them in your hands. GitHub Agentic Workflows are now available in technical preview. In this post, we’ll explain what they are and how they work. We invite you to put them to the test, to explore where repository-level AI automation delivers the most value. AI repository automation: A revolution through simplicity  The concept behind GitHub Agentic Workflows is straightforward: you describe the outcomes you want in plain Markdown, add this as an automated workflow to your repository, and it executes using a coding agent in GitHub Actions. This brings the power of coding agents into the heart of repository automation. Agentic workflows run as standard GitHub Actions workflows, with added guardrails for sandboxing, permissions, control, and review. When they execute, they can use different coding agent engines—such as Copilot CLI, Claude Code, or OpenAI Codex—depending on your configuration. The use of GitHub Agentic Workflows makes entirely new categories of repository automation and software engineering possible, in a way that fits naturally with how developer teams already work on GitHub. All of them would be difficult or impossible to accomplish traditional YAML workflows alone: Continuous triage: automatically summarize, label, and route new issues. Continuous documentation: keep READMEs and documentation aligned with code changes. Continuous code simplification: repeatedly identify code improvements and open pull requests for them. Continuous test improvement: assess test coverage and add high-value tests. Continuous quality hygiene: proactively investigate CI failures and propose targeted fixes. Continuous reporting: create regular reports on repository health, activity, and trends. These are just a few examples of repository automations that showcase the power of GitHub Agentic Workflows. We call this Continuous AI: the integration of AI into the SDLC, enhancing automation and collaboration similar to continuous integration and continuous deployment (CI/CD) practices. GitHub Agentic Workflows and Continuous AI are designed to augment existing CI/CD rather than replace it. They do not replace build, test, or release pipelines, and their use cases largely do not overlap with deterministic CI/CD workflows. Agentic workflows run on GitHub Actions because that is where GitHub provides the necessary infrastructure for permissions, logging, auditing, sandboxed execution, and rich repository context. In our own usage at GitHub Next, we’re finding new uses for agentic workflows nearly every day. Throughout GitHub, teams have been using agentic workflows to create custom tools for themselves in minutes, replacing chores with intelligence or paving the way for humans to get work done by assembling the right information, in the right place, at the right time. A new world of possibilities is opening for teams and enterprises to keep their repositories healthy, navigable, and high-quality. Let’s talk guardrails and control  Designing for safety and control is non-negotiable. GitHub Agentic Workflows implements a defense-in-depth security architecture that protects against unintended behaviors and prompt-injection attacks. Workflows run with read-only permissions by default. Write operations require explicit approval through safe outputs, which map to pre-approved, reviewable GitHub operations such as creating a pull request or adding a comment to an issue. Sandboxed execution, tool allowlisting, and network isolation help ensure that coding agents operate within controlled boundaries. Guardrails like these make it practical to run agents continuously, not just as one-off experiments. See our security architecture for more details. One alternative approach to agentic repository automation is to run coding agent CLIs, such as Copilot or Claude, directly inside a standard GitHub Actions YAML workflow. This approach often grants these agents more permission than is required for a specific task. In contrast, GitHub Agentic Workflows run coding agents with read-only access by default and rely on safe outputs for GitHub operations, providing tighter constraints, clearer review points, and stronger overall control. A simple example: A daily repo report   Let’s look at an agentic workflow which creates a daily status report for repository maintainers. In practice, you will usually use AI assistance to create your workflows. The easiest way to do this is with an interactive coding agent. For example, with your favorite coding agent, you can enter this prompt: Generate a workflow that creates a daily repo status report for a maintainer. Use the instructions at https://github.com/github/gh-aw/blob/main/create.md The coding agent will interact with you to confirm your specific needs and intent, write the Markdown file, and check its validity. You can then review, refine, and validate the workflow before adding it to your repository. This will create two files in .github/workflows:  daily-repo-status.md (the agentic workflow)   daily-repo-status.lock.yml (the corresponding agentic workflow lock file, which is executed by GitHub Actions)  The file daily-repo-status.md will look like this:  — on: schedule: daily permissions: contents: read issues: read pull-requests: read safe-outputs: create-issue: title-prefix: “[repo status] ” labels: [report] tools: github: — # Daily Repo Status Report Create a daily status report for maintainers. Include – Recent repository activity (issues, PRs, discussions, releases, code changes) – Progress tracking, goal reminders and highlights – Project status and recommendations – Actionable next steps for maintainers Keep it concise and link to the relevant issues/PRs. This file has two parts:  Frontmatter (YAML between — markers) for configuration  Markdown instructions that describe the job in natural language in natural language The Markdown is the intent, but the trigger, permissions, tools, and allowed outputs

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