Context matters when you’re working with AI. Every codebase has its own house rules: how components are structured, which scripts to run, and which conventions apply to different frameworks and file types. Copilot can help you make changes across a project, but it works best when it knows those rules, too. Much of that knowledge can live inside the repository, so Copilot can pick it up automatical

Someone bursts through the door and announces, “There’s pizza!” Nearby, a team has duct tape and cardboard holding its prototype together. Another is debugging a model that won’t detect their movements. This is a common scene during hackathons, and for a lot of people they’re the place to learn to build. During several recent hackathons, we followed dozens of participants from the badge line to fi

As we kick off Cybersecurity Awareness Month, the GitHub Bug Bounty team is excited to spotlight one of the top-performing security researchers participating in the GitHub Security Bug Bounty Program: @vaib25vicky! The security research community makes GitHub safer for everyone—that’s the simple idea behind GitHub’s Bug Bounty Program . For more than a decade, researchers from around the world hav

Today, one in three pull requests on GitHub involves an AI agent . A year ago, that number was fewer than one in 10. If that pace holds, within the next two years, most of the code pushed to GitHub could be written by an agent. Much of it may never be fully read by a human. If developers and agents move faster, we have a responsibility to ensure protection keeps up with the accelerated rate of cod

Every day on GitHub, millions of developers build the products their customers rely on, contribute to open source, and pursue personal projects. GitHub’s architecture has changed steadily over the years to support that work and the growing demands of the developers and organizations who depend on it. Agentic software development is driving the next architectural shift. With developers and agents w

Agentic code review is becoming an essential piece of how development happens. It helps you inspect pull requests, catch issues, and decide what deserves attention before code ships. But the quality of existing AI reviewers can be hard to measure, and you need to know the strengths of a reviewer before you know if it will help you. Some reviewers surface more issues, some produce less noise, and s

AI is changing how developers work and apply their skills. Writing code is still essential, but developers increasingly need to know how to direct AI, evaluate its output, communicate tradeoffs, and make sound technical decisions. The good news? You can start preparing today. Here’s where to focus: Tip #1: Learn to direct AI, not just use it. AI is changing what execution looks like. Increasingly,

I’m looking for ideas I can take back to my own work, from verifying agent-written code and securing dependencies to building software that works where internet access is unreliable. There are more sessions at GitHub Universe than I can fit into two days. To narrow the list, I built my agenda around questions I’m working through right now: How do we know an agent’s code works? What should it remem

Policy decisions increasingly shape how developers build, collaborate, and participate in open source. That makes it important not only to be transparent about how GitHub responds to government requests, but also to help developers understand policy proposals that could affect their work and create opportunities for the open source community to engage. With that in mind, we’re sharing our latest T

With the rise of AI in the security space, our team created the GitHub Security Lab Taskflow Agent as a way for security researchers to easily automate, package, and share the AI prompts and workflows that they find effective for their work. In this blog post, I’ll share how I created auditing taskflows to find vulnerabilities in Android applications. While new models are getting better at underst