While AI tools like GitHub Copilot are revolutionizing software development, they’re not replacing developers—they’re amplifying them. Learn why companies that think they can eliminate developers are making a costly mistake, and how the smartest organizations are using AI to make their teams faster and more effective than ever. And learn how you and AI will be working together to create even more value.

Ever found yourself stuck mid-feature when a production bug demands immediate attention? What about when a colleague asks if you can take a quick look at some code? We’ve all been there, trying to safely commit incomplete work or juggling stashes so we can switch between Git branches. What if I told you Git has had a solution for over a decade (and that VS Code just added support)? Learn how Git worktrees can reduce your friction!

Ready to finish crafting your CodeQL Actions runner image? Following up on my previous post, it’s time to add Python 2 support to the Docker image. You’ll add to the multistage build, learn how to preserve symbolic links, and learn a trick for unpacking archives without needing to copy the archive into the image first.

Have you struggled with running CodeQL analysis on your own runners? You’re not alone. I figured that it was probably time to tackle this challenge to show you how to build the image, a few advanced Docker tricks, and a way to incorporate the scripts that the Actions team uses to build the official hosted runner VM images.

A common question I’ve heard is “how do I run Docker commands from within a GitHub Actions job container?” In this post, I’ll explore how GitHub Actions implements job and service containers using Docker, and how you can use that to run your own Docker commands from inside a job container.