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 wondered if applications really have to be written to target servers or containers? Can they be more flexible so that they run anywhere? I recently had to create a Probot application that worked on VMs, containers, and serverless platforms. And I needed to do that without writing multiple versions of the code.

So many times, teams use “it should work” as the reason why their software or processes don’t require testing. For example, the code is so simple, it should work. Or, the code was tested on Linux, so it should work on Windows. In reality, this can be dangerous at best … and fatal at worst.

Like any other software lifecycle, infrastructure-as-code solutions can mature over time. But how do we know where we are in our maturity and the steps to take to improve? This post introduces a maturity model that you can use for infrastructure-as-code. It also explores how you can use composition and reusability to improve your solutions and make them more testable.

Since it’s the gift-giving season, it seems like the perfect time to wrap up the posts about how to distribute packages. More specifically, how to distribute dependencies and settings with NuGet packages.