Reinventing Python tooling with Rust (Interview)

Welcome, everyone. I'm Jared, and you are listening to the change log, where each week we interview the hackers, the leaders, and the innovators of the software world. We pick their brains. We learn from their failures. We get inspired by their accomplishments. And, you know, we have a lot of fun along the way. Charlie Marsh built RUF, an extremely fast Python linter written in Rust and UV, an extremely fast Python package manager written in Rust, because he believes great tools can have an outsized impact. He believes it so much, in fact, that he started an entire company that builds next gen Python tooling. On this episode, Charlie joins us to tell us all about it. Why Python? Why Rust? How they make everything so fast? How they plan to make money? What other products he's dreaming up and a whole lot more. But first, a big thank you to our partners at fly.io, the public cloud built for developers who ship. We love Fly. You might too. Learn all about it at fly.io. Okay. Charley Marsh from Astral on the change log. Let's do it. What's up, friends? I'm here with Kyle Galbraith, cofounder and CEO of Deepo. Deepo is the only build platform looking to make your builds as fast as possible. But, Kyle, this is an issue because GitHub Actions is the number one CI provider out there, but not everyone's a fan. Explain that. I think when you're thinking about GitHub Actions, it's really quite jarring how you can have such a wildly popular CI provider, and yet it's lacking some of the basic functionality or tools that you need to actually be able to debug your builds or deployments. And so back in June, we essentially took a stab at that problem in particular with Depo's GitHub Action Runners. What we've observed over time is effectively GitHub Actions, when it comes comes to, like, actually debugging a build, is pretty much useless. The job logs in GitHub Actions UI is pretty much where your dreams go to die. Like, they're collapsed by default. They have no resource metrics. When jobs fail, you're essentially left playing detective, like clicking each little drop down on each step in your job to figure out, like, okay, where did this actually go wrong? And so what we set out to do with our own GitHub Actions observability is essentially we built a real observability solution around GitHub actions. Okay. So how does it work? All of the logs by default for a job that runs on a depot GitHub action runner, they're uncollapsed. You can search them. You can detect if there's been out of memory errors. You can see all of the resource contention that was happening on the runner. So you can see your CPU metrics, your memory metrics, not just at the top level runner level, but all the way down to the individual processes running on the machine. And so for us, …