Python documentary companion pod (Interview)

Welcome friends. 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 we have a lot of fun along the way. On this episode, we celebrate the launch of Colt Repo's epic Python documentary by sitting down with Travis Oliphant, creator of NumPy, SciPy, and more. We get his perspective on how Python took over the software world. Stick around for the twist ending. We set aside Python and dissect Travis's big idea to make open source projects financially sustainable through direct investment. But first, a big thank you to our partners at fly.io, the public cloud built for developers who like to ship. We love fly. You might too. Learn all about them at fly.io. Okay. Travis Oliphant talking Python 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 Deepos GitHub Action Runners. What we've observed over time is effectively GitHub Actions, when it 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 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, this is our take on the first step forward of actually building a real observability solution around GitHub actions so that developers have real debugging tools to figure out what's going on in their builds. Okay, Fran. You can …