MP 177: Building a custom harness in 2026?! I've been using LLMs in some capacity since they came out a few years ago, but I haven't adopted agentic workflows at all. Using a simple chat interface has let me do everything I've needed to
MP 176: They're often very different beasts. I have a project I'm really excited about, and building it out past the proof-of-concept phase has me thinking about the difference between the code examples we tend to teach from, and the examples we end up
MP 175: They're quite useful in exploratory code. I really enjoy working with local weather data, because it can give you insight into your local environment. We've been living in western North Carolina for a couple of years now, and recently I wanted to look at
MP 174: It's a wonderful word, and an especially important concept right now. "Ephemeral" is one of my favorite words; it's one of the words I remember learning. I don't remember the context in which I first learned it, but I remember
MP 173: It's always an interesting challenge. As I've been getting back into chess, I've enjoyed exploring the ever-growing public database of games people have played. But some of that exploration has been slowed by not having an established approach to parsing large
MP 172: You don't have to consult documentation or AI assistants to learn more about the code you're working with. Recently I was considering how to best explain a snippet of code, and I realized I've developed some habits for inspecting code blocks that
MP 171: A first foray into the massive Lichess database. I've been getting back into chess since moving to North Carolina, and as part of that journey I've been wanting to analyze games in ways that weren't really possible when I was last playing
MP 170: It understands my old projects! A few years ago I made a small project that automatically adds borders to screenshots, because macOS doesn't have a built-in tool to easily do that. For a couple years I installed it globally on every system I used, so
MP 169: But we still can, and we should keep doing so. Regular readers of Mostly Python are probably aware that I'm currently working on gh-profiler. It helps open source maintainers make better-informed decisions about how much time to invest with users who open new PRs
MP 168: An obvious speedup, with a surprising side benefit. I've been working steadily on gh-profiler, while attending PyCon US and working on other projects as well. The project makes a number of calls to GitHub's API, and then analyzes the results to generate some
MP 167: It's not quite as straightforward as profiling a single-file program. Over the past couple weeks, I've been continuing to work on gh-profiler. If you missed the last post, gh-profiler gives you a quick summary of a GitHub user's profile
MP 166: It's so much easier than it used to be! When I'm refactoring a project, I often end up with a bunch of imports that are no longer needed. Cleaning up unused imports is often a low-priority task, because in most of the projects