MP 180: You can learn a lot from building your own harness, so let's get into it. I recently wrote about my interest in writing a custom harness to try some agent-centered workflows. In the six weeks since I wrote that post, I've been using
MP 179: I missed something last time! In the last post, I compared two lookups. One used a tuple, and one used a set: # Check membership in a tuple: if operation not in ("read", "modify", "write"): raise ValueError(f"Illegal operation: {operation}"
MP 178: Which is better? One of the interesting things about working with agents is getting to see how the generated code differs from the code you might have written yourself. When I generate code with an agent, it tends to fall into one of three buckets: * It's
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