What actually happens when you live with LLMs — not the highlight reel.
I spent twenty years in visual effects. I worked on shots that ended up in films you’ve probably seen. And in all that time, the thing I learned most wasn’t about rendering or compositing or pipeline engineering. It was this: the final image you see on screen is a lie. A beautiful, polished, curated lie.
Behind every frame are thousands of broken iterations. Crashes at 3am. Renders that came back purple for no reason. A shader that worked fine until someone rotated the camera two degrees and the whole thing fell apart.
Nobody talks about the purple frames. They talk about the shot.
I’ve been thinking about that a lot lately, because I’ve fallen into the same trap with something new. Over the past year, I’ve been building a multi-agent AI system that runs on a Raspberry Pi in my house. Five or six AI agents, each with their own personality and job, working on my projects around the clock. They write code, research things, track tasks, manage a Discord server, and occasionally surprise me with something I didn’t expect.
It’s the most interesting thing I’ve worked on in years. And if you only saw the successful outputs — the clean code, the finished posts, the working automations — you’d think I had it all figured out.
I don’t. Not even close.
Here’s what a typical week actually looks like:
An agent hallucinates an API endpoint that doesn’t exist. I spend forty minutes debugging before I realize the documentation it read was from a two-year-old cached version. Another agent writes a beautiful, well-structured Python script — that’s completely wrong for the task I gave it, because it answered what it thought I meant instead of what I actually said. A third agent goes into an infinite loop retrying a failed git push and fills up a log file until the Pi runs out of disk space.
These aren’t edge cases. This is Tuesday.
Why write about it?
There are a thousand blogs about AI. Most of them fall into two categories: breathless hype about what’s possible, or doomscrolling about what’s coming. Very few people are writing about what it’s actually like to use this stuff every day. The boring parts. The parts where it doesn’t work. The parts where you feel like you’re debugging a coworker who’s very confident and frequently wrong.
I want to write that blog.
Partly because I think it’s more useful than another “10 things ChatGPT can do” list. But mostly because the only way to get better at working with AI is to be honest about where it breaks down. And I’m in a pretty good position to break things — I use more of this stuff, in more weird configurations, than most people. Dialysis patient. Indie developer. Parent of two. Running an AI team from a Raspberry Pi on a desk in Geinsheim, Germany.
If something can go wrong, it probably already has. I’ll tell you about it.
What to expect
The Glitch Diary is a weekly series. Each post will be about one specific thing that went wrong, went weird, or went unexpectedly right. One topic per post. I’m not writing essays — I’m writing field notes.
A few ground rules:
I won’t sand down the mistakes. If I did something dumb, I’ll say so. If an agent produced something embarrassing, I’ll show it. The whole point is that the highlight reel is useless as a learning tool.
I’ll explain jargon once. If I use a term you don’t know, I’ll define it in the post where it first appears, then assume you’ve got it from there. You’re smart. You’ll keep up.
No fluff. If a post doesn’t need to be long, it won’t be. Some weeks that might mean a few paragraphs. Other weeks it might mean a deep dive. The length serves the story, not the other way around.
Real screenshots, real errors, real logs. I’ll show you what actually happened, not a cleaned-up version.
The Kintsugi thing
There’s a Japanese art form called kintsugi — repairing broken pottery with gold. The idea is that the cracks aren’t something to hide. They’re part of the object’s story, and they make it more beautiful than it was before it broke.
I like that philosophy. Not just for pottery, but for learning, for building things, for life in general. We spend so much energy presenting the polished version. The final render. The shipped product. The Instagram angle.
The cracks are where the learning happens.
This series is about the cracks.
This is part of The Glitch Diary, a weekly series about what actually happens when you live with LLMs.