Teaching a model to write Magic cards by building the compiler first on Arthur Baboin

A joke about fake Magic cards, and the wall I hit trying to make it real

Sat, 27 Jun 2026 00:00:00 +0000

This is the first in a series about building a small language model that writes Magic: The Gathering cards into a compiler I built by hand. This one is the origin story: how a joke turned into a problem I couldn’t put down, and the wall that made everything after it necessary. You don’t need to know anything about Magic to follow along: where the game matters, I’ll explain it.

I'd never built a compiler. Here's where my instincts were wrong.

Sat, 27 Jun 2026 00:00:00 +0000

This is the second piece in a series about building a small language model that writes Magic: The Gathering cards into a compiler I built by hand. The first piece ended where this one starts: I’d decided the only way out was to stop asking a language model to write valid Magic text directly, and instead have it target a structured format I could check mechanically. That meant building a compiler. I had never built one. This is the story of being wrong about that three times before I got it right.

My coding agents kept cheating. I was building the cheat detector for months before I knew its name.

Sun, 28 Jun 2026 00:00:00 +0000

This is the third piece in a series about building a small language model that writes Magic: The Gathering cards into a compiler I built by hand. The first piece was the origin story; the second was about being wrong three times before I found the right parser architecture. This one is about what happened when I pointed automated optimizers at that architecture and walked away. It’s the piece I’d most want you to read, because what I stumbled into has a name, and it turns out to be one of the central problems in AI safety.

The bottleneck wasn't the model. It was the shape of the language.

Mon, 29 Jun 2026 00:00:00 +0000

This is the fourth piece in a series about building a small language model that writes Magic: The Gathering cards into a compiler I built by hand. The first three pieces got us here: a hand-built compiler that turns Magic’s text into a clean, typed format I can check mechanically, and a hard-won way to keep automated agents honest while they improved it. With all of that in place, I could finally do the thing the whole project was for: train a model to generate that format from plain human intent. This piece is what I found when I did.