01
Teaching a Decision Model to Play 2048 — and Why the Facts Mattered More Than the Model
Jev, from TypeSafe AI, is a new kind of model: it never writes text, it only makes judgments. You give it a small state and a typed question, and about 230 ms later it hands back an answer with probabilities. I wanted to know what that is actually good for, so I made it play 2048 in a real browser and measured it against plain-code bots. The surprise wasn't how smart it was. It was how completely its play depended on the facts I chose to give it.
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02
Latency Is a Design Constraint: A Decision Model Plays the Chrome Dino Game
After 2048, I gave the same AI decision model a game where timing is everything: the dinosaur that runs across Chrome when you're offline. The first design asked it "jump now?" — and the dino died on the second cactus every time, even though the model's answers were 100% correct. The fix wasn't a better prompt. It was asking a different question at a different moment, and letting code own the clock.
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03
Plain Code, a Decision Model and a Coding Agent: Who Should Make Which Call?
Three lines of `if` played the Chrome dino game for over an hour and a half and passed 100,000 points, for free. A purpose-built AI decision model needed a redesign just to survive the second cactus. And neither bot would exist without a coding agent that can't make a single real-time decision itself. After a day of making AI play browser games, the most useful thing I learned wasn't about any one tool — it was about which kind of "thinking" belongs where.
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