I built an AI that reads poker players in real time

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I built a real-time poker decision engine that watches a live table, models opponent tendencies from hand history, and returns a bet sizing recommendation in under 3 seconds.

One hand stands out. I got double barreled on the flop and turn. The bot still recommended firing a 30% pot bluff on the river. Villain folded.

That's not a GTO play. That's the system reading a specific opponent, recognizing they were capped, and committing to the exploit.

GTO is designed to be unexploitable against a perfect opponent. But most players aren't perfect. They have leaks and tendencies that repeat across hundreds of hands. A GTO model ignores that by design. This system is built entirely around it.

The hardest engineering problem was LLM hallucination. If you ask a model to evaluate a hand and act on that evaluation in the same step, it will sometimes get the first step wrong. The fix was pre-computing all hand facts in code before the prompt is sent. Nothing left to hallucinate.

Happy to answer questions about the poker logic or the technical side. Full breakdown in the comments.