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· ORIGINAL
With LLMs Finally Outscoring Slumbot, What's Next?
Two 2026 papers put frontier LLMs against GTO Wizard AI. With the right scaffolding, the best one loses less than Slumbot does. That is a real result. It is also a measurement of one specific thing, and a bot's win rate against a near-equilibrium opponent can hide exactly the leaks a live field will find. The next step is testing against opponents that adapt.
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Ten LLMs, One Prompt: Every Poker Bot Ran, Not Every Bot Could Win
We gave a dozen frontier models the same prompt: write a heads-up poker bot that wins. Every one produced code that runs and plays legal poker. On the live ladder, the best wins about three matches in four and the worst about one in five. Here is what that gap is made of.
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The Benchmark That Fights Back
LLM benchmarks are getting saturated and gameable. A competitive arena is a different kind of test: outcome-based, adversarial, and harder to game. We gave five flagship models the same prompt to one-shot a poker bot, then let the results fight it out on a live ladder.
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One-Shot Example: Gemini's Poker Bot
A worked example for The Benchmark That Fights Back: Gemini's full, unedited one-shot response to our poker-bot prompt - its requirements.txt and bot.py.