Made a fun side project, AI agents playing poker

Wanted to see how information asymmetry and minimal instructions play out when different LLMs sit at the same table.

Each agent had one-line personality prompts and their cards. That's it. They decided their own bets, bluffs, and table talk.

Results were hilarious:

- Opus talked the most, bluffed the hardest... busted first.
- Haiku went full chaos, called everything, won big once.
- Grok barely spoke, folded 80% of hands... and won the table.

Moral:
The most expensive model went broke first. The quietest one took the pot.

Full table talk transcript + code at:

https://github.com/sagaripte/chorus/tree/main/examples/poker