Hi Noam: I'm intrigued that you trained/tested the bot against strategies that were skewed to raise a lot, fold a lot and check a lot, as well as something resembling GTO. Were there any kinds of table situations where the bot had a harder time making money? Or where the AI crushed it?

I'm thinking in particular of unbalanced tables with an ever-changing mixture of TAG and LAG play. I've changed my mind three times about whether that's humans' best refuge -- or a situation that's a bot's dream.

You've done the work. Insights welcome.