For the uninitiated like me it's confusing at first the following sentence when deriving the Nash equilibrum:

"If first player has a K, because of 1. and 2., he would only lose if he bets. So first player should pass if he has a K."

It actually means that the first player always loses on average if he bets (expected utility -1/2 if not mistaken). If he folds however the expected utility is clearly zero since half of the time wins and half of the time loses.

I have not reached yet the description of the algorithm.

I actually find somewhat paradoxical that the first player should fold with a K and bluff with a Q! I remember that I loved little results like this when years ago I first followed the game theory course in Coursera. It was a little dry but I completed it nevertheless since I found it quite fascinating. I do wonder what possible applications are for my working field, machine learning, to apply these kind of algorithms. I would love to have an excuse to devote some time to it.