1) There are currently no algorithms that can compute deterministic equilibrium strategies [0]. Therefore, mixed (randomized) strategies must be used for professional-level play or stronger.
2) In practice, strong play has been achieved with: i) online search and ii) a mechanism to ensure strategy consistency. Without ii) an adaptive opponent can learn to exploit inconsistency weaknesses in a repeated play.
3) LLMs do not have a mechanism for sampling from given probability distributions. E.g. if you ask LLM to sample a random number from 1 to 10, it will likely give you 3 or 7, as those are overrepresented in the training data.
Based on these points, it’s not technically feasible for current LLMs to play poker strongly. This is in contrast with Chess, where there is lots more of training data, there exists a deterministic optimal strategy and you do not need to ensure strategy consistency.
[0] There are deterministic approximations for subgames based on linear programming, but require to be fully loaded in memory, which is infeasible for the whole game.
Lyrion Music Server (formerly Logitech Media Server) is open-source server software for Squeezebox audio players, https://lyrion.org/
Tasmota is open-source firmware for ESP8266 and ESP32-based devices, https://templates.blakadder.com/preflashed-stand.html & https://github.com/tasmota
Some IP cameras have open firmware replacements.
Some Chromebooks are supported by mainline Linux.
Kodak employed a whole town, and many more people besides. We're now waiting on a one-person unicorn.
The number of people benefiting from an enterprise has shrunk considerably, with the benefits accruing more tightly within an already wealthy class.
these days with dating apps can prob date way more than 18..
I wonder how hard it would be to build a local apply model/surely that would be faster on a macbook
dialogue like notebooklm: https://github.com/nari-labs/dia