This skill is a great idea. I’ve been trying to figure out how to integrate this with Woogles.io for automatic game analysis.

i am also using Opus for a hobby teaching agent, and the way it writes the prompts is "cringy" but they seem to work well. i almost want it to continue doing this internally, it understands best this way.

- https://domino14.github.io/macondo/ - my AI, written in Go. Mostly meant as a "research" tool and doesn't have a proper GUI yet.

- https://github.com/andy-k/wolges - an implementation of a Scrabble AI from scratch in Rust. Andy is very smart and came up with his own algorithm and data structure that we now use throughout our projects

- https://github.com/jvc56/MAGPIE - a friend started this as a rewrite of Macondo, but in C. A lot of new development has gone into this and I've contributed a bit as well. Also no official GUI, but it is _absurdly_ fast, so fast that we can do nested Monte Carlo simulations. Some algorithms have been backported to Macondo with the help of AI.

These are the main 3, but there are people contributing in other ways, someone else has made a CNN and it has good results (I built a CNN as well in the Macondo source, and it has decent results). We're slowly going to continue to improve this until we can use it in real-time for Monte Carlo evaluations, maybe by translating it into a NNUE architecture.

Currently on Macondo I've been working on AI explainability (it's quite good already) and Bayesian inference with a stats PhD candidate. We are likely going to publish a paper on the latter. It's very mathy but it ends up beating the best bot we have so far around 52% of the time (that's actually a pretty significant edge).

You can read about some algorithms on my blog here: https://cesardelsolar.com (very in-depth discussions on inference, CNN, and my exhaustive endgame/pre-endgame algorithm)