This is super interesting, thanks for sharing! I did a similar thing a few years ago which I'd been meaning to properly finish and share, and your post was the inspiration needed to make mine public (albeit still in a state much too messy for my liking, hopefully having it public will force me to improve it).

We took fairly different approaches, but I really enjoy the visual explanation element of yours! Well done.

My investigation stemmed from wondering if the seemingly useless 1st, 10th, and 1000th nearest word similarity scores were enough to uniquely ID the word. Turns out—yes, pretty much! It's effectively just a kind of reverse engineering, similar to how you also made your own version of the game. Can definitely improve on a lot.

Tried today's puzzle and got it in two (first was 999/1000).

Here's my code & write up: https://github.com/OisinMoran/Solving-Semantle/blob/main/Sol...

Regarding Semantle, I found that Pimantle (https://semantle.pimanrul.es/) is a much more satisfying implementation to actually play. It provides a 2D visualization of guesses, which lets you see the clusters and lines of similarity more clearly.

This has Peter Norvig sudoku energy, in that it describes a game that is tricky enough for humans to solve that it's become a whole pastime, but is a trivial solver away from reliably defeating, and with a tiny amount of code. Once you see what they're doing with this, you're like, oh of course. Very cool.

I was hoping this would have some application for human solving. I gave up on Semantle because I couldn't figure out any sort of strategy.