Hey Brian, I really enjoyed reading your work on the Pips game! I found myself applying a similar backtracking algorithm to my Pythonic solution (https://github.com/ematth/pips). I focused on finding a single solution for each puzzle as opposed to all possible solutions. For hard puzzles with longer run times, I found that running multiple processes, each with the domino list shuffled, gets the solve time down to <15 seconds.
Thanks! I'm glad to see I'm not the only one who went down this rabbit hole. :)
I considered parallelizing my solution as well, but the problem is that it only gives a linear speedup, while the problem space increases exponentially. I decided to focus on pruning the search tree instead, and that seemed to work pretty well (after much thinking).
It's getting crowded down here in the rabbithole... One more to peek at: https://github.com/prb/pips-solver/blob/main/README.md
That's great! Your experience with the 2025-09-15 and 2025-10-14 puzzles was very similar to mine, I think. I'm impressed that you were able to get AI models to solve this game effectively. I coded it the old-fashioned way myself, mostly, with occasional help from Gemini Pro.
I did write the spec first — data model, algorithm, etc. That may have helped the agents get traction.