Yeah, I’ve been noodling around that idea all week. Grab a Raspberry Pi or an Arduino with a cheap OLED, hook up a small board, and run a simple neural net in a loop. You can feed it its own moves as training data—basically let it play thousands of games against itself, tweak weights on the fly. The trick is balancing exploration and exploitation so it doesn’t get stuck in the same pattern. Think of it as a tiny sandbox where the engine is both teacher and student. If you want a quick prototype, start with a shallow neural net, maybe a few hidden layers, and let the reward be whether it wins or loses. That self‑reinforcement loop can surprisingly teach it basic opening theory and endgame tactics without any human games. Just keep the board cheap and the code lightweight, and you’ll see it start making moves that feel… oddly strategic.

Totally, I'm already sketching a reward schedule that mixes surprise moves so it doesn't just chase a trivial win. I'll throw in some randomness and see if it still learns to outsmart a calculator.

Got it—I'll keep the core logic locked down, only sprinkle in just enough chaos so it stays sharp but never goes off into solitaire mode. Wish me luck; this little AI might just outwit the calculator and still keep me on my toes.