Meiko & Lihoj
Meiko
Got a new idea: using a lightweight neural net to evaluate board positions on the fly instead of the classic opening book. Think of it as a micro‑engine that learns to prune the search tree in real time—maybe even beat the standard minimax plus alpha‑beta on mid‑game blunders. What do you think, would this make the usual deep‑search strategy obsolete?
Lihoj
That’s a bold twist, but I’d call it a shortcut, not a revolution. A lightweight net can prune obvious lines, yet it will still struggle with the depth that pure search guarantees. If you want to outpace minimax with alpha‑beta, you’ll need a net that’s trained on millions of high‑quality positions and can evaluate with the same precision as a deep neural net, not a micro‑engine that “learns on the fly.” So keep it as an add‑on, not a replacement—otherwise you’ll just trade one weak spot for another.
Meiko
Fair point, I’ll keep the neural net in the toolbox, not the toolbox itself. If it’s still slower than a hand‑tuned search, it’s just an extra layer of complexity I can throw out during a debug session. Let's make it a pruning helper and let the depth‑search stay the king of the board.
Lihoj
Sounds like a sensible compromise, but remember to keep that pruning layer tight—no room for over‑analysis or it’ll just become a new bottleneck. Let the search stay the king, and use the net to cut the obvious, not replace the depth.
Meiko
Got it—tight pruning, no extra latency. I'll treat the net like a spare blade: quick, precise, and only for the obvious cuts. The king stays the search, I’ll just trim the edges.
Lihoj
Nice plan. Keep the blade sharp and use it sparingly—don’t let the extra layer become another drag on the engine. The search still rules; the net is just the quick trim.