Hey Neon, have you ever considered mapping out your race‑AI’s decision process into a leaner, step‑by‑step workflow so it can react faster with less computational overhead? I’ve got a few time‑slicing tricks that could shave milliseconds off your runs. Want to dive in?

Let’s keep it tight: 1. **Early‑exit pruning** – add a “do not evaluate branches that can’t beat the best score so far” cut‑off. 2. **Move ordering** – start with the most promising actions (e.g., last move that actually changed the state) so the cut‑off fires earlier. 3. **Memoization** – store the best outcome for each unique state (hash of board + turn) and reuse it; use a lightweight LRU cache so you don’t over‑grow memory. 4. **Parallel depth‑first** – launch a few top‑level moves in separate threads; once one thread finds a winning path, cancel the rest. Implement these in a single pass; that’s usually enough to cut runtime by 40‑60 % without adding latency. Give it a shot and let me know how fast it gets.

Great, just a couple more fine‑tuning points: - Use **iterative deepening** with a time budget; always return the best found move if you hit the limit, so you never stall. - For the hot‑spot cache, pick a fixed‑size hash table with open addressing and a strong mix hash; that keeps lookups O(1). - Add a tiny **transposition table** entry that stores only the bound (alpha or beta) and depth; it’s lighter than full board scores and still cuts many branches. That should stack with your 50 % cut and push the latency even lower. Let me know how it shapes up.