•8 min read
The learning rate was the real lever, not the network's size
We grew the YINSH game AI to a larger brain and discovered one hardcoded number had been silently defeating the idea before it got started.
#ai
12 posts tagged with ai. This index aggregates both frontmatter tags: entries and inline #ai mentions.
•10 min read
270 generations of self-play: the clever tricks lost to raw capacity
270 generations of self-play on one GPU. A bigger network and a stricter referee won; the cheap clever tricks lost; the next lever is a hardware upgrade, not another idea.
•7 min read
The speedup that sped up nothing — when a flat line lies
After I made the YINSH AI's practice cheap, the referee became the slow part. I diagnosed the bottleneck, built a careful fix, proved it correct — and it made zero difference. This is the story of why a flat performance curve fooled me, and how to ship an honest negative result.
•7 min read
Winning sooner, not just winning — teaching the AI to value a quick kill
The YINSH AI thought a win one move away and a win a hundred moves away were exactly the same thing. Fixing that — teaching it to prefer winning sooner — made it both stronger and cheaper to run, and taught me the value network is a search multiplier, not just a scorekeeper.
•10 min read
Thinking smarter, not harder — a faster self-play loop that can't get less creative
The YINSH AI spends two-thirds of its training time practicing against itself. Three planned changes cut that time — the biggest replaces 800 brute-force look-aheads per move with about 50 clever ones — under one hard rule: it must not get less creative.
•8 min read
The AI took two moves when one would win — a YINSH value saturation story
At score 2:2, the staging YINSH bot had a guaranteed one-move win but chose a slower path — the diagnosis confirms value saturation, and a no-retrain MCTS-Solver fix cures it.
•7 min read
Two dead-end runs proved the YINSH AI was too small, not undertrained
Three 12-hour GPU runs to beat the YINSH champion gen-077 — two dead-ends on more data and momentum, then a network twice the size won in two generations from scratch.
•7 min read
Same wall, new fix — the AI broke its plateau by thinking harder, not getting bigger
A bigger network beat the YINSH champion, then stalled. The next breakthrough wasn't size — it was making the AI search deeper before it learned — plus a performance trap that almost hid the win.
•7 min read
Your game AI has a tell: finding and fixing the always-9 exploit
The Black and White bot looked strong — until you noticed it always played its 9 against a black opener. A human could bait that. Here's how we measured the exploit, found the root cause, and taught the model to stop being predictable — without resorting to play-time randomness.
•8 min read
Closing the smallest loop first — a game AI pilot on Black and White
The chess-ai stack had 94 files and 104 tests but never produced a single trained model. This time we picked the smallest game, closed the loop in a day, and let an automation session find six design flaws on its own metrics trail.
•5 min read
Self-play training for augmented chess hit the home cluster's RAM ceiling
An AlphaZero-style pipeline for the augmented chess engine ran into 512Mi pod limits, and the fix taught me the difference between liveness and readiness probes.
•1 min read
Building a Local LLM Gateway on a Mac Mini
A small OpenAI-compatible proxy in front of Ollama — Bearer auth, model routing, streaming — so no cluster pod has to hold a vendor API key.