Ops Console gives ALPHA FORGE a 3D cockpit for its four AI models

The article is a write-up of a personal project by the developer of ALPHA FORGE, a Japanese-equity AI screening and continual-learning terminal. The system runs four model types — XGBoost, Random Forest, LSTM and Transformer — that score market data daily, and a continual-learning loop lets a challenger model take over when its validation accuracy beats the current champion. The Ops Console is a side project meant to make that background activity visible on a sub-monitor, styled after a film-style mission control room.

The build's central constraint is isolation. The console runs on its own Next.js and FastAPI ports, separate from the main web console, so that a crash or heavy query on the monitoring side cannot reach the trading inference or scheduled batches. The author states this is the top design principle, and the write-up returns to it in the summary as the main practical benefit of the approach.

The more tangible result is on the knowledge-graph side. Serial scanning of 4,800-plus Obsidian notes hit file-system metadata waits on Windows and pushed initial scans past 45秒, causing API timeouts. An index cache that checks update times, serializes a structured dictionary, and parses only changed files cut later responses to 1.6秒 — about 28× faster — and the author says the UI now comes up instantly. Whether that pattern scales to tens of thousands of notes or to a multi-user deployment is not addressed, so the broader payoff hinges on how far a single-machine cache can carry it.