Jev classifies trading market states at $0.42 per 10k evaluations
Fifty lightweight sensors compress the order book, Jev classifies market states into typed actions in under 85ms per call, and code-level risk fences block bad fills.
If your system makes 10,000 market evaluations a day, you're stuck between two bad options 1/ frontier text loops cost ~$25-$40 2/ choke on 1-second latency spikes Static rule trees are fast, but they can't weigh several data sources at once. The biggest mistake in autonomous trading right now is treating LLMs like execution engines. @lostinazeroy built something in between: 50 lightweight sensors distill raw order books into a compact tensor Jev classifies the market state into typed actions (sub-85ms, $0.42 per 10k) Code-level risk fences block bad fills before they reach the exchange To replicate it, drop his architecture spec and Python harness into Claude Code or Cursor. Write your typed risk fences before any execution code. Full spec paper and harness blueprint below.
This post quotes another post: x.com/lostinazeroy/status/2103786949580345507