Jev as a trading execution layer: 10k evaluations for $0.42
Treating LLMs as execution engines is the wrong approach: 50 lightweight sensors compress the order book, and Jev classifies market state into typed actions at under 85ms per call.
The biggest mistake in autonomous trading right now is treating LLMs like execution engines. If your system makes 10,000 market evaluations a day: Frontier text loops cost ~$25–$40 and choke on 1-second latency spikes. Static rule trees are fast, but they can't handle complex multi-source state nuance. @lostinazeroy built the right bridge: 50 lightweight sensors distill raw order books into a compact tensor Jev classifies the market state into typed actions (sub-85ms, $0.42 / 10k) Code-level risk fences block bad fills before they hit the exchange How to replicate this: Drop his architecture spec and Python harness into Claude Code or Cursor Build your typed risk fences before you write a single line of execution code Full specification paper and harness blueprint below.
This post quotes another post: x.com/lostinazeroy/status/2103786949580345507