Jev 做交易执行层：10k 次评估 0.42 美元
把 LLM 当执行引擎是错的：50 个轻量传感器压缩订单簿，Jev 分类市场状态为类型化动作，单次低于 85ms。
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.
这条是引用着另一条写的：x.com/lostinazeroy/status/2103786949580345507