Jev 架构实测：快 193 倍、便宜 444 倍
主张把路由、打分、审批从 LLM 挪到确定性层，只让推理模型在真正需要思考时触发，并类比交易公司的策略层与执行层分离。
193X FASTER. 444X CHEAPER. SAME OUTPUT. that's a real benchmark on a stack where the LLM stopped making every decision. the model didn't get better. the architecture did. most agent setups right now look like this: a request comes in, GPT-5 decides the route, GPT-5 scores relevance, GPT-5 approves the tool call, GPT-5 writes the response. four model calls to do the work of one. everyone eats the latency and calls it "the price of intelligence." it isn't. it's the price of using a $15/M-token generalist to answer yes-no questions that a lookup table would answer in 3ms for free. Jev Engineering is the fix. one reasoning pass at the top of the stack, then everything downstream (routing, scoring, gating, approval) runs on deterministic layers that don't generate prose. they route, they score, they block, they approve. the expensive model only fires when the task actually needs to think. the interesting part isn't the speedup. the interesting part is that this is exactly how trading firms have run for twenty years. the strategy layer figures out the edge. the execution layer just executes. nobody lets the researcher press the buy button in prod. we've been building agents backwards. we let the researcher press every button, and then we're surprised when a customer support workflow costs $4 and takes 11 seconds. the tell is simple. if your agent calls the LLM more than once per user turn to decide what to do next, most of those calls should not exist. they should be a router, a rule, a cached embedding, an approval gate with a threshold. the model touches the flow once, at the moment reasoning actually earns its cost. reasoning is expensive on purpose. decisions should be cheap on purpose. mixing them is what broke agent economics.
这条是引用着另一条写的：x.com/yurshevv/status/2100978237547458936