Jev 当廉价决策层，替前沿模型挡掉琐碎分支
文章讲怎么把 Jev 接进 agent 做路由：大模型只管思考和写作，Jev 负责「下一个 agent 是谁」「要不要回复」这类小决策，并给出分类论文、邮件、浏览器循环的具体成本。
whoever built this realized we've been using our smartest AI for the dumbest possible jobs your $20 to $200/mo frontier model is researching, writing code, planning… and then you're paying that same brain to decide YES or NO. Jev flips that. it's a tiny decision model that sits in front of the expensive stuff and handles the boring forks: which agent goes next? is this worth researching? reply or ignore? publish, wait or escalate? BUY / SELL / HOLD? click this or keep looking? then I saw the numbers and the whole architecture clicked. ~1,000 papers classified for around $0.08 ~500 emails sorted for around $0.035 browser agent loops running at ~7 seconds for $0.0039 at the quoted pricing, roughly 10,000 decisions on ~1K tokens comes out to about $0.42. that's not because Jev outsmarted Claude. it's because nobody asked it to be Claude. the setup is dead simple: big LLM gets the hard thinking, research and writing. Jev gets the thousands of tiny decisions between those steps. code actually clicks, saves, sends and runs. and once you see it, these little decisions show up everywhere. your inbox. your X feed. support queue. meetings. leads. video clips. browser agents. even paper trading loops. that's what most agent builders are missing. the expensive model doesn't need to touch every fork just because it can. let Claude think. let Jev decide what deserves Claude. let code do the actual work. I broke down how to wire Jev into your agent and the exact router setup in the article below