Jev 成本拆解：一万次决策 42 美分
把 agent 的「写字」和「挑选」拆开：固定选项交给 Jev、精确规则交给代码，并给出本周上手四步（列出决策点、playground 试一个、按置信度设闸、匹配问题类型），附 Browser Use 7.1 秒跑完航班搜索等公开数据与 72.5% 准确率的提醒。
10,000 decisions for 42 cents. That's the pitch behind Jev, and the math checks out: $0.042 per million input tokens, output free. At 1,000 tokens a decision, 10,000 decisions cost $0.42. The idea is simple. Most of what your agent does all day isn't writing. It's picking. Which tool next. Is this source relevant. Approve or send back. And every one of those picks wakes up a frontier model. So split the work: If it writes text, the big model does it. If it picks from a fixed menu, Jev does it. If it's an exact rule, plain code does it. How to start this week: 1. List the picks Open one agent's transcript and mark every step with a fixed set of answers: routing, scoring, approvals. 2. Test one with no code One state, one question, three options, in the playground. 3. Gate on confidence Below your threshold, the job goes to you or the big model. Set the threshold from your own labeled examples, not a guess. 4. Match the question "Which one" is a choice. "How good" is a score. "Yes or no" is true or false. What it already did in public: → Browser Use's Jev agent ran a Google Flights search in 7.1 seconds for $0.0039. Browser calls per task fell from 1,092 to 101. → 1,018 research papers sorted into 24 topics for $0.08. The fine print nobody posts: Jev picks fast, not perfectly. One independent study put it at 72.5% accuracy, level with mid-price models. "Can't hallucinate" means it won't answer outside your menu. It can still pick wrong. Cheap picks, a confidence gate, and a human on the uncertain ones. That's the whole system.
这条是引用着另一条写的：「长文：如何搭好 Jev 驱动的 agent 技术栈」