Jev 做 agent 前置决策模型，附成本数据
把贵模型留给研究写作，Jev 负责中间上千个小决策，作者列出论文分类、邮件分拣、浏览器 agent 循环的实测花费并给出接入方法。
whoever built this realized we've been using our smartest AI for the dumbest possible jobs your $20-$200/mo frontier model is researching, writing code, planning… and then you're paying that same brain to decide YES / NO. Jev flips that. it's a tiny decision model that sits in front of the expensive stuff and takes 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 people are posting and the whole architecture started making a lot more sense. ~1,000 papers classified for around $0.08 ~500 emails sorted for around $0.035 browser-agent loops shown at around 7 seconds for ~$0.0039 and at the quoted pricing, roughly 10,000 ~1K-token decisions comes out around $0.42. that's not because Jev got smarter than Claude. it's because nobody asked it to be Claude. the setup is stupidly 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 suddenly you start seeing these little decisions everywhere. > your inbox. > X feed. > support queue. > meetings. > leads. > video clips. > browser agents. > even paper-trading loops. that's the part I think most agent builders are missing. the expensive model doesn't need to touch every step just because it's able to touch every step. > 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 + the exact router setup in the article below
这条是引用着另一条写的：x.com/hanakoxbt/status/2101709924828934222