用 Jev 承接 agent 里的小决策以省钱
视频讲解把昂贵大模型只留给研究与写作，让 Jev 处理是/否类分叉，并列出分类论文、邮件、浏览器循环的成本数字。
The guy who built Jev realized we’ve been using our smartest AI for the dumbest possible jobs your $25-$250/mo frontier model is researching, writing code, planning… and then you’re paying the same brain to decide YES / 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 people are reporting and the whole architecture started making a lot more sense. ~2,000 papers classified for around $0.05 ~800 emails sorted for around $0.028 browser-agent loops demonstrated around 6 seconds for ~$0.0029 and at the quoted pricing, roughly 20,000 ~2K-token decisions comes out around $0.62. that’s not because Jev became 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 executes and suddenly you start noticing these little decisions everywhere. > your inbox. > X feed. > browser agents. > support queue. > meetings. > leads. > video clips. > 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 capable of touching every step. > let Claude think. > let Jev decide what deserves Claude. > let code do the actual work. I broke down 10 builds you can wire into an agent today in the article below ↓ ↓
这条是引用着另一条写的：x.com/0x_rody/status/2103165281149354256