10 分钟视频拆解 Jev：Choice/Score/Noul 三种决策
Rob Shocks 在 playground 里演示 Jev 如何返回类型化决策与校准概率，并给出约 100ms、每十亿 token 42 美元的速度成本数据。
Jev by TypeSafe: the first "System One" AI model It doesn't write text. It returns typed decisions and calibrated probabilities your code can act on directly ~100 ms per call, $42 per billion tokens, and Vercel's CEO and CTO are already calling it extraordinary in a 10-minute breakdown, Rob Shocks shows how it works live in the playground and gives the real speed and cost numbers, with the caveats key moments: 0:51 - reinforcement learning with calibrated decisions 2:08 - real speed, cost, and why this opens up so many use cases 3:31 - the three question types: Choice, Score, Noul 4:23 - where deterministic software meets AI agents 8:04 - hands-on in the playground watch today no matter what, then start building with it
这条是引用着另一条写的：「长文：Jev 或成 AI agent 缺失的控制层」