10-minute video breaks down Jev's Choice, Score and Noul decision types
Rob Shocks demonstrates in the playground how Jev returns typed decisions and calibrated probabilities, and gives speed and cost figures of about 100ms per call and $42 per billion tokens.
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
This post quotes another post: “Jev could become the control layer AI agents have been missing”