Topic
TypeSafe's Jev returns typed answers with probabilities instead of text. Its documentation, the first developer reports and early independent papers show where it fits in a pipeline, what the published price works out to, and why its probabilities need checking on your own data.
TypeSafe's Jev returns typed answers with probabilities instead of text. The idea borrows Kahneman's System 1, a calibration objective called RLCD, and a strong claim about hallucination. The concepts, the evidence behind them, and where each one stops.
Routing, gating, judging and choosing an agent's next step are decisions, not writing. Splitting decision from generation is the right architecture, and TypeSafe's Jev is the first product built for it. Trust will come from independently measured calibration, not a promise that it cannot hallucinate.
TypeSafe AI came out of stealth on September 15 with $40 million in seed funding and Jev, a model that answers typed questions about a block of text with probabilities instead of prose. The pricing is unusual, the speed claims are large, and the first independent tests are mixed.