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Jev, the first model from TypeSafe AI, does not generate text. You give it a state and a set of typed questions, and it returns a probability distribution over the answers you defined, in a single parallel pass that costs you nothing for output. Your code then branches on the result, so Jev works as a smart if-statement rather than a chatbot. TypeSafe trains it with what it calls RLCD, Reinforcement Learning for Calibrated Decisions: a new training target, in the way RLHF was, whose goal is probabilities that mean what they say rather than text a rater likes or an answer a verifier accepts. This post explains what Jev is and how to use it, then goes through RLCD in pictures. It covers what calibration means, why RLHF's reward destroys it and why RLVR's reward cannot produce it, and why a proper scoring rule is the only objective that fits the stated goal. It also shows why that makes the 'reinforcement' in the name mostly a story about where the data comes from, and why the one number where Jev still leads its open clones is confidence ordering, which temperature scaling cannot fix. Nothing about RLCD has been published, so every claim is labelled by who made it.