Jev versus text-generating models
Jev does not generate free-form text. A side-by-side video compares parallel decision-making with token-by-token text generation.
Jev's typed System One gate: zero-hallucination means schema match, not correct deny
TypeSafe AI shipped Jev on 15 Sep as a typed System One gate: state in, Choice/Score/yes-no out, 70 to 500ms, no free-form string. The post notes Jev cannot invent a tool but can still pick the wrong one, and zero-hallucination means schema match, not a correct deny.
Jev cannot invent a tool. It can still pick the wrong one. @typesafeai shipped Jev on 15 Sep as a typed System One gate: state in, Choice/Score/yes-no out, 70 to 500ms, no free-form string. Zero-hallucination here means schema match, not a correct deny. @CompleteSkeptic said as
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Pricing, evaluations, and the name Jev
The launch thread explains code-and-AI workflow evaluations, the input price announced at launch, and free output tokens.
Jev Model: Decision-Making Probability Generation, 200× Faster and 400× Cheaper Than LLMs
This model is built for decision-making rather than text generation, outputting probabilities for each option in parallel instead of token-by-token responses. The author shares a simplified comparison of normal LLMs versus this Jev model.
