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.
Where Jev Fits in an Agent Loop: Typed Routing and Risk Gating
The post outlines a five-step agent loop and positions Jev as the typed layer for routing, gating, scoring, risk and urgency, able to re-check before destructive actions.
Where each belongs in an agent loop: 1. Observe state (ticket, tool args, logs, messages) 2. Jev — route / gate / score / risk / urgency (typed) 3. LLM — plan, write, debug, explain (open-ended) 4. Tools / APIs execute 5. Loop — Jev can re-check before destructive actions
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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.
