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 closed-decision boundary: enumerable slots vs. LLM
The post argues Jev fits closed decisions: pick the tool, fill enumerable slots, and use an LLM only when input can't be enumerated—calling that the right line to draw.
Jev for the closed decision -- pick the tool, fill the enumerable slots -- and an LLM only where the input can't be enumerated. That's the right place to draw the line.
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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.
