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.
Tuning Jev to Your Decision Criteria with GEPA
Richard Artoul shares a demo showing how to use GEPA to tune Jev, a model trained only on synthetic data, to your exact decision criteria by manipulating the prompt and labeling a few examples.
cool demo on how to use GEPA to take an off the shelf model like Jev (trained only on synthetic data) and tune it to your exact decision criteria simply by manipulating the prompt and labeling a few examples you should follow Seth, he’s way ahead of the curve on AI functions
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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.
