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 Differentiable IF and Tensor Logic
The post highlights two special aspects of Jev: implementing tensor logic without calling it that, making IF arguments differentiable, and not wasting generation tokens on known context.
There are two things that are special that Jev did 1. They are doing tensor logic but don’t call it that, which is that they make the args for your IF differentiable 2. They don’t waste gen tokens on context you know 1 is just telling you that true false needs a gradient to be
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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.
