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.
How Jev Composes and Manipulates UI in Real Time
Jev takes one line of text and answers nine closed questions (era, glowing or reflective, colour, typeface, wear, density, mood, layout, scene), then scores fifteen dials against those answers in a second call, with no LLM in the loop.
Jev composes and manipulates UI in real time one line of text goes in. Jev answers nine closed questions: era, glowing or reflective, colour, typeface, wear, density, mood, layout, scene. a second call scores fifteen dials against those answers. no LLM in the loop @typesafeai
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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.
