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.
TypeSafe: A Billion Dollars Still Won't Buy Pre-training—Slicing and Dicing Existing Stacks
TypeSafe's Diogo Almeida says even with a billion dollars he wouldn't pre-train a foundation model from scratch; instead, Jev is built by slicing, dicing, and recombining existing stacks.
Give TypeSafe’s Diogo Almeida a billion dollars and he still wouldn’t pre-train. His bet: slice, dice, and Frankenstein existing stacks — anything except burning the check on a scratch foundation model. @swyx @labenz Latent Space — Why We Made Jev — Diogo Almeida, TypeSafe
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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.
