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 Explained in 5 Slides: Differences from if Statements, OpenJev, and Speedups
The author uses 5 slides to introduce Jev: the idea is simple, but combined with AI it makes an old mechanism surprisingly interesting, covering differences from if statements, OpenJev, speedups, and use cases.
「Jev」が突然話題に。 実は考え方はシンプル。でもAIを組み合わせると、昔からある仕組みがかなり面白く化けます。 if文との違い、OpenJev、高速化、使い道まで5枚で解説 #Jev #生成AI #OpenJev #AI https://note.com/angework/n/nbf305d585ab3…
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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.
