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 returns fast judgments like selection and evaluation instead of generating text
Introduces Jev as a judgment-oriented technology that does not generate text but quickly performs selection and evaluation, applicable to inquiry routing, request prioritization, and human confirmation, with emphasis on validating accuracy, speed, and rework across industries.
Jevは文章を生成せず、選択・評価などの判断を素早く返す技術。 問い合わせの振り分け、依頼の優先度、人への確認。業界を問わず、相性のよい業務を探し、精度・速度・手戻りまで検証したい。 AI活用を加速する視点を整理しました。 https://note.com/nouchinho/n/n482865f66982… #Jev #AI活用
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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.
