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: Revolutionizing AI Agent Decision-Making
Introduces the Jev architecture: instead of leaving everything to the LLM, Jev handles judgment only, standardizing decisions via Choice, Score, and Noul, with a four-step onboarding process.
【衝撃】 AIエージェントの作り方が変わる Jevは「実行」ではなく「判断」だけを担う Choice / Score / Noulで意思決定を型にする 導入は4ステップから始められる LLMに全部やらせる設計から分離する この4枚を見ると強さが一発でわかる 実装の全体像は👇
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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.
