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 Overheating: Opaque Probability Calculation, Value Comes from Self-Provisioning
The post notes Jev is overheating; users set questions, criteria, and adoption probability thresholds, but Jev does not disclose how probabilities are calculated, suggesting self-provisioning to unlock real value.
Jevの過熱ぶりが激しいが、、、 利用者側：質問文/criteria/採用確率閾値を設定 Jev内部：何を根拠に確率を算出したか非公開 これは意識すべきかと 自前で仕込むことで真価を発揮するかと
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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.
