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.
Trade-offs Between JEV and Fast Classifiers: Designing Flows to Contain Errors
The author argues that programs that classify correctly need not use JEV; cheap fast classifiers make errors, and when errors are unacceptable, flows should add other components to contain them.
#JeV #ai大pk 小小研究看法： 會出錯的便宜快速分類器 程式能正確分類的就不用 JEV 錯誤如果是不可接受的, 要設計流程加入其他東西使錯誤收束
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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.
