For the task of making judgments, I've always been using

@Wizarp_zz Wizardp zz · Sep 19, 2026 · case #133159 · listed via Jev Hub

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Reviewed Jev's docs and OpenRouter listing, noting it returns typed decisions with confidence probabilities that drop straight into code.

The original post, translated

For the task of making judgments, I've always been using large models like using a cannon to shoot mosquitoes. TypeSafe launched Jev yesterday specifically for this. After going through OpenRouter and the official docs, 5 key points: 1. It's a System One decision model, specifically for judgment/classification tasks: give it state + typed questions, and it returns typed decisions with confidence probabilities, no JSON parsing layer, results can be directly plugged into code 2. For classification tasks, it's 200 faster than LLMs

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做判断这个活，我一直拿大模型当大炮打蚊子。 TypeSafe 昨天上线的 Jev 专干这个，我对着 OpenRouter 和官方翻完，5 个重点： 1. 它是 System One 决策模型，专做判断/分类的活：给状态 + 带类型提问，回带置信概率的类型化决策，没有 JSON 解析层，结果能直接塞代码 2. 分类任务比 LLM 快 200

The post above is a machine translation from zh; the untranslated text is in the fold-out.

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Filed under coding & developer tools. In the pattern Jev is built for, the model answers a bounded question per step — and ordinary code acts on the answer, because the answer is already a value rather than a paragraph. Other posts in the same family are on the coding & developer tools page.

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