I made an AI-smell checker tool with Jev.

@lucky_note_lab ラッキー｜AIを武器に。 · Sep 20, 2026 · case #685482 · listed via Jev Hub

Still from the clip posted with this case; watch it in the original post on X.

Built an AI-text detector that uses Jev to score how AI-generated a piece of writing is.

The original post, translated

I made an AI-smell checker tool with Jev. ・Codex: planning, writing, implementation ・Jev (typesafe-ai/jev): quantify the AI smell of text ・HyperFrames: screen production, video export ・FFmpeg: MP4 conversion, final check ・TypeScript/Node.js: execution environment for processing The AI-smell score is not an effect, it is actually measured with Jev. The article is 👇️

Show the original post in its source language ▾

JevでAI臭チェッカーツール作りました。・Codex：企画・文章・実装・Jev（typesafe-ai/jev）：文章のAI臭を数値化・HyperFrames：画面制作・動画書き出し・FFmpeg：MP4化・最終チェック・TypeScript／Node.js：処理の実行環境 AI臭の数値は演出ではなく、Jevで実際に計測しています。記事は👇️

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

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Numbers are a snapshot taken from X when the case was added to the library (schema v1, collected 2026-09-19); they will not match today.

Where this case fits

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.

🛠️ Coding & developer tools