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I'm using Jev more than Opus 5.5 or GPT-6. Here's why.

Claire Vo opens the hood on her week in Jev: nine cents to classify two years of ChatPRD pull requests, her Claude and Codex sessions sorted in minutes, and a real-time voice app where the quote API, not the decision model, was the slowest part.

Source screenshot of I'm using Jev more than Opus 5.5 or GPT-6. Here's why.
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What it does

The through-line is a question she now asks before building anything: does this step need prose, or just a decision? Decisions go to Jev at fractions of a cent; the interesting clusters go to GPT-6 Astra. One pattern from the episode sticks: her AI usage fell from nearly all engineering in January to under 40% by September as agents and media work took over. ChatPRD hosts written walkthroughs of every workflow she shows.

How you can use it

You can sort large archives of past work, such as team project logs or code updates, into clear topics. Before starting, gather the written records you want to organize. A developer can then set up Jev to compare items side by side and decide whether they share the same subject.

Jev only makes quick choices between fixed options and cannot write summaries or sentences. After it groups related records together, your developer can send each cluster to a standard text-writing model to name the topic.

Maker-reported (not independently measured by JevMade)

Author-reported: 1,700 pull requests compared across 17,000 pairs for about nine cents · Author-reported: 200,000 classifications for about $4 on the Jev side of a product-insights graph · Author-reported: AI usage shifted from nearly 100% engineering in January to under 40% by September

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