Jev for beginners: how to use it and what to build
Jev is TypeSafe AI’s new decision model. It returns type-safe structured values (a choice, a score, a probability) instead of generated text, at 4 cents per million input tokens with no output charge. This week I ran it on five real projects: PR categorization, a meta-analysis of my own Claude and Codex sessions, Gmail triage, the ChatPRD product insights graph, and a live audience dashboard built from 4,500 YouTube comments. What you’ll learn: What makes Jev fundamentally different from every other model I’ve used How I analyzed 1,700 PRs for 9 cents and what I found out about where my engineering effort actually went The personal meta-analysis you can run on your own Claude and Codex sessions right now Why I stopped using Jev alone, and what I pair it with now How I turned 4,500 YouTube comments into a searchable audience dashboard for almost nothing The real-time app I built in an afternoon that shows something surprising about Jev’s speed Why Jev’s pricing model is different from any LLM I’ve used, and what it makes practical to build The ChatPRD product insights project: 1,100 signals, 200,000 classifications, and what it cost me — Brought to you by: OpenArt —An all-in-one AI creation platform for images, videos, music, audio, and more — In this episode, we cover: (00:00) Jev launch and what makes it different from every other model (02:49) Type-safe values e
