@pro.glitch: What Jev actually is, why it's so fast, and what you can build with it. Classification is as old as AI that can read: give it text, ask a question with fixed answers (complaint yes/no · which team · urgency 1–5), and the label drives a rule. The problem: we've been using models built for writing, a few seconds per answer. Jev (TypeSafe, launched Sept 15, founded by one of the OpenAI researchers behind ChatGPT's instruction-following) only decides. It never writes. Send the text once with every question; it scores every answer in one pass: yes/no, pick one, or a score, each with a confidence. 70–500 ms. Input ~$0.042 per million tokens, output free. TypeSafe measured up to ~200× faster and ~400× cheaper than top models on its own workflows, and says real-world gains are usually lower. Builders: a resume screened against 6,000+ companies in 25 seconds for $0.37; 98,000 items labeled in ten minutes. ~13% of paid Vercel AI Gateway teams used it within a day. Calibrated: "70% sure" should be right ~70% of the time, so software can act alone above a threshold and route the rest to a person. Build with it: every competitor ad, same five questions · every ticket, review, call note and CRM entry across systems, tagged the same way · an agent's actions checked before they run (LangChain already does this). Edges: text only; weak at math, counting, dates; reads your question literally; you design the questions and categories up front. That design is the skill. #jev #aiagent

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Sunday 27 September 2026 19:55:58 GMT