JEV - The Model that doesn't generate text

This AI model doesn't write a single word. It only makes decisions. Jev is a new "System One" AI model from TypeSafe AI. Instead of generating text like ChatGPT, Claude, or Gemini, it answers multiple-choice questions about your text and returns calibrated probabilities your code can act on directly. In this video, I break down what Jev is, how it works, how to use its API, and where it actually fits in your app. 📚 What you'll learn: ✅ Why most apps waste money sending small decisions to big LLMs ✅ System 1 vs System 2 thinking, and how Jev and LLMs work together ✅ Calibration: why "90% confident" should mean right 90% of the time ✅ The Jev API: state, questions, and the Noul, Choice, and Score types ✅ Confidence bands: when to act automatically vs hand off to a human ✅ Pricing, latency, rate limits, and the 200× faster / 400× cheaper claims ✅ Real use cases, including routing, content safety, and AI agent guardrails ✅ What Jev can't do, and how to test it safely before switching ⚠️ Note: The speed and cost figures in this video are TypeSafe's own best-case estimates, not independent benchmarks. Test them on your own data. ⏱️ Timestamps 00:00 Introduction [00:12] The problem: essay prices for one-word answers [00:33] What JEV returns: probability, distribution, confidence [01:29] Who built it: TypeSafe AI [01:55] System 1 vs System 2 [02:58] JEV decides, the LLM writes [03:47] Confidence bands: act, confirm, or hand off [05:38] Request type [06:41] Three question types [07:13] Confidence of JEV [07:40] Endpoint and SDKs [07:53] Speed: ~100 ms per call [08:27] Pricing: input is cheap, output is free [08:58] The 200× / 400× claim (and the catch) [09:34] Where JEV works best [10:16] AI agent guardrails example [10:50] What JEV can't do [00:00] How to test it before switching #AI #Jev #TypeSafeAI #LLM #AIForDevelopers