Jev explained: A hands-on introduction
Registration for this session is closed. Join our next JEV workshop on October 10 at 10 am Pacific: https://luma.com/9v2t909b
Jev is an AI model that can't write a single sentence. It can only choose & that's exactly what makes it powerful. Most frontier models are built to answer free-response questions. Jev is built to answer multiple-choice ones, in about a tenth of a second, at 4.2 cents per million input tokens (output tokens are free).
In this one-hour hands-on session, we'll cover three things: what Jev actually is, how you use it, and one example that shows how far the idea goes.
What you'll learn:
Why Jev exists. When a decision is a multiple-choice question, why pay for a slow and expensive frontier model that can generate anything? Jev answers in about 0.1 seconds, costs a fraction of frontier models, and tells you how confident it is.
How to use it, live. We'll work through the TypeSafe playground together: State (what changes every call) vs. Questions (the fixed "form" you write once), and the three question types: Choice, Score, and Noul (a boolean that comes back as a probability).
How it works in code. We'll build an Enterprise App decision router: three questions asked in parallel against every incoming query, with confidence scores deciding whether to route automatically or send to a human. Jev decides and then your code controls what happens next.
How far the idea goes. A real-world example: a browser agent rebuilt around Jev that solved 49/49 benchmark tasks (same accuracy as a frontier model), ~30x cheaper and ~4x faster. The lesson: Jev is purpose-built for bounded semantic decisions, and Agentic AI systems are full of them.
Who it's for: Developers, technical leaders, AI practitioners, and anyone interested in how agentic systems make decisions. You don't need prior TypeSafe experience; basic Python familiarity will help with the hands-on portion.
You'll leave with a clear mental model of Jev, working code in hand, and the ability to recognize where it fits (and perhaps more importantly where it doesn't) in real agentic AI systems.