Jev: 8 real use cases for the fastest, cheapest model I’ve ever used | John Lindquist

About this episode

John Lindquist created egghead.io, a developer education platform used by hundreds of thousands of working engineers. These days he’s building mega.dev, a hands-on program specifically for developers who want to do real work with AI agents, not just prototype them. What you’ll learn: • Why Jev is a decision engine, not a chatbot, and what that distinction actually changes about how you build • How John built a real-time voice to-do app that classifies and executes commands with no visible pause • The data deduplication pattern that merges messy records in milliseconds using confidence scores • Why Jev works best as a router, and how a single text input can navigate users deep into an app • What a chess match between Jev and a low-reasoning LLM reveals about speed, cost, and when to use which • The multi-step classification pattern John reaches for when one Jev pass isn’t enough • Where Jev falls short, and when you should still reach for a full generative model — Brought to you by: Vanta—Automate compliance and simplify security — In this episode, we cover: (00:00) John Lindquist returns for Jev week (04:32) What Jev actually outputs (06:15) Demo: real-time voice to-do