Jev: 8 real use cases for the fastest, cheapest model I’ve ever used | John Lindquist
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Jev: 8 real use cases for the fastest, cheapest model I’ve ever used | John Lindquist
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Jev: 8 real use cases for the fastest, cheapest model I’ve ever used | John Lindquist
🎙️ Educator and developer John Lindquist shares eight Jev demos, from live voice classification and deduplication to agent routing, showing how low costs make once-impractical ideas worth trying
Claire Vo
Sep 30, 2026
Transcript
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.
Listen or watch on YouTube , Spotify , or Apple Podcasts
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 app
( 08:17 ) How sequential Jev calls chain together
( 10:38 ) Demo: plain English to function name (grocery cart)
( 11:50 ) Demo: data deduplication and record merging
( 13:45 ) Confidence scores and multi-model validation
( 15:06 ) Demo: Jev as a multi-level app router
( 18:23 ) Architecting around Jev
( 19:35 ) Demo: Jev vs. traditional LLM at chess (speed and cost benchmarks)
( 24:29 ) DOM interactions as a decision set, not an infinite canvas
( 28:21 ) Demo: Wikipedia “path to philosophy” route mapper
( 30:28 ) Demo: multi-agent coordination and collision avoidance
( 33:36 ) Demo: real-time presentation coach
( 36:56 ) Quick recap
( 39:54 ) Lightning round and final thoughts
Tools referenced:
• Jev (TypeSafe AI decision model): https://typesafe.ai/blog/introducing-system-one-models-and-jev
• Vercel AI Gateway: https://vercel.com/docs/ai-gateway
• OpenRouter: https://openrouter.ai
• Opus 5.5 (mentioned in context of iterative demo building): https://www.anthropic.com/claude-opus-5-5
Where to find John Lindquist:
LinkedIn: linkedin.com/in/john-lindquist-84230766
X: https://x.com/johnlindquist
Mega.dev: https://mega.dev/
Egghead.io: https://egghead.io/
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
X: https://x.com/clairevo
Production and marketing by https://penname.co/ . For inquiries about sponsoring the podcast, email [email protected] .
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How I AI
How I AI, hosted by Claire Vo, is for anyone wondering how to actually use these magical new tools to improve the quality and efficiency of their work. In each episode, guests will share a specific, practical, and impactful way they’ve learned to use AI in their work or life. Expect 30-minute episodes, live screen sharing, and tips/tricks/workflows you can copy immediately. If you want to demystify AI and learn the skills you need to thrive in this new world, this podcast is for you.
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