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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

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:

  1. Why Jev is a decision engine, not a chatbot, and what that distinction actually changes about how you build

  2. How John built a real-time voice to-do app that classifies and executes commands with no visible pause

  3. The data deduplication pattern that merges messy records in milliseconds using confidence scores

  4. Why Jev works best as a router, and how a single text input can navigate users deep into an app

  5. What a chess match between Jev and a low-reasoning LLM reveals about speed, cost, and when to use which

  6. The multi-step classification pattern John reaches for when one Jev pass isn’t enough

  7. Where Jev falls short, and when you should still reach for a full generative model


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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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