Jev: 8 real use cases this fast, cheap model

ℹ️ EPISODE INFO

Released

September 30, 2026

Duration

46m

Channel

How I AI

Watch on YouTube

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📝 EPISODE DESCRIPTION

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:*

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

*Brought to you by:* Vanta—Automate compliance and simplify security: https://www.vanta.com/howiai *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 *Blog and detailed workflow walkthroughs from this episode:* John Lindquist’s 6 Jev Workflows for Apps, Data, and AI Agents: https://www.chatprd.ai/how-i-ai/john-lindquist-jev-workflows ↳ How to Build a Real-Time Voice-Controlled To-Do List App with Jev: https://www.chatprd.ai/how-i-ai/workflows/jev-voice-controlled-todo-app ↳ How to Deduplicate and Clean Large Datasets Using AI Decision Models: https://www.chatprd.ai/how-i-ai/workflows/jev-data-deduplication ↳ How to Build a Smart Command Bar and App Router with Jev: https://www.chatprd.ai/how-i-ai/workflows/jev-smart-command-bar-app-router *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 *Have a really cool use case of how you use AI in your work? Let us know:* https://docs.google.com/forms/d/e/1FAIpQLSfP8wYCeDo5K0DxscDmoqgkxYY8ngA3B2anUDDvC4xadGTerg/viewform _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email jordan@penname.co._

🎤 SPEAKERS

• Claire Vo

  host

  Product leader and AI-focused creator who hosts “How I AI with Claire Vo.”

• John Lindquist

  guest

  Developer/educator who demos and explains practical AI workflows and tools, including work associated with mega.dev.

🧾 EPISODE SUMMARY

In this episode of How I AI, featuring Claire Vo and John Lindquist, Jev: 8 real use cases this fast, cheap model explores How Jev enables real-time, low-cost decision-driven product features Claire Vo and John Lindquist explain why Jev (a fast, cheap decision model) changes what product teams can build by turning natural language into structured choices, scores, and function calls instead of text.

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