How I AI

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
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Brought to you by:
Vanta—Automate compliance and simplify security
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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
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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
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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/
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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
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

I spent the day at OpenAI’s DevDay in San Francisco, and I have good news and bad news: OpenAI released a lot of stuff.

In this episode, I break down the announcements worth paying attention to - and show you what happened when I tested some of them early. We’ll meet my Dot, explore why Spaces and Sites could matter for how teams work, and get into the model and API updates I’m most excited about as a developer.

I use the Decisions API to find podcast thumbnails where nobody looks awkward, build a collaborative sketchpad with Astra ultrafast, and let my kids redesign a 3D world in real time. That last experiment cost about $97. My wallet has thoughts.

These are my early impressions: what’s promising, what still feels rough, and what I think you should try first.

What you’ll learn:
What OpenAI’s Dots can do, how I’ve been using mine, and why I’m waiting to give a full verdict
Why Spaces might be one of the most underhyped announcements for collaboration between humans and agents
How Sites with connectors and plugins could help teams share internal tools with the right data permissions
Where GPT-6.1 Sol fits in my model stack—and why speed and cost matter
What vision adds to the Decisions API, including my thumbnail-selection and hot dog demos
What Astra ultrafast makes possible for interactive AI apps, from collaborative drawing to a changing 3D game
Where the speed feels magical, where the experience still needs work, and what it costs
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In this episode, we cover:
(00:00) OpenAI DevDay recap—and pressing the Codex reset button
(00:58) Dots: early impressions and rough edges
(06:57) Spaces: working with humans and agents
(10:37) Sites, connectors, and sharing internal tools
(13:06) Models and platform: GPT-6.1 Sol
(14:36) Decisions API: fast decisions with vision
(15:27) Finding better podcast thumbnails with AI
(16:29) Hot dog or not hot dog?
(17:17) Astra ultrafast: speed, pricing, and possibilities
(18:50) The Other Pencil: drawing alongside AI
(19:45) Little Starship: a 3D world you can change with a prompt
(21:24) The $97 AI game—and what it makes possible
(22:25) Agents API, computer use, plugins, and plan updates
(23:03) What I’d try first
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Tools referenced:
• ChatGPT: Dots, Spaces, and Sites: https://chatgpt.com/
• Codex: https://openai.com/codex/
• OpenAI API — GPT-6.1 Sol, Decisions API, and Astra ultrafast: https://platform.openai.com/
• Jev: https://typesafe.ai/
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Other references:
• OpenAI DevDay 2026: https://devday.openai.com/
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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
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Jev is TypeSafe AI’s new decision model. It returns type-safe structured values (a choice, a score, a probability) instead of generated text, at 4 cents per million input tokens with no output charge. This week I ran it on five real projects: PR categorization, a meta-analysis of my own Claude and Codex sessions, Gmail triage, the ChatPRD product insights graph, and a live audience dashboard built from 4,500 YouTube comments.

What you’ll learn:
What makes Jev fundamentally different from every other model I’ve used
How I analyzed 1,700 PRs for 9 cents and what I found out about where my engineering effort actually went
The personal meta-analysis you can run on your own Claude and Codex sessions right now
Why I stopped using Jev alone, and what I pair it with now
How I turned 4,500 YouTube comments into a searchable audience dashboard for almost nothing
The real-time app I built in an afternoon that shows something surprising about Jev’s speed
Why Jev’s pricing model is different from any LLM I’ve used, and what it makes practical to build
The ChatPRD product insights project: 1,100 signals, 200,000 classifications, and what it cost me
—
Brought to you by:
OpenArt—An all-in-one AI creation platform for images, videos, music, audio, and more
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In this episode, we cover:
(00:00) Jev launch and what makes it different from every other model
(02:49) Type-safe values explained
(05:28) Understanding Jev outputs
(07:39) Use case 1: PR categorization and pairwise clustering
(11:12) Use case 2: analyzing your own local Claude Code and Codex sessions
(13:00) Use case 3: Gmail triage with Jev scoring and LLM follow-up
(14:30) Use case 4: ChatPRD’s product insights graph
(18:17) Demo: How I AI audience signal dashboard
(22:14) Demo: voice-to-color emotion-mapping app
(25:16) Jev week recap and what’s coming in episode 2
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Tools referenced:
• Jev (TypeSafe AI): https://typesafe.ai
• Vercel: https://vercel.com/ai
• GitHub API: https://docs.github.com/en/rest
• YouTube Data API v3: https://developers.google.com/youtube/v3
• OpenAI Realtime Voice API: https://platform.openai.com/docs/guides/realtime
• Gemini 3.5 Flash-Lite: https://ai.google.dev/gemini-api/docs/models/gemini-3.5-flash-lite
• API Ninjas Quotes API: https://api-ninjas.com/api/quotes
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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
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

I got up early to record an Opus 5.5 review. Then Anthropic and OpenAI dropped new models on the same morning, and I decided to do something I’d never done before: take the How I AI bench live. I put GPT-6 Astra, GPT-6 Sol, Claude Opus 5.5, and more through the work I actually care about: emails, PRDs, frontend prototypes, backend work, long-running agents, SVGs, and video editing. I scored the outputs without knowing which model made them, so you get to watch me make predictions, change my mind, and reveal my own very inconsistent taste. Astra won my heart. Opus 5.5 won my week. Sol still has me split. There’s a creative result I got completely wrong, an LLM judge that disagreed with me, and a return to Barbie Bench: the 3D fashion game that keeps reminding me how far we have to go. The hands are tragic. AGI has not arrived.

What you’ll learn:
How I run the How I AI bench blind, and what gets an output a bad score before I even know which model made it
Why Astra won my heart while Opus 5.5 might be overall strongest, especially for long-running agents and B2B frontend
Where Sol still wins me over on clear writing, readable PRDs, and price
The character SVG results that completely overturned my prediction about Anthropic
What happened when I asked these models to edit video, and why I think skills explain part of the disappointment
Why an LLM judge disagreed with my rankings, and what it was rewarding that I wasn’t
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In this episode, we cover:
(00:00) LIVE setup and new model launches
(01:30) What’s new in Opus 5.5, Sol, and Luna
(04:11) Guardrails, personality, and speed
(09:00) The How I AI bench and blind evaluation process
(11:31) Email and personal-productivity results
(13:50) Frontend prototype vibe checks
(24:10) Backend, agent personality, and long-running tasks
(28:25) SVG illustration test
(29:48) AI video-editing results
(30:43) Predictions before the reveal
(31:20) Barbie Bench: the 3D fashion-game test
(34:17) Results: Astra, Sol, and Opus 5.5
(35:04) Writing clarity and creative surprises
(36:51) Why the LLM judge disagreed with me
(37:24) What each model is actually best for
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Tools referenced:
• Claude Opus 5.5: https://www.anthropic.com/claude-opus-5-5
• GPT-6 Sol and Luna: https://openai.com/index/introducing-gpt-6-sol-and-luna/
• Codex (OpenAI): https://openai.com/codex
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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 jordan@penname.co.

I’ve been off Claude for months. Not because it got dumb, but because it got annoying. The rambling, the hedging, the preachy little disclaimers on tasks that didn’t need them. I moved most of my daily work to Codex and I didn’t miss it. Then Anthropic shipped Opus 5.5: 40% cheaper than Opus 5, faster, and with what they’re calling a fundamentally different alignment approach. I ran it for a week across real work, including four long-running agentic tasks, a full ChatPRD homepage redesign, an SVG benchmark, and one very firm refusal, and I’m ready to give you the honest verdict. There’s a lot to like. There are still two things that drive me a little crazy. And there’s one capability I genuinely wasn’t expecting.

What you’ll learn:
Why I walked away from Claude entirely, and what it took for me to come back
The real cost math on Opus 5.5 and why pricing matters more for agentic work than single prompts
What happened when I ran four long-running agentic tasks, including one that tried to manipulate Claude mid-run
Why Opus 5.5 is now my go-to for frontend prototyping, and where it still lets me down
The one capability I genuinely didn’t see coming, and no other model in my stack can match it
The moment Opus 5.5 told me flat-out no, and what that says about where Anthropic’s safety posture actually lands in practice
Where Codex still wins, and how I’m splitting my model stack after a full week of testing
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In this episode:
(00:00) Why I stopped using Claude
(01:02) What Anthropic says Opus 5.5 is
(01:54) Cost, speed, and benchmark overview
(03:20) Safety, alignment, and the cybersecurity limits
(05:02) How I AI bench
(05:39) Voice test: is it actually not annoying?
(07:54) Long-running agentic task results
(10:50) Frontend prototyping
(17:23) Writing voice and email
(19:41) SVG illustrations
(20:46) Video editing
(21:42) My verdict: what it’s good at, what it still isn’t
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Tools referenced:
• Claude Opus 5.5: https://www.anthropic.com/claude-opus-5-5
• ElevenLabs MCP connector: https://elevenlabs.io/mcp
• Codex (OpenAI): https://openai.com/codex
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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 jordan@penname.co.