How I AI

I put Meta’s new Muse Wonder (and Wonder Full) frontier models through the How I AI bench, which covers nearly 20 tasks across prototyping, agentic workflows, coding, writing, and creative work. I scored everything with an LLM-as-a-judge pass and my own voice notes, then pulled together the first-look results you see here.

What you’ll learn:
Why Meta’s “personal and professional agent” framing mostly holds up, and where it doesn’t
What active focus mode actually does on long-running tasks, and whether it’s real
The three agentic tasks where Muse ranked first, and why those categories matter
What Muse’s design output actually looks like (it’s not good)
How it compares to Sonnet on voice assistant tasks
Where Muse lands on SVG and short-form video versus Opus 5.5, Astra, and Fable
Whether I’m actually putting Muse into any of my agent workflows
—
Brought to you by:
DX—Engineering intelligence for the AI era
Jira AI SDLC—Get your tokens’ worth with Jira
—
In this episode, we cover:
(00:00) Intro and what Muse Wonder is
(01:19) Meta's frontier capability claims
(04:36) Muse code
(06:06) Benchmark overview and how the How I AI bench works
(07:51) Overall results and TL;DR verdict
(11:53) Agentic tasks
(13:36) Email writing and voice assistant performance
(14:35) Where Muse falls short
(18:28) SVG illustration results
(19:33) Short-form video editing
(21:18) Fast on one-shot work, slow on agent tasks
(22:15) Final verdict and where I'll use Muse Wonder next
—
Tools referenced:
• Meta Muse Wonder / Muse Wonder Full: https://research.meta.ai/blog/muse-wonder
• Muse Code: https://developer.meta.com/ai/lp/muse-code
—
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.

Alex Cornell is a design director at Meta, where he leads design on Muse, Meta’s consumer AI agent. He came to the role with a long background in software product design, including time as a founder, and has spent the past year building one of the most thoughtful agent interfaces in the consumer AI space. Muse ships with a custom avatar, a three-layer status display, a personalized ideas surface, and a generative feed. Alex has been sharing the reasoning behind each of those design decisions publicly on Threads.

What you’ll learn:
Why Alex built Muse to feel like messaging, not an AI app
How Muse’s three-layer status system accommodates different levels of technical expertise
What generative UI actually means in a shipped consumer product
How Alex turned years of forgotten Instagram saves into a daily shopping catalog
Why his mom apologized to her Muse, and what it means for AI design
How he uses Muse to track his golf game, shot by shot, as he works toward a single-digit handicap
The mindset shift designers need when the output is never pixel-perfect
When to use the main chat versus a side chat, and where the architecture still falls short
—
Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
—
In this episode, we cover:
(00:00) Welcome and guest intro
(02:50) Designing Muse
(05:33) Choosing which primitives to surface: characters, ideas, feed
(08:50) Speed of model and response times
(11:01) Generative UI and personalization
(14:25) SkyMall demo: Instagram saves turned into a shopping catalog
(17:16) Shopping, monitoring, and price alerts with Muse
(19:07) Instagram and Pinterest connectors for visual data
(21:40) Golf use case: round recaps, shot data, practice breakdowns
(25:28) Main chat versus side chats
(27:56) Old blog turned morning podcast: 20 years of posts via the Wayback Machine
(29:39) Chess dashboard: game analysis with a snarky coach
(30:59) What designers need to learn for AI, and what still works
(34:33) Prompting strategy when Muse is being obstinate
(36:31) Where to find Alex
—
Tools referenced:
• Meta Muse: https://muse.ai
—
Other references:
• The Wayback Machine: https://web.archive.org
• Instagram: https://www.instagram.com
• Pinterest: https://www.pinterest.com
• Chess.com: https://www.chess.com
• SkyMall/Sharper Image aesthetic: https://www.skymall.com
—
Where to find Alex Cornell:
LinkedIn: linkedin.com/in/alex-cornell-5449a99
X: https://x.com/alexcornell
—
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.

Kath Korevec is a member of the Product staff at OpenAI working on Codex, and she spent over a year building and using ChatGPT Sites internally before its public launch. She’s been on the front lines of shipping Plugin Insights, MCP plugin hosting, and the connector ecosystem, which now includes around 60 integrations.

What you’ll learn:
What Plugin Insights actually does, and why it changes who can use a site
The incident command site Kath built for her OpenAI team, and how it uses live Slack and Notion connectors
The one phrase that tells Codex to wire up connectors for you
The infrastructure layer inside Sites that most people haven’t touched yet
How Kath fixed her Spotify after her kids wrecked it, using Reddit and computer use
The skill distribution model behind her community dungeon crawler, and why it’s a new way to think about collaboration
Why model speed is what actually determines how creative you get
Where Kath draws a hard line on AI acting in her name
—
Brought to you by:
Merge—Connective infrastructure for production AI
Vanta—Automate compliance and simplify security
—
In this episode, we cover:
(00:00) Welcome and intro
(01:00) Sites: internal testing and use cases
(05:53) Sites infrastructure
(09:00) Kath’s favorite connectors
(11:10) Unique ways to use Sites
(12:28) Curating a custom Spotify playlist
(15:50) Game design and development
(25:42) Bringing inference into Sites: the widget experiment
(30:00) Awesome Sites gallery
(31:57) Kath’s prompting strategy
(34:20) Wrap-up and how to find Kath
—
Tools referenced:
• ChatGPT Sites: https://chatgpt.com/sites
• OpenAI Codex: https://openai.com/codex
—
Other references:
• OpenAI DevDay: https://openai.com/devday
• Awesome Sites: https://awesomesites.ai
—
Where to find Kath Korevec:
LinkedIn: https://www.linkedin.com/in/kathleensimpson/?isSelfProfile=false
X: https://x.com/simpsoka
—
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.

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 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 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
—
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
—
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/
—
Other references:
• OpenAI DevDay 2026: https://devday.openai.com/
—
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.