How I AI

Brooke Lacey has been making videos since before Facebook had video, and she built a TikTok audience of more than 290,000 by learning to cut short-form videos herself. With over 25 years in tech, she now builds her own apps, including Zosha, a free mobile app for learning AI. In this episode, Brooke walks me through the Claude Code, FFmpeg, and Whisper workflow that edits her TikToks in about 15 seconds, how she mines her comments for new scripts, and how she runs a weekly short-form operation with no editor.

What you’ll learn:
How Claude Code edits a raw phone video on her computer in about 15 seconds
How she taught Claude her jump-cut cadence
Why her TikTok comments became her content brief
How she keeps growing her audience while posting just once a week—and what’s next
What over 25 years in tech taught her about this AI moment
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Brought to you by:
Firecrawl—Give your AI agents web data and beyond
Auros—The human network behind AI
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In this episode, we cover:
(00:00) Intro and guest welcome
(02:30) Brooke’s background in video
(06:56) Her first session cutting videos with Claude Code
(08:51) Live demo: Claude edits a video in real time
(11:05) Walking through the edit chain
(15:58) How she taught Claude her cadence, her hooks, and what funny sounds like
(25:36) Quick recap
(26:20) Posting frequency, live streaming plans, and TikTok ads
(28:00) Lightning round: Gen X, wisened experience, and YOLO energy
(32:43) When Claude disappoints: the lane OS, status files, and how she prompts it back
(34:50) Where to find Brooke and the Zosha app
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Tools referenced:
• Claude Code: https://claude.ai/code
• FFmpeg: https://ffmpeg.org
• Whisper (OpenAI): https://openai.com/research/whisper
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Other reference:
• How I AI episode with John Blackman (91-year-old vibe coder): https://www.lennysnewsletter.com/p/how-a-91-year-old-vibe-coded-a-complex?utm_source=publication-search
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Where to find Brooke:
TikTok: https://www.tiktok.com/@brookejlacey
X: https://x.com/brookejlacey
Zosha app: http://zosha.app/
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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 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
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Brought to you by:
DX—Engineering intelligence for the AI era
Jira Product Discovery—Prioritize with insights, build with confidence
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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
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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
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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.

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
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Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
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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
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Tools referenced:
• Meta Muse: https://muse.ai
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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
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Where to find Alex Cornell:
LinkedIn: linkedin.com/in/alex-cornell-5449a99
X: https://x.com/alexcornell
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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.

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
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Brought to you by:
Merge—Connective infrastructure for production AI
Vanta—Automate compliance and simplify security
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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
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Tools referenced:
• ChatGPT Sites: https://chatgpt.com/sites
• OpenAI Codex: https://openai.com/codex
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Other references:
• OpenAI DevDay: https://openai.com/devday
• Awesome Sites: https://awesomesites.ai
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Where to find Kath Korevec:
LinkedIn: https://www.linkedin.com/in/kathleensimpson/?isSelfProfile=false
X: https://x.com/simpsoka
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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.

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
—
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
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.