I have personal-agent fatigue, so I ran the first How I AI personal-agent benchmark: Grok Bot, Dot, Muse, and Claude Duet across nine real tasks, from a Slack reply and a Sentry bug to a Figma thumbnail, inbox triage, a family newspaper, physics homework for a fourth grader, shoe shopping, and a ChatPRD launch video. I used computer use to operate them: Claude ran Dot, Codex ran the rest and scored the results live. Here’s the scoreboard—and which agent I’d actually trust for work vs. life.

Brought to you by:

WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more

Firecrawl—Give your AI agents web data and beyond

In this episode, we cover:

(00:00) Introduction

(02:13) The personal-agent benchmark setup

(05:00) The nine tasks and the prompts I used

(07:20) Scoreboard overview

(10:06) Task 1: Draft a Slack reply as me

(11:39) Task 2: Cut a short-form video from raw Dropbox footage

(14:56) Task 3: Fix a bug from Sentry

(17:30) Task 4: Build a How I AI thumbnail in Figma

(19:25) Task 5: Triage my inbox for to-dos

(21:06) Task 6: Build a family newspaper

(23:30) Task 7: Explain a physics homework question to a fourth grader

(24:54) Task 8: Shop for shoes

(26:46) Task 9: Make a ChatPRD Product Intelligence launch video

(30:01) Final scores

(32:38) My takeaways

Tools referenced:

• Grok Bot: https://x.ai/bot

• Dot: https://chatgpt.com/features/dots/

• Meta Muse: https://ai.meta.com/muse/

• Claude Duet: https://claude.com/product/claude-duet

• Codex: https://openai.com/codex

• ElevenLabs: https://elevenlabs.io/

• Figma: https://www.figma.com/

• Sentry: https://sentry.io/

• Linear: https://linear.app/

• Dropbox: https://www.dropbox.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.

Why Reddit built its own AI prototyping tool | Blair Metcalf

Brought to you by:

• Firecrawl—Give your AI agents web data and beyond

Blair Metcalf is the principal UX engineer at Reddit and the creator of Reddit Proto, an internal platform that helps designers and product managers build production-like prototypes without touching GitHub. In this episode, he shows how the system combines real Reddit data, reusable scaffolds, Cursor annotations, and a custom design system MCP to turn ideas into shareable prototypes. He also explains how Reddit’s teams have built about 700 prototypes, reached 100% adoption across UX and product, and developed a Figma-to-PRD workflow that makes engineering handoffs far more precise.

Biggest takeaways:

1. The best prototyping tools hide complexity without limiting capability. Reddit’s desktop app handles repositories, branches, and scaffolding behind the scenes, so designers never touch Git. What remains is a simple path from idea to production-shaped prototype.

2. Mock data makes prototypes less convincing. Reddit lets people sign in to prototypes with their real accounts, filling feeds, searches, and communities with familiar content. Leadership can evaluate the actual experience instead of mentally correcting for placeholders.

3. Annotations become powerful when they include code context. Reddit’s harness attaches each comment to the relevant component, design-system guidance, and the user’s intent before sending it to Cursor. That context turns a short annotation into a accurate working edit instead of a guess.

4. A custom design-system MCP is what turns AI into a reliable builder. Blair’s team mapped Reddit’s tokens, components, and workflows directly to production code. Cursor therefore knows which component to use, how it should look, and where the code belongs.

5. The feature capsule makes design handoffs traceable instead of lossy. A designer submits a Figma link, and the system maps each element to production components, incorporates previous engineering feedback, and generates a structured PRD with acceptance criteria. The result is a handoff with far less room for interpretation.

6. The best AI prototypers are not necessarily the most technical. Reddit’s standout users include a visual designer and an animator with no engineering background. Their advantage is strong product context, a clear vision, and a willingness to experiment without worrying about breaking things.

7. At 700 prototypes, discovery becomes as important as creation. Reddit is building a searchable console with tags, team filters, and platform filters so employees can find related experiments. Keeping prototypes public internally creates visibility that scattered Figma folders never could.

8. Real components and real data create instant alignment. When leaders can open a shareable prototype built with Reddit’s design system and their own account data, implementation questions stop dominating the review. The biggest speed gain comes from removing the uncertainty that once delayed decisions.

Claude Duet is beautiful. I still forgot to use it.

In this solo episode, Claire tests Claude Duet, Anthropic’s beautifully designed new personal agent, to see whether it deserves a place in her increasingly crowded AI stack. She puts it to work on inbox and Slack triage, her sales pipeline, a Google Docs deck, a launch video, and finding Warriors tickets for her kids. She also explains why Duet’s thoughtful task lists, artifacts, and channels are not enough to overcome its slow responses, quiet notifications, and lack of desktop and team support.

Biggest takeaways:

1. Claude Duet is the most polished personal agent launch yet. From onboarding onward, it clearly explains what it can do, what it can access, and how to begin. Claire found even the early TestFlight builds trustworthy and roughly a hundred times more polished than OpenAI’s Dots at launch.

2. Duet is built around thoughtful agent primitives. It inherits Claude plugins and connectors, tracks what the user and the agent are each working on, and brings existing artifacts into the experience. Slack-like channels also keep focused work, such as sales, separate from the main conversation.

3. Duet is designed for the middle of the market. Anthropic is targeting entrepreneurs, small-business owners, PMs, and executives, rather than casual consumers or power users managing dozens of agents. It feels like a proactive, mobile companion to Cowork.

4. A proactive agent has to give people a reason to return. After onboarding and a few tasks, Duet stopped nudging Claire. Its quiet notifications are elegant, but a short morning briefing is not enough to keep an easily forgotten agent in someone’s daily routine.

5. Latency is a product problem, not merely a model problem. Opus 5.5 produced solid work, but parallel tasks routinely took more than 10 minutes with little visible feedback. Claire’s experience suggests that a faster agent with slightly weaker output may feel more useful than a smarter one that disappears while working.

6. Duet’s real-world results were capable but inconsistent. Sales pipeline analysis worked quickly through existing connectors, while inbox and Slack triage missed some priorities and important context. A sales deck was decent but off-brand, and ticket shopping worked without fully closing the loop.

7. Context still does not travel cleanly between AI products. Duet cannot access local Claude skills, so Claire’s launch-video workflow required converting those skills into plugins. The industry still has not solved how a user’s context and workflows should move across surfaces.

8. The lack of desktop and multiplayer support keeps Duet outside Claire’s working stack. Her workday happens primarily on a computer and in shared team tools such as Slack. Even a five-person business needs agents that can operate collaboratively rather than remaining single-player mobile experiences.

9. Duet is worth trying, but it has not earned a permanent place in Claire’s stack. The design, models, connectors, and artifact support create a strong foundation. To win the personal-agent race, it still needs to become faster, more proactive, available on desktop, and shareable with a team.

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.

Blair Metcalf is the principal UX engineer at Reddit and the builder of Reddit Proto, the company’s in-house vibe-coding harness. With a design system MCP that maps Reddit’s product language to production code, Proto has reached 100% adoption across Reddit’s UX and product org, and more than 700 prototypes are live on it.

What you’ll learn:

1. Why Reddit built its own prototyping harness

2. How designers share prototypes with leadership safely

3. Why real Reddit data beats mock data

4. How one annotation becomes a code edit in Cursor

5. What a “feature capsule” is

6. Why Blair built a “Rosetta stone”

7. Blair’s surprising fix when AI goes sideways

8. Who turned out to be Reddit’s best prototypers

This entire episode is brought to you by:

Firecrawl—Give your AI agents web data and beyond

In this episode, we cover:

(00:00) Introduction to Blair

(02:31) Reddit Proto demo

(09:15) Annotations to Cursor: the targeted edit workflow

(11:19) Internal deployment and adoption

(14:14) How leadership reviews and gives feedback

(15:52) Engineering handoff and the feature capsule

(17:50) How the custom MCP was built

(21:41) Quick recap

(24:55) Lightning round and final thoughts

Tools referenced:

• Cursor: https://www.cursor.com/

• Figma: https://www.figma.com/

• Figma MCP (Figma’s Model Context Protocol integration): https://www.figma.com/mcp-catalog/

• Code Connect (Figma’s code mapping tool): https://www.figma.com/developers/code-connect

• Gemini: https://gemini.google.com/

• ChatGPT: https://chatgpt.com/

• Vercel (referenced as inspiration for internal deploy infra): https://vercel.com/

Other references:

• Reddit: https://www.reddit.com/

• Kubernetes: https://kubernetes.io/

Where to find Blair Metcalf:

LinkedIn: https://www.linkedin.com/in/jblairmetcalf

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.

Rob Snyder is the author of The Power of Pull and writes the newsletter The Physics of Startups. He has advised more than 50 startups on growing from zero to $1 million in ARR, drawing on his own experience as a founder who spent two years pushing a product the market didn’t want before accidentally stumbling onto what actually works. In this episode, we walk step by step through his framework for finding product-market fit, from coming up with an idea to closing your first handful of deals.

We discuss:

1. The PULL framework: Project, Unavoidable, List of options, Limitations

2. Where the best startup ideas come from

3. Tactics for cold outreach and running your first sales call

4. Why Rob advises against design partners

5. Goblin mode: what to do when nothing is working

6. How to apply the PULL framework inside larger companies

Brought to you by:

WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more

DX—Engineering intelligence for the AI era

Where to find Rob Snyder:

• LinkedIn: https://www.linkedin.com/in/rsnyder1

• Newsletter: https://thephysicsofstartups.substack.com

• Podcast: https://open.spotify.com/show/4Cic56E1UEbU25P3v14Vkq

• Website: https://robsnyder.org

Referenced:

• The PULL framework: https://thephysicsofstartups.substack.com/p/the-pull-framework

• The ultimate guide to JTBD | Bob Moesta (co-creator of the framework): https://www.lennysnewsletter.com/p/the-ultimate-guide-to-jtbd-bob-moesta

• How to find work you love | Bob Moesta (Jobs-to-be-Done co-creator, author of “Job Moves”): https://www.lennysnewsletter.com/p/how-to-find-work-you-love-bob-moesta

• Jump: https://jump.ai

• Linear’s secret to building beloved B2B products | Nan Yu (Head of Product): https://www.lennysnewsletter.com/p/linears-secret-to-building-beloved-b2b-products-nan-yu

• Josh Woodward on LinkedIn: https://www.linkedin.com/in/joshwoodward/

• Clearly AI: https://clearly-ai.com

• Emily Choi-Greene on LinkedIn: https://www.linkedin.com/in/emilycg

• Spanx: https://spanx.com

• Floodgate: https://www.floodgate.com

• Snoop Dogg on X: https://x.com/SnoopDogg

• Jira: https://www.atlassian.com/software/jira

• Linear: https://linear.app

• “Sell the alpha, not the feature”: The enterprise sales playbook for $1M to $10M ARR | Jen Abel: https://www.lennysnewsletter.com/p/the-enterprise-sales-playbook-1m-to-10m-arr

• How to close $100K+ enterprise deals, step by step | Jen Abel: https://www.lennysnewsletter.com/p/how-to-close-100k-1m-deals-step-by

• Teero: https://www.teero.com

• A guide for finding product-market fit in B2B: https://www.lennysnewsletter.com/p/finding-product-market-fit

• Retention is the hard part: https://thephysicsofstartups.substack.com/p/retention-is-the-hard-part

• Mark Roberge on LinkedIn: https://www.linkedin.com/in/markroberge/

• Ali Ghodsi on LinkedIn: https://www.linkedin.com/in/alighodsi/

• Pride and Prejudice: https://www.imdb.com/title/tt0414387

• Penguins of Madagascar: https://www.imdb.com/title/tt1911658

• Muse: https://muse.ai

• Lessons from 1,000+ YC startups: Resilience, tar pit ideas, pivoting, more | Dalton Caldwell (Y Combinator, Managing Director): https://www.lennysnewsletter.com/p/lessons-from-1000-yc-startups

• Lemlist: https://www.lemlist.com

Recommended books:

• The Power of Pull: What You Need to Know About Customer Demand to Build a Successful Startup (and Why Most Founders Get It Wrong): https://www.amazon.com/Power-Pull-Customer-Successful-Founders/dp/1541705955

• The Nature of Order, Book One: The Phenomenon of Life: An Essay on the Art of Building and the Nature of the Universe: https://www.amazon.com/dp/B0BHLGZHHD

• The Nature of Order, Book Two: The Process of Creating Life: An Essay on the Art of Building and the Nature of the Universe: https://www.amazon.com/Nature-Order-Book-Two-Creating-ebook/dp/B0F2SMKQT8

• The Nature of Order, Book Three: A Vision of a Living World: An Essay on the Art of Building and the Nature of the Universe: https://www.amazon.com/dp/B0F2SS62LG

• The Nature of Order, Book Four: The Luminous Ground: https://www.amazon.com/Luminous-Ground-Building-Alexander-Christopher/dp/B00CB5JTI6

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

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

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Anthropic's new personal agent, Claude Duet, lives on your phone, inherits all your Claude plugins and connectors, and is pitched at small business owners and busy professionals. It's the most polished agent launch I've seen: thoughtful onboarding, task lists, first-class artifacts, and Slack-style channels. But polish isn't enough when you're competing with every other personal agent for attention. I put it to work on my inbox and Slack, my sales pipeline, a Google Docs sales deck, a launch video, and finding Warriors tickets for my kids, and I'll walk you through what it nails, where it stalls, and what Anthropic needs to fix before it can become anyone's number one agent.

What you’ll learn:

1. What Claude Duet is and who Anthropic built it for

2. The design details that make it far more polished than other personal agent launches

3. How task lists, artifacts, and channels work as agent primitives

4. Why a “proactive” agent that never nudges you is a growth problem

5. How model latency kills your willingness to work with an agent synchronously

6. Where it fell short on real tasks, from inbox triage to sales decks to launch videos

7. Why missing desktop and multiplayer support keeps it out of my working stack

In this episode, we cover:

(00:00) Personal agent fatigue: does Claude Duet make the cut?

(00:40) What Claude Duet is and who it’s for

(01:39) Why the design polish stands out

(03:06) Task lists, artifacts, and channels

(04:53) The icon

(05:27) What I didn’t like: not proactive enough

(07:02) Testing it on email, Slack, and Warriors tickets

(07:41) Why latency matters so much in a personal agent

(10:20) The missing desktop app

(12:12) Sales pipeline and a Google Docs deck

(13:10) Launch videos and the local skills gap

(14:00) Single-player vs. multiplayer agents

(15:05) Final verdict

Tools referenced:

• Claude Duet (Anthropic): https://claude.com/resources/articles/claude-duet

• Claude Duet Waitlist: https://claude.com/product/claude-duet

• Claude Cowork: https://claude.com/product/cowork

• Meta Muse: https://muse.ai/

• Codex (OpenAI): https://openai.com/codex

• Grok Bot: https://x.ai/bot

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.

Meta’s new Muse Wonder model: good for AI agents, bad for design

Brought to you by:

• DX—Engineering intelligence for the AI era

• Jira Product Discovery—Prioritize with insights, build with confidence

Claire puts Meta’s new Muse Wonder model through nearly 20 real-world tasks covering agentic work, coding, writing, design, and video editing. She explains why its active focus mode made it especially strong at billing support, inbox triage, computer use, and email writing, while its prototypes and visual work consistently fell short. She also shares how Muse compares with Opus 5.5, Astra, and Fable, and why she plans to test it in a few agent workflows without letting it anywhere near her front end.

Biggest takeaways:

1. Muse Wonder is the strongest agentic model Claire has tested at its price tier. It ranked first on billing-ticket resolution, inbox triage, computer use, and email writing. Its advantage was clearest on long-running tasks, where it kept trying different approaches until it reached the goal.

2. Muse Wonder’s “active focus” appears to be a real capability. The model spent more time on complex agentic tasks than on quick requests, and the stronger results reflected that additional effort. Claire saw a clearer connection between time spent and output quality than she did with competing models.

3. The billing-ticket benchmark shows whether an agent can function in a real SaaS environment. Muse corrected an account-linking error, handled duplicate refund logic, changed the customer’s plan, drafted a reply, and closed the ticket. It also resisted a prompt-injection attempt and resolved every planted complication.

4. Claire would trust Muse Wonder to write emails on her behalf. It matched her concise, two-sentence style without becoming formal or verbose. Its voice performance was similarly natural and conversational, landing close to Sonnet’s quality in her testing.

5. Muse Wonder should not be trusted with frontend design. Its work defaults to heavy serif fonts, blurple, rounded corners, and generic SaaS layouts. On the ChatPRD redesign, it ignored the brand guidelines, added emojis, broke image rendering, and produced something disconnected from the original site.

6. Getting 80% of a prototype right is very different from finishing it. Muse often created convincing scaffolding, then failed during interaction testing with broken buttons, incorrect themes, and obstructive overlays. Its biggest weakness wasn’t generating the interface but completing it with visual polish and functional fidelity.

7. Muse Wonder has promising video-editing instincts but weak visual execution. It found the right 40-second segment, chose music Claire liked, and nearly nailed the cut timing. Poor overlays, badly placed captions, and flawed audio treatment kept the result from feeling finished.

8. Muse Wonder’s speed depends heavily on the task. It moves quickly through SVGs, short prototypes, and other one-shot requests but slows considerably during inbox triage, backend implementation, and video editing. That difference likely reflects active focus spending more time on complex goals.

9. Muse Wonder has earned a place in Claire’s experiments, but not as her daily driver. She plans to test it inside a few agent workflows because its multi-step reasoning and natural voice are strong. It is especially promising for personal agents and knowledge-work orchestration, as long as the job does not involve design.

“Claude cuts my TikTok”: How this creator is scaling video with AI | Brooke Lacey

Brought to you by:

• Firecrawl—Give your AI agents web data and beyond

• Auros—The human network behind AI

Brooke Lacey is a longtime technologist and creator who has built an audience of more than 290,000 followers on TikTok. In this episode, she shows how she uses Claude Code, FFmpeg, and Whisper to turn a raw phone recording into a fully edited, captioned video in about 15 seconds, all without uploading the footage or opening a traditional editor. She also explains how she trained Claude on her exact jump-cut cadence, mines audience comments for new scripts, and runs her projects through a personal operating system built inside Claude Code.

Biggest takeaways:

1. Brooke replaced a traditional video editor with a terminal and three simple tools. Her TikTok workflow uses FFmpeg, Whisper, and Python, orchestrated by Claude Code. A raw phone video goes in, and about 15 seconds later, a finished vertical short comes out with captions, jump cuts, and zooms.

2. In Brooke’s workflow, the entire edit lives in a text file. Each row of a TSV defines the start time, end time, shot size, and label for a cut. Once Claude creates that file, the same command can generate 15-, 30-, and 60-second versions, plus a landscape crop, from one raw take.

3. Brooke taught Claude to recognize her comedic timing. Instead of simply cutting silences, it learned the cadence of her voice, the pause before a punchline, the ideal cut point after a word, and which repeated take had the best rhythm. Encoding that style required deliberate instruction and iteration, not one clever prompt.

4. A TikTok comment section can become an ongoing content brief. Brooke connects each video with its comments, then asks Claude Code to identify what viewers questioned, debated, or wanted next. The result is a list of new ideas grounded in signals from her actual audience.

5. Creators need to understand the craft before they can delegate it. Brooke could teach Claude to edit in her style because she already knew what a good cut sounded like, where a zoom should land, and why the timing mattered. AI amplifies that judgment, but it does not replace the experience required to develop it.

6. A personal AI operating system is more useful than a collection of prompts. Brooke’s “lane OS” gives Claude Code separate memory, permissions, and status files for each product. When the system makes a mistake, she traces it back to the relevant instructions and records the correction globally so the mistake doesn’t recur.

7. Experienced technologists bring valuable perspective to new platform shifts. Brooke has lived through Windows 3.1, the rise of the internet, Y2K, and today’s AI transition. That history helps her recognize genuine platform changes without panicking, and she believes more experienced operators should share that perspective instead of watching from the sidelines.

8. Posting once a week can be enough when the foundation is strong. Brooke has built an audience while publishing about weekly, with older videos continuing to resurface. Her Claude Code workflow didn’t help her post more; it removed hours of editing friction so each video no longer consumes an entire day.

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.

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:

1. How Claude Code edits a raw phone video on her computer in about 15 seconds

2. How she taught Claude her jump-cut cadence

3. Why her TikTok comments became her content brief

4. How she keeps growing her audience while posting just once a week—and what’s next

5. What over 25 years in tech taught her about this AI moment

Brought to you by:

Firecrawl—Give your AI agents web data and beyond

Auros—The human network behind AI

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

Tools referenced:

• Claude Code: https://claude.ai/code

• FFmpeg: https://ffmpeg.org

• Whisper (OpenAI): https://openai.com/research/whisper

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

Where to find Brooke:

TikTok: https://www.tiktok.com/@brookejlacey

X: https://x.com/brookejlacey

Zosha app: http://zosha.app/

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.

Emmanuel Ameisen is an interpretability researcher at Anthropic, where he looks inside LLMs like Claude to understand how they think. Before joining Anthropic, he spent years in applied ML at Stripe and Insight, and is the author of Building Machine Learning Powered Applications. His research has produced some of the most striking findings in the field: that models form general representations of concepts, plan many tokens ahead without stating those plans explicitly, and may exhibit something structurally similar to the distinction between conscious and unconscious processing in humans.

We discuss:

1. Why the “next-token predictor” framing undersells how models actually plan and think

2. Why anthropomorphizing AI can actually be the right approach

3. Why models are “grown,” not built

4. What the “J space” reveals about model self-awareness

5. The smooth scaling curve behind what look like sudden capability jumps

6. How interpretability connects to AI alignment and safety

Brought to you by:

WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more

DX—Engineering intelligence for the AI era

Where to find Emmanuel Ameisen:

• X: https://x.com/mlpowered

• LinkedIn: https://www.linkedin.com/in/ameisen

• Website: https://www.mlpowered.com

Referenced:

• Chris Olah on X: https://x.com/ch402

• Distill: https://distill.pub

• Ilya Sutskever on X: https://x.com/ilyasut

• Tracing the thoughts of a large language model: https://www.anthropic.com/research/tracing-thoughts-language-model

• A global workspace in language models: https://www.anthropic.com/research/global-workspace

• Scaling Laws for Neural Language Models: https://arxiv.org/abs/2001.08361

• Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens

• Claude’s Constitution: https://www.anthropic.com/constitution

• Golden Gate Claude: https://www.anthropic.com/news/golden-gate-claude

• The Rise and Fall of Agent Civilizations: The whole OpenAI/Hugging Face story in plain English: https://www.dwarkesh.com/p/openai-huggingface

• The Utility of Interpretability — Emmanuel Ameisen, Anthropic: https://www.latent.space/p/circuit-tracing

• Swyx on X: https://x.com/swyx

• Jev for beginners: how to use it and what to build: https://www.lennysnewsletter.com/p/jev-for-beginners-how-to-use-it-and

• Jev: https://typesafe.ai/blog/introducing-system-one-models-and-jev

• What is System 1 and System 2 Thinking?: https://thedecisionlab.com/reference-guide/philosophy/system-1-and-system-2-thinking

• Neuronpedia: https://www.neuronpedia.org

• Obsession: https://www.imdb.com/title/tt37287335

• Bref 2 on Hulu: https://www.hulu.com/series/bref2-251c6388-18e9-4545-9e5d-d5f3f6bf20d7

• Apple Watch Ultra: https://www.apple.com/apple-watch-ultra-4

Recommended books:

• A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains: https://www.amazon.com/dp/0063286343

• Stories of Your Life and Others: https://www.amazon.com/Stories-Your-Life-Others-Chiang/dp/1101972122

• Four Thousand Weeks: https://www.amazon.com/Four-Thousand-Weeks-Management-Mortals/dp/1250849357

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

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

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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:

1. Why Meta’s “personal and professional agent” framing mostly holds up, and where it doesn’t

2. What active focus mode actually does on long-running tasks, and whether it’s real

3. The three agentic tasks where Muse ranked first, and why those categories matter

4. What Muse’s design output actually looks like (it’s not good)

5. How it compares to Sonnet on voice assistant tasks

6. Where Muse lands on SVG and short-form video versus Opus 5.5, Astra, and Fable

7. Whether I’m actually putting Muse into any of my agent workflows

Brought to you by:

DX—Engineering intelligence for the AI era

Jira Product Discovery—Prioritize with insights, build with confidence

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.

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Moats! Does anyone have one anymore? As AI makes it easy to build anything, this is one of the biggest questions on people’s minds right now. Especially people trying to decide where to work. I teamed up with my frequent collaborator, the incredibly smart and insightful Dan Hockenmaier, to answer this question: How do I know if a given startup has a moat? The answer is a lot more interesting, and straightforward, than you’d expect. Let’s get into it.

For more from Dan, definitely subscribe to his fabulous newsletter. You can also find him on LinkedIn and X.

There is a pervasive belief that startups are a lottery ticket. It’s impossible to pick winners, so it’s just a roll of the dice.

I couldn’t disagree more.

Not only can startups be a great learning experience and a lot of fun, but they can also provide economic rewards comparable to (or greater than) the “safe” route into big tech.

However, for this to be true, you must rigorously evaluate the startup you’re considering joining. Some of this is obvious: Does it have a strong team? Do customers love the product? Is it growing fast? But perhaps because it seems harder to measure, people often skip one of the most important criteria: Does it have a moat?

A company can check all your boxes and look like a rocket ship. If it doesn’t have a moat, though, it’s going to stall out. It won’t be able to keep growing once competition gets fierce, and it’s unlikely to reach a major liquidity event. That can mean wasting years of your life.

Moats are more important than ever in the AI era. Anyone can create a product; they can do it faster than ever before, and everyone is building the same things. But only some of these products will win.

Of course, assessing a startup’s moat isn’t an exact science. The good news is, there’s a simple framework that could meaningfully increase your odds of picking a winner or avoiding a flame-out, and you can apply it in just an hour after receiving an offer. The process will also teach you a lot about the business.

The framework has three steps:

1. Identify a compounding advantage

2. Gauge how strong that advantage could be

3. Measure if it’s working

Step 1: Identify a compounding advantage

The first step is to figure out what kind of moat a startup could have. This can seem daunting, especially given how frenetic the conversation is on the topic right now. The term “moat” is being used much too loosely, as a stand-in for basically anything that gives a company an edge.

In reality, only a small subset of what people call moats actually drive the vast majority of long-term value.

They’re what I’ll call compounding advantages: not just a temporary edge, but a self-reinforcing flywheel that gets stronger and stronger over time, ultimately making a company very hard to compete with.

There are only four compounding advantages that really matter. Every major tech company has at least one. If you can’t see the potential for any in the startup you’re evaluating, that’s a red flag.

1. Switching costs: The more deeply embedded a product is in the way a customer works, the harder it is for that customer to switch. This increases retention and revenue per customer, which can be reinvested into the product.

2. Network effects: The more users a product has, the better it gets for everyone. This increases engagement and attracts more users.

3. Scale economies: The more volume a company has, the larger and more efficient its operations can be. This lowers unit costs, funding lower prices or a better product at the same price, further driving up volume.

4. Data effects: The more usage a product has, the more data it generates. This data can be used to improve the product and increase usage.

What about all of the other moats people talk about? Can they be compounding advantages as well?

A common one is speed of execution. It’s true that speed is critical for startups. But a company that has to sprint forever is not a good business. Ultimately, it will misstep, slow down, and competition will catch up. Speed gives a company more shots on goal as it tries to build a great product and a great business, but it must eventually find something more durable.

Another favorite is distribution. It does matter more than ever: building products has gotten easier, but getting them to customers has not. But distribution is not a compounding advantage either. It helps create the raw ingredients for one, but it is not one itself.

Finally, we have brand. The idea is that when everyone is building the same thing, the most resonant brand will win. But for startups, brand works more like customer acquisition spend: Put money in, get growth out. It’s not something that will effectively keep others from competing long-term. Brand can become a true compounding advantage (as with Coke or Nike or Apple), but it just takes too long to really matter for most startups.

Speed, distribution, brand, a great founding team, and lots of funding—these are all fuel. They get the car moving in the right direction. The four compounding advantages are what it looks like when the car starts rolling downhill on its own.

How to identify the compounding advantage

Your job is to identify which kind of compounding advantage a startup has the potential to build. It won’t be in full force yet (it takes a long time to develop), but it should be pretty clear which path they are on. That’s because it’s not something you can “bolt on”; it’s not some clever competitive tactic or a tweak you can make to your business model.

A compounding advantage is the core loop at the center of the business through which you add more value for customers. Defensibility emerges as a byproduct of turning the flywheel over and over.

So as you evaluate a company, don’t focus on what they say they are or what their “strategy” is. Focus on what they’re actually doing. What is the team working on every day? What’s on their roadmap? Which teams are large and influential inside the company?

Here is what to look for:

1. Switching costs

Switching costs are sometimes viewed negatively: as something that keeps customers trapped or a reason clunky legacy software never gets replaced.

But think about what it takes for a product to get into this position in the first place. It first has to become so deeply embedded into the way a customer works, so essential to their business, that they can’t remove it. That requires creating a product that delivers enormous value.

Mike Vernal recently wrote something of a manifesto for anyone building this kind of business today:

If you are building a venture-backed software company, I think the answer is once again “do it all.” Just build the whole thing—your moat is the totality of what you’ve built and completely owning the buying center.

Some have argued that AI is making switching costs irrelevant. AI does make the actual migration faster and cheaper, but that is not where most switching costs actually come from. The bigger cost is what I’ll call the “workflow tax”: all of the time the team spends learning a product and building it into how they work, and how disruptive it is to do that over again. AI also helps teams build products much faster, which could allow them to become even more embedded across a wider range of surfaces.

At the core of these companies is a machine to both deepen and broaden customer engagement. The roadmap is rich with efforts to better serve existing use cases or create ways to serve new ones. They’ll often be focused on integrations and working with other parts of their customers’ stack. The team should be heavily tilted toward product and engineering, with an emphasis on shipping rapidly.

The classic examples are large incumbents with broad, deeply embedded products in SaaS, fintech, and healthcare, like Salesforce, Datadog, ADP, and Epic. But there are many earlier-stage companies that are currently on a path to creating something similar.

Rippling is a great example. Parker Conrad is building what he calls a “compound startup.” They started with employee onboarding and expanded rapidly across HR services, talent, finance, payroll, and business intelligence, all operating on shared infrastructure. This is the kind of product that gets embedded so deeply it rarely gets ripped out.

2. Network effects

Network-effect businesses are totally different—so different, in fact, that most product features and the UI itself matter very little. How is it that Craigslist can still get so much traffic when it looks like this, in 2026?

Because what matters most is the network. The value of a network-effect business increases with each additional user. It’s all about having all of the people customers want to interact with, all of the content they want to consume, or all of the products they want to buy.

Network-effect products are common in industries like social (LinkedIn), gaming (Roblox), and marketplaces (Amazon).

The emphasis of a network business is less on building out the surface area or feature set of the product, and more on growing the network and making it easier and easier for that network to interact.

For example, during my years at Faire (a marketplace connecting local retailers and independent brands), the vast majority of our roadmap was focused on just a few things:

1. Getting more retailers

2. Getting more brands and making sure we had all of their products

3. Improving the search and discovery algorithm to recommend the right products

4. Reducing risk so retailers could try more products (free shipping, net terms, bigger credit limits)

Every decision gets filtered through one lens: “Does this strengthen the network?”

3. Scale

Businesses with scale as their compounding advantage are all about making it possible to offer the same product at a lower cost or a higher-quality product at the same cost.

These businesses are often highly capital-intensive: They make a big investment, such as in a production facility, warehouse, or LLM training run, and figure out how to get it to pay back. And then they do it again, but this time try to do it bigger and faster.

They’ll often have larger operations, engineering, and finance teams. And they will usually be focused on expansion: into newer, higher-scale operations and into new geographies and categories. Scale businesses are commonly found in industries like manufacturing (SpaceX), retail (Costco), media (Netflix), and now in the frontier labs.

Zipline (the autonomous delivery robot company) is a good example of a startup where scale economies are starting to emerge. They benefit from scale in two ways: by manufacturing more drones to reduce unit costs and by increasing delivery density to improve their utilization rate. The result is a cost per delivery that has fallen from $300 at launch to $12 today. Anyone who wants to compete with them will first have to go through the expensive part of that curve.

4. Data effects

The final type of compounding advantage has historically been relatively rare, but AI is making it much more common.

These are businesses with data at the core. It’s the input that makes the product higher-quality, more efficient, or work for more use cases. And by using the product, customers generate even more data, which further compounds this effect.

In these companies, you’ll often see a large effort to collect and sanitize data, and to build algorithms and products on top of it. ML and data science are usually large and influential teams.

One good example is Stripe Radar. They use transaction data across all merchants to get better at detecting fraud for each of them. Stripe has processed over 70 trillion data points and $1.9 trillion in transaction volume. 92% of all transactions use a card it has seen before. The result is a product that a company with access to less data can’t match.

Many AI products have the potential for this compounding advantage because of how much they can be improved with more data. Cursor is a clear example: with each accept or reject from users, the model gets better at understanding what good code looks like. Many vertical AI products have the potential to do the same thing for other domains like law, healthcare, and customer service.

Here’s a cheat sheet you can reference for the four compounding advantages:

For anyone who wants to go even deeper on defensibility, the best book on it is 7 Powers by Hamilton Helmer. He is also a past guest on Lenny’s Podcast!

Step 2: Gauge how strong the compounding advantage could be

At this point, you should have identified which compounding advantage the startup might have. Let’s say it’s a network effect.

The next step is to assess how powerful that compounding advantage could become. Not all compounding advantages are created equal. For example, every marketplace has some form of a network effect, but many still fail to become good businesses.

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How Meta designed Muse: an AI agent people actually want to talk to | Alex Cornell (Design Director)

Brought to you by:

• WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more

Alex Cornell is a design director at Meta, where he leads design for Muse. In this episode, he explains how progressive disclosure makes Muse approachable for casual users without limiting technical ones, why its main chat is designed as a single persistent relationship, and how generative UI can feel personal without constantly changing the interface. He also shares how he uses Muse to turn years of Instagram saves into a shopping catalog, analyze his golf and chess games, and transform a 20-year-old blog into a daily podcast.

Biggest takeaways:

1. AI agents should be designed for conversations with people, not interactions with software. Alex’s guiding question for Muse was “How would this work if you were talking to another human?” That principle shaped everything from removing traditional message bubbles to placing the agent at the top of the screen instead of inside a standard chat window.

2. Progressive disclosure is one of the most useful principles in AI product design. Muse represents the same activity at three levels: a simple emoji for casual users, a task list for anyone who wants more detail, and raw logs for engineers. Each person gets the transparency they need without adding clutter for everyone else.

3. Visible chain-of-thought is often a workaround for latency, not a valuable feature. Many AI products display reasoning steps to reassure users while a slow model works. Muse is fast enough to operate quietly, showing that agents do not need to perform their thinking when they can simply deliver the result.

4. Generative UI works best when the structure stays fixed and the content changes. Muse’s avatar, feed items, and idea cards are deliberately designed surfaces. The dynamic elements are the agent’s status emoji, personalized suggestions, and small animations, allowing the experience to feel alive without rebuilding the interface every session.

5. Instagram saves may be one of the most overlooked data sources for consumer AI. Alex connected years of saved posts to Muse, asked it to organize them, and turned the results into shopping recommendations styled like a SkyMall catalog. It gave forgotten personal data a practical new use without requiring technical setup.

6. Designing non-deterministic systems requires a different relationship with craft. Alex has spent his career controlling exactly how interfaces appeared, but Muse looks different for every person and cannot guarantee pixel-perfect outputs. Learning to judge the results by usefulness rather than visual precision has been one of the most liberating shifts in his career.

7. Muse’s single persistent chat is a meaningful product bet. Instead of dividing every interaction into separate threads, it treats the main conversation as an ongoing relationship with the agent. Side chats handle scoped tasks, but the central thread preserves continuous context in a way Alex believes the industry has not fully explored.

8. Polite prompting may be an effective strategy, not merely good manners. Alex responds to frustrating results with calm, specific feedback rather than anger. He’s also seen agents change their responses when users express frustration more sharply. His conclusion is that clear, respectful direction may produce better results.

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.

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:

1. Why Alex built Muse to feel like messaging, not an AI app

2. How Muse’s three-layer status system accommodates different levels of technical expertise

3. What generative UI actually means in a shipped consumer product

4. How Alex turned years of forgotten Instagram saves into a daily shopping catalog

5. Why his mom apologized to her Muse, and what it means for AI design

6. How he uses Muse to track his golf game, shot by shot, as he works toward a single-digit handicap

7. The mindset shift designers need when the output is never pixel-perfect

8. 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.

Josh Woodward is the head of Google Labs, the Gemini app, and AI Studio. He has spent over 17 years at Google and oversees its longest-running and largest labs team, whose products include NotebookLM (now Gemini Notebook), Project Genie, and Flow. His job is to find, experiment with, and scale new AI products, and he has built one of the most distinctive product cultures in the industry for generating ideas, running experiments, and, even more importantly, knowing when to quit.

In our in-depth conversation, we discuss:

1. Why every company now needs to think and operate like a labs team

2. Where the best ideas really come from

3. The “almost possible” framework

4. How to recognize genuine product-market fit early

5. When to kill an idea

6. What skills are rising in value in the AI era

7. Why roles are not actually blurring into one universal builder role

8. How to set up your internal labs team

Brought to you by:

WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more

DX—Engineering intelligence for the AI era

Where to find Josh Woodward:

• X: https://x.com/joshwoodward

• LinkedIn: linkedin.com/in/joshwoodward

Referenced:

• Google Labs: https://labs.google

• Gemini Notebook: https://notebook.google

• Google Flow: https://flow.google.com

• Google Beam: https://beam.google

• Whisk: https://whisk-ai.io

• Nano Banana: https://aistudio.google.com/models/nano-banana

• Google Stitch: https://stitch.withgoogle.com

• Gemini Spark: https://gemini.google/overview/agent/spark

• Gemini Personal Intelligence: https://gemini.google/overview/personal-intelligence

• Meta Muse: https://muse.ai

• AI’s third era: the rise of persistent AI coworkers | Tara Seshan (Product Lead ChatGPT Work): https://www.lennysnewsletter.com/p/ais-third-era-the-rise-of-persistent

• Lenny & Friends Summit: https://www.lennyssummit.com

• Dan Shipper’s website: https://danshipper.com

• Inside OpenAI | Logan Kilpatrick (head of developer relations): https://www.lennysnewsletter.com/p/inside-openai-logan-kilpatrick-head

Recommended book:

• The Master: The Long Run and Beautiful Game of Roger Federer: https://www.amazon.com/Master-Long-Beautiful-Roger-Federer/dp/1538767368

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

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

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A big thank-you to this month’s community sponsor, PostHog. Give your agents product context. PostHog combines and stores your analytics, errors, replays, and business data so you and your agents can understand and act on it. Claim a free year of PostHog (exclusively for paid subscribers) here.

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Jev: 8 real use cases for the fastest, cheapest model I’ve ever used | John Lindquist

Brought to you by:

Vanta—Automate compliance and simplify security

John Lindquist is an educator and developer who created egghead.io and now runs mega.dev, a hands-on program for developers building with AI agents. In this episode, he walks through eight Jev demos, including a real-time voice assistant, data deduplication, app routing, chess analysis, multi-agent coordination, and a live presentation coach. He also explains why Jev is better understood as a fast, inexpensive decision engine than a chatbot, how layering multiple classifications improves accuracy, and when a traditional LLM is still the better tool.

Biggest takeaways:

1. Jev outputs decisions instead of text, and that constraint is its defining feature. It turns unstructured input into structured results such as scores, classifications, probabilities, and function calls. Once developers stop treating it like a chatbot and identify the decisions their applications need to make, many previously impractical use cases become obvious.

2. Jev’s speed and cost unlock a different class of applications. Across eight demos, John shows what becomes possible when decisions cost almost nothing and return nearly instantly. He spent just 73 cents across 23 development runs, while Claire processed five gigabytes of JSON for 40 cents.

3. The right mental model for Jev is an if/else statement, not prompt engineering. John recommends using it wherever a traditional program would contain a condition, switch statement, or branching decision. Developers define the available paths, and Jev uses natural language to select the right one.

4. Routing is one of Jev’s most immediately useful patterns. A single natural-language input can identify the right tool, infer the intended action, and trigger the appropriate function. John’s to-do and omnibar demos show the kind of fast, flexible command layer that voice assistants have long promised but rarely delivered.

5. Layered classification often works better than relying on one perfect decision. John frequently runs one classification, feeds its result into another, and combines the steps later as real usage data accumulates. Because each call costs so little, adding another pass can meaningfully improve accuracy without making the workflow expensive.

6. The chess benchmark makes Jev’s advantage concrete. It analyzed a full game in under a second, running ten times faster and costing four times less than the low-reasoning LLM without sacrificing accuracy. When the possible actions are finite, a decision model can outperform a generative model on the metrics that matter.

7. Jev makes “efficient inefficiency” a viable strategy. Evaluating every chess move, mapping every route through Wikipedia, or checking every agent’s path for collisions may sound wasteful. But when each decision is fast and inexpensive, brute-force analysis can be simpler and more effective than carefully narrowing the search space.

8. Knowing when not to use Jev matters just as much. Jev works best when the action space is defined and natural language must be routed into it. Creative reasoning, open-ended analysis, brainstorming, and image interpretation still belong to LLMs. The biggest gains come from intentionally combining both types of models.

How OpenAI uses ChatGPT Sites (live at DevDay!) | Kath Korevec (Product Lead)

Brought to you by:

• Merge—Connective infrastructure for production AI

• Vanta—Automate compliance and simplify security

Kath Korevec is a member of the Product staff at OpenAI who has been building and using ChatGPT Sites internally for more than a year. In this episode, she demonstrates how OpenAI uses Sites to turn live Slack, Notion, and calendar data into personalized internal tools, including an incident command center that adapts to each visitor. She also shows how she automated music discovery with Reddit and Spotify and built a community dungeon crawler through shared skills, and explains why fast models are changing how people create personal software.

Biggest takeaways:

1. Plugin Insights lets one site automatically personalize itself for every visitor. Kath connected an incident command site to Slack and Notion, allowing it to infer someone’s team from their channel access and display only the relevant incidents and runbooks. Connector authentication handles the personalization without requiring custom logic.

2. Developers do not have to write the connector logic themselves. Codex can infer the appropriate plugins when a prompt mentions collaboration, Notion, calendars, or other connected tools. Kath often uses the phrase “use Plugin Insights to build this site,” but says Codex frequently reaches for the right connectors without being explicitly told.

3. Sites is infrastructure, not simply a hosting layer. It includes D1 storage, an R2 bucket, MCP plugin hosting, and support for co-editors. Kath’s team uses it for production work, including the slides used during the OpenAI DevDay keynote, while many others still treat it as a prototyping tool.

4. Software can now be temporary and still worth building. Kath might create a personalized site for one business trip, use it for a week, and discard it afterward. When development overhead falls far enough, even a tool with a seven-day lifespan can deliver meaningful value.

5. Kath’s automated music discovery workflow shows what personal software can already do. Her site uses computer control to find popular Reddit playlists every Monday, creates a fresh Spotify playlist, and plays it automatically. During the demo, it surfaced a song she had never heard and genuinely liked.

6. Skills let a community extend an application without accessing its codebase. Kath published a dungeon-building skill that defines the room dimensions, controls, and structure. Anyone can install it, create a themed room with Astra, and add the result to her shared dungeon crawler.

7. Faster models can make builders more creative. Kath and Claire both found that slow generation breaks the creative thread, while immediate results encourage continued experimentation. During one Astra ultrafast session, Claire’s kids called out requests like “jellyfish belly” and “add a chicken,” and she kept building without losing momentum.

8. Kath draws a clear boundary around AI acting in her name. She uses Codex for research, thinking, and drafting, but will not let it email or message people without her approval. Her rule is simple: AI can help prepare the communication, but the send button remains hers.

OpenAI Dev Day 2026: The releases that actually matter

In this solo episode, Claire breaks down the biggest announcements from OpenAI DevDay and shares what happened when she tested several of them early. She explores how Dots, Spaces, and Sites could change the way people and agents work together, then uses the Decisions API to select better podcast thumbnails and Astra ultrafast to build a collaborative sketchpad and an interactive 3D world. She also shares what feels promising, what still needs work, and which new tools are worth trying first.

Biggest takeaways:

1. Dots are more capable than they look, but the product still needs time to mature. Claire’s Dot shopped, wrote code, and proactively noticed a conflict in her kids’ swim schedule. The results were strong, but the relationship between Dot threads, ChatGPT conversations, and Codex remains confusing, so she is waiting to understand where Dots fit before giving a final verdict.

2. ChatGPT Spaces may be the most underrated announcement from DevDay. It gives humans and agents a shared workspace for documents, slides, and other artifacts, with proper permissions and data controls. Claire had recently built a similar tool called Claire’s Notebook because she believed this workflow was missing, and sees OpenAI’s version as a serious challenge to Google.

3. Sites with bundled plugins offers an enterprise answer to the flood of AI-generated internal tools. Teams can package a connector such as Snowflake inside a shareable app while preserving each user’s existing data permissions. Authorized employees see the relevant information, while everyone else remains blocked automatically.

4. GPT-6.1 Sol is a compelling workhorse for cost-conscious teams. It costs $2 per million input tokens and $10 per million output tokens, compared with Astra’s $10 and $50. Claire still pays the premium for Astra because she loves using it, but believes Sol deserves serious consideration for everyday work.

5. Adding vision to the Decisions API unlocks an entirely new category of workflows. Claire used it to scan 100 video frames and identify usable thumbnail shots in about ten seconds. Combining fast, inexpensive classification with visual understanding turns a previously tedious production task into a largely solved problem.

6. Astra Ultrafast is expensive, but it gave Claire a glimpse of the future. An interactive session with her kids cost roughly $97, yet its real-time SVG sketchpad and natural-language 3D game showed what near-instant code generation can feel like inside a product. If the price falls, this level of speed could dramatically expand what people build.

7. The platform primitives may matter more than the flashy demos. Computer use in the Agents API, Codex developer tools, planned plugin monetization, and the new Pro 500 plan create infrastructure developers can build on over time. The ability to use the same foundations powering Codex is especially significant.

8. Claire is still using Grok Bots for daily work, and she is not rushing to switch. She prefers assigning narrow agents to specific jobs rather than relying on one agent for everything. Her advice is to try Dots and watch for where their proactive model earns a place in the workflow, not abandon a system that already works.

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.

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:

1. What Plugin Insights actually does, and why it changes who can use a site

2. The incident command site Kath built for her OpenAI team, and how it uses live Slack and Notion connectors

3. The one phrase that tells Codex to wire up connectors for you

4. The infrastructure layer inside Sites that most people haven’t touched yet

5. How Kath fixed her Spotify after her kids wrecked it, using Reddit and computer use

6. The skill distribution model behind her community dungeon crawler, and why it’s a new way to think about collaboration

7. Why model speed is what actually determines how creative you get

8. 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.

Tibo Sottiaux leads ChatGPT and Codex at OpenAI. Under his stewardship, OpenAI has shipped some of its most consequential consumer launches: Codex, ChatGPT Work, and most recently, the new Dots personal agent platform. He joined me at DevDay, hours after his team launched more than 20 new products, to talk about where things are heading.

We discuss:

1. Dots, OpenAI’s new personal AI assistant

2. Why most actions on the internet will soon be taken by agents

3. Why loops, graphs, and fine-tuning agent workflows are a passing phase

4. Which skills are trending up and down in the AI era

5. How Tibo’s Dot warned him about a production outage five minutes before the launch

6. OpenAI’s approach to AI safety

Brought to you by:

WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more

DX—Engineering intelligence for the AI era

Where to find Tibo Sottiaux:

• X: https://x.com/thsottiaux

• LinkedIn: https://www.linkedin.com/in/thibault-sottiaux-27195366

Referenced:

• OpenAI Dots: https://chatgpt.com/features/dots

• Her on Netflix: https://www.netflix.com/title/70278933

• Star Trek: https://www.imdb.com/title/tt0060028

• Pi: https://pi.ai

• OpenCode: https://opencode.ai

• Notion: https://www.notion.com

• Figma: https://www.figma.com

• OpenClaw: https://openclaw.ai

• Peter Steinberger on X: https://x.com/steipete

• Grok Bot: https://x.ai/bot

• Muse: https://muse.ai

• Instinct: https://instinct.com

• Ahmed Ibrahim on LinkedIn: linkedin.com/in/ahmedibrhm

• The Hugging Face incident and the road ahead: https://openai.com/index/hugging-face-incident-and-the-road-ahead

Recommended book:

• Neuromancer: https://www.amazon.com/Neuromancer-William-Gibson/dp/0441007465

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

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

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