Lenny RachitskyModify

2026-10-22 04:25:18

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 [email protected].

2026-10-19 23:03:35

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.

2026-10-19 20:04:52

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 [email protected].

2026-10-18 20:31:36

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 [email protected].

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

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2026-10-17 21:53:57

👋 Hello and welcome to this week’s edition of ✨ Community Wisdom ✨ a subscriber-only email, delivered every Saturday, highlighting the most helpful conversations in our members-only Slack community.

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2026-10-14 20:04:05

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 [email protected].