I break down every loop type from scratch—what a heartbeat, cron, hook, and goal loop actually are, when each one fits, and the five things any effective loop needs before it touches production. Then I build two live loops: a daily aging-PR reviewer in Claude Code that schedules itself at 10:15 a.m. and spins off its own subagents, and a weekly skills-identification loop in Codex that spawns goal-based subagents to validate its own output in real time.

What you’ll learn:

1. The plain-English definition of a loop—and why it’s just an automated prompt, not a scary new paradigm

2. The four loop types (heartbeat, cron, hook, and goal) and when each one actually fits your workflow

3. How to think about loop design using the “onboarding an employee” mental model

4. The five things every effective loop needs: work trees, skills, plugins/connectors, subagents, and state tracking

5. How to build a scheduled PR-review routine in Claude Code that babysits aging PRs and alerts your team

6. How to set up a weekly skills-identification automation in Codex that spawns its own validating subagents

7. Why goal-based loops are the hardest to write well—and where most people burn tokens for nothing

8. The two warning signs that your loop is going to get expensive before it gets useful

Brought to you by:

WorkOS—Make your app enterprise-ready today

Runway—The creative AI platform for images, video, and more

In this episode, we cover:

(00:00) Prompts are out and loops are in

(02:30) Defining a loop

(03:03) The four ways to automate a prompt: heartbeat, cron, hooks, and goals

(06:03) Five things every effective loop needs

(09:26) The “onboarding an employee” framework for designing loops

(11:58) Live build #1: Daily aging PR loop in Claude Code

(17:08) Subagents inside loops

(19:00) Live build #2: Weekly skills identification loop in Codex

(22:57) Watching subagents spin up in real time

(25:28) Warning signals around loops

(27:31) What listeners are doing with loops

Tools referenced:

• Claude Code: https://claude.ai/code

• Codex: https://chatgpt.com/codex

• OpenClaw: https://openclaw.ai/

Other references:

• Claire’s article “Why OpenClaw Feels Alive Even Though It’s Not”: https://x.com/clairevo/article/2017741569521271175

• Addy Osmani’s article on loop engineering: https://addyosmani.com/blog/loop-engineering/

• Using Goals in Codex: https://developers.openai.com/cookbook/examples/codex/using_goals_in_codex

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.

Claude Fable 5 review: what the new Mythos model gets right (and very wrong)

Claire puts Claude Fable 5, Anthropic’s first generally available Mythos-class model, through a series of real-world tests: product specs, agent workflows, design tasks, vision tasks, and multi-agent orchestration. She breaks down what Anthropic is claiming, where the model genuinely feels like a leap forward, and where it surprisingly falls short.

Biggest takeaways:

1. Fable 5 is Anthropic’s first “Mythos-class” model to reach general availability, and it’s crushing benchmarks across the board. It hit 80% on SWBench Pro, significantly outperforming Opus 4.8, GPT-4.5, and Gemini 3.1 Pro. Claire found the model excels in specific areas while falling short in others that matter for everyday product work.

2. The model is expensive by design: $10 per million input tokens and $50 per million output tokens. That’s a new tier above Opus, and it consumes tokens at roughly twice the rate of other models. You need to be strategic about when to deploy this level of intelligence versus using cheaper models like Sonnet or Opus for simpler tasks.

3. Fable 5 works like a “seasoned engineer”—which is both its superpower and its Achilles’ heel. It’s thorough, autonomous, and will investigate every corner of a problem to be 120% sure it’s shipping the right thing. Sometimes you need a model that’s a little less thorough, a little “dumber,” to actually ship something useful quickly.

4. The model is exceptionally good at vision tasks, particularly document formatting and PDF parsing. Claire tested it on creating handwriting worksheets for her 7-year-old and found it dramatically outperformed Opus 4.8—better spacing, clearer layout, appropriate white space. This extends to other vision tasks where you want something to look good or need to parse complex documents.

5. The writing is nearly unreadable for specs and PRDs. Claire found that Fable 5 produces extremely detailed, technically complete documents that are almost impossible to parse. It gets wrapped around the axle on details, creates big blocks of dense paragraphs with internal references, and makes it hard to see the forest for the trees.

6. Design output is shockingly bad, at least for one-shot design tasks. When Claire asked Fable to design a skills registry, it produced fundamentally terrible design: gray, black, red, simple outlines. This was a real surprise given the model’s benchmark performance.

7. The model is conservative on execution and takes “minimal” very literally. When Claire asked it to ship an MVP that would deliver customer value, Fable produced something extremely narrow and not actually that useful. This conservatism may stem from the safety guardrails built into the model.

8. Fable 5 includes specific safeguards for cybersecurity, biology, chemistry, and distillation tasks. Instead of blocking you entirely, it uses a new “fallback” concept—if you get classified into one of these categories, it gracefully falls back to Opus 4.8. Anthropic reports that 95% of sessions don’t hit a fallback, and they maintain a 30-day retention policy solely to catch misuse.

9. Multi-agent orchestration is technically possible but not yet reliable. Claire tested the dynamic workflows and subagent capabilities extensively and had some successful multi-agent runs, but also encountered frequent stalls and errors. She walked away from her laptop and came back to find subagents had stalled after about three hours.

10. The key insight: match model intelligence to task complexity. Claire recommends using it for hard technical problems where extreme detail matters, long-horizon work, and vision tasks. But for front-end work, strategy, specs, and design, other models in the ecosystem will serve you better and cost less.

11. This is “baby Mythos,” not the full Mythos model. Fable 5 has guardrails that the unrestricted Mythos model (available only to Project Glasswing partners) doesn’t have. The underlying model is the same, but Fable is tuned for safety and general availability.

How Braintrust uses AI agents, evals, and CI to ship better software | Ankur Goyal

Brought to you by:

• Guru—The AI layer of truth

• Persona—Trusted identity verification for any use case

Claire sits down with Ankur Goyal, the founder and CEO of Braintrust, to unpack how top engineering teams are using AI agents, evals, and CI to ship better software faster. They get into why agents are now capable of tackling hard infrastructure problems, how to decide what work sits “below the agent line,” and why evals are quickly becoming the modern version of a PRD. Ankur’s core message: the best teams won’t just use AI to write more code; they’ll build the feedback loops, benchmarks, and systems that let AI improve the quality of the product itself.

Biggest takeaways:

1. There’s no staff engineer running as many rigorous benchmarks as someone using an agent. Ankur viscerally disagrees with engineers who say AI can’t handle complicated problems. While models might not be perfect at writing highly concurrent code, they excel at running exhaustive experiments—testing every column store format, every execution engine, every optimization strategy. The baseline of rigor you get from agents is incredible, and there’s simply no excuse anymore to skip benchmarks because they’re tedious.

2. The agent line keeps going up—and you need to identify what’s below it. Many interactions, decisions, and directions that feel like they need human judgment actually fit “below the agent line.” If you took the information from a meeting and gave it to an agent, would it solve the same problem? Increasingly, the answer is yes. The best teams push this line higher by building smart skills and integrations that expand what agents can handle autonomously.

3. Practical quality beats theoretical quality every time. In theory, a human engineer with infinite time and focus might produce better code than an AI agent. In practice, humans lose context over days, have decaying attention spans on hard-but-tedious problems, and skip benchmarks they know they should run. AI agents maintain consistent focus, run every test, and can work on problems continuously for days or weeks. The practical quality of AI-assisted engineering is higher because of sustained rigor, not because the code is theoretically better.

4. You can now bite off much harder technical problems than before. Companies historically avoid major infrastructure changes because the cost of testing alternatives is prohibitively high and the unknown unknowns are risky. With AI agents, you can exhaustively test six different database solutions, run thousands of benchmarks on production-scale data, and make informed decisions about platform shifts that would have been impossible before. The business case for deep technical work becomes much easier when agents do the heavy lifting.

5. Run four to six foreground agents simultaneously—that’s the human concurrency limit. Ankur runs different agents working on different problems. This matches the personal concurrency limit most people can manage; you can’t effectively context switch between more than that. Some agents run locally, and others run remotely on cloud infrastructure with production-scale data. The key is isolation: each agent has its own environment, ports, and services.

6. Evals are the modern PRD—they define what success looks like, not how to achieve it. Machine learning shifts programming from defining implementation details to defining success criteria. Just like the best PRDs include user stories and examples, the best evals include concrete test cases and scoring functions. The difference is that evals quantify success in ways that can be automatically measured and improved. This lets you focus on outcomes while AI figures out the implementation.

7. Build a feedback loop that automatically turns real-world data into evals. For AI product teams, the #1 engineering priority isn’t prompt engineering or picking an agent framework—it’s building a pipeline that summons real-world data and converts it into evals. This is the same principle as investing in CI for traditional software: you’re building the platform that lets agents do the work engineers used to do manually. Without this feedback loop, you’re stuck in whack-a-mole mode, fixing individual cases without systematic improvement.

8. Quantify your designer’s taste so it scales across your product. Ankur runs hundreds of evals to improve things quantitatively, then asks David (their tastemaker designer) for a vibe check every few days. When David destroys his work, Ankur captures the feedback (“David thinks it’s OK to show both languages as long as . . .”) and improves the scoring functions to encode David’s palette. This doesn’t replace David; it amplifies him. They’re able to apply David’s quality bar to more things than he could ever review manually.

9. Product building is now carving, not constructing. It’s extremely fast to create something with too many features, too many buttons, and too much code. The hard part is removing stuff. When customers complain, Braintrust removes the thing causing confusion 90% of the time, making the system work better by eliminating complexity. This is the opposite of traditional product development, where you carefully add features one by one.

10. Invest in CI to earn the ability to move faster—it’s the platform for AI-powered engineering. Every engineer is now building a platform upon which agents do the work engineers used to do manually. For traditional software, that platform is CI. If you feel constrained by velocity, don’t ship crappy stuff faster. Instead, pause and improve CI so you earn the ability to move faster safely. The same principle applies to AI products: build the eval pipeline first, then let agents optimize within that system.

11. When agents fail, close the session and improve the evals—don’t yell or bribe. Ankur’s back-pocket strategy is remarkably disciplined: he doesn’t try to prompt his way out of problems. He closes the session, improves the evaluation criteria or success metrics, and starts fresh. Sometimes this means hand-writing code to better understand the problem (like when he spent a weekend hand-writing a 3,000-line eval that had become trash through vibe coding). The solution is always better evals, not better prompting.

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.

In this episode, I sit down with Ankur Goyal, founder and CEO of Braintrust, the AI evals and observability platform used by teams like Notion, Stripe, Vercel, and Zapier. This one is for the senior engineers, staff engineers, VPs of engineering, and CTOs in my audience. We get into how coding agents can take on deeply technical architecture and infrastructure work that no single human engineer could tackle before, and then we demystify evals so you can use them to make your AI products better without touching the implementation.

What you’ll learn:

1. How Ankur uses Codex to run week-long benchmark experiments across database indexes, column store formats, and execution engines to speed up slow queries

2. Why he argues there’s no excuse to skip rigorous benchmarking now that agents can run them tirelessly

3. The “agent line” framework: how to decide which decisions, directions, and interactions you can hand off to an agent

4. How I think about the practical vs. theoretical quality of AI on hard technical problems, and why human attention decays on tedious work

5. Why evals are the modern version of a PRD, and how to encode “what good looks like” so a model can figure out the “how”

6. How to build a scoring function live and let an agent improve your prompt inside a safe playground

7. How Ankur turned his designer David’s taste into a repeatable eval so quality scales beyond one person

8. Why fixing your CI is the highest-leverage way to speed up engineering velocity

Brought to you by:

Guru—The AI layer of truth

Persona—Trusted identity verification for any use case

In this episode, we cover:

(00:00) Introduction to Ankur Goyal

(03:00) Using AI agents for database optimization

(06:10) Running exhaustive benchmarks with coding agents

(09:03) Why staff engineers are wrong about AI limitations

(11:30) The “agent line” framework for delegation

(14:00) Ankur’s workflow: running 4 to 6 concurrent agents

(17:16) Technical setup: foreground agents, background agents, and cloud environments

(20:32) Spending time with AI tools

(23:06) Demystifying evals

(26:02) Live demo: Building an eval for documentation answers

(30:20) The alternative to evals: vibe checks and whack-a-mole

(32:09) Capturing designer taste in scoring functions

(33:13) Quick recap

(33:44) Managing velocity and throughput

(35:40) Why CI/CD investment is critical for AI-accelerated teams

(37:30) Ankur’s prompting strategy when agents fail

(39:10) Closing thoughts and how to connect

Tools referenced:

• Braintrust: https://www.braintrust.dev/

• Codex: https://openai.com/codex/

• GPT 5.4: https://developers.openai.com/api/docs/models/gpt-5.4

• Claude: https://claude.ai/

Other references:

• GPT 5.5 just did what no other model could: https://www.lennysnewsletter.com/p/gpt-55-just-did-what-no-other-model

• Paul Graham’s Maker vs. Manager Schedule: http://www.paulgraham.com/makersschedule.html

• tmux: https://github.com/tmux/tmux

• Chris Tate at Vercel: https://www.linkedin.com/in/ctatedev/

Where to find Ankur Goyal:

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

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.

Mark Pincus founded Zynga—the company behind Words With Friends, FarmVille, and Zynga Poker—and has arguably created more hit consumer products than anyone in history. At Zynga, eight of 10 major game launches became massive hits, reaching over a billion players. Over the past five years, Mark has been synthesizing everything he’s learned about building successful consumer products and turning it into a book, Life at the Speed of Play, which comes out on June 23. This is the first interview he’s done about the book.

In our in-depth conversation, we discuss:

1. His “Proven, Better, New” framework: copy what’s proven, make it better so that 10 out of 10 people say “f*ck yes, I’ll use this”—then add something new

2. Why being less ambitious is the path to the most ambitious ideas

3. His rule of thumb that your instincts are right 95% of the time, but your ideas are wrong 75% of the time

4. “Kill hope before hope kills you”

5. How to raise kids in the age of AI

Brought to you by:

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

Vanta—Automate compliance, manage risk, and accelerate trust with AI

Where to find Mark Pincus:

• X: https://x.com/markpinc

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

• Website: https://www.lifeatthespeedofplay.com

Referenced:

• Tribe.net: https://en.wikipedia.org/wiki/Tribe.net

• Zynga: https://www.zynga.com

• Sid Meier: https://en.wikipedia.org/wiki/Sid_Meier

• Electronic Arts: https://www.ea.com

• CityVille: https://en.wikipedia.org/wiki/CityVille

• Words With Friends: https://wordswithfriends.com/

• Scrabble: https://playscrabble.com

• Reddit: https://www.reddit.com

• TED Radio Hour, MIT Media Lab founder, 1984 TED talk.: https://www.ted.com/talks/nicholas_negroponte_5_predictions_from_1984

• Peter Thiel on LinkedIn: https://www.linkedin.com/in/peterthiel

• FarmVille: https://en.wikipedia.org/wiki/FarmVille

• Craig Newmark: https://en.wikipedia.org/wiki/Craig_Newmark

• How to consistently go viral: Nikita Bier’s playbook for winning at consumer apps (co-founder of TBH, Gas, advisor, investor): https://www.lennysnewsletter.com/p/how-to-consistently-go-viral-nikita-bier

• Angry Birds: https://www.angrybirds.com/

• OMGPop: https://en.wikipedia.org/wiki/OMGPop

• Draw Something: https://en.wikipedia.org/wiki/Draw_Something

• Slack founder: Mental models for building products people love ft. Stewart Butterfield: https://www.lennysnewsletter.com/p/slack-founder-stewart-butterfield

• Brian Chesky’s new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach

• Garry Tan on LinkedIn: https://www.linkedin.com/in/garrytan

• Brian Armstrong on LinkedIn: https://www.linkedin.com/in/barmstrong

• Jason Citron on X: https://x.com/jasoncitron

• Stanislav Vishnevskiy on LinkedIn: https://www.linkedin.com/in/svishnevskiy

• Jeff Bezos on X: https://x.com/JeffBezos

• Andy Jassy on X: https://x.com/ajassy

• Niantic: https://nianticlabs.com

• Pokémon Go: https://pokemongo.com

• Bing Gordon on LinkedIn: https://www.linkedin.com/in/binggordon

Recommended book:

• Life at the Speed of Play: Launch Products People Love!: https://www.amazon.com/Life-Speed-Play-Launch-Products/dp/0063352575/ref=tmm_hrd_swatch_0

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:

Read more

A big thank-you to this month’s community sponsor, Strella. Strella is an AI-powered qualitative research platform that allows teams to run, analyze, and share customer interviews at scale without sacrificing depth. Usability testing, concept validation, discovery research, and more, Strella delivers insights fast and makes them accessible right where you work, including Claude, ChatGPT, and Figma, so your research keeps working long after the initial readout.

🏕️ Lenny and Friends Summit returns this fall

The Lenny and Friends Summit returns on September 10 in San Francisco. This will be the greatest assembling of product leaders in history.

We’re keeping it intentionally small (about 1,000 people)—every attendee will be handpicked—and along with in-depth talks from the world’s top operators, we’ll have interactive workshops, tons of opportunities to get to know other attendees, and a few fun surprises.

Here’s the initial lineup of speakers (with more to be announced soon):

Given that we’re anticipating lots of interest and the venue has limited capacity, we’re asking people to apply to attend. Paid newsletter subscribers will get priority access.

Apply now

P.S. We’ve expanded capacity at the venue, so if you’ve received an email saying there wasn’t a spot for you, reply to this email and we’ll take another look at your application.

📆 Upcoming community meetups

Click the city name to RSVP:

• Amsterdam. June 17th. Thanks to @Adriana Mosnoi, @Ruslan Doronichev & @Luke Rynne Cullen!

• Asheville. June 17th. Thanks to @Nathan Phillips!

• Atlanta. June 25th. Thanks to @Ravish C.!

• Austin. June 25th. Thanks to @Mark Vandegrift & @Andy Keil - Austin!

• Bangalore. June 18th. Thanks to @Sumant!

• Bellevue. June 23rd. Thanks to @Aman Goyal!

• Boston. June 17th. Thanks to @David Jorjani!

• Chicago. June 17th. Thanks to @Jason Siegel!

• Boulder. June 25th. Thanks to @Dave Carlyle!

• Detroit. June 25th. Thanks to @Ken Yesh!

• Hong Kong. June 17th. Thanks to @Manny Reimi!

• Lisbon. June 22nd. Thanks to @Gabriela Naumnik & @Nina Un!

• Munich. June 16th. Thanks to @Lukas Gerhardt!

• Philadelphia. June 17th. Thanks to @Keriann Sabatini & @Doug Clark!

• San Diego. June 17th. Thanks to @Vidit!

• San Francisco. June 25th. Thanks to @Tarek Sadi!

• Santa Barbara. June 15th. Thanks to @Joni Hoadley, @Cody Landstrom, & @Oliver Barton!

• Shenzhen Afternoon Meetup. June 18th. Thanks to @Ivan Xu!

• Toronto. June 24th. Thanks to @Jessie Wang!

• Valencia. June 26th. Thanks to @Paul Boudet!

• Vancouver. June 23rd. Thanks to @Charles!

• Vienna. June 16th. Thanks to @Serge Versille!

• Warsaw. June 16th. Thanks to @Matt Swulinski!

Photos from recent meetups

📚 Book club: The Mom Test

We have a special event for the community’s #book-club this month: June’s book club selection is The Mom Test by Rob Fitzpatrick, and the man himself, Rob, will be joining us on June 24 for a Q&A!

Join the #book-club channel to participate, and be sure to RSVP by June 23. Huge shoutout to Akil Bhagat for running the current iteration of our book club.

✍️ Final chance to participate in the tech worker sentiment survey

We’d love to get your perspective on what it’s like to work in tech right now. The survey will take less than 5 minutes to complete, and we’ll send you the anonymized raw data so you can perform your own analysis, along with an early look at the results before we share them publicly.

Take the survey

🎙️ New podcast episodes this week

Father of the iPod and iPhone on building taste, judgment, and creativity in the AI era | Tony Fadell: YouTube, Spotify, Apple Podcasts

Claude Fable 5 review: what the new Mythos model gets right (and very wrong): YouTube, Spotify, Apple Podcasts

5 Career Questions Your Old Playbook Can’t Answer: YouTube, Spotify, Apple

💥 Top threads this week

1. Tracking work stress using wearables

Thought of posting this on #talk-lol, but this is such an interesting use case and it seems real. Someone “hooked my Whoop to my work calendar to find which coworker gives me the most stress.” One of the replies: “HR reviews could never, this has actual data. Somewhere an Anthropic PM is adding ‘coworker stress forensics’ to the use case list. Legend behavior, monetize it.” ↗

—Guy Peled

Jeremy: I did a similar thing a few months ago, but used Apple Watch heart rate data. I only tried it once, but noticed that my heart rate was lower in larger meetings, but certain 1:1s definitely caused a spike. I’m fairly confident it was related to the level of engagement, but it was a fun vibe-coded experiment.

Guy Peled: Love it. So many factors go into this. Definitely beyond the people in the room:

• Amount of people

• Your host/participant role

• Content (e.g. department restructuring announcement meeting)

etc.

Potential phase 2.

Abdussamad Bello: Has to be a PM!

Miroslav Pavelek: I love the idea! But in comments it is also correctly pointed out that, well, Whoop (or any other device on the market) is not really good at measuring stress.

Jeremy: @Miroslav Pavelek It’s best to view them as directional instead of completely accurate. Exploring the concept with this level of data feels reasonable to me.

2. Weekly status reports for managers of managers

For managers of managers, how do you have your leaders provide weekly status reports for non-sprint/dev-related work? For example, initiatives that involve other divisions/teams, GTM work, research, etc.—how do you stay up to date with progress and what their teams are focused on? ↗

Ian McAllister: For anything weekly sent via email, do your teams a favor and enforce a very short/crisp format. Green/Yellow/Red status for key projects with a path to Green for anything Yellow or Red. Anything longer simply won’t get read, so all the effort writing it is wasted.

Save the thoughtful descriptions and details for monthly reviews when there is time to process and discuss.

Aaron Nichols: I think this depends greatly on why you want those updates. I’ve operated in teams where those updates needed to go to someone else—in which case I can just observe what gets reported through those normal means. I’ve also operated in orgs where my teams operated fine without my oversight on regular execution and MBR/QBR updates were sufficient to keep tabs. I tend to be more worried about whether my teams are getting good context and signal from me and their stakeholders than about status updates to me—but this all depends on your level, the size of your org, maturity, etc.

There’s a reality here that you simply do not stay up to date on what everyone is doing. You’ll spend all your energy keeping tabs instead of doing the work you actually should be doing.

Cindy Cohen: I’ve found the most effective updates focus on outcomes, risks, and decisions needed.

For cross-functional initiatives, I typically ask leaders to provide:

• Objective / desired outcome

• Current status (green/yellow/red)

• Key accomplishments since last update

• Next 1–2 priorities

• Risks, dependencies, or blockers

• Decisions or support needed from leadership

Then I use 1:1s to go deeper where needed rather than reviewing everything in detail.

The biggest mistake I see is reporting activity (“met with X, researched Y”) instead of progress toward an outcome. A short update tied to objectives usually provides enough visibility without creating reporting overhead.

Adam Thackeray: Put @Ian McAllister recommendation in bold. Simple is better. Get to the point and what help is needed (if at all).

3. Marketing and GTM for tiny teams

Question for solo founders and 2–5-person teams where everyone is an engineer or PM: how are you handling marketing/GTM day-to-day?
Roughly three paths I keep seeing, curious which one you took and how it went:

1. Hire an agency/freelancer—pay someone external to run SEO, content, ads, etc.

2. Hire a GTM/marketing person—first marketing hire, in-house

3. DIY—founder learns the tools and systems (SEO, email, social, ads) and just does it

React with (1), (2), (3) for what you actually did, and if you have 2 minutes I’d love to hear:

• Roughly what it costs you per month (money and/or founder hours)

• What you’d do differently if starting today

(Context: solo founder with a product background here—figuring out how much of marketing I should learn vs. buy. Not selling anything in this thread.) ↗

—Jake Luo

Shawn Jones: Hermes agent + company context brain (LLM-wiki) + growth skills + management tool (Notion/Linear/etc.).

Jake Luo: Cool solution. Would you mind sharing what growth skills you use? Do you use the Hermes agent to run your SoMe and ads campaigns as well?

Shawn Jones: I recommend creating your own custom skills for your industry/business, but have your agent reference these for inspiration: https://github.com/coreyhaines31/marketingskillsi
Primarily have my agent focused on the content development and management side. I’m planning to set up ad management next.

Christos Apartoglou: I think it is hard to assess what the right direction for your team is with the information you provided. My recommendation is to do the following exercise:

• Spend some time understanding what your team needs at this life stage. Is it validating PMF? Is it demand generation? Something else?

• For the needs you have identified, it will be important to then understand if they are one-offs or evergreen and the relative degree of prioritization. This can guide you on identifying the skills and competencies you may need to add to the team and for how long.

This information can help guide your staffing approach between the three options you are contemplating. If you already have done this work I am happy to jump on a quick 30-minute call and help make sense of it. I was an in-house marketing lead for many companies in the past and now have a fractional consulting practice (though I am not attempting to sell you anything—I don’t have capacity for more clients atm).

Matthew Stublefield: Founder and solopreneur here. I recommend the books Obviously Awesome and Crossing the Chasm and learning about positioning and copywriting. I have built and bought some AI tools that help me with copywriting and SEO, and that combined with some reading and learning has helped. I also paid for some inexpensive digital courses that helped (Csaba Borzasi and Katelyn Bourgain).

Bal Sieber: Path 3, with an asterisk. Solo consultancy here, product design background, so writing was never the scary part. The system was. What I actually run: I do the thinking and the voice, and a set of scheduled AI jobs does the reps. Sourcing who to talk to, drafting engagement, keeping the daily floor. My time lands around an hour a day of reviewing and steering rather than producing. Tooling is a few hundred a month all in. No agency, no hire. What I’d do differently: skip the months of treating marketing as the thing I’d get to after the real work. It only started compounding once it got the same seriousness as client work, a daily floor and a weekly review. One caveat: This shape works because my buyer hangs out where text lives. If your customers need ads or SEO at scale, my setup says nothing about that.

Jake Luo: Seems like many folks focus on content writing and SEO, which is great for building organic pipeline. But sometimes I feel like it’s slow. I would consider doing performance marketing to accelerate user acquisitions. Then how to run paid ads efficiently and effectively is another challenge to tackle.

Mih Fodor: Buy the Demand Curve program and go through it. You’ll have everything planned after that, and you can use what @Shawn Jones suggested.

Bal Sieber: Paid is the one lane I can’t speak to with real numbers, so season this hard. The one thing I’d check before buying acceleration, since it’s the part I do see every week: what the first session after the click does. Ads multiply whatever your signup-to-first-value path already does, so if that path leaks, paid mostly accelerates the leak, at CPC prices. The slow organic phase is annoying, but it’s also cheap rehearsal. You find the funnel holes while the traffic is free. I’d want evidence the path holds before paying to fill it.

Dinuka Wijesinghe: As a starting point before you think about execution, I’d encourage you to spend high-quality time developing the following:

1. Target market/ICP

2. Market dynamics incl. competitors and alternatives

3. Your positioning in relation to market dynamics (value prop, problem & solution statements, pricing etc.)

4. Likely acquisition channels based on where you think your ICP is and where you’re likely to recognize their trigger point

5. One primary GTM metric (e.g. # of users, amount of usage, $s etc.) and a goal for that metric—start with only one metric

Interrogate these with your team/investors/trusted advisors—once you feel like you know this back to front and have confidence in it, then think about how you could systematically execute outreach. For example, pick 2-3 channels to test, what would be an ideal cadence of outbound activity you’d need to execute to get meetings/users. At first this will be an educated guess, but you can refine once you get more data. Only then should you consider the mechanics of execution (i.e. the options you and others have listed here)—and the answer will become clearer. More importantly, you’ll have the rationale for the decision you made, which will make it easier for you to adjust if the data you get is different. Take your GTM as seriously as your product. Throwing AI at it or hiring an agency from the start won’t deliver good results if you as the founder don’t have the clarity to ask the right questions. If you want to chat specifics in relation to your market, I can do a short call.

Harshil Bhimani: I have worked with founders before, going 0 to 1. Usually they have a very good understanding of ICP. So the main goal is to find message-market fit. I think all options have tradeoffs. Imo you can do it yourself if you have bandwidth, otherwise outsource it to a good agency/freelancer. Hiring FT is very costly if you don’t have a playbook imo. Wish you luck!

4. How AI is changing product operating models

I’m curious to know, how has AI actually changed the way your product team operates?

For context, I’m working on re-organising the product operating model in my org and want to understand what’s working for others.

Two things I’m keen to learn from people who’ve gotten their hands dirty:

1. What’s one thing that’s genuinely changed in how your team works—not just speed, but how you’re structured, how you hand off to engineering, or how decisions get made?

2. I’m hearing PMs or designers pushing changes directly to GitHub a lot. We are not there yet, so for those who do, what does that look like in practice, and what did you need to be in place for it to work?

If you’ve worked with teams experimenting with BMAD or similar approaches, I’d love to hear what the PM side of that looks like.

If this has been answered before, happy to be pointed to the relevant thread. ↗

—Nishanth D’Souza

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Claude Fable 5 is the first Mythos-class intelligence model to be generally available, and I got early access to test it before launch. I walk through what Anthropic is promising, what actually stood out when I used it on real work, and where I think it fits in your AI stack.

In this episode, we cover:

(00:00) Introduction: Fable 5 is finally here

(00:31) What Anthropic says about the model

(05:14) Token-intensive by design

(06:28) Safety classifiers and the new fallback concept

(07:46) Is this or is this not Mythos?

(08:30) New product launches: Managed Agents and more

(09:20) Crushing benchmarks

(09:55) What it’s actually like to use (the good and the bad)

(11:40) Test 1: product graph spec

(12:56) Test 2: designing a skills registry

(14:04) Conservative on execution

(14:43) Test 3: multi-agent orchestration

(15:39) My takeaways

Tools referenced:

• Claude Fable 5: https://www.anthropic.com/news/claude-fable-5-mythos-5

• Claude Managed Agents: https://platform.claude.com/docs/en/managed-agents/overview

Other reference:

• SWBench Pro benchmark: https://www.swebench.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.

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On the heels of part 1 of my essential books for product builders series, I’m excited to share part 2.

As with last time, the books are organized by their jobs-to-be-done in your work and life. I’m again limiting myself to only three per category that I’ve personally read (and completed), and only books that have stood the test of time (i.e., no new books). As a bonus, at the end of the post I’ve also included a dozen fan favorites that either fell just below the ones I picked or I haven’t had a chance to read yet.

I know it’s hard to find time to read a whole book, and when you do, it’s hard to retain anything. As I mentioned in part 1, what’s worked for me is to read 10 minutes before bed as part of my wind-down routine. This has the added benefit of helping me sleep better! And as I read, I try to find one nugget or tactic that I can bring into my work that week. I take a photo of it and email it to myself (using this sweet app) for the next morning. My philosophy is that if I retain just one golden nugget per book over the years, I’m happy. That’s how it usually ends up working out anyway.

So, as I share the book recommendations, I’ll also highlight a nugget of wisdom that’s still with me over the many years since I first read these books.

Let’s get into it.

I want to get better at design

Before reading these books, I thought design was a squishy, subjective thing. It’s not. Don’t Make Me Think taught me how to objectively make a product UI work (and feel) better. The Design of Everyday Things showed me that when I struggle with a product, it’s not my fault—it’s the design’s. Refactoring UI gave me a ton of specific design tactics.

1. Don’t Make Me Think by Steve Krug

2. The Design of Everyday Things by Don Norman

3. Refactoring UI by Adam Wathan and Steve Schoger

I want to improve my taste/craft

I’ve always looked up to people who have great taste, and lucky for me, guests on my podcast consistently remind me that it isn’t something you’re born with—taste is something you can learn. The War of Art helped me learn how to recognize and overcome the internal “resistance” that comes with creating something new and different. The Work of Art showed me the creative process of dozens of high-taste creators. Creativity, Inc. taught me to protect my (and my team’s) “ugly babies”—early half-baked ideas that otherwise get squashed.

1. The War of Art by Steven Pressfield

2. The Work of Art by Adam Moss

3. Creativity, Inc. by Ed Catmull

I want to get better at influence

Influence—maybe you’ve heard it’s important? It’s something I was very bad at early in my career, and I’ve had to learn how to do it well. How to Win Friends and Influence People showed me the power in being interested vs. interesting. Influence taught me the fundamentals of how people change their mind: social proof, authority, scarcity, and simply being liked. Never Split the Difference taught me how to shift a negotiation from “you vs. me” to “us” working together to solve the problem.

1. How to Win Friends and Influence People by Dale Carnegie

2. Influence by Robert Cialdini

3. Never Split the Difference by Chris Voss

I want to start a company

I’ll be honest, I didn’t read a lot of books when I was starting my company. But I should have. When I finally read The Lean Startup, it showed me how to be smart about where to start, and how to iterate efficiently. Crossing the Chasm taught me what a good early user looks like. Fall in Love with the Problem, Not the Solution finally gave me a very practical guide for every step of the founder journey.

1. The Lean Startup by Eric Ries

2. Crossing the Chasm by Geoffrey Moore

3. Fall in Love with the Problem, Not the Solution by Uri Levine

I want to advance my career

There are certain books you read and you’re just like, “Wow, this really explains what’s going on around here.” These three did that for me. Great at Work showed me that people who rise fastest focus on fewer things but do them extremely well. 7 Rules of Power taught me that, often, it isn’t the most talented or nice people who end up winning. The Effective Executive helped me understand that efficiency is doing things well, while effectiveness is doing the right (highest-leverage) things.

1. Great at Work by Morten T. Hansen

2. 7 Rules of Power by Jeffrey Pfeffer

3. The Effective Executive by Peter Drucker

I want to be happier (continued)

A few readers suggested that this category should have come first last time—what else matters if you can’t be happy? So I’m including another three books that made me a happier person. The Subtle Art of Not Giving a F*ck taught me that freedom from work isn’t the real goal. Instead, it’s your opportunity to figure out what problems you want to spend time solving, because the most lasting fulfillment comes from solving problems we care about. A Guide to the Good Life gave me the skill of “negative visualization,” which I use to this day. Stumbling on Happiness showed me that we’re often (very) wrong about what will make us happy.

1. The Subtle Art of Not Giving a F*ck by Mark Manson

2. A Guide to the Good Life by William B. Irvine

3. Stumbling on Happiness by Daniel Gilbert

I want some great children’s books

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Gemini Omni: Clone yourself with AI in under 15 minutes

Brought to you by:

• Merge—Connective infrastructure for production AI

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

In this solo episode, Claire puts Google Flow and Gemini Omni to the test by cloning herself into an AI avatar and using it to build a full hype reel in about 15 minutes. She walks through the whole workflow live: scanning her face, generating scenes, troubleshooting weird outputs, stitching the video together, and reacting to the very real uncanny-valley moments along the way. It’s part tutorial, part tech demo, and part “wait, this is already possible?” glimpse into how AI video tools are making high-quality creative production accessible to anyone with an idea and a laptop.

Biggest takeaways:

1. AI video tools are unlocking creative capabilities for non-video professionals. Claire, who describes herself as “creative, but not video-creative,” was able to produce a complete one-minute hype video without any prior video production experience. The entire process—from creating an avatar to final video—took roughly 15 minutes, demonstrating how these tools democratize creative work that previously required specialized skills and expensive equipment.

2. AI can serve as a creative collaborator, not just a tool. Rather than just generating videos, Google Veo acted as a creative partner, helping Claire brainstorm scenes, develop a storyboard, and think through the overall narrative arc. The AI asked clarifying questions about setting, tone, and style, then proposed a seven-scene structure that Claire could refine and execute.

3. Character consistency remains a major challenge in AI video generation. Throughout the generated videos, Claire’s avatar appeared with different hair lengths, varying backgrounds (some with books, some with plants, different wall colors), and inconsistent environmental details. While the AI pulled some accurate elements from her original photos (like posters in the background), it couldn’t maintain perfect consistency across scenes.

4. Emotional expression is still a weak point for AI avatars. While some scenes looked remarkably realistic—particularly side profiles and serious expressions—scenes requiring emotion fell flat. Claire described one laughing scene as “100% uncanny valley,” noting she looked like she was “on some kind of medication perhaps.” The technology hasn’t quite mastered the subtle muscle movements that make human expressions feel authentic.

5. The workflow from idea to finished video is remarkably fast. The entire process included creating the avatar (a few minutes), brainstorming with AI (a few minutes), generating seven video scenes (several minutes total), and stitching them together in the built-in editor (about five minutes). What would have traditionally required a production team, studio time, and significant budget happened in a single session at a desk.

Shopping with Claude: How to find quality brands, automate returns, and buy things that last 100 years | Nicole Ruiz

Brought to you by:

• Orkes—The enterprise platform for reliable applications and agentic workflows

• Metaview—The agentic recruiting platform for winning teams

Nicole Ruiz has built a Claude-powered shopping system to help her family buy fewer, better things—and avoid the endless noise of Amazon, drop-shippers, and low-quality brands. In this episode, she shares how she uses Claude Projects to vet every household purchase against criteria like craftsmanship, materials, brand history, and return policies, plus how she uses Claude Cowork to make returns faster when something doesn’t hold up. It’s a practical look at how AI can reduce decision fatigue, surface higher-quality products, and help busy parents spend less time managing stuff.

Biggest takeaways:

1. The modern internet shopping experience is broken for people who want quality over convenience. Between paid ads, drop-shipping brands, and knockoff products on Amazon, it’s incredibly difficult to find thoughtfully made items that will last for years. Nicole’s solution: build a Claude Project that holds all her purchasing criteria and trusted brands in one place, so she never has to start from scratch.

2. Keep a running list of brands you trust, and let AI search through them for you. Nicole maintains a list of shops with decades of history, strong return policies, and proven craftsmanship. When she needs something, she asks Claude to search through these trusted vendors first. This flips the typical shopping flow: instead of searching the entire internet and filtering out garbage, she’s searching a pre-vetted list and only expanding if needed.

3. Your purchasing criteria should be written down and reusable. Nicole has specific requirements: natural materials, made to last and repair, decades of business history, strong return policies, and no trendy direct-to-consumer brands that over-invest in advertising. By codifying these criteria in a Claude Project, she removes the mental overhead of running through an invisible checklist every time she needs to buy something.

4. AI can surface brand history and quality signals that would take hours to research manually. When Nicole queries a product, Claude explains why each brand is trustworthy, surfacing details like “This brand has been manufacturing the same tote bag for over 80 years” or “This company got acquired two years ago and reviews have been abysmal since then.” These insights help her make informed decisions without hours of research.

5. The worst websites often belong to the best manufacturers. Heritage brands that have been making quality products for decades frequently have terrible websites that are hard to navigate. This puts them at a disadvantage compared with Amazon or well-funded DTC brands. AI levels the playing field by making it just as easy to shop from a 100-year-old manufacturer with a clunky website as from Amazon.

6. Format your AI shopping results to surface the information that matters most to you. Nicole’s Claude Project presents each product with specific details: product name, photo, price, materials (especially important for avoiding plastic), care and maintenance notes, purchase link, and a brief note on the brand’s trustworthy history. This consistent format makes it easy to compare options and make quick decisions.

7. Use AI to automate the tedious parts of returns and refunds. When a product fails—like J.Crew pants that wore through after six months—Nicole uses Claude Cowork to pull the original receipt from her email, find the order details, and draft a customer service email requesting a refund. What would normally take 10 to 15 minutes now takes 2 to 3 minutes of voice dictation from her phone.

8. AI can identify manufacturing issues by analyzing review patterns. When Nicole requests a return, Claude often discovers that other customers had the same problem with the same product from the same time period, suggesting a manufacturing defect rather than normal wear. This strengthens her refund request and helps her avoid brands with known quality-control issues.

9. Build your shopping system for multiple use cases. Nicole uses her Claude Project in three main ways: “Help me find a can opener” (specific item search), “I have $30 for L.L.Bean; what should I buy?” (budget-constrained search), and “What’s your analysis of this brand I found?” (vetting a new brand). This flexibility makes the system useful for different shopping scenarios.

10. Buying quality items up front reduces household maintenance over time. Nicole’s philosophy is to move as much vetting upstream as possible. She lives in a small Brooklyn apartment with two young children, and every item needs to stand the test of time. By investing time in building a shopping system that prioritizes quality, she spends less time dealing with broken items and processing returns. The goal: buy things that will last for multiple children and can be mended rather than replaced.

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.

Nicole Ruiz is a writer and parent who has built a comprehensive AI-powered shopping system to help her family buy high-quality, long-lasting items while avoiding the noise of drop-shipping brands, paid ads, and poorly made products. She writes an interview series on Substack about how technology is changing the household.

What you’ll learn:

1. How to build a Claude Project with custom instructions for vetting brands based on heritage, craftsmanship, and return policies

2. The shopping criteria that help surface century-old manufacturers over trendy direct-to-consumer brands

3. How to use Claude to search through trusted vendor websites that have terrible UX

4. Why AI actually helps small artisans and heritage brands compete against Amazon’s infrastructure

5. How to use Claude Cowork to automate returns by finding receipts in your email and drafting refund requests

6. The technique for getting Claude to analyze whether a brand is legitimate or just a drop-shipping operation

7. How to shop within a specific budget or with gift cards using AI assistance

Brought to you by:

Orkes—The enterprise platform for reliable applications and agentic workflows

Metaview—The agentic recruiting platform for winning teams

In this episode, we cover:

(00:00) Introduction to Nicole and AI-powered shopping

(02:29) The problem

(04:55) Building a Claude Project for household purchasing

(07:44) The “anti-to-do list” concept for reducing mental overhead

(10:30) Shopping for a can opener: the system in action

(15:53) How AI helps century-old brands with terrible websites

(18:45) Processing returns with Claude Cowork

(25:06) Using gift cards strategically

(26:33) Vetting brands

(29:40) Recap, lightning round, and final thoughts

Tools referenced:

• Claude: https://claude.ai/

• Claude Cowork: https://www.anthropic.com/product/claude-cowork

Other references:

• Boston General Store: https://bostongeneralstore.com/

• L.L.Bean: https://www.llbean.com/

• Manufactum: https://www.manufactum.com/

• 5 OpenClaw agents run my home, finances, and code | Jesse Genet: https://www.lennysnewsletter.com/p/5-openclaw-agents-run-my-home-finances

• From a $6.90 newsletter to $3M API: How a non-coder built Memelord | Jason Levin: https://www.lennysnewsletter.com/p/from-a-690-newsletter-to-3m-api-how

Where to find Nicole Ruiz:

X: https://x.com/nwilliams030

Substack (The Third Oikos): https://www.thirdoikos.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.

Tony Fadell created the iPod, co-created the iPhone, and founded Nest (which he sold to Google for $3.2 billion). He’s co-authored over 300 patents, was part of the legendary team at General Magic, and wrote one of the most important and inspiring books for builders, called Build.

In our in-depth conversation, we discuss:

1. The heated internal debates about whether the iPhone should have a physical keyboard

2. Why opinion-based decisions are essential for v1 products

3. Why marketing matters as much as the product itself, and how the iPod almost failed

4. Why voice will eventually become the primary interface with AI

5. Why cognitive surrender to AI is the biggest risk facing product builders today

Brought to you by:

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

Vanta—Automate compliance, manage risk, and accelerate trust with AI

Where to find Tony Fadell:

• X: https://x.com/tfadell

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

• Website: https://www.buildc.com

Referenced:

• BlackBerry: https://www.netflix.com/title/81725542

• Functional systems: https://x.com/bhalligan/status/2051873396896518558/photo/1

• Nest: https://en.wikipedia.org/wiki/Google_Nest

• Everyone’s an engineer now: Inside v0’s mission to create a hundred million builders | Guillermo Rauch (founder and CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch

• Hermann Hauser on LinkedIn: https://www.linkedin.com/in/hermannhauser

• Acorn Computers: https://en.wikipedia.org/wiki/Acorn_Computers

• Skunkworks project: https://en.wikipedia.org/wiki/Skunkworks_project

• Netscape Navigator: https://en.wikipedia.org/wiki/Netscape_Navigator

• Unpacking Amazon’s unique ways of working | Bill Carr (author of Working Backwards): https://www.lennysnewsletter.com/p/unpacking-amazons-unique-ways-of

• General Magic: https://www.imdb.com/title/tt6849786

• Dario Amodei’s website: https://www.darioamodei.com

• Flighty: https://flighty.com

• Dave Chappelle: https://en.wikipedia.org/wiki/Dave_Chappelle

• Humane Inc.: https://en.wikipedia.org/wiki/Humane_Inc

• Her: https://www.imdb.com/title/tt1798709

• Spike Jonze: https://en.wikipedia.org/wiki/Spike_Jonze

• Waymo: https://waymo.com

• Snapchat CEO: Why distribution has become the most important moat | Evan Spiegel: https://www.lennysnewsletter.com/p/snapchat-ceo-why-distribution-is

• Simbe Robotics: https://www.simberobotics.com

• Greyparrot: https://www.greyparrot.ai

• Grok: https://grok.com

• Cerebras: https://www.cerebras.ai

• Esther 4:14: https://www.biblegateway.com/verse/en/Esther%204%3A14

• iPod Inventor and Nest Founder Tony Fadell Named MAD’s Inaugural Designer in Residence: https://mad.mit.edu/news/ipod-inventor-and-nest-founder-tony-fadell-named-mit-morningside-academy-for-design-s-inaugural-designer-in-residence

Recommended books:

• Build: An Unorthodox Guide to Making Things Worth Making: https://www.amazon.com/dp/0063046067

• Working Backwards: Insights, Stories, and Secrets from Inside Amazon: https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250275717

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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In this experimental episode, I document my real-time attempt to create an AI avatar of myself using Google Flow and the new Gemini Omni video generation model. I walk through the entire process—from scanning my face with my phone to generating a complete one-minute hype video for the podcast, all in about 15 minutes.

What you’ll learn:

1. How to create an AI avatar using Google Flow in under five minutes

2. Why video AI tools unlock creative possibilities for people with zero video production skills

3. The step-by-step process of generating a full storyboard using AI as your creative producer

4. How to use character consistency features to generate multiple video scenes with the same avatar

5. The uncanny-valley moments you’ll encounter when your AI clone doesn’t quite nail emotions or physics

6. How to stitch together AI-generated scenes into a complete video using built-in editing tools

Brought to you by:

Merge—Connective infrastructure for production AI

Jira Product Discovery—Prioritize with insights, build with confidence

In this episode, we cover:

(00:00) Getting started with Google Flow and Gemini Omni

(01:38) The avatar creation process: scanning and photo capture

(02:55) Using Flow to brainstorm a hype video storyboard

(06:59) Generating the first video scene with the avatar

(08:41) Troubleshooting: accidentally generating images instead of videos

(09:32) Generating all seven scenes for the complete video

(11:37) Reviewing the avatar videos

(13:13) Stitching the videos together in the browser-based editor

(14:32) The complete How I AI hype video

(15:32) What worked and what didn’t

(19:04) Final thoughts

Tools referenced:

• Google Flow: https://labs.google/fx/tools/flow

• Gemini Omni: https://gemini.google/overview/video-generation/

• Veo 3: https://deepmind.google/technologies/veo/

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.

Building an iPhone app with zero technical skills | Bryce Rattner Keithley

Brought to you by:

• WorkOS—Make your app enterprise-ready today

• Metaview—The agentic recruiting platform for winning teams

Bryce Rattner Keithley spent her career in talent and recruiting and had never written a line of code. Then she used AI to build Daily Hundred, a fitness app with custom AI-generated videos of animals doing exercises, and shipped it to the App Store. In this episode, Bryce shares the exact workflow she used with Replit, Claude, Gemini, Higgsfield, and Kling; why being non-technical became an advantage; and what her journey reveals about how AI is changing who gets to build software.

Biggest takeaways:

1. You can build and ship a production iPhone app with zero technical background. Bryce spent her entire career in talent and recruiting, had never written code, and still managed to build Daily Hundred—a fitness app with custom AI-generated videos—and get it approved in the App Store. The entire process took a few months of weekend work.

2. The workflow that worked: Claude as architect, Claude Code as engineer, Terminal as executor. Bryce used regular Claude as her “friend in the cockpit” to plan what to do and how to approach problems. Claude would tell her when to use Claude Code to write actual code. She’d bring the code back to Claude for confirmation, then Claude would tell her what to paste into Terminal. This three-step dance—plan, execute, deploy—let her ship production code without having to know exactly how it all worked.

3. Screenshots and iteration are your best debugging tools. When AI wasn’t understanding what Bryce wanted, she’d either get more literal in her descriptions, completely restart the prompt (not just edit it), or send screenshots showing what she was seeing. Sometimes she’d even draw what she wanted or photograph her own starting position to give the AI a visual reference. The key was trying different approaches rather than getting stuck in one failed pattern.

4. The role of technical expertise is fundamentally changing. Bryce observed that engineers who come into technical interviews focused only on finding a working solution fastest are missing the point—“the robots can find a working solution faster than they can.” The human role has shifted to something broader: understanding the full suite of tools, knowing when to use AI versus when to step in personally, and bringing taste and judgment to the process. What got people here won’t get them there.

5. Hiring for adaptability and openness matters more than ever. In Bryce’s view, people who get territorial about what they used to do or what other people used to do will struggle with relevance. The winners will be those with “the humility and the curiosity to work with others in ways that you haven’t before” and who recognize that “people can contribute in ways that they haven’t before.” The best idea should win, regardless of where it comes from.

The Codex feature that works while you sleep

Brought to you by:

• Mercury—Radically different banking loved by over 300K entrepreneurs

Claire Vo breaks down one of her favorite Codex features: /goal. In this solo episode, she shows how Goals turn AI from a tool you have to constantly babysit into an agent that can work for hours on multi-step tasks. She walks through real examples, including eliminating Sentry errors, cleaning nearly 4,000 emails, and organizing Linear tasks, and shares the six-part framework to write Goals that actually run.

Biggest takeaways:

1. Goals enable AI to work autonomously for hours without supervision. Claire ran a goal in Codex that worked for five hours and 45 minutes—the longest she’s ever had an AI agent run successfully. Unlike standard prompts that require turn-by-turn interaction, Goals create a loop where the AI works, verifies, checks, and continues until it hits the defined outcome.

2. The difference between a prompt and a Goal is fundamental. A prompt is an instruction of what to do (“Rewrite this code”). A Goal is a description of what a good outcome looks like and how to get there (“Reduce P95 checkout latency below a defined threshold while keeping the correctness suite green”).

3. Claire eliminated hundreds of error logs by pointing Goals at her Sentry data. She gave Codex access to every trace of invalid operations, then set a goal: categorize each issue, fix it, then replay all historical examples until every error is solved. The result: zero errors remaining, and instead of bandaid fixes scattered throughout the code, she got a systematic, intelligent framework.

4. Goals work incredibly well for non-technical tasks. Claire cleaned 3,900 emails down to 68 in under four hours by setting a simple goal: categorize all emails, unsubscribe from unnecessary ones, and clean up the inbox. The AI read every email, created labels, clicked unsubscribe links, and left her with only the emails requiring judgment.

5. Strong Goals have six key components: outcome (what should be true when done), verification (how to test it), constraints (what can’t regress), boundaries (what tools and files to use), iteration policy (how to decide what to try next), and stopping conditions (when to ask for help). Product managers who’ve written good OKRs will recognize this framework immediately.

6. Working with Goals feels like managing a colleague, not babysitting a tool. You assign a task, the AI goes away for the time required (whether that’s 30 minutes or five hours), and comes back with completed work for you to review. Claire found herself “twiddling her thumbs” because so much of the work was now handled autonomously.

7. Goals aren’t token-cheap, but they’re worth it. Claire’s email cleanup used about 6 million tokens over four hours. But the alternative—manually categorizing thousands of emails or chasing down hundreds of error logs—would take far longer and be far more tedious.

Claude Opus 4.8 is here. Is it as good as they say?

Claire put Anthropic’s new Opus 4.8 model through real coding, design, and strategy tests across Claude Code and Claude Cowork. She shares where the model shines, where it breaks down, how it compares to Opus 4.7, and what builders should know before using it in production.

Biggest takeaways:

1. The voice and ergonomics are excellent. Opus 4.8 is easy to read, doesn’t have “slop tells,” is token-efficient, and feels conversational without being annoying. It talks enough but not too much, and with fast mode enabled, the experience is snappy. The writing quality is strong and the model follows instructions well.

2. Anthropic is shipping new features alongside Opus 4.8 that expand agentic capabilities. Claude Code now has dynamic workflows that let you spin off hundreds of parallel sub-agents. Both Claude.ai and Cowork now offer effort control from low to max, giving users more control over how deeply the model thinks through problems.

3. Use Opus 4.8 for greenfield prototypes and design work, but test carefully for production codebases. The model excels at one-shot features, has improved design aesthetics (no more italicized emphasis words), and is good at tool use. But for existing codebases, edge cases, and strategy work requiring numerical analysis, you’ll need careful prompting and should double-check anywhere the model expresses high confidence.

4. The model hallucinates when it gets stuck, which is a significant regression. Claire experienced straight-up hallucinations multiple times—something she hadn’t seen in a very long time with modern models. When debugging, Opus 4.8 would make up explanations based on hypotheses rather than actual data. It would confidently state things like “No, I didn’t search GitHub” or “No, I didn’t actually validate that bug” when asked to verify its work.

5. Opus 4.8 struggles to orient itself in existing codebases. When Claire asked it to rebase branches and fix conflicts in her production codebase, it required cycle after cycle of fixes because it kept shipping edge-case bugs. The model couldn’t understand the elevation at which it should be operating or how to properly insert itself into existing code.

6. The model isn’t ambitious enough for truly agentic work. Claire asked it to suggest fun things to build that would impress a 9-year-old, pushing it to explore the edges of agentic coding. While it shipped working code, the results were serviceable but not impressive—not the 10x agentic coding experience she expected from a state-of-the-art model.

7. For business strategy work, Opus 4.7 significantly outperforms Opus 4.8. Claire tested both models on the same strategy prompt, giving them access to three months of business context. Opus 4.7 delivered numbers-anchored, structured analysis rooted in real data. Opus 4.8 was hand-wavy, over-rotated on small data points, and had a harder time discovering relevant information.

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.

Bryce Rattner Keithley has spent her career in talent and recruiting, working with technical leaders but never writing a line of code herself. Yet she managed to build Daily Hundred—a fitness app featuring custom AI-generated videos of anthropomorphic animals demonstrating exercises—and ship it to the App Store before her software engineer friends. Using Replit, Claude, Gemini, and a relentless beginner’s mindset, Bryce proves that in the AI era, execution is no longer the constraint on good ideas.

What you’ll learn:

1. How to build and ship an iPhone app using Replit without any coding knowledge

2. The step-by-step process for creating custom AI-generated workout videos by combining Gemini images with real exercise footage

3. How to use Claude as your technical architect and Claude Code as your software engineer

4. How to navigate App Store submission requirements (including fixing rejection feedback)

5. Why being hyper-literal in your prompts unlocks better AI results

6. Why a beginner’s mind is actually an advantage when building with AI tools

Brought to you by:

WorkOS—Make your app enterprise-ready today

Metaview—The agentic recruiting platform for winning teams

In this episode, we cover:

(00:00) Introduction to Bryce and Daily Hundred

(04:48) Building with Replit

(06:16) The beginner’s mindset advantage

(11:17) Creating anthropomorphic animals

(22:55) Moving from static image to video

(27:15) The floating genie and other anthropomorphic animal generations

(30:46) Shifting from web app to App Store submission

(36:24) User feedback

(37:41) Lightning round and final thoughts

Tools referenced:

• Replit: https://replit.com/

• Lovable: https://lovable.dev/

• Claude: https://claude.ai/

• Claude Code: https://claude.ai/code

• Gemini: https://gemini.google.com/

• Higgsfield: https://higgsfield.ai/

• Kling: https://kling.ai/

• Railway: https://railway.app/

• TestFlight: https://developer.apple.com/testflight/

Other references:

• How a 91-year-old vibe coded a complex event management system using Claude and Replit | John Blackman: https://www.lennysnewsletter.com/p/how-a-91-year-old-vibe-coded-a-complex

• What Got You Here Won’t Get You There: https://www.amazon.com/What-Got-Here-Wont-There/dp/1401301304

• How Women Rise: https://www.amazon.com/How-Women-Rise-Holding-Careers/dp/0316440124

• A Whole New Mind: https://www.amazon.com/Whole-New-Mind-Right-Brainers-Future/dp/1594481717

• How to Win Friends and Influence People: https://www.amazon.com/How-Win-Friends-Influence-People/dp/0671027034

Where to find Bryce Rattner Keithley:

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

GitHub: https://github.com/brk-bot/

Daily Hundred on the App Store: https://apps.apple.com/us/app/daily100-fitness-challenge/id6762108062

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.

Benedict Evans is an independent analyst and former partner at Andreessen Horowitz, where he spent years as their in-house “thinker” tracking the most important technology trends. For the past six years, he’s been publishing deeply researched presentations on where tech is heading, most recently focused on AI’s transformation of the economy. His work is read by founders, investors, and operators trying to make sense of a noisy field. His most controversial opinion: AI is as big a deal as the internet or mobile—and only as big.

In our in-depth conversation, we discuss:

1. Why we’re in “1997” for AI—early, exciting, and deeply uncertain about what comes next

2. Where value will actually accrue in the AI stack

3. The anti-AI backlash, and where it may lead

4. The surprising boom in consulting and professional services at AI companies

5. Why distribution is becoming the ultimate moat as software gets easier to build

6. Why the right question about your job isn’t “What percent can AI do?” but “Is this a task or a job?”

7. Why things will probably be okay—and what you need to do to prepare

Brought to you by:

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

Vanta—Automate compliance, manage risk, and accelerate trust with AI

Where to find Benedict Evans:

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

• Newsletter: https://www.ben-evans.com/newsletter

• Website: https://www.ben-evans.com

Referenced:

• Andreessen Horowitz: https://a16z.com

• AI Eats the World: https://youtu.be/niJpDnNtNp4

• VisiCalc: https://en.wikipedia.org/wiki/VisiCalc

• McKinsey & Company: https://www.mckinsey.com

• Bain & Company: https://www.bain.com

• Accenture: https://www.accenture.com

• Jevons paradox: https://en.wikipedia.org/wiki/Jevons_paradox

• Benedict’s post on LinkedIn about Excel: https://www.linkedin.com/posts/benedictevans_younger-people-may-not-believe-this-but-activity-7303217994459938816-PNqu

• The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code | Dan Shipper (co-founder/CEO of Every): https://www.lennysnewsletter.com/p/inside-every-dan-shipper

• Dario Amodei on X: https://x.com/DarioAmodei

• Marc Andreessen: The real AI boom hasn’t even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom

• Frame.io: https://frame.io

• Food Marketing Institute: https://en.wikipedia.org/wiki/Food_Marketing_Institute

• Llama: https://www.llama.com

• Steven Sinofsky on X: https://x.com/stevesi

• Drake meme: https://imgflip.com/memegenerator/343699919/Drake-Hotline-Bling-Transparent-Background

• Ex-Google CEO Gets Booed While Discussing AI in Commencement Speech | WSJ News: https://www.youtube.com/watch?v=tNH43a1EI7s

• Jonathan Swift’s quote: https://www.goodreads.com/quotes/9838985-you-cannot-reason-a-person-out-of-a-position-he

• George Carlin’s quote: https://www.brainyquote.com/quotes/george_carlin_391403

• Fujitsu: https://global.fujitsu

• O*NET OnLine: https://www.onetonline.org

• Pete Holmes’s website: https://peteholmes.com

• The Seventh Seal: https://www.imdb.com/title/tt0050976

• Ericsson R310s phone: https://en.wikipedia.org/wiki/Ericsson_R310s

• i-mate phone: https://en.wikipedia.org/wiki/I-mate

• J-Phone: https://www.mobilephonemuseum.com/phone-detail/j-phone-j-t06

Recommended books:

• Three Men in a Boat: https://www.amazon.com/Three-Men-Boat-Jerome-K/dp/1512099899

• Nature’s Metropolis: Chicago and the Great West: https://www.amazon.com/Natures-Metropolis-Chicago-Great-West/dp/0393308731

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:

Read more

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I got a few hours of early-access testing with Anthropic’s newly released model Opus 4.8. I walk through real coding, design, and strategy tasks across Claude Code and Claude Cowork, and give you my unfiltered view on what impressed me and what didn’t.

What you’ll learn:

1. Where Opus 4.8 excels: greenfield prototypes, one-shot features, and fast execution

2. Where it struggles: the last 10%, edge cases in existing codebases, and hallucinations

3. How Opus 4.8 compares to Opus 4.7 on business strategy work

4. Why I’m still reaching for Opus 4.7 on data-heavy strategy and roadmap work

5. The new features shipping alongside the model: dynamic workflows with parallel subagents and effort control in Claude.ai and Cowork

6. The prompting and harness strategy I’d use to get the most out of it

In this episode, we cover:

(00:00) Introduction to Opus 4.8

(00:44) Benchmark performance and pricing

(01:53) First coding test: Building a prototyping tool

(03:00) Where it failed: The last 10% problem

(03:27) The hallucination problem

(04:23) Testing Opus 4.8 on existing codebases

(05:24) The ambition test: Building games for a 9-year-old

(07:03) Business strategy test: 4.7 vs 4.8

(08:23) The roadmap test

(09:17) Final verdict

References:

• System Card: Claude Opus 4.8: https://cdn.sanity.io/files/4zrzovbb/website/c886650a2e96fc0925c805a1a7ca77314ccbf4a6.pdf

• Introducing Claude Opus 4.8 on X:

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.

In this 30-minute episode, I walk through my favorite feature in Codex: the /goal command. I show how Goals transform AI from a turn-based assistant that needs constant ‘what’s next?’ prompting into an autonomous agent that can work for hours on complex, multi-step tasks. I share three real examples: eliminating thousands of Sentry errors, cleaning 3,900 emails down to 68, and organizing hundreds of Linear tasks.

What you’ll learn:

1. What Goals are and how they differ from standard prompts

2. How I used /goal to eliminate hundreds of error logs in my codebase over a five-hour autonomous run

3. The non-technical use cases that make Goals incredibly powerful: cleaning up 3,900 emails in under four hours and organizing hundreds of project management tasks in Linear

4. How to write effective /goal prompts with measurable outcomes, verification methods, and constraints

5. When not to use Goals and what makes a strong versus weak Goal

6. Why Goals represent a fundamental shift in how we work with AI, from babysitting the model to managing it

Brought to you by:

Mercury—Radically different banking loved by over 300K entrepreneurs

In this episode, we cover:

(00:00) Introduction

(01:50) What is /goal and when should you use it?

(02:45) The difference between prompts and Goal-based loops

(04:06) Claire’s first five-hour 45-minute autonomous coding task

(05:05) How to manage a Goal lifecycle: view, pause, resume, and clear

(06:06) How to write strong goals: outcomes vs. outputs

(07:34) The six components of effective Goals

(08:57) Example: Reducing P95 checkout latency with /goal

(09:36) Demo: Using /goal to eliminate Sentry errors in ChatPRD

(13:18) Demo: Burning down Vercel API errors

(17:28) Non-technical use case: Cleaning 3,900 emails with /goal

(21:24) Demo: Using /goal to clean up Linear project tasks

(24:41) When not to use /goal

(26:10) Why /goal changes everything

Tools referenced:

• Codex: https://openai.com/codex/

• Sentry: https://sentry.io/

• Vercel: https://vercel.com/

• Linear: https://linear.app/

Other reference:

• OpenAI blog post “Using Goals in Codex”: https://developers.openai.com/cookbook/examples/codex/using_goals_in_codex

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.

Subscribe now

P.S. Get a full free year of Cursor, Google AI, Lovable, Replit, Notion, Manus, Gamma, n8n, Canva, ElevenLabs, Factory, Wispr Flow, Fin, Supabase, Bolt, Linear, PostHog, Framer, Railway, Granola, Warp, Gumloop, Magic Patterns, Mobbin, Stripe Atlas, and ChatPRD, by becoming an Insider subscriber. Yes, this is for real.

I was a shy kid growing up, so I spent a lot of time reading books. I devoured sci-fi, nonfiction, and every programming book I could find. When the library didn’t have the C++ book I wanted, I taught myself to code by buying book after book from our local Borders (RIP). I was tearing through so many coding books that my dad started returning the ones I’d finished to the bookstore so we could afford to buy more.

I’ve always thought it was so cool that the smartest person in the world on a topic I care about spent years of their life distilling their best ideas into an enjoyable read, and I can get this for just $20. Amazing.

There’s endless free content flying at us now—newsletters, podcasts, tweets, oh my—but how many blog posts or tweets have had anywhere near the lasting impact on your life as a great book?

To continue my essential-reads series (don’t miss part 1 and part 2 of my favorite online essays), I’ve put together a collection of my all-time favorite books, organized by their jobs-to-be-done. When your manager tells you to work on a particular skill—or if you’re just feeling the itch for self-improvement—these are the books I recommend.

To keep this list extremely high signal-to-noise, I forced myself to pick only three books per category (so hard!), and only books I’ve completed. I included both classics and under-the-radar gems. I also agree with Marc Andreessen’s take that you should mostly read books that are over 10 years old (because those are the ones that have stood the test of time), so you’ll notice very few newer books.

There are so many great books that I didn’t include, either because I haven’t had a chance to read them or they just missed the cut. I probably forgot some important titles, too. And don’t miss part 2!

P.S. If you feel like you have no time to read, I was in the same boat. Bryan Johnson’s suggestion to read a book for 10 minutes before bed changed my life. I started reading more books, and I got better sleep! Try it out.

I want to improve my communication skills

I had never written anything online before starting this newsletter, so once I realized this was going to be a thing, I decided I needed to learn something about writing. Friends recommended many different books, and I read them all. But these three had the most practical, impactful, and lasting advice, which is still lodged in my head even now.

1. Nobody Wants to Read Your Sh*t by Steven Pressfield

2. On Writing Well by William Zinsser

3. Storyworthy by Matthew Dicks

Check out my conversation with Matthew Dicks.

I want to get better at executing

If you love this newsletter, you crave actionable, tactical advice and frameworks that you can put into practice immediately. The first two books will give you exactly that, and the third will give you a meta-framework that’ll level up how you approach everything at work.

1. The Great CEO Within by Matt Mochary

2. Scaling People by Claire Hughes Johnson

3. The Goal by Eliyahu Goldratt

Check out my conversations with Matt Mochary and Claire Hughes Johnson.

I want to get better at strategy

I remember reading these books and feeling like, okay, I finally understand what “strategy” is.

1. Good Strategy/Bad Strategy by Richard Rumelt

2. Playing to Win by Roger L. Martin

3. Working Backwards by Colin Bryar and Bill Carr

Check out my conversations with Richard Rumelt, Roger Martin, and Bill Carr.

I want to be inspired to build something great

This bucket of books might be my favorite of the bunch. They showed me how much a singular (relentless) mind can accomplish. Read them, and you’ll be ready to run through walls.

1. The Making of Prince of Persia by Jordan Mechner

2. Build by Tony Fadell

3. Shoe Dog by Phil Knight

I want to become a better manager

These books are among the most mentioned on my podcast. I remember reading High Output Management when I was a baby manager, and it finally taught me what my job actually was. The Making of a Manager is the modern version of that book. And Radical Candor forever shifted how I think about hard feedback.

1. High Output Management by Andy Grove

2. The Making of a Manager by Julie Zhuo

3. Radical Candor by Kim Scott

Check out my conversations with Kim Scott and Julie Zhuo.

I want to become a better leader

I love how the combination of these three books sums up great leadership: push people to do the best work of their lives, but be human about it, and, in the end, the score will take care of itself.

1. Amp It Up by Frank Slootman

2. The 15 Commitments of Conscious Leadership by Jim Dethmer, Diana Chapman, and Kaley Klemp

3. The Score Takes Care of Itself by Bill Walsh

I want to increase my product success rate

These classics taught me the fundamentals of product management: talking to customers, prioritizing roadmaps, and what it takes to build something valuable and impactful.

1. The Mom Test by Rob Fitzpatrick

2. Escaping the Build Trap by Melissa Perri

3. Continuous Discovery Habits by Teresa Torres

Check out my conversations with Melissa Perri and Teresa Torres.

I want to level up my product org

These books are also some of the most mentioned on my podcast, because they’ll shift how you think about the role of product within your organization, and how to make decisions in uncertain environments.

1. Empowered by Marty Cagan

2. Inspired by Marty Cagan

3. Thinking in Bets by Annie Duke

Check out my conversations with Marty Cagan (and, again, Marty Cagan) and Annie Duke.

I want to get better at sales and marketing

With AI making it easier to build, distribution is becoming the bigger bottleneck. This is not a natural strength for most builders, which is why you need to develop your marketing and sales muscle. I learned more about marketing and sales from these three books than from anywhere else.

1. Purple Cow by Seth Godin

2. Obviously Awesome by April Dunford

3. Founding Sales by Peter Kazanjy

Check out my conversations with Seth Godin, April Dunford, and Peter Kazanjy.

I want to be more productive

Read more

How the engineer behind Claude Cowork actually uses Claude | Felix Rieseberg (Anthropic)

Brought to you by:

• Magic Patterns—Prototypes that look like your product

• Guru—The AI layer of truth

Felix Rieseberg, the engineering lead for Claude Cowork and Claude Code Desktop at Anthropic, joins Claire to show how he actually uses Claude in his own life and work. In this episode, Felix walks through building a 3D floor planner from a 2D house plan, using email as a personal inventory database, creating live dashboards from connected apps, and hacking together a $20 hardware “Claude buddy.” He also shares his philosophy for getting more out of AI: go one abstraction layer up, let Claude work in the background, and stop assuming computers can’t solve some of the annoying little problems in your life.

Biggest takeaways:

1. The biggest barrier to AI adoption is people not realizing they can ask AI to solve almost any problem. Felix sees this constantly—the tools are incredibly powerful, but users haven’t built the muscle memory to reach for them. His advice: whenever you’re doing something annoying that doesn’t feel creative, pause and ask yourself if Claude could do it instead. The gap isn’t technical; it’s psychological.

2. Your email is an untapped gold mine of personal data. Felix used his email to inventory all his furniture when moving houses: every purchase receipt, every confirmation, every dimension. Claude parsed it all and built him a 3D floor planner with his actual furniture. This same principle applies to clothing, medical records, travel history, or any domain where you’ve been emailing receipts and confirmations for years. You already have a structured database—you just need to point Claude at it.

3. Go one abstraction layer up, then do it again. Felix started manually entering furniture dimensions into his floor planner, then stopped and asked Claude to figure out what furniture he had. Then he went another layer up and told Claude to find the furniture in his emails. This is the key pattern: every time you catch yourself doing tedious work, ask how Claude could do it instead. Then ask how Claude could figure out what to do without your telling it.

4. Live artifacts are Claude’s answer to keeping your personal dashboards always up-to-date. Unlike static artifacts, live artifacts refresh with real-time data from your connected services—Spotify, Gmail, Calendar, Notion, whatever you’ve authorized. Felix built a personal dashboard that looks like early-2000s software that updates throughout the day. The killer feature: you never have to manually update your pitch deck, your daily briefing, or your personal reports again.

5. Choose Opus when you don’t know what you’re really asking for. Felix’s heuristic for model selection: use Sonnet when the problem is well-scoped and specific. Reach for Opus when you need Claude to interpret what you actually want, not just what you said. It’s the difference between “make me a floor plan with units” (Sonnet territory) and “help me figure out how to organize my life” (Opus territory). For most tasks, Sonnet is perfectly capable, but when you need that extra layer of problem decomposition, Opus is worth it.

6. Kids are the best AI users because they aren’t afraid to ask for things. Felix gets videos from parents showing what their kids build with Claude—custom video games with hand-drawn characters, interactive stories, tools that would have required a software engineer just a few years ago. Adults have spent 20 years in a “mind prison” learning what computers can’t do. Unlearning that is the unlock.

7. When Claude makes mistakes, debug your workflow, not the model. Felix doesn’t curse at Claude (though he notes it’s useful for the team to know when people do). Instead, he asks it: “Here’s what I expected. Can you walk me through where things went differently? How can we prevent this in the future?” Usually the fix isn’t “Claude can’t do this”; it’s “I need to change the prompt, clean up the data source, or set up a dry run.” Treat Claude like a collaborator who needs better instructions, not a tool that’s broken.

What launched at Google I/O 2026 (30-minute day 1 recap)

Brought to you by:

• Magic Patterns—Prototypes that look like your product

• ThoughtSpot—Build AI-powered analytics into your product

Claire breaks down the biggest launches from Google I/O 2026—from Gemini 3.5 Flash and Antigravity 2.0 to Google AI Studio, Omni, Flow, Stitch, and Pomelli. In this episode, she tests the tools live, shares what actually works, and explains where Google is catching up, where it may be pulling ahead, and why its launch-to-availability gap is still such a problem for builders.

Biggest takeaways:

1. Gemini 3.5 Flash rivals leading frontier coding models in Google’s benchmarks while running four times faster. Google positions this as their agentic coding model, optimized for tasks requiring both high reasoning and rapid execution. If the benchmarks hold in practice, this speed advantage could shift the coding agent landscape toward Google’s tools.

2. Antigravity 2.0 brings Google’s IDE to feature parity with Claude Code and Codex—but it’s playing catch-up. The update includes projects (folder-constrained workspaces), scheduled tasks on Cron, and subagents for specific tasks. The UI looks nearly identical to Codex, and the features match what Anthropic and OpenAI shipped months ago. The advantage is speed: if Gemini 3.5 Flash delivers, developers might choose Antigravity for well-scoped tasks that need to ship fast.

3. The /grill-me slash command is Antigravity’s aggressive take on Claude Code’s polite clarification tool. Instead of gently asking questions, /grill-me promises to interrogate your requirements and get to the heart of what you’re building. Whether this is actually more hardcore or just clever branding remains to be seen, but it signals Google’s attempt to differentiate on personality.

4. Google AI Studio now integrates directly with Workspace apps—or it’s supposed to. The promise: build no-code apps that read Sheets, draft Gmails, organize Drive, and see Calendar without setup. Claire couldn’t get it to work during testing. If it delivers, it would capture internal enterprise productivity use cases and personal assistant workflows where Google already owns the data layer.

5. Omni is Google’s answer to Sora, focused on longer, production-quality video. The model creates 10-second videos (versus Sora’s 6 or 7 seconds), maintains character consistency across edits, and allows conversational editing. Claire tested it by animating her kid’s drawing, and the output was impressive. The real power will be in production workflows where you iterate on the same characters and scenes multiple times.

6. Flow is Google’s production-grade video editor built on Omni. It lets you define characters, create avatars, and edit videos conversationally while maintaining cinematic quality. The tool targets creators and marketers who need consistent, high-quality video at scale. Claire tried creating an avatar of herself, but the feature failed—a recurring theme throughout I/O announcements.

7. Stitch and Pomelli are Google’s design and marketing tools. Stitch is like in-browser Figma with streaming design generation, inline AI edits, and code sync. Pomelli creates brand books, campaign assets, and websites from a URL. Both show promise but suffer from “Google slop,” the generic aesthetic of AI-generated design.

8. Gemini’s multimodal capabilities remain its strongest differentiator. For work involving files, videos, or transformative work across modalities (document to video, image to text), Gemini models excel. Claire uses them for generating blog posts from podcast videos and animating drawings. The 3.5 family continues this strength; for these use cases, Gemini’s multimodal performance is best-in-class.

9. The biggest problem: half the features don’t actually work yet. Claire encountered broken features, missing integrations, and “coming soon” disclaimers throughout testing. Workspace integration in AI Studio? Couldn’t access it. Avatar creation in Flow? Didn’t work. When you announce features that aren’t ready, people lose patience and stop trusting your roadmap.

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.