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:

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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. That’s why there’ll be a 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

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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.

Felix Rieseberg is the engineering lead for Claude Cowork and Claude Code Desktop at Anthropic. He previously spent five years at Slack building developer tools. In this episode, Felix demonstrates how he uses Claude to solve real-life problems: analyzing floor plans to build interactive 3D house walkthroughs, automatically tracking promises he makes on Twitter, and building a $20 hardware device that physically approves Claude actions with a button press.

What you’ll learn:

1. How to use Claude Cowork to turn a 2D floor plan into an interactive 3D walkthrough where you can move furniture around

2. The “go one abstraction layer up” philosophy: why you should never manually enter data Claude can find itself

3. How to use your email as an inventory database for furniture, clothing, and personal purchases

4. When to use Opus vs. Sonnet 4.6 (hint: it’s about how well you can scope the problem, not technical complexity)

5. How live artifacts work and why they’re powerful for dashboards that refresh with real-time data from your connectors

6. The product philosophy behind making latency delightful

7. How to build your own $20 hardware device using Claude Code (no hardware experience required)

8. Why Felix never reads the code Claude writes and judges it purely on output

Brought to you by:

Magic Patterns—Prototypes that look like your product

Guru—The AI layer of truth

In this episode, we cover:

(00:00) Introduction to Felix Rieseberg

(02:40) Felix’s role at Anthropic

(03:25) The multiple tabs in Claude and why they exist

(05:55) Using Claude Cowork to design a new house using floor plans

(09:52) When to use Opus versus Sonnet 4.6

(12:37) Building an interactive 3D furniture planner

(14:30) Using your email as a source of truth for personal inventory

(15:58) The anti-to-do list: going one abstraction layer up

(23:14) Introduction to live artifacts

(26:02) Building a personal dashboard with live data

(28:37) Being polite to Claude (and why it matters for your humanity)

(30:28) Claude interaction tips

(32:33) Looking at the daily dashboard

(33:55) How live artifacts work with connectors

(35:02) Redesigning the dashboard

(37:55) The biggest gap: people don’t know what problems AI can solve

(41:52) The reverse interview

(42:30) Making latency delightful through asynchronous design

(44:05) The redesigned dashboard

(45:28) AI should free up your creative energy

(46:44) Building a $20 hardware Claude buddy

(52:33) Why kids are magical AI users

(54:30) Recap and final thoughts

Tools referenced:

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

• Claude Code: https://claude.ai/code

• Claude for Chrome: https://code.claude.com/docs/en/chrome

• Claude Desktop: https://claude.ai/download

• Live Artifacts: https://support.claude.com/en/articles/14729249-use-live-artifacts-in-claude-cowork

• Connectors (Spotify, Gmail, Calendar, Notion): https://claude.ai/settings/connectors

• Slack: https://slack.com/

Where to find Felix Rieseberg:

Website: https://felixrieseberg.com/

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

X: https://x.com/felixrieseberg

GitHub: https://github.com/felixrieseberg

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.

Dan Shipper is the co-founder and CEO of Every, a media and software company that’s become a living laboratory for the future of work. Everyone at his company of about 30 people is an AI early adopter; from editors to ops people, they use AI to do much of their work, giving Every a unique lens into where the world is heading. A year ago on this show, Dan predicted that people were sleeping on Claude Code for nontechnical work, which proved to be remarkably prescient. Today he’s back with another set of calls: the SaaS apocalypse is dumb, CLIs are over, the forward deployed engineer is the most valuable new hire, and the only thing you need to do to stay employed is ride the models.

Dan’s predictions:

1. The future of work will happen inside Codex or Claude Code.

2. Every company will have one “super-agent” inside their Slack that every employee talks to regularly.

3. SaaS is not dead—in fact, Dan is bullish on SaaS stocks. His contrarian take: “I would buy SaaS stocks right now.”

4. SaaS economics will shift: users will bring their own AI tokens into apps, which actually improves SaaS margins.

5. PMs will thrive in the AI era.

6. Full-stack designers will become superheroes.

7. The AI job apocalypse is not happening.

8. Forward deployed engineer is the new most essential role.

9. CLIs are over.

10. Automation is a lie.

11. We will read way more AI-generated writing and we will like it.

12. We’ll be building software for humans and agents to use together.

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 Dan Shipper:

• X: https://x.com/danshipper

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

• Podcast: https://every.to/podcast

• Website: https://danshipper.com

Referenced:

• 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

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

• Codex: https://chatgpt.com/codex

• Everyone should be using Claude Code more: https://www.lennysnewsletter.com/p/everyone-should-be-using-claude-code

• Every: https://every.to

• Kieran Klaassen on X: https://x.com/kieranklaassen

• Cora: https://cora.computer

• Kate Lee: https://every.to/@kate_1767

• METR (Model Evaluation and Threat Research): https://metr.org

• OpenClaw: https://openclaw.ai

• Shopify: https://www.shopify.com

• Ramp: https://ramp.com

• Brandon Gell on LinkedIn: linkedin.com/in/brandongell

• Proof: https://every.to/on-every/introducing-proof

• Devin: https://devin.ai

• Cursor: https://cursor.com

• The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell

• SpaceX: https://www.spacex.com

• GitHub: https://github.com

• Lenny’s post on X on AI changing job responsibilities: https://x.com/lennysan/status/2054664138039992600

• General Assembly: https://generalassemb.ly

• Marcus Moretti: https://every.to/@marcus_fd8302_1

• Spiral: https://every.to/on-every/introducing-spiral

• Axios: https://www.axios.com

• Hermes: https://hermes-agent.nousresearch.com

• Viktor: https://www.viktor.ai

• The Dark Wizard on HBO: https://www.hbomax.com/shows/dark-wizard/76e78c26-21c9-45b4-b84b-4c3e1f8ff763

• Alex Honnold: https://en.wikipedia.org/wiki/Alex_Honnold

• 100 Foot Wave on HBO Max: https://www.hbomax.com/shows/100-foot-wave/34e489ad-2eca-4078-adb6-6d2b521b407e

• Rob Burbea talks: https://www.youtube.com/@boubabuddha/playlists

• David Goggins: https://en.wikipedia.org/wiki/David_Goggins

Recommended books:

• The Golden Compass: https://www.amazon.com/Golden-Compass-His-Dark-Materials/dp/0807204714

• The Writing Life: https://www.amazon.com/Writing-Life-Annie-Dillard/dp/0060919884

• The Second World War: https://www.amazon.com/Second-World-War-Volumes/dp/B005NS30ZG

• The Rigor of Angels: Borges, Heisenberg, Kant, and the Ultimate Nature of Reality: https://www.amazon.com/Rigor-Angels-Heisenberg-Ultimate-Reality/dp/0593315073

• The Power Broker: Robert Moses and the Fall of New York: https://www.amazon.com/Power-Broker-Robert-Moses-Fall/dp/0394720245

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

Read more

• Twitter: https://twitter.com/lennysan

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

• Podcast: https://www.youtube.com/@LennysPodcast

• Newsletter: http://lennysnewsletter.com/

I'll see you there.

Today is day one of Google I/O 2026, and I walk through every major announcement live—from the new Gemini 3.5 model family to Anti-Gravity 2.0, Google AI Studio, Gemini’s consumer redesign, the Omni video model, Flow, Stitch, and Pomelli. I test them in real time and tell you exactly which ones delivered.

What you’ll learn:

1. How Gemini 3.5 Flash benchmarks against Claude and GPT models on speed and agentic coding tasks

2. How Anti-Gravity 2.0’s new features (projects, scheduled tasks, subagents, slash commands) compare to Codex and Claude Code

3. Why the /grill-me slash command could be a more aggressive alternative to Claude Code’s clarification flow—and how to use it

4. How Google AI Studio’s new Workspace integration is designed to own the internal productivity app use case

5. How Google’s new creative tools work in practice: Omni (video generation), Flow (cinematic video editing and character consistency), Stitch (streaming UI design with inline edits), and Pomelli (brand identity and asset generation)

6. Why Google’s launch-to-availability gap is still a problem—and what to do when a featured product doesn’t actually work yet

Brought to you by:

Magic Patterns—Prototypes that look like your product

Thoughtspot—Build AI-powered analytics into your product

In this episode, we cover:

(00:00) Google I/O 2026 day 1 overview

(01:47) Gemini 3.5 flash

(04:19) Antigravity updates

(06:32) CLI test and agent features

(07:59) Core agent features released today—May 19th, 2026

(09:43) New slash commands

(11:20) Antigravity test results and takeaways

(12:25) AI Studio updates

(13:52) Access issues

(15:20) Gemini redesign

(17:24) Gemini image gen test

(19:16) Omni (video generation)

(22:56) Flow (cinematic editing)

(24:31) Avatar creation test

(26:45) Pomelli and Stitch

(31:13) Recap and final thoughts

Tools referenced:

• Gemini 3.5 Flash: https://deepmind.google/technologies/gemini/

• Antigravity: https://antigravity.google/

• Google AI Studio: https://aistudio.google.com/

• Google Gemini: https://gemini.google.com/

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

• Google Flow: https://flow.google/

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

• Pomelli (Google brand tool): https://labs.google.com/pomelli/about/

Other references:

• Google I/O 2026 announcements: https://blog.google/innovation-and-ai/sundar-pichai-io-2026/

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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Two years ago, we held the first-ever Lenny and Friends Summit. It was one of the most meaningful days of my life. Full of in-depth talks, intimate roundtables, and a lot of human connection. At the end of the day, an attendee handed me this note before quickly walking away:

There’s such power in IRL events, especially these days and especially for product leaders, who often lack peers they can be real with. Ever since the first summit, I’ve been getting messages about how much y’all want another event like this.

Today, I’m excited to share that the Lenny and Friends Summit returns on Thursday, September 10, in San Francisco! Apply now.

My vision is to make this the best product event in the world. We’re keeping it intentionally small—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.

My three main goals for this summit are to help you:

1. Deeply understand where the product role is heading

2. Learn tangible skills to thrive in that future

3. Build lasting connections with other world-class leaders

Here’s the initial lineup of speakers, each of whom has a unique lens into the future of building (with more to be announced soon):

Apply now

Given that we’re expecting lots of interest and the venue has limited capacity, we’re asking people to apply to attend. Paid newsletter subscribers will get priority consideration. This summit is for senior product and AI leaders; typical titles include Chief, VP, Director, Senior, Group, Lead, or Principal.

If you’re just starting your career in product or growth, the content won’t be as relevant for you. I may host a future event geared toward early-career folks.

To give you a sense of what the last summit felt like, a few messages I got from attendees last year:

“The highest-ROI networking event I have been to.” —Senior PM

“Every aspect of the event was absolutely remarkable.” —CPO

“So many insightful discussions, learnings, and connections, and many tactical takeaways I plan to immediately put in practice.” —Product Lead

“The hit rate of good talks was higher than at any other conference I’ve been to.” —Sr. Director of Product

“A powerful reminder of why I fell in love with building products.” —Founder

Finally, some of my favorite moments from the day:

I’m so excited to be doing this again. Whether you made it to the first summit or missed the chance then, we’ve put a lot of thought into making this one even better: more hands-on workshops, more opportunities to meet other attendees, and more options for how to spend your day.

See you there!

Apply now

Any questions? Please email info@lennyssummit.com, and we’ll do our best to reply promptly.

If you’re finding this newsletter valuable, share it with a friend, and consider subscribing if you haven’t already. There are group discounts, gift options, and referral bonuses available.

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Sincerely,

Lenny 👋

HTML is the new Markdown: How Anthropic engineers are building with Claude Code | Thariq Shihipar

Brought to you by:

• Celigo—Intelligent automation built for AI

• Persona—Trusted identity verification for any use case

Thariq Shihipar is an engineer on Anthropic’s Claude Code team. In this episode (recorded live at Anthropic’s Code with Claude event), Thariq shows how he uses HTML artifacts to plan projects, create interactive specs, build throwaway micro-apps, and maintain living design systems that help humans stay in the loop as agents do more of the work. He also explains why engineers are becoming “compute allocators,” why most AI-generated tokens won’t end up in production, and how richer interfaces can lead to better products.

Biggest takeaways:

1. HTML has become the superior format for communicating with AI agents, replacing Markdown for planning and specs. While Markdown was popular because it’s both human- and machine-readable, HTML offers far richer expression—interactive elements, visual mockups, scrollable sections, and better information density. As Claude’s context windows have expanded and plans have grown to thousands of lines, HTML makes it actually possible to engage with the content rather than just skimming or ignoring it entirely.

2. Engineers are becoming “compute allocators” rather than code writers. When Claude can run for eight hours on a single task, you’re really deciding how to spend $500 of compute. The critical skill is no longer writing code; it’s deciding what’s worth building, defining the boundaries of what you need to know, and staying in sync with the agent throughout the process. This happens primarily in the spec and planning phase, making that work more important than ever.

3. You can build custom, throwaway UIs for editing specific parts of your plans. Thariq demonstrates this by taking a data visualization rules table from his implementation plan and asking Claude to create an ideal interface for editing just that section. The result is a beautiful, gamified UI that makes engaging with the content actually enjoyable. This “micro software on top of micro software” approach means you can have the perfect tool for every specific problem, then discard it when you’re done.

4. The future of agent output isn’t more text. It’s more readable interfaces. Thariq says he stopped reading thousand-line Markdown plans and started asking Claude to edit them instead, which made him less involved in the work. HTML changed that: by turning plans into visual, scrollable, interactive artifacts, Claude makes the output easier to engage with, critique, and improve. The lesson isn’t to read less. It’s to make the work legible enough that you actually want to read it.

5. Living design systems in HTML are more effective than traditional design tools. Instead of pointing Claude at a Figma file or GitHub repo, Thariq maintains an HTML file that represents his entire design system: colors, typography, spacing, components. This compressed understanding can be passed around to any project, and Claude can extract design systems from existing codebases and encode them in HTML. It’s both human-readable and machine-readable, with no tradeoff between the two.

6. The best prompts give Claude enough direction but leave room for creativity. Thariq’s prompts are remarkably simple: “Create an HTML file with a plan. Help me visualize. Include excerpts, mockups, code, whatever is needed to give me maximum context.” The key is the ending—“whatever is needed”—which signals trust and gives Claude permission to make decisions. Over-constraining with elaborate system prompts often produces worse results than simple, trusting instructions.

7. Only about 1% of the tokens Thariq generates go into production code. The vast majority go into dashboards, custom interfaces, weekly status updates, and tools for understanding what he wants to build. This is what abundance looks like—when tokens are cheap, you can afford to make everything you interact with beautiful and tailored to your specific needs. The hope is that this richness in the process translates into better final products.

8. Test verification is not the same as testing. This is a nuanced point that Thariq says deserves its own podcast episode, but the core idea is that traditional unit tests are being replaced by verification rubrics, managed agents checking outcomes, and Claude recording videos of what it did. The testing landscape is evolving rapidly, and teams need to think beyond conventional approaches.

9. Just-in-time documentation in whatever format works is better than centralized, templated systems. When creating content is nearly free and AI can find anything, the old anxieties about “source of truth” and standardized templates matter less. What matters is the quality of the ideas and whether the documentation helps you build better products. Thariq sends his manager weekly HTML status updates because they’re more likely to actually get read than Markdown or plain text.

10. Being nice to Claude probably produces better outputs, and it definitely creates a better world. While no one has run a rigorous A/B test, Thariq prefers to build the world where being kind to AI produces better results. When you’re stern with models, their reasoning gets sad (“the user is right to be disappointed in me”), and that’s not the interaction pattern anyone wants to normalize. Treat Claude like a colleague you respect, not a tool you command.

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

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