Lenny RachitskyModify

2026-06-08 23:01:21

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.

2026-06-08 20:03:27

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 [email protected].

2026-06-07 20:31:29

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 [email protected].

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

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2026-06-07 01:14:54

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

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2026-06-03 20:04:09

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 [email protected].

2026-06-01 23:01:46

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.