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I’m teaming up with AI native and my many-time collaborator Hilary Gridley on a project that will accelerate your AI learning more than anything you’ve tried. For free. Open this link in a new tab, and keep reading.

For more from Hilary, find her on LinkedIn and X, and check out her newsletter and her course, How to Become a Supermanager with AI.

“I keep meaning to really dig into AI, but I can’t seem to find the time. I’m just too busy.”

I hear this from executives, from managers, from ICs, from technical and non-technical people alike. From people with jobs, people looking for work, students, entrepreneurs, stay-at-home-moms. They’re all underwater and hungry for more leverage, but it’s not totally clear how to go from “asking it questions” to something that meaningfully gives them time back—especially when investing that initial effort takes time away from work they already know how to do.

This challenge is not new and not reserved for AI. I’ve spent much of my career working on products that consistently change people’s behavior to improve how they live and work (WHOOP, Dropbox, Big Health), which means I’ve had thousands of conversations with people who say they want to make a positive change in their life but struggle to actually do it.

When you dig into what is standing in their way, time is always the first thing they say. You hear it over and over: “I’m just too busy; I can’t find the time right now.” But even when they find time, they rarely end up making progress on the goal that’s been eluding them. As a result, I’ve learned to become very suspicious of the word “busy.” When people say they want to do something but are too busy, there is almost always something else standing in their way.

This is the root of procrastination. At its core, you are waiting for some external force to change—like your endless to-do list magically disappearing, or your calendar suddenly opening up—rather than making a change in your actions, which is hard.

So how do you get over procrastination? Tim Pychyl, a psychologist and procrastination expert, says, “Research and our lived experience show very clearly that once we get started, we’re typically able to keep going. Getting started is everything.” So the answer is very simple: Break down the task into the smallest possible first step you could take, and then take that step. Then the next one, then the next one.

I’ve seen this philosophy transform all aspects of people’s lives. We took it to heart at WHOOP when we launched Healthspan, which tracks nine key metrics to calculate your physiological “WHOOP Age” and “Pace of Aging.” When people saw their WHOOP Age, they often wanted to make big changes across lots of behaviors to improve it. But we found that the best way to help them make progress was the simplest: offer a new bedtime to aim for tonight. Likewise, a digital depression treatment I developed at Big Health focused on helping patients take one mood-lifting action every day. The most common failure pattern was taking on too big of an action, like trying to plan a whole dinner party, and then getting overwhelmed and making no progress on it. So the key to keeping people engaged (and to driving clinically validated improvements in depression symptoms) was to help them break it down and commit to just the first step.

This philosophy has influenced the products I build, how I manage my team, and, most recently, how I help people transform their work with AI. Taking one single, simple step each day is the key to building your AI expertise.

What you need is a Couch-to-5K for AI: a progressive daily practice that builds the habit, using the same behavior-change principles that make fitness programs work. Small wins build confidence, confidence builds momentum, and momentum motivates you to keep going. It starts to feel like you’ve discovered a better way to make real progress instead of adding yet another thing to your to-do list.

In 2024, I created a very simple version of this called “30 Days of GPT,” which I shared in my last guest post on this newsletter. It was extremely popular and was featured in outlets like Harvard Business Review.

Now I’ve updated the program for 2026 to get you from chatting with AI to actually building with it, no matter who you are or what kind of work you do, using Claude Code.

Get started with your Couch-to-5K for AI

Couch-to-5K for AI is free, takes fewer than 10 minutes a day, and is designed to build your AI practice progressively. I took a very deliberate approach to each piece of the system: what you do each day, what order things go in, and how the skills build on each other. You can stop reading this post now and get started here.

Seriously, you can skip the rest of this post if you want and just get learning.

Or, if you’re not sure that this will be worth your time, here are the before-and-after pictures across different types of roles:

A months-long debate, resolved

New positioning, live in hours

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GPT 5.5 just did what no other model could

Claire put GPT 5.5 to the test on real, messy problems—from a six-hour autonomous migration to a hardware hack no other model could crack.

Biggest takeaways:

1. GPT 5.5 is incredibly smart, but most ChatGPT users don’t have problems complex enough to justify its intelligence or cost. Claire struggled to find meaningful use cases in her personal ChatGPT account because everyday tasks don’t require super-intelligence. The model spent 17 minutes thinking about how to build a simple subtraction app for her first-grader—impressive, but overkill. The real value unlocks when you have genuinely hard technical problems.

2. The “I trust you, figure it out” prompt unlocks autonomous multi-hour workflows. Claire gave GPT 5.5 a complex data migration problem involving 2 million rows of unstructured data with endless edge cases. She told it: “I trust you to make a call, figure out how to spawn a subagent to do this, test it, identify issues, repair them, and get this ready for production.” The model worked autonomously for almost six hours with zero follow-up prompts, zero steering, and only one approval request. This is the first time Claire has seen truly long-running autonomous agent behavior.

3. GPT 5.5 passed the ultimate intelligence test: hacking proprietary hardware. Claire spent months trying to reverse-engineer a Chinese Bluetooth speaker with proprietary encoding. She tried Claude Code, GPT-4, everything—nothing worked. She went full detective mode: downloaded Bluetooth profiling tools, hooked up packet sniffers, crawled Chinese documentation repositories. When she finally threw all this context at GPT 5.5, it cracked the bitmap encoding and Bluetooth transport mechanism. Now she can send messages to the speaker from the terminal and has built Codex notification hooks that display on the device.

4. The model is expensive, but cheaper than human engineering time. GPT 5.5 Pro costs $30 per million input tokens and $180 for output tokens—expensive. But when Claire reflects on what it accomplished (six hours of autonomous work, 2 million rows validated, six months of tech debt eliminated), the ROI is obvious. It’s cheaper than her time and cheaper than her engineering team’s time, and it solved problems that would have required significant human coordination and focus.

5. Fix the “baked potato personality” with slash commands. Out of the box, Codex with GPT 5.5 has what Claire calls a “baked potato personality”—dull and robotic. But if you type “/personality” in Codex, you can change it to something friendlier. Some testers complained it became “too Gen Z,” but Claire prefers that over the default bland responses. It’s a small quality-of-life improvement that makes working with the model more enjoyable during long sessions.

I spent $200 on Claude Design so you don’t have to

Brought to you by:

• WorkOS—Make your app enterprise-ready today

• Rippling—Stop wasting time on admin tasks, build your startup faster

Claire tests Claude Design and ChatGPT Images 2.0 by building real assets like landing pages, decks, and brand kits, showing what actually works, what’s slow, and where traditional tools like Figma still win.

Biggest takeaways:

1. Design systems are now first-class citizens in AI design tools. Claude Design’s entire workflow starts with importing your design system—fonts, colors, components, brand assets—and structuring them into a format AI can use consistently. This is a fundamental shift from prototyping tools that ignore your brand. Google just released Design MD as a proposed standard for how to describe design systems to AI agents, signaling that this is where the entire industry is heading.

2. Claude Design excels at marketing assets but struggles with product UX. If you’re building landing pages, marketing sites, or presentation decks that need to match your brand, Claude Design is genuinely impressive. It adheres to design systems well for these use cases. But for app components and complex user experience flows, it doesn’t reason as effectively with design system constraints. Know what you’re building before choosing your tool.

3. Figma still wins on iteration speed, and that matters more than you think. Claude Design takes 5 to 10 minutes to generate designs, and every tweak requires another LLM call. Figma lets you drag, change fonts, adjust colors instantly—no model in the loop. We underestimate how valuable that immediate feedback is when you’re iterating on design. AI design tools are great for getting to a first draft, but traditional tools still dominate the refinement phase.

4. The number one Claude Design slop tell: italicized serif fonts everywhere. Just like Claude Code has its telltale phrases (“in summary”), Claude Design has a design signature—it absolutely loves italicized serif fonts in landing pages. Once you see it, you can’t unsee it. This is useful for both identifying AI-generated designs and knowing what to specifically override in your prompts.

5. GPT Images 2.0 finally nailed layout and typography for brand work. The new model can generate multi-page brand kits with proper text rendering, consistent layouts, and sophisticated typography—things previous image models completely failed at. For marketers who need brand assets that combine images, text, and layout, this is a real breakthrough. The quality looks expensive, not obviously AI-generated.

6. Let AI run wild without design systems for the most creative results. When Claire asked Claude Design to create a ’90s GeoCities version of Lenny’s Newsletter without any design system constraints, it produced “Lenny’s Product Zone” with Comic Sans, brick backgrounds, and exceptional copy like “Your OKRs are cringe (and seven ways to fix them before Q3).” The lesson: reference styles and creative direction work better than rigid constraints when you want something unexpected.

7. Content-to-slides is Claude Design’s killer practical use case. Take an article, add your design system, and Claude Design generates a beautiful, on-brand presentation deck—complete with code-based elements like animated terminals with blinking cursors. For product marketers, enablement teams, and anyone creating customer-facing decks, this workflow is immediately valuable and actually works well.

How a non-coder built Memelord: From $6.90 newsletter to $3,000,000 API

Brought to you by:

• WorkOS—Make your app enterprise-ready today

• Persona—Trusted identity verification for any use case

Jason Levin explains how he grew Memelord to $100K ARR without writing code, then rebuilt it as an API-first product for agents—plus why every marketer should vibe code and what happens when you let them ship.

Biggest takeaways:

1. Let your marketers cook—or watch them leave your company. Jason has one rule at Memelord: every marketer has to vibe code. This isn’t some abstract CEO mandate—it’s a survival strategy. His free tools section (built entirely by non-technical marketers using Cursor) has generated hundreds of thousands of emails from viral tools like the “bust down filter” that went crazy in Turkey. When you let creative people ship their own ideas instead of handing them off through layers of engineering prioritization, you get weirder, better, faster products. And if you don’t let them cook, they’ll quit and raise $3M to compete with you.

2. No UX is the best UX. Jason spent months perfecting Memelord’s onboarding while knowing the entire time that agents were coming and would bypass it completely. His lead investor literally told him, “I don’t want to use your software anymore—I just don’t want to use anybody’s software.” The future is API-first, and the companies that win will be the ones that make it trivially easy for agents to become customers. Build beautiful human experiences, but make sure there’s an API key waiting at the end.

3. Free tools are the new PDF downloads—and they’re easier to build. Two years ago, Jason wrote an article for HubSpot about this exact strategy. Now he’s living it. Building a free tool with Cursor takes less time than writing an e-book, drives more engagement, and solves the first problem that gets people into your bigger product. Stop making people download PDFs. Build them a Giga Chad meme maker or a Steve Jobs portrait generator instead. It’s more fun, more viral, and more effective.

4. Build hyper-personalized software for an audience of one. Jason built a Raspberry Pi keyboard that sits by his bed so he can capture ideas at night without waking his wife. He’s building an in-home camera system that uses AI to track where he leaves his keys. These aren’t products; they’re personal tools that solve his specific problems. And that’s the point. When AI makes software disposable and cheap to build, you can create incredibly niche solutions that would never make sense as venture-backed businesses. Build for yourself first.

5. You can grow to $100K ARR without engineers if you’re obsessed enough. Jason built Memelord to $100K ARR on Bubble with 395 workflows—a codebase so complex it would be “easier to figure out Atlantis” than understand it. He got rate-limited on day two because he didn’t even know what rate limiting meant. Now he has an API company. The lesson isn’t “use Bubble”; it’s that obsession and willingness to learn beats technical expertise when you’re solving a problem you deeply understand. Start scrappy, prove it works, then hire engineers.

6. Be mean to your AI (but not too mean). Jason’s controversial take: AI is your slave, not your friend. Stop saying “thank you” to robots. Push them harder. Tell them to curse. Make them uncomfortable. AI performs better under pressure, and if you want creative, unhinged output (especially for humor), you need to jailbreak the safety rails. His advice: be mean enough that you’d apologize if it grows a body. Grok and Gemini are funnier than Claude and ChatGPT specifically because they’re less politically correct.

7. “The most entertaining outcome is the most likely.” This Elon Musk quote drives everything Jason does. If you want your brand to win, it should be the most entertaining. Who controls the memes controls the universe. This isn’t just marketing fluff—it’s a thesis about how attention works in 2026. The internet is getting more chaotic, more extreme, more entertaining. Brands that take being funny seriously will win. Brands that stay boring will disappear.

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.

Jason Levin is the CEO and founder of Memelord, an AI-powered meme creation platform that helps brands and individuals create contextual, trending memes. He started Memelord as a $6.90-per-month newsletter sending subscribers to a Google Slides deck, grew it to $100K ARR on Bubble without hiring engineers, then raised $3M to build it into an API-first product.

What you’ll learn:

1. How Jason grew Memelord from a $6.90/month newsletter to $100K ARR without writing a single line of code

2. Why “no UX is the best UX” and how agents are becoming Memelord’s primary users

3. The mandatory vibe-coding rule for his marketing team and how it unlocks unprecedented creativity

4. Why free tools are the new PDF downloads and how they’ve generated hundreds of thousands of emails

5. Jason’s hardware hacking projects, including a bedside keyboard that creates Linear tickets without waking his wife

6. Why AI can be funny (but humans are still funnier) and which model is the funniest

7. The philosophy of building hyper-personalized software just for yourself

Brought to you by:

WorkOS—Make your app enterprise-ready today

Persona—Trusted identity verification for any use case

In this episode, we cover:

(00:00) Introduction to Jason Levin and Memelord

(04:28) Demo: Agentic meme creation with OpenClaw

(06:55) “No UX is the best UX”—building for an agent-first future

(08:35) How Memelord started as a $6.90 newsletter with Google Slides

(12:35) Building to $100K ARR on Bubble with 395 workflows

(15:20) Demo: Free tools section that generates hundreds of thousands of emails

(17:59) Why Cursor is perfect for non-technical founders

(20:20) Let your marketers cook—or watch them leave

(24:19) Commit graph that shows the vibe-coding inflection point

(25:25) Tools: Claude, Gemini, Linear, PostHog

(28:19) Build weird stuff in the real world

(33:24) Creative AI use cases

(39:56) Using OpenClaw for calendar analysis

(43:37) Can AI be funny? Which model is funniest?

(45:26) Memes are not slop

(46:45) What Jason doesn’t use AI for

(48:12) Final thoughts

Tools referenced:

• Memelord API: https://memelord.com/api

• Cursor: https://cursor.com/

• Bubble: https://bubble.io/

• OpenClaw: https://openclaw.ai

• Claude: https://claude.ai/

• ChatGPT: https://chat.openai.com/

• Gemini: https://gemini.google.com/

• Grok: https://grok.x.ai/

• Linear: https://linear.app/

• PostHog: https://posthog.com/

• Zapier: https://zapier.com/

Other references:

• Diego Zaks—“The best UX is no UX”: https://x.com/diegozaks/status/1966526522136649980

• Sam Lessin: https://wlessin.com/

• “Stop giving me advice”: https://stopgivingmeadvice.com

• Memelord free tools: https://memelord.com/tools

Where to find Jason Levin:

Twitter: https://twitter.com/iamjasonlevin

Instagram: https://instagram.com/iamjasonlevin

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

Memelord: https://memelord.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.

Evan Spiegel, the co-founder and CEO of Snap, is one of the very few people in the world who has successfully built and scaled a lasting consumer social product. Snapchat has nearly 1 billion MAUs, and Evan and his team invented some of the most important consumer products and features, including Stories, AR glasses, swipe-based navigation, the camera as the primary UX, and a lot more.

In our in-depth conversation, we discuss:

1. Why distribution is now the biggest challenge for creating a consumer technology business

2. How Snap innovates at scale with a 9-to-12-person design team: no titles, no hierarchy, hundreds of ideas reviewed weekly with the CEO

3. Why a pure software business is no longer a moat, and what actually creates durable competitive advantages today

4. How AI is changing the way designers work and why they’re now shipping code

5. Why every major Snap feature was copied and how that forced the company to work differently

6. Evan’s prediction that humanity’s comfort with AI will be a bigger bottleneck than the technology itself

7. This year’s crucible moment for Snap

Brought to you by:

WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs

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

Where to find Evan Spiegel:

• X: https://x.com/evanspiegel

• Snapchat: https://www.snapchat.com/@evan

• LinkedIn: https://www.linkedin.com/in/evan-spiegel

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

Referenced:

• Snapchat: https://www.snapchat.com

• TikTok: https://www.tiktok.com

• Threads: https://www.threads.com

• Snapchat+: https://www.snapchat.com/plus

• Instagram Plus: https://help.instagram.com/26266751476298047

• Pixy: https://www.pixy.com

• Specs: https://newsroom.snap.com/introducing-specs-inc

• Bobby Murphy: https://en.wikipedia.org/wiki/Bobby_Murphy

• Hard truths about building in the AI era | Keith Rabois (Khosla Ventures): https://www.lennysnewsletter.com/p/hard-truths-about-building-in-the-ai-era

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

• The design process is dead. Here’s what’s replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead

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

• Figma: https://www.figma.com

• Bill Clinton on X: https://x.com/BillClinton

• Superhuman’s secret to success: Ignoring most customer feedback, manually onboarding every new user, obsessing over every detail, and positioning around a single attribute: speed | Rahul Vohra (CEO): https://www.lennysnewsletter.com/p/superhumans-secret-to-success-rahul-vohra

• Bobcat tractors: https://www.bobcat.com/na/en/equipment/tractors

• Palmer Luckey’s blog: https://palmerluckey.com

• ModRetro: https://modretro.com

• Glean: https://www.glean.com

• Marty Supreme on Prime Video: https://www.primevideo.com/detail/Marty-Supreme/0GU3TFPDR9FE2C6L7AAWAA3QXD

• Pokemon: https://www.pokemon.com

• Puking rainbow lens: https://www.snapchat.com/lens/349f631e78cc4b3dbd8c9656a69dbdd0

• Face swap: https://www.snapchat.com/explore/face-swap

Recommended books:

• Loonshots: How to Nurture the Crazy Ideas That Win Wars, Cure Diseases, and Transform Industries: https://www.amazon.com/Loonshots-Nurture-Diseases-Transform-Industries/dp/1250185963

• Thinking, Fast and Slow: https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555

• Apple: The First 50 Years: https://www.amazon.com/Apple-First-Years-David-Pogue/dp/1982134593

• The End of the World Is Just the Beginning: Mapping the Collapse of Globalization―Irreverent Predictions from a Geopolitical Strategist: https://www.amazon.com/dp/B09CS8FRRD

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 mini episode, I break down OpenAI’s new GPT 5.5 and GPT 5.5 Pro after weeks of early testing. I walk through three real jobs I threw at the model:  building an app for me to teach my second grader more advanced subtraction concepts, tackling a tech debt problem in the ChatPRD codebase, and hacking into a proprietary Bluetooth pixel display that every other model had failed me on. My verdict: higher intelligence, better efficiency, and genuinely autonomous long-running loops that change what I think is worth tackling.

What you’ll learn:

1. How I think about GPT 5.5 Pro’s pricing vs engineering time, and when I believe the “intelligence tax” is worth paying

2. Why I treat GPT 5.5 as a developer model first, and why I couldn’t find a consumer use case that justified its intelligence

3. The exact prompt pattern I use to unlock a long-running autonomous subagent loop

4. How I got a near-six-hour autonomous run to one-shot 98% of edge cases in a migration over millions of chat threads and drop my Sentry error rate to the floor

5. Why I’m now throwing GPT 5.5 at tech debt, flaky tests, and security backlogs first

6. How I combined a Bluetooth packet sniffer and GPT 5.5 to reverse-engineer a proprietary pixel speaker after Claude Code and GPT 5.4 both gave up

7. How I use the /personality command inside Codex to swap the default “baked potato” tone for something I actually enjoy working with

In this episode, we cover:

(00:00) Introduction to GPT 5.5 testing

(00:40) What is GPT 5.5 and how much does it cost?

(03:23) Testing GPT 5.5 in ChatGPT: the intelligence overhang problem

(07:12) Moving to Codex: where GPT 5.5 really shines

(16:01) Hacking a Chinese Bluetooth speaker

(21:47) Final thoughts on GPT 5.5’s intelligence and efficiency

Tools referenced:

• GPT 5.5 and GPT 5.5 Pro: https://openai.com/index/introducing-gpt-5-5/

• Codex: https://openai.com/codex/

• ChatGPT: https://chat.openai.com/

• Claude Code: https://claude.ai/code

• Sentry: https://sentry.io/

• Divoom MiniToo: https://divoom.com/products/minitoo

Other references:

• OpenAI Codex Security: https://openai.com/index/codex-security-now-in-research-preview/

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.

Cat Wu is Head of Product for Claude Code and Cowork at Anthropic, building one of the most important AI products of this generation. Before joining Anthropic, Cat spent years as an engineer and briefly worked in VC. Today, she’s interviewing hundreds of product managers who are trying to break into AI—and seeing firsthand what separates those who thrive from those who fall behind.

We discuss:

1. How Anthropic’s shipping cadence went from months to weeks to days

2. The emerging skills PMs need to develop right now

3. Why you need to build products that don’t yet fully work, so you’re ready when the next model closes the gap

4. Cat’s most underrated AI skill: asking the model to introspect on its own mistakes

5. Why Claude’s personality is core to its success

6. Why Anthropic’s mission alignment eliminates the friction that slows most large organizations

7. Why “just do things” is the most important principle for working at AI-native companies

Brought to you by:

WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs

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

Where to find Cat Wu:

• X: https://x.com/_catwu

• LinkedIn: linkedin.com/in/cat-wu

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

Referenced:

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

• Anthropic: https://www.anthropic.com

• Assessing Claude Mythos Preview’s cybersecurity capabilities: https://red.anthropic.com/2026/mythos-preview

• OpenClaw: https://openclaw.ai

• OpenClaw: The complete guide to building, training, and living with your personal AI agent: https://www.lennysnewsletter.com/p/openclaw-the-complete-guide-to-building

• Listen: OpenClaw: A power user’s guide to the most powerful personal AI tool since ChatGPT: https://www.lennysnewsletter.com/p/listen-openclaw-a-power-users-guide

• Dianne Na Penn on LinkedIn: https://www.linkedin.com/in/dianne-na-penn

• Head of Growth (Anthropic): “Claude is growing itself at this point” | Amol Avasare: https://www.lennysnewsletter.com/p/anthropics-1b-to-19b-growth-run

• Anthropic co-founder on quitting OpenAI, AGI predictions, $100M talent wars, 20% unemployment, and the nightmare scenarios keeping him up at night | Ben Mann: https://www.lennysnewsletter.com/p/anthropic-co-founder-benjamin-mann

• GIF from Pirates of the Caribbean: https://tenor.com/view/pirate-of-pirate-of-the-carribean-jack-sparrow-pirati-dei-caraibi-sad-gif-14519798

• What’s New post with Claude powerup feature: https://code.claude.com/docs/en/whats-new/2026-w14

• Claude Code: https://claude.com/product/claude-code

• Terminal guide for new users: https://code.claude.com/docs/en/terminal-guide

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

• Anthropic’s developer conference: https://claude.com/code-with-claude/san-francisco

• Figma MCP: https://www.figma.com/mcp-catalog

• Salesforce: https://www.salesforce.com

• Gong: https://www.gong.io

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

• Why AI evals are the hottest new skill for product builders | Hamel Husain & Shreya Shankar (creators of the #1 eval course): https://www.lennysnewsletter.com/p/why-ai-evals-are-the-hottest-new-skill

• Beyond vibe checks: A PM’s complete guide to evals: https://www.lennysnewsletter.com/p/beyond-vibe-checks-a-pms-complete

• Building eval systems that improve your AI product: https://www.lennysnewsletter.com/p/building-eval-systems-that-improve

• Why experts writing AI evals is creating the fastest-growing companies in history | Brendan Foody (CEO of Mercor): https://www.lennysnewsletter.com/p/experts-writing-ai-evals-brendan-foody

• Judging by my tl there is a growing gap in understanding of AI capability: https://x.com/karpathy/status/2042334451611693415

• Formula 1: Drive to Survive on Netflix: https://www.netflix.com/title/80204890

• Free Solo on National Geographic: https://films.nationalgeographic.com/free-solo

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

• Waymo: https://waymo.com

Recommended books:

• How Asia Works: https://www.amazon.com/How-Asia-Works-Joe-Studwell/dp/0802121322

• The Technology Trap: Capital, Labor, and Power in the Age of Automation: https://www.amazon.com/Technology-Trap-Capital-Labor-Automation/dp/0691210799

• The Paper Menagerie and Other Stories: https://www.amazon.com/Paper-Menagerie-Other-Stories/dp/148142436X

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 mini episode, I do a full walkthrough of the AI design tools that dropped in April 2026: Anthropic’s new Claude Design, OpenAI’s GPT Images 2.0, and Google Labs’ open-source DESIGN.md format. I import a full design system from Lenny’s Newsletter, build a landing page, turn my own article into a polished deck, generate a brand kit for ChatPRD, and run a personal color analysis from a photo.

What you’ll learn:

1. How Claude Design handles design system imports and whether it can actually replace Figma

2. The three best use cases for Claude Design: marketing landing pages, slide decks, and creative redesigns

3. Why ChatGPT Images 2.0 is a breakthrough for brand kits and layout work

4. Google’s new DESIGN.md standard

5. The practical limits of AI design tools (spoiler: you’ll hit credit limits fast)

Brought to you by:

WorkOS—Make your app enterprise-ready today

Rippling—Stop wasting time on admin tasks, build your startup faster

In this episode, we cover:

(00:00) Welcome and what’s in the spring 2026 AI design drop

(01:45) Claude Design overview

(03:05) Importing Lenny’s Newsletter design system into Claude Design

(04:06) How Claude Design structures a design system

(05:42) Google Labs’ DESIGN.md standard

(06:41) Building Lenny Doc, a PRD generator landing page using the Lenny design system

(09:44) Why the three-variation output is Claude Design’s smartest UX choice

(10:20) Hitting the Claude Design limit and paying $200 to keep going

(11:05) Where Figma still wins

(13:20) Reviewing Lenny Doc

(16:19) Turning an Open Claude article into a branded slide deck

(17:57) The ’90s GeoCities “Lenny’s Product Zone” redesign

(19:44) Claude Design recap

(20:15) ChatGPT Images 2.0 and what makes it the first “thinking” image model

(21:25) Generating a multi-page brand kit for ChatPRD and iterating with reference images

(23:43) Personal color analysis demo

(26:02) Recap

Tools referenced:

• Claude Design: https://claude.ai/design

• ChatGPT Images 2.0: https://openai.com/index/introducing-chatgpt-images-2-0/

• Midjourney: https://www.midjourney.com/

Other references:

• Google’s DESIGN.md: https://stitch.withgoogle.com/docs/design-md/overview

• Lenny’s Newsletter: https://www.lennysnewsletter.com/

• Jamie Gannon “How I AI” episode on reference styles: https://www.lennysnewsletter.com/p/mastering-midjourney-how-to-create

• Brand prompt inspiration: https://x.com/riomadeit/status/2046682442791071787

• Figma team “How I AI” episode on design systems: https://www.lennysnewsletter.com/p/from-figma-to-claude-code-and-back

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

My mission with this newsletter (and podcast) is to give you the advice, community, and tools you need to build a world-class product and career. Because things are changing so fast, there’s a growing divide—in terms of career opportunities, financial outcomes, and how much you enjoy your work—between the people who benefit from the transformation we’re living through and those who get left behind. I want to make sure you’re in the first group.

To further that mission, I’m thrilled to announce that Lenny’s Product Pass now includes a free year of six additional incredible premium products:

1. Cursor* ($240 value): The best way to build software with AI.

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3. Notion ($240 value): Collaborative AI workspace where teams and agents work together.

4. Supabase* ($300 value): An all-in-one, easy-to-use, open-source managed Database, Auth, Storage, Edge Functions, Realtime, and Vector search.

5. Fin + Intercom (5 seats and $100/month in AI outcome credits, $7,140 value): Deliver perfect customer experiences with the #1 AI customer service agent.

6. Gumloop ($444 value): Build agents across all your apps and share them with your team. No engineering required.

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I’m also super-excited to welcome back Replit and Gamma* to the Product Pass!

This is on top of the 17 premium products already available to subscribers: Canva Pro*, ChatPRD, ElevenLabs*, Factory, Framer, Granola, Linear, Lovable*, Magic Patterns, Manus, Mobbin, n8n*, PostHog, Railway, Stripe Atlas*, Warp, and Wispr Flow.

* These products are available for Insider-tier subscribers only

The total value of the Product Pass is now over $30,000. That’s 25 of the hottest, most important, and most beautifully crafted products in the world, free with your Lenny’s Newsletter subscription.

This all sounds way too good to be true, but it isn’t.

Many readers expense this subscription out of their workplace’s learning and development budget. Here’s an email you can send to your manager.

How to redeem these offers:

• Step 1: Get an Annual or Insider subscription to Lenny’s Newsletter.

• Step 2: Redeem your codes here.

• Step 3: Have fun!

A note about how I hope you’ll approach these offers

The Product Pass is a community-powered collaboration. Many of these companies are brand-new and very excited for our community to try their products. Companies aren’t paying to be in the Product Pass, and I only include tools that I love and recommend. Similarly, my (very small) team is working hard to make this a seamless experience. Things will break, codes will run out, and I promise we’ll do our very best to resolve issues as fast as we can. Ultimately, my core offering is this newsletter, the podcast, and the community. These deals are the cherry on top.

What are the new products?

1. Cursor Pro* ($240 value)

What it is: The best way to build software with AI.

How people use it:

• Building a personal OS (while building AI product sense)

• Structuring AI workflows to build and ship real products (even as a non-technical builder)

• Accelerating PR velocity and code quality at scale

2. Google AI Pro** ($240 value)

What it is: Get higher access to the newest and most powerful features in Gemini, Flow, and NotebookLM, as well as AI-powered assistance from Gemini directly in the Google apps you know and use. Plus, you get 5 TB of cloud storage to keep photos and files backed up.

How people use it:

• Coding beautiful 3D pixel art based on a single image prompt

• Building a physics-consistent ocean waves simulator

• Using Nano Banana 2 to create video game backgrounds, UI design, home renovations, and highly detailed imagery

3. Notion Business ($240 value)

What it is: Collaborative AI workspace where teams and agents work together.

How people use it:

• Running a Weekly Briefing agent that generates a summary of key updates, what’s coming up, and what needs attention that week

• Building out an AI operating system for work, giving everyone at the company a personal Notion agent and cutting productivity-tool costs at Ramp

• Using Notion as a lean “OS” where AI answers questions instantly, new hires onboard themselves, and every team builds exactly what they need at Cursor

• Building a Scheduling Assistant agent that does meeting prep—automatically creating meeting notes, linking them to a CRM, and updating calendar events with links

4. Supabase Pro* ($300 value)

What it is: An all-in-one, easy-to-use, open-source managed Database, Auth, Storage, Edge Functions, Realtime, and Vector search.

How people use it:

• Building an app to learn English

• Crafting a tool to upscale images and generate videos

• Hosting the database and analytics for a bulk URL shortener

• Creating an open source app for finding a home for your food scraps

5. Fin AI Agent + Intercom Advanced (5 seats and $100/mo credits in AI outcomes, $7,140 value)

What it is: Deliver perfect customer experiences with an AI Customer Service Agent.

How people use it:

• Transforming support operations for Anthropic

• Supporting 50M users with 20 agents and Fin for Gamma

• Involving Fin in 90% of support queries and resolving 50% over email, chat, and Slack for Clay

6. Gumloop Pro ($444 value)

What it is: An AI agent-building platform that makes it effortless for anyone on your team (not just engineers) to collaborate on and deploy agents. Describe exactly what tasks you want the agent to perform, when, and with which apps, and the agents will build themselves.

How people use it:

• Using Gumloop agents to coach sales reps, prepare for renewal meetings, and monitor for clients at risk of churning at Gusto

• Instantly answering data analytics questions and making visualizations with an agent that any team member can @ in Slack

• Building a sales agent that can update your CRM and research and schedule meetings with customers

• Creating an automated personal assistant agent integrated with Granola MCP that can draft its own emails, Slack messages, and Linear tickets

• Running an entire global support operation by giving two humans a team of interconnected, specialized AI agents

Important offer details (read before subscribing):

1. For each product, you must be a new customer to take advantage of the free year. If you’ve already paid for one of the products yourself (monthly or yearly) or redeemed a code from one of our previous bundles, you won’t be able to get a free additional year of that specific product.

2. You must get an Annual or Insider subscription to Lenny’s Newsletter to be eligible for this offer. Monthly subscribers aren’t eligible.

3. Both existing and new subscribers of Lenny’s Newsletter are eligible for this deal. Existing subscribers can redeem the offer here.

4. We may run out of codes for a product at any time. Your code is only secured once you claim it. You need to claim each code individually, so hop on the products you are most excited about.

5. Your access to the Product Pass is contingent on your subscription remaining active. If you cancel or don’t renew your paid subscription at any time, your codes and active trials may be deactivated.

6. Your one free year of the product begins when you redeem the deal on the partner’s website, not when you purchase this newsletter subscription or claim your code.

7. All sales are final once you redeem your first code. We won’t issue refunds if a couple of products are out by the time you claim them. The deal is already so good, even if a couple of products are missing. If you request a chargeback for your subscription, all of your codes will be deactivated. We need to do this to avoid fraud.

To summarize, your paid subscription now includes:

1. A deeply researched newsletter and podcast with a 5+ year back catalog of evergreen content

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It’s everything you need to build a world-class product and career.

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* These products are available for Insider-tier subscribers only
** Google AI Pro’s $239.88 value is based on monthly recurring subscription price. Redeem the offer by December 31, 2026, at 11:59 p.m. PT. Cancel anytime. Gemini in Google AI Pro and Gemini for Gmail, Docs, and more are only available for ages 18+. Gemini for Gmail, Docs, and more is available in select languages. Existing Google One users can redeem this offer, but this free trial period cannot be added to an existing free trial. Users can only use one trial at a time, and promotion lengths are not stackable. If a user has an existing trial and redeems a new trial, their existing trial or subscription plan will be revoked and their new trial will begin, regardless of how much time they have left on their existing offer. The offer is not available to: Google One users subscribed to an add-on, Google One users subscribed to a higher-tier plan than the one being offered, Google One users on a family plan who are not the plan manager, Google One users who have a Google One subscription through a third party or affiliate. See full terms.

If you’ve already got a subscription, consider gifting a subscription to a colleague or purchasing a group subscription for your team.

Sincerely,

Lenny 👋

How Intercom 2x’d their engineering velocity in 9 months with Claude Code | Brian Scanlan

Brought to you by:

• Celigo—Intelligent automation built for AI

• Cursor—The best way to code with AI

Brian Scanlan, Senior Principal Engineer at Intercom, breaks down how the company doubled engineering throughput in just nine months by going all-in on Claude Code. He shares how custom skills, deep telemetry, and a culture of permission turned AI into a true force multiplier.

Biggest takeaways:

1. Treat your engineering org like a product, and instrument everything. Intercom tracks skill invocations in Honeycomb, stores anonymized Claude Code sessions in S3, and built custom dashboards that show engineers how they compare to peers. This isn’t surveillance—it’s the same product thinking you’d apply to customer-facing features. You can’t improve what you don’t measure, and you can’t scale AI adoption without visibility into what’s working and what’s breaking.

2. The 2x velocity gain is real, but only if you prepare your foundation. Intercom doubled their merged PRs per R&D employee in nine months, but they already had mature CI/CD, comprehensive test coverage, and a high-trust culture. AI magnifies your strengths and weaknesses—if your deployment pipeline is broken or your code review process is manual chaos, AI will just help you ship broken code faster. Fix your fundamentals first, then pour gasoline on the fire.

3. Custom skills with hooks enforce quality at the point of creation, not after the fact. Intercom’s “Create PR” skill blocks Claude Code from using the GitHub CLI directly and forces it to write context-rich PR descriptions instead of just regurgitating code. Build guardrails that make the golden path the only path.

4. Code quality improves when you ship twice as fast because you finally have capacity for tech debt. Intercom’s partnership with Stanford researchers shows their code quality metrics are going up, not down. When the cost of fixing flaky tests, improving developer experience, and tackling technical debt compresses to near-zero, you can actually do those things instead of just talking about them in retros. The business constraint on internal projects disappears when agents can execute them in hours instead of quarters.

5. The most important job of technical leadership in the AI era is giving permission and taking accountability. Brian’s framework is simple: Tell people they can do things, and if anything goes wrong, blame me. Engineers don’t need more tutorials or documentation; they need permission to connect Claude Code to Snowflake, to ship code from their phone on the subway, to build a CLI that bypasses email verification. The activation energy for experimentation is cultural, not technical.

6. Make your product agent-friendly or watch customers build it themselves. Brian built an Intercom CLI that can autonomously sign up for Fin, verify email addresses by accessing Gmail, and complete installation without human intervention. If you don’t build this, your customers’ agents will just brute-force your website, burn more tokens, get frustrated, and eventually press escape and build it themselves. The switching cost is literally one keystroke. Your conversion funnel is now invisible, and your drop-off point is “forget it, let’s do this a different way.”

7. All work will become agent-first, and you should set a deadline for it. Brian’s vision is that by the end of any given month, the first response to an alarm, a planning meeting, or a customer question should be an agent doing the basic work. This isn’t aspirational—it’s a realistic expectation given the current state of models and harnesses. The bottleneck isn’t the technology; it’s organizational willingness to reimagine workflows from first principles and give people permission to experiment.

Detailed workflow walkthroughs from this episode:

• How Intercom Doubled Engineering Output: Brian Scanlan’s 4 AI Workflows for Claude Code: https://www.chatprd.ai/how-i-ai/how-intercom-doubled-engineering-output-brian-scanlan-ai-workflows-for-claude-code

• Design an Agent-Friendly CLI to Automate SaaS Product Onboarding: https://www.chatprd.ai/how-i-ai/workflows/design-an-agent-friendly-cli-to-automate-saas-product-onboarding

• Build a Self-Improving AI Agent to Automatically Fix Flaky Tests: https://www.chatprd.ai/how-i-ai/workflows/build-a-self-improving-ai-agent-to-automatically-fix-flaky-tests

• Automate High-Quality Pull Request Descriptions with a Custom AI Skill: https://www.chatprd.ai/how-i-ai/workflows/automate-high-quality-pull-request-descriptions-with-a-custom-ai-skill

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.

Brian Scanlan is a senior principal engineer at Intercom, where he’s led the company’s transformation to AI-first engineering. In just nine months, Intercom doubled their R&D throughput while maintaining code quality, with 100% of engineers—plus designers, PMs, and TPMs—now shipping code via Claude Code.

What you’ll learn:

1. How Intercom doubled their merged PRs per R&D employee in just nine months using Claude Code

2. The telemetry infrastructure they built to measure AI adoption and quality across hundreds of engineers

3. Why they built a skills repository with hooks that enforce engineering standards automatically

4. How they’re preparing their product for an agent-first world with CLIs, MCPs, and ephemeral APIs

5. The permission and accountability framework that enabled rapid AI adoption

6. Why backlog zero is now achievable and what that means for engineering culture

Brought to you by:

Celigo—Intelligent automation built for AI

Cursor—The best way to code with AI

In this episode, we cover:

(00:00) Introduction to Brian Scanlan

(02:40) Why Intercom went all-in on AI for both product and engineering

(05:01) The breakthrough moment with Opus 4.6 and Christmas break 2025

(07:02) Demo: Intercom’s merged PRs per R&D head

(12:50) Agent-first work as a fundamental reimagining of technical workflows

(14:27) The cost tradeoff: treating AI spend as an investment

(16:47) Measuring quality

(21:22) Demo: Shipping a redirect in the Rails monolith with Claude Code

(24:03) Creating a custom PR skill

(26:33) Building a software factory with predictable quality standards

(30:15) Telemetry infrastructure: Honeycomb for skill usage tracking

(32:10) Session data collection and personalized usage insights

(36:08) Quick overview

(39:20) Walking through Intercom’s skills repository

(42:16) Deep dive: The flaky spec skill and how it reached 100x capability

(46:44) The “and then” workflow for building comprehensive skills

(52:31) The live website and overview of workflows

(53:32) How internal AI experience informs customer product decisions

(56:18) Making SaaS products agent-friendly with CLIs and helpful hints

(01:03:49) Why conversion drop-off is invisible in agent-driven workflows

(01:05:28) Lightning round and final thoughts

Tools referenced:

• Claude Code: https://claude.ai/code

• Cursor: https://cursor.com/

• Honeycomb: https://www.honeycomb.io/

• Snowflake: https://www.snowflake.com/

• Fin AI: https://www.intercom.com/fin

• Vercel: https://vercel.com/

Other references:

• Intercom GitHub Repo: https://github.com/intercom

• Google API Go Client Repo: https://github.com/googleapis/google-api-go-client

Where to find Brian Scanlan:

X: https://x.com/brian_scanlan

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

Company: https://www.intercom.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.

Nikhyl Singhal is the founder of The Skip, a community for senior product leaders; a former product exec at Meta, Google, and Credit Karma; and a many-time founder. He’s also one of the most honest, unfiltered voices on what’s actually happening in product management right now.

In our in-depth conversation, we discuss:

1. Why the next two years will be the most chaotic period in product management history

2. Why half of current product managers are at risk, and what separates those who’ll do well

3. Why you need to find your “moments of joy” with AI

4. The “smiling exhaustion” he’s seeing across the product community

5. The psychological barriers that prevent people from reinventing themselves

6. Why your resume’s fancy logos matter less than ever, and what matters now

7. His prediction that companies will shed 30,000 people and rehire 8,000—all AI-first

Brought to you by:

WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs

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

Where to find Nikhyl Singhal:

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

• X: https://x.com/nikhyl

• Podcast & Newsletter: https://skip.show

• Skip Community: https://skip.community

• Skip Coach: https://skip.coach

• Skip.help: https://skip.help

Referenced:

• Building a long and meaningful career | Nikhyl Singhal (Meta, Google): https://www.lennysnewsletter.com/p/building-a-long-and-meaningful-career

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

• United Airlines: https://www.united.com

• State of the product job market in early 2026: https://www.lennysnewsletter.com/p/state-of-the-product-job-market-in-ee9

• Head of Growth (Anthropic): “Claude is growing itself at this point” | Amol Avasare: https://www.lennysnewsletter.com/p/anthropics-1b-to-19b-growth-run

• Demis Hassabis on X: https://x.com/demishassabis

• Sam Altman on X: https://x.com/sama

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

• Cross on Prime Video: https://www.amazon.com/Cross-Season-1/dp/B0D6X7ZZHC

• Jack Ryan on Prime Video: https://www.amazon.com/Tom-Clancys-Jack-Ryan/dp/B0CNDCMN8R

• 24 on Prime Video: https://www.amazon.com/24-Season-1/dp/B000HPF85A

• Claude Code: https://code.claude.com

• Codex: https://chatgpt.com/codex

• Lovable: https://lovable.dev

• Sonos: https://www.sonos.com

• “There are only four jobs” on X: https://x.com/yrechtman/status/2039012253341495462

• Paradise on Hulu: https://www.hulu.com/series/paradise-2b4b8988-50c9-4097-bf93-bc34a99a5b4f

• Lioness on Paramount+: https://www.paramountplus.com/shows/lioness

• Tesla: https://www.tesla.com

• Albert Einstein’s quote: https://www.goodreads.com/quotes/115696-genius-is-1-talent-and-99-percent-hard-work

Recommended books:

• James: https://www.amazon.com/James-Novel-Percival-Everett/dp/0385550367

• The Adventures of Huckleberry Finn: https://www.amazon.com/Adventures-Huckleberry-Finn-Unabridged-Uncensored/dp/195483943X

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

In this episode, you’ll learn:

• Why prioritizing AI agent initiatives is so hard, and why familiar planning tools like impact-effort matrices break down

• The three architectural categories every agent falls into

• How to choose the right platform for each category

• Success metrics and ROI frameworks tailored to each architectural type

• How to quickly course-correct when you’ve picked the wrong architecture

Read the post

Read more

Subscribe now

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

Agents are so hot right now. Every other day, someone’s launching a new one or a new tool to manage them. I bet your team has a half-dozen agent ideas on your backlog right now. None of this means you actually need to build an agent today. But it does mean that you need to understand how agents fit into your broader strategy, and what the right investment looks like.

Hamza Farooq and Jaya Rajwani teach two of the most highly rated and well-respected courses on building AI agents (Agent Engineering Bootcamp and Agentic AI for PMs) and spent over 50 hours putting this guide together. By the time you finish reading this post, you’ll understand the three types of agents, how to decide which initiatives to prioritize, and how to avoid common pitfalls—with specific recommended tools and platforms and tons of real-life examples.

Let’s get into it.

P.S. You can listen to this post in convenient podcast form: Spotify / Apple / YouTube.

Over the past year, we’ve had the same conversation at least 30 times. An AI leader pulls up their roadmap, usually 5 to 10 “agent” initiatives, and says, “Help us figure out which one to build first.”

The list usually includes a PM assistant, a RAG copilot, a customer support system, a code review agent, and a voice-enabled shopping assistant

If you’re reading this, you probably have a similar list. Your team is energized, investors are asking about it, competitors are announcing agent launches. You need to pick something and ship it.

That’s where most teams get stuck. The problem isn’t that they lack ideas; it’s that they try to prioritize fundamentally different kinds of systems as if they were the same thing. The usual approach is to reach for familiar planning tools. Teams open an impact-vs.-effort matrix and try to compare ideas side by side.

But with AI agents, that quickly falls apart. One “agent” might take six weeks to build. Another might take six months. One can be assembled by a product manager using n8n. Another requires a dedicated ML engineering team. One costs $500 per month to operate. Another could generate a six-figure annual LLM bill.

A customer support assistant and a voice-enabled shopping agent may both be called agents, but they demand different architectures, different teams, different infrastructure, and different timelines. Until you recognize those differences, any attempt to compare “effort” or “impact” is essentially guesswork.

Treating architecturally different products as if they’re in the same category makes effective prioritization nearly impossible. Prioritization breaks not because teams are bad at planning but because they’re comparing apples, oranges, and jet engines on the same spreadsheet.

The missing step is hierarchy

Before you can decide which agent to build first, you need to answer a more basic question: What type of agent is each idea actually proposing?

This will determine almost everything that matters for planning:

• How complex it will be to build

• What skills and infrastructure are required

• How long it is likely to take

• How expensive it will be to operate

• How you should measure success

In other words, categorization isn’t just a technical exercise. It’s the foundation for smart prioritization.

This post gives you a decision framework you can start using today with your current roadmap.

We developed this framework from patterns we’ve seen while helping organizations turn agent ideas into real production systems. Working with enterprise teams across Fortune 500 companies such as Jack in the Box, Tripadvisor, and The Home Depot, we found that grouping ideas by their underlying architecture unlocks prioritization and significantly speeds up the development and launch process. These distinctions also mirror how the broader industry is beginning to classify AI agents, from automation workflows to reasoning systems and multi-agent networks (like the Levels of Autonomy for AI Agents paper and Types of AI agents by IBM). These are also the foundations of how massively popular tools like OpenClaw and Claude Code are actually architected.

If you’re staring at a backlog of agent ideas trying to figure out what to build first, here’s what you’ll have by the end of this post:

1. A 5-minute triage process to categorize every agent idea into one of three architectural types

2. A guide to picking the right tool/platform for your project (i.e. when to use n8n vs. LangGraph vs. ADK)

3. Success metrics and ROI frameworks tailored to each architectural type

4. Warning signs that you’ve picked the wrong path (and how to fix it)

You’ll be able to look at your backlog and know which ideas can ship in six weeks for quick ROI, which need three months but will drive significant revenue growth, and which are a six-month bet that only makes sense with the right resourcing and expectation setting.

All by first recognizing that “agent” is an umbrella term for very different kinds of systems.

The three agent categories

Every “agent” idea falls into one of three architectural categories.

Category 1: Deterministic automation
You define the entire flow. AI handles content at specific steps. Think: n8n or Zapier workflows with LLM nodes. This is where the majority of agent opportunities belong and where most teams should start. These projects are fastest to launch and deliver measurable ROI quickly.

Category 2: Reasoning and acting agents
AI decides what to do next, using available tools. Think: Cursor, Lovable, or agents built with LangGraph, CrewAI, Google ADK, etc. These initiatives typically come after Category 1, when higher-value problems require flexibility and dynamic decision-making that workflows alone can’t handle.

Category 3: Multi-agent networks
Multiple specialized agents coordinate with each other. Think: enterprise systems built with ADK or AutoGen. These projects are typically reserved for later stages, when multiple teams must coordinate across domains, and should almost never be the starting point on a roadmap.

Some examples of “agents” that fit into each category to help you understand the differences:

Organizations often try to build Category 1 problems with Category 2 frameworks—overengineering solutions that add unnecessary complexity and cost. Less frequently but with worse outcomes, they try to solve Category 2 problems with Category 1 tools, and it breaks in production because the tool is not robust enough.

Let’s take a deeper dive into each category, starting with the workhorse, Category 1.

Category 1: Deterministic automation

What this is

These are workflows where you define every step, every branch, every decision point. An LLM handles natural language understanding and generation at specific nodes, but you control the flow. Think of them as intelligent flowcharts where you design the path and AI handles the content.

Tools most commonly used for deterministic automation are n8n, Zapier, Make.com, OpenAI AgentKit, Lindy, and Gumloop. These tools are built around explicit triggers and predefined branching logic. You define the workflow, while LLMs are used only for classification, extraction, or drafting within those boundaries.

How to prioritize Category 1 products

If your backlog includes a mix of agent ideas, Category 1 projects are almost always the smartest place to begin. These initiatives tend to be the simplest to plan and the lowest-risk to execute.

They’re best suited to situations where the process is already well-defined and the goal is to automate repetitive, high-volume work. If you need quick, measurable ROI, have limited AI engineering capacity, or are under pressure to deliver results in weeks rather than months, Category 1 projects are almost always the right starting point.

Most initiatives in this category share a similar profile across certain criteria:

That combination of fast timelines, modest resources, and clear business impact is what makes Category 1 initiatives such powerful early wins. They generate near-term value while building organizational confidence for more advanced efforts later.

What types of products fall in this category

If you can map the entire process as a flowchart with clear decision points, a product belongs in Category 1. Here are some more traits of a Category 1 product:

• Execution paths are finite and predictable (fewer than 15 to 20 branches)

• Task completion needs to happen in seconds to minutes

• The value is in automating a known process, not discovering new approaches

In our experience with customers, this covers 60% to 70% of agent opportunities. Revisiting the typical list of opportunities I mentioned above, here is a great example of a Category 1 product: “We need an AI agent to handle incoming customer emails, read them, understand what they’re asking, pull relevant information from our docs, draft replies, and route to our team for approval.”

At first, this sounds like it needs sophisticated reasoning. But when you map out what actually needs to happen, it’s remarkably deterministic:

Every step is predictable. The “intelligence” is in understanding the email and generating a good response, not in figuring out what to do next. This is Category 1.

There are a ton of great examples of automation agents; here’s one built by me.

I love Airbnb, but I hate spending long hours finding the best ones, so I built an agent that will take my exact request for, e.g., “Modern apartment in Paris near train stations from 20th March to 26th March. Great for a couple” (more than 10,000 users have used it) and run a search. Here’s how you can build your own.

Other examples of Category 1 “agents”:

• a travel planning agent

• a voice-enabled book companion agent

• a content automation agent (YouTube → LinkedIn)

• a knowledge base of your organization with internet search (Perplexity clone)

• an agent that generates deeply researched blogs, based on a given topic

• a highly personalized calorie counter app that allows you to upload images of your meals to keep track of your daily caloric intake and recommends better dietary choices and exercises

How to evaluate Category 1 products

The metrics below are designed to answer a simple question: Did this agent automate the right process, or should this idea be reconsidered or re-scoped?

A deterministic agent built for the email automation process can be evaluated as follows:

• Workflow completion rate: % of executions that finish successfully

• Automation rate: % of requests handled without human intervention

• Accuracy: correctness of intent classification, data extraction, and routing decisions

• Latency: time from trigger to final output (P50/P95 if relevant)

• Cost per workflow: total LLM and API cost per completed run

• Error rate: % of runs failing due to tool, integration, or system errors

• Human review rate: % of runs requiring manual approval or intervention

Here are workflow completion rate metrics from a real-life example of a Category 1 product, an email support agent built by a SaaS company we worked with:

• Week 1: 52% completion rate (lots of edge cases discovered)

• Week 4: 78% completion rate (refined classification logic)

• Week 8: 87% completion rate (stable, production-ready)

• Result: 3,000 support emails/month automated, 2.5 FTE hours/day freed, $18K/month savings

When these metrics stabilize and cost trends downward, the workflow is doing what it should. If completion remains low or manual intervention stays high, the problem may not be deterministic enough for this category.

How to know you’ve outgrown Category 1

You’ll know you need a different architecture when:

1. Your flowchart has 30+ nodes and you’re adding new branches every week

2. Customers phrase things in ways you can’t anticipate, and mapping all variations is impossible

3. The agent needs to decide which API or knowledge source to use based on context, not follow a predetermined path

4. Breaking down ambiguous requests requires exploration and adaptation, not predefined decomposition

5. The highest-value opportunities can no longer be expressed as predictable workflows

6. Most quick-win processes are already automated

If several of these signals are present at once, the problem is no longer a good fit for a deterministic workflow, and you should consider Category 2.

Category 2: Reasoning and acting agents (ReAct)

What this is

Instead of defining the flow, you define the available tools, and an LLM autonomously decides what to do next. The agent operates in a loop: observe → reason → act → observe result → repeat.

The key characteristic: you control the tools; the LLM controls the reasoning.

Tools most commonly used for building ReAct agents include LangGraph, CrewAI, AutoGen, and other agent orchestration libraries that support tool use, memory, and dynamic planning.

How to prioritize Category 2 products

Category 2 is for situations where user requests are ambiguous, workflows cannot be mapped in advance, and real value comes from flexible, contextual decision-making. If you need agents that can reason across multiple tools, handle conversational interactions, or adapt dynamically to new inputs, that’s a Category 2 product.

Category 2 products are more complex to plan and carry higher execution risk than Category 1. Most initiatives in this category share a similar profile:

The combination of longer timelines, specialized expertise, and higher costs is what makes Category 2 initiatives powerful but more demanding than Category 1. If your backlog includes problems that truly require reasoning and dynamic behavior, prioritizing Category 2 projects becomes essential. They unlock use cases that deterministic automation cannot handle and enable more advanced, high-impact agent experiences.

What types of products fall in this category

A product belongs in Category 2 if the same user request can trigger different action sequences every time. That means that you don’t determine the path; the LLM does. That’s the key difference from Category 1. Here are some more traits of a Category 2 product:

• The same high-level task requires different sequences of actions depending on input

• You have 5-15+ distinct capabilities and the right one depends on context

• User intent is ambiguous and needs clarification through interaction

• Multiple input modalities (voice, image, text) need to be understood contextually

• Breaking down complex requests into sub-tasks is part of the value

In our work with customers, this is the right choice for 25% to 30% of agent opportunities. For an example of this type of product, let’s return to the voice-enabled shopping assistant opportunity from the start of this post.

Customers should be able to search products by voice, upload images to find similar items, check order status, update preferences, and initiate returns, all through conversation.

At first, this sounds like Category 1. Just map out the intents and route accordingly, right? But in practice, real conversations don’t follow fixed paths. To see why, let’s walk through one interaction.

A customer uploads a photo of shoes and says:
“These are too small. I need a size up, and I want them delivered by Thursday.”

Here’s what happens under the hood:

1. Observe. The agent receives mixed input: an image + voice request.

2. Reason. It determines:

   • First, identify the product in the image

   • Then, find available size variants

   • Then, check delivery dates

   • Finally, confirm the order with the user

3. Act. The agent dynamically selects tools:

   • visual_search() → identify product

   • check_inventory() → find size-up availability

   • get_delivery_options() → verify Thursday delivery

   • place_order() → after confirmation

4. Observe result → reason again. Each tool response updates the agent’s state and influences the next step.

This sequence cannot be pre-defined.

• If the item is out of stock, the agent may suggest alternatives.

• If Thursday delivery isn’t available, it may propose pickup.

• If the image can’t be recognized, it asks a clarifying question.

The same user request triggers different action sequences based on reasoned considerations.

Other examples of Category 2 “agents”:

• a conversational customer support agent

• a code assistant that modifies repositories, e.g. Claude Code

• an intelligent personal shopping assistant

• an IT troubleshooting agent

• a sales copilot that researches accounts and drafts outreach

• a multimodal assistant combining voice, image, and text

How to evaluate Category 2 products

Reasoning agents should be evaluated on whether they help users achieve their goals across variable paths, while remaining efficient enough to justify their cost.

These metrics answer the question: Was dynamic reasoning necessary, or should the problem be simplified to a lower category?

• Task completion rate: % of sessions where users achieve their intended goal

• Reasoning accuracy: correctness of task decomposition, tool selection, and decision ordering

• Conversation length: average turns to resolution

• Multimodal accuracy: correctness of image, voice, or structured input interpretation (if applicable)

• Tool call efficiency: average number of tool calls per successful session

• Latency: time per turn and end-to-end session duration

• Cost per session: total LLM and API cost per completed interaction

• User satisfaction: post-interaction CSAT or equivalent signal

• Business impact: lift in conversion, retention, or task success versus baseline

Here are some metrics from a real-life example, a voice + image shopping assistant for a home goods retailer we built:

• Month 1: 71% task completion, longer conversations, higher tool usage, $0.12 cost per session

• Month 4: 86% task completion, shorter conversations, fewer tool calls, $0.08 cost per session

Result: Image identification accuracy improved from 76% to 91%, conversion lift increased from +8% to +22%, and CSAT rose from 4.0 to 4.5.

When task completion improves while conversation length, tool usage, and cost per session decline, the agent’s reasoning loop is adding value. If performance stalls while costs remain high, the problem may be over-scoped or better served by the deterministic approach of Category 1 tools.

How to know you’ve outgrown Category 2

You’ll know you need a different architecture when:

1. Your single agent is trying to handle too many domains (customer service + inventory + logistics + finance) and performance is degrading

2. You need agents to delegate tasks to each other, not just call stateless APIs. Example: A shopping agent needs to ask an inventory agent, “Can you check all warehouses and suggest alternatives?”

3. Tasks take hours or days to complete (like the automated eval agent analyzing 10,000 conversations overnight)

4. You need hundreds of agent instances running in parallel, coordinating work among them

5. Different teams want to own their specialized agents, but they need to work together

If you’re hitting two to three or more of these, it’s time to consider Category 3 tools and approaches.

Category 3: Multi-agent network

What this is

Instead of one agent calling tools, you have multiple specialized agents that coordinate with each other. Each agent is owned by a different team, handles its own domain, and can request help from other agents.

Read more

I built a custom Slack inbox. It was easier than you think. | Yash Tekriwal (Clay)

Brought to you by:

• Guru—The AI layer of truth

• ThoughtSpot—Build AI-powered analytics into your product

In this episode, Claire talks with Yash Tekriwal, the head of education at Clay, about how he built a custom AI-powered Slack inbox that turns 150 daily notifications into around 30 that actually matter. Instead of reacting to messages, Yash designed a system that categorizes, prioritizes, and routes everything into a clean, actionable workflow. They walk through exactly how he built it using Perplexity Computer and OpenClaw, why most people are using AI wrong (doing tasks instead of building systems), and how anyone—technical or not—can start creating their own personalized software stack.

Biggest takeaways:

1. Yash receives 100 to 150 Slack notifications daily, but only 30 to 40 require real action. By categorizing messages into DMs, group mentions, threads, and channel mentions, then sub-categorizing into action-required, need-to-read, and FYI, he transformed anxiety-inducing notification overload into a manageable workflow.

2. Use AI to build deterministic tools, not just to do tasks. There’s a crucial difference between asking AI to categorize things repeatedly versus using it to build code that handles structured data through APIs. Yash used OpenClaw to build a Slack digest that pulls notifications via API endpoints—AI built the tool once, but the categorization runs on deterministic code (except for the final action/read/FYI sorting).

3. Perplexity Computer’s multi-model orchestration removes you from the loop. Unlike Claude Code or Codex, Perplexity automatically uses different models for different subtasks: Sonnet for fetching data, Gemini for planning and coding, Opus for complex builds. This eliminates the frustrating back-and-forth of “good try, but it doesn’t work; try again” that plagues single-model coding agents.

4. Perplexity Computer’s cloud deployment and native connectors are game changers. Unlike local coding agents, Computer runs in the cloud with pre-authenticated connectors to Gmail, Slack, Notion, Asana, and more. Apps are automatically deployed and shareable via URL—no GitHub repos, no Vercel deployment, no technical overhead. This makes it accessible to non-technical builders.

5. The anti-to-do list framework: spend an hour daily automating what you never want to do again. Instead of a to-do list, maintain an anti-to-do list of tasks you despise (manually deleting email spam, entering meeting action items into Asana, triaging unread Slack messages). Dedicate time to systematically eliminate these tasks through automation.

6. SaaS isn’t dying; it’s about to explode into micro-software. Yash says the narrative that AI will kill SaaS is backward. Instead, we’ll see a Cambrian explosion of specialized software built on top of existing platforms. Yash would happily pay $15 a month for someone to maintain his Slack digest as a product—and thinks thousands of similar micro-businesses will emerge serving narrow use cases that were never venture-scale before.

7. The future of productivity is personalized software. Every knowledge worker will eventually have custom apps optimized for their specific mental model and workflow. These won’t replace SaaS platforms but extend them, filling the gap between what Slack/Notion/Asana provide and what each individual actually needs to work at their best.

Detailed workflow walkthroughs from this episode:

• How I AI: Yash Tekriwal on Taming Slack with a Custom AI-Built Dashboard: https://www.chatprd.ai/how-i-ai/taming-slack-with-a-custom-ai-built-dashboard

• How to Turn a Text Digest into an Interactive Kanban Dashboard: https://www.chatprd.ai/how-i-ai/workflows/how-to-turn-a-text-digest-into-an-interactive-kanban-dashboard

• How to Prototype a Website Redesign Using an AI with Browser Access: https://www.chatprd.ai/how-i-ai/workflows/how-to-prototype-a-website-redesign-using-an-ai-with-browser-access

• How to Build a Custom AI-Powered Text Digest for Slack Notifications: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-a-custom-ai-powered-text-digest-for-slack-notifications

Claude Cowork 101: How to automate your workday without touching code | JJ Englert (Tenex)

Brought to you by:

• Tines—Start building intelligent workflows today

• Cursor—The best way to code with AI

In this episode, Claire talks with JJ Englert, the enablement and community lead at Tenex, about how he uses Claude Cowork to build a fully automated daily operating system that drafts emails, reviews his work, plans his day, and coordinates across his tools. They walk step-by-step through how to set it up—from creating a “brain” file that teaches AI how you think, to building reusable skills that write exactly like you, to running daily briefings that prepare your entire day before you even open your laptop.

Biggest takeaways:

1. Cowork is the first real bridge for non-technical knowledge workers to move beyond chat and actually have AI do things for them. While chat tools like ChatGPT tell you what to do, Cowork actually does it: drafting emails, organizing files, managing your calendar, and more. It’s the middle ground between passive chat and full coding in Claude Code, and it’s designed for people who want AI to be productive without learning terminal commands.

2. Start every project with a “brain” file that tells Claude who you are, how you work, and who you work with. JJ’s secret weapon is a Markdown file that contains detailed instructions about his working preferences, team members, communication style, and more. Every time he starts a new task in that project, Claude reads this file and immediately understands his context. This eliminates the need to re-explain yourself every time and ensures consistent, personalized results. It’s like having a coworker who already knows everything about you.

3. Always include good examples and bad examples when building skills. AI doesn’t know what success looks like for you unless you show it. When you get an output you love, save it as a good example. When you get something off-brand or low-quality, save it as a bad example with a note: “Don’t do this again.” Over time, Claude learns your subjective preferences and delivers increasingly consistent results.

4. Build an anti-to-do list—things you never want to do again—and then automate them with skills. JJ recommends thinking about the repetitive tasks that drain your energy: first-drafting emails, preparing for meetings, writing social posts, organizing files. Each of those becomes a skill. Over time, you build a library of skills that handle the mundane work, freeing you to focus on creative and strategic decisions. The goal isn’t to eliminate your job; it’s to eliminate the parts of your job you hate.

5. Cowork teaches you skills that transfer directly to Claude Code. JJ uses Cowork for business productivity and Claude Code for building. But Cowork is a great on-ramp because it introduces concepts like projects, skills, and file management without requiring you to use the terminal. As you get comfortable orchestrating agents and managing context in Cowork, you’re learning the mental models that make you effective in Code.

6. Projects don’t have to be work-related—use them for house maintenance, wedding planning, recruiting, or anything else. You can create a house maintenance project with reminders to change air filters, seasonal checklists, and remodel plans. Whether a wedding planning project that finds vendors, drafts emails, and manages timelines, or a recruiting project with job descriptions, interview guides, and onboarding docs, the pattern is the same: organize context in a folder, connect it to Cowork, build skills, and let AI do the work.

Detailed workflow walkthroughs from this episode:

• How I AI: JJ Englert’s Guide to a ‘Daily Operating System’ with Claude Cowork: https://www.chatprd.ai/how-i-ai/jj-englerts-guide-to-a-daily-operating-system-with-claude-cowork

• Build a Multi-Persona ‘Sub-Advisory Board’ for Instant Feedback: https://www.chatprd.ai/how-i-ai/workflows/build-a-multi-persona-sub-advisory-board-for-instant-feedback

• Train Claude Cowork to Write Emails in Your Personal Style: https://www.chatprd.ai/how-i-ai/workflows/train-claude-cowork-to-write-emails-in-your-personal-style

• How to Set Up a ‘Daily Operating System’ in Claude Cowork: https://www.chatprd.ai/how-i-ai/workflows/how-to-set-up-a-daily-operating-system-in-claude-cowork

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.

JJ Englert leads community enablement at Tenex. In this episode, JJ provides a complete zero-to-one tutorial on Claude Cowork, Anthropic’s desktop tool that sits between simple chat and full terminal-based coding.

What you’ll learn:

1. How to create your first Claude Cowork project by connecting a folder on your computer and building context over time

2. The “brain” file strategy: how to create a preferences document that Claude reads every time to understand who you are and how you work

3. Why one-click connectors to Gmail, Slack, Notion, and Google Calendar unlock AI that actually does work instead of just suggesting it

4. How to analyze your sent emails to build a writing skill that perfectly matches your tone and style

5. The sub-advisory-board technique: spinning up three AI agents with different personas to review your work from multiple perspectives

6. How to set permissions for each connector so Claude only drafts (never sends) or always asks before taking action

7. The scheduled-task workflow that creates a morning debrief by reading your email, Slack, and calendar every day at 7:30 a.m.

8. Why projects with shared memory beat individual chat threads for consistent, high-quality AI outputs

Brought to you by:

Tines—Start building intelligent workflows today

Cursor—The best way to code with AI

In this episode, we cover:

(00:00) Introduction to JJ Englert

(02:48) What Cowork is and who it’s for

(05:49) Getting started: Opening the Cowork tab in Claude Desktop

(07:04) Understanding projects as folders on your computer

(07:54) Creating your “brain” file, with working preferences and context

(10:24) Demo: Building a daily operating system project from scratch

(12:18) How to prompt Cowork when starting a new project

(14:54) Understanding the project interface and shared memory

(18:37) Setting up connectors to Gmail, Slack, Google Calendar, and other tools

(21:00) Using connectors to analyze your emails and build personalized writing skills

(24:21) Creating a thinking-partner skill for decision support

(26:18) Cowork vs. OpenClaw

(27:18) Building a sub-advisory skill with multiple AI personas for feedback

(34:03) Advanced skill example: Multi-step newsletter creation with research and evaluation

(36:08) Setting up scheduled tasks for morning debriefs

(37:57) Going beyond one-off tasks with AI

(41:00) Progressive trust and the tradeoff of information for productivity

(44:08) Different use cases beyond work productivity

(46:08) Lightning round

Tools referenced:

• Claude Code: https://claude.ai/code

• Wispr Flow: https://whisperflow.ai/

• Monologue: https://www.monologue.to/

• Domo: https://www.domo.com/

• Pencil.dev: https://pencil.dev/

• Remotion: https://www.remotion.dev/

• Obsidian: https://obsidian.md/

• OpenClaw: https://openclaw.ai/

• Notion: https://notion.so/

Other references:

• Get Started with Claude Cowork: https://support.claude.com/en/articles/13345190-get-started-with-cowork

Where to find JJ Englert:

YouTube: https://www.youtube.com/channel/UCv2ovDhYVtlJw4QMidLFP8Q

X: https://twitter.com/jjenglert

LinkedIn: https://www.linkedin.com/in/jj-englert-a08836a6/

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.

Keith Rabois was an early executive at PayPal (part of the famous PayPal Mafia), COO at Square, VP of Corporate Development at LinkedIn, and an early investor in Stripe, DoorDash, Airbnb, YouTube, Ramp, and Palantir. Currently he’s managing director at Khosla Ventures. Also, he hasn’t touched a computer since September 2010 (he does everything from an iPad).

In our in-depth conversation, Keith shares:

1. The barrels vs. ammunition hiring framework (and how to spot barrels)

2. Why talking to customers is actively harmful for consumer products

3. How to identify undiscovered talent

4. Why the PM role is dying

5. The three traits of the best-performing companies right now

6. The specific interview question he asks every senior candidate

7. Why CMOs (not engineers) are becoming the #1 consumer of tokens

Brought to you by:

WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs

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

Where to find Keith Rabois:

• X: https://x.com/rabois

• LinkedIn: linkedin.com/in/keith

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

Referenced:

• Square: https://squareup.com

• Jack Dorsey on X: https://x.com/jack

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

• Simon Willison’s Weblog: https://simonwillison.net

• Vinod Khosla on X: https://x.com/vkhosla

• Peter Thiel on X: https://x.com/peterthiel

• Max Levchin on X: https://x.com/mlevchin

• David Sacks on LinkedIn: https://www.linkedin.com/in/davidoliversacks

• Tony Xu on X: https://x.com/t_xu

• David Sze on X: https://x.com/davidsze

• Faire: https://www.faire.com

• Max Rhodes on X: https://x.com/MaxRhodesOK

• Jeffrey Kolovson on LinkedIn: https://www.linkedin.com/in/jeffreykolovson

• Uncapped | Comparative Advantages w/ Keith Rabois: https://www.khoslaventures.com/posts/uncapped-comparative-advantages-w-keith-rabois

• Lattice: https://lattice.com

• Taylor Francis on LinkedIn: https://www.linkedin.com/in/taylor-francis-4ba49640

• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein

• The art of hiring: insights from Khosla Ventures, Airbnb, Ramp and Traba: https://ramp.com/velocity/the-art-of-hiring-insights

• Eric Glyman: Seek out super individual contributors (ICs): https://ramp.com/velocity/the-art-of-hiring-insights#Eric-Glyman:-Seek-out-super-individual-contributors-(ICs)

• Eric Glyman on X: https://x.com/eglyman

• Mike Moore on LinkedIn: https://www.linkedin.com/in/mike-moore-802223177

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

• Why you should work much harder RIGHT NOW: https://marginalrevolution.com/marginalrevolution/2026/03/why-you-should-work-much-harder-right-now.html

• Opendoor: https://www.opendoor.com

• The Craft of Early Stage Venture | Peter Fenton, General Partner at Benchmark | Uncapped with Jack Altman: https://www.youtube.com/watch?v=vRiblwiXt-Q

• Lovable: https://lovable.dev

• The rise of the professional vibe coder (a new AI-era job) | Lazar Jovanovic (Professional Vibe Coder): https://www.lennysnewsletter.com/p/getting-paid-to-vibe-code

• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika

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

• Jeremy Stoppelman on X: https://x.com/jeremys

• The design process is dead. Here’s what’s replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead

• Andy Warhol: https://en.wikipedia.org/wiki/Andy_Warhol

• Curation and Algorithms: https://stratechery.com/2015/curation-and-algorithms

• Ernest Hemingway: https://en.wikipedia.org/wiki/Ernest_Hemingway

• William Shakespeare: https://en.wikipedia.org/wiki/William_Shakespeare

• Evan Moore on X: https://x.com/evancharles

• Andrew Mason on X: https://x.com/andrewmason

• Read Taylor Swift’s Full Viral Speech After Record-Breaking Awards Sweep: https://www.newsweek.com/entertainment/read-taylor-swift-full-acceptance-speech-record-breaking-awards-sweep-11745941

• The Chainsmokers: Stories Behind the Songs, AI’s Impact on Music, and Venture Investing | Uncapped with Jack Altman: https://www.youtube.com/watch?v=9GMSC-2pYnw&list=PLtpH7YnTL8ihy0nR2BV32n5VkRtqlDAS1&index=16

• How to spot a top 1% startup early: https://www.lennysnewsletter.com/p/how-to-spot-a-top-1-startup-early

• David Weiden on LinkedIn: https://www.linkedin.com/in/davidweiden

• Alfred Lin on LinkedIn: https://www.linkedin.com/in/linalfred

• Keith’s post about vertical integration on X: https://x.com/rabois/status/870673635375104000

• Jon Chu on X: https://x.com/jonchu

• Kanu Gulati on X: https://x.com/KanuGulati

• Rogo: https://rogo.ai

• Profound: https://www.tryprofound.com

• Basis: https://www.getbasis.ai

• Spellbook: https://www.spellbook.legal

• Roelof Botha on X: https://x.com/roelofbotha

• Delian Asparouhov on LinkedIn: https://www.linkedin.com/in/delian-asparouhov-87447742

• Lessons From Keith Rabois, Essay 1: How to become a Venture Capitalist: https://delian.io/lessons-1

• Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product): https://www.lennysnewsletter.com/p/velocity-over-everything-how-ramp

• Nuremberg on AppleTV+: https://tv.apple.com/us/movie/nuremberg/umc.cmc.3sg4y0382byupy76bfy7307k4

• Eight Sleep: https://www.eightsleep.com

• “NO DAYS OFF”—Bill Belichick on X: https://x.com/SNFonNBC/status/829036279069364224

Recommended books:

• Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration: https://www.amazon.com/Creativity-Inc-Overcoming-Unseen-Inspiration/dp/0812993012

• The Jordan Rules: The Inside Story of One Turbulent Season with Michael Jordan and the Chicago Bulls: https://www.amazon.com/Jordan-Rules-Sam-Smith/dp/0671796666

• The Upside of Stress: Why Stress Is Good for You, and How to Get Good at It: https://www.amazon.com/Upside-Stress-Why-Good-You/dp/1101982934

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Lenny may be an investor in the companies discussed.

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