<![CDATA[Lenny's Newsletter]]>https://www.lennysnewsletter.comhttps://substackcdn.com/image/fetch/$s_!8MSN!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F441213db-4824-4e48-9d28-a3a18952cbfc_592x592.pngLenny's Newsletterhttps://www.lennysnewsletter.comSubstackSun, 17 May 2026 10:34:10 GMT<![CDATA[🧠 Community Wisdom: Screening AI slop in hiring, Wispr Flow alternatives for voice transcription, multi-agent pipeline vs. MCP, and more]]>https://www.lennysnewsletter.com/p/community-wisdom-screening-ai-slophttps://www.lennysnewsletter.com/p/community-wisdom-screening-ai-slopSat, 16 May 2026 21:46:34 GMT👋 Hello and welcome to this week’s edition of ✨ Community Wisdom ✨ a subscriber-only email, delivered every Saturday, highlighting the most helpful conversations in our members-only Slack community.

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]]><![CDATA[🎙️ How I AI: Quests, token leaderboards, and the elite AI adoption playbook & Notion’s spec-driven development ]]>https://www.lennysnewsletter.com/p/how-i-ai-quests-token-leaderboardshttps://www.lennysnewsletter.com/p/how-i-ai-quests-token-leaderboardsMon, 11 May 2026 16:02:49 GMT

Quests, token leaderboards, and a skills marketplace: The elite AI adoption playbook | John Kim (Sendbird)

Brought to you by:

  • WorkOS—Make your app enterprise-ready today

  • ThoughtSpot—Build AI-powered analytics into your product

John Kim is the co-founder and CEO of Sendbird. In this episode, John shares how his marketing team built a live swag store with Stripe integration without engineering support, why he tracks every token spent across the org, how he identifies “AI Gods” inside the company, and why the future of work belongs to people with curiosity, agency, and energy, not just years of experience.

Biggest takeaways:

  1. The most successful AI transformations treat internal tooling as a product, not a program. John built the “Automators” platform—a gamified internal marketplace where anyone can create a “quest” (a request for automation or tooling), and engineers or AI agents can pick it up and build it. Each quest shows the risk level, weeks saved, and who benefits. People earn experience points for completing quests, which they can exchange for gift cards, tea with executives, or the chance to present their work to the entire company at Wednesday standups. This isn’t a top-down mandate but a product that makes AI adoption fun, measurable, and rewarding.

  2. When you give creative teams builder capabilities, they’ll create things that would never make it onto a traditional product roadmap. John’s marketing team built a fully functional e-commerce swag store with Stripe integration, custom designs, and even a Konami Code Easter egg that unlocks secret conference details. In the old world, this would have required two sprints of engineering time and probably would have been deprioritized. Now it shipped in days, delights customers, and generates actual revenue.

  3. The biggest unlock for non-technical builders is creating secure, compliant templates they can build on top of. John’s team created app templates where authentication, environment setup, databases, and security are pre-configured and vetted by InfoSec. Marketers, salespeople, and CSMs just extract the template and build their idea on top. This removes the biggest barrier to non-engineers shipping to production: the fear of doing something wrong or insecure.

  4. Measure token usage without shame, and create tiers that make it aspirational. John created five tiers: Beginner (under 1M tokens/day), Intermediate, Expert, Architect, Catalyst, and AI God (over 100M tokens/day). Every manager can see where their team members are and tailor enablement accordingly. This isn’t about performance reviews; it’s about bringing people along the journey and making AI fluency visible and celebrated.

  5. The goal isn’t just to use AI during work hours but, rather, to smooth the curve so AI works around the clock. John monitors token usage over time and looks for smoothness in the curve. Dips mean people are on weekends or vacation and AI isn’t working. When the curve smooths out, it means AI partners are working 24/7. This is a fundamentally different vision: not just augmenting human work, but having AI fill the gaps when humans aren’t available.

  6. Build a cross-functional AI task force that meets weekly to unblock challenges. John created a role called AI Engineer for Internal Operations that reports directly to him and the chief of staff. This person works cross-functionally with the CTO, engineering, and InfoSec to vet tools, set up compliant tech stacks, and remove barriers. They meet weekly as a task force to discuss what’s blocking people and how to enable faster iteration.

  7. The most important hiring criteria for AI-first companies are curiosity, agency, and energy—not tenure or experience. John rewrote job descriptions to optimize for people who are curious, willing to go deep, and figure things out on their own. He lowered the bar on years of experience and raised the bar on learning ability. In a world where you can build a custom learning center for any topic in 20 minutes, the constraint isn’t access to knowledge; it’s the drive to learn.

  8. Start with your champions, not your skeptics. John’s advice to CEOs struggling with AI adoption: find the people in your organization who are already curious and have agency. Make them the champions. Give them the spotlight. Let them share their work at all-hands meetings. Build energy around their stories. Innovation doesn’t start from theoretical structures—it starts with people who have energy and a story to tell. Once others see what’s possible, adoption spreads organically.

  9. Leadership has to model the behavior, not just mandate it. The top token consumers at Delight.ai are the executives. When leaders show up with new capabilities and ship things faster, it signals to the team that this is real and important. John also does one-on-ones with people who aren’t using tokens: “We noticed you haven’t been spending any tokens. Can we help you? What’s stopping you?” This combination of top-down modeling and bottom-up support is what drives transformation.

Blog & detailed workflow walkthroughs from this episode:

How I AI: John Kim’s Playbook for AI Transformation with Quests, Skills, and ‘AI Gods’: https://www.chatprd.ai/how-i-ai/john-kims-playbook-for-ai-transformation

↳ How to Create an Internal AI Marketplace to Crowdsource Automations: https://www.chatprd.ai/how-i-ai/workflows/how-to-create-an-internal-ai-marketplace-to-crowdsource-automations

↳ How to Build a Personal AI-Generated Learning Center on Any Topic: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-a-personal-ai-generated-learning-center-on-any-topic

↳ How to Automate Personal Knowledge Management with an AI ‘Gardener’: https://www.chatprd.ai/how-i-ai/workflows/how-to-automate-personal-knowledge-management-with-an-ai-gardener

Spec-driven development: The AI engineering workflow at Notion | Ryan Nystrom

Listen now on YouTube • Spotify • Apple Podcasts

Brought to you by:

  • WorkOS—Make your app enterprise-ready today

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

Ryan Nystrom is a software engineer and engineering manager at Notion, where he works on Notion AI, Custom Agents, and internal systems that help engineers move faster with less busywork. He joined Notion after the company acquired Campsite, the team communication platform he co-founded. In this episode, Ryan shares how his team automates standup prep, ships PRs from a single Notion comment, uses specs as the new source of truth, and why fast CI is becoming essential for AI coding agents. He explains why engineering managers should still write code, how AI is changing meetings, and why great developer experience matters more than ever.

Biggest takeaways:

  1. Never prep for standups again by auto-generating meeting notes from all your workstreams. Ryan’s Notion AI agent pulls from Slack conversations, closed tasks, merged PRs, telemetry metrics, and yesterday’s meeting transcript to create a comprehensive pre-read every morning at 9 a.m. This transforms standups from rote status updates into high-bandwidth problem-solving sessions where the team spends 100% of their time on decisions, blockers, and next steps instead of “I did this thing yesterday.”

  2. Fast CI is the mathematical limit on your AI coding velocity. If your CI takes an hour to run, your agents sit idle for an hour waiting for test results. If it takes three minutes, you can run 20x more iterations in the same time frame. Ryan’s team is aggressively cutting Notion’s CI to 25% of current time specifically to unlock agent productivity—because agents don’t get tired, don’t sleep, and can work in parallel across VMs if your infrastructure supports it.

  3. Background agents that ship PRs from Slack mentions eliminate context switching. Ryan’s “Boxy” system lets him @mention Codex from a Notion task, and 20 minutes later he gets back a PR with implementation, screenshots of UI verification, and a preview URL. This morning a friend texted him a feature request; Ryan wrote four sentences and dropped a screenshot in a Notion task, mentioned Codex, and had a shipped PR before lunch. No IDE, no local environment, no context switch.

  4. Specs as source of truth beats code as source of truth for AI-powered development. Notion engineers now maintain Markdown spec files in their repo that describe features in plain English with code pointers and verification steps. When they need to update a feature, they update the spec and point Codex at it—the agent implements everything, runs verification, and ships. The spec’s version history becomes the changelog, and non-technical stakeholders can actually read it.

  5. “Yap your spec” is a legitimate development workflow now. Ryan opens Whisper, talks through how a feature should work, gives that transcript to Codex with examples of other specs, and gets back a comprehensive technical document. This is faster than writing and more thorough than typing because you naturally explain edge cases and context when speaking that you’d skip when writing bullet points.

  6. Make AI defend its technical decisions. When Codex suggests a change Ryan doesn’t understand, he doesn’t ask, “Are you sure?” He says, “You’re wrong; defend your argument with evidence.” This forces the model to provide cited reasoning instead of just agreeing with whatever the human says. This is especially critical when working on infrastructure you don’t fully understand—you need the AI to teach you, not just comply.

  7. The era of the hard skill means engineering leaders should write code again. Ryan manages six people and writes code daily. He works until the minute standup starts without prep because AI handles meeting notes. He ships features from his phone on the subway. The AI tools have eliminated so much meeting prep and information synthesis work that managers can be hands-on again—and in Ryan’s opinion, line managers should be writing code, fixing bugs, and staying close to the work.

  8. Changing your tools constantly is energizing, not exhausting, when you’re learning. Ryan changed IDEs, terminals, and workflows more than 10 times in the past year. Instead of feeling chaotic, it feels fresh and joyful. He’s working faster and harder than ever, but in a good way—because he’s experimenting, learning, and building instead of maintaining the same workflow he’s used for over 12 years. The pace of change is the point.

  9. Good developer experience for humans creates good developer experience for agents. Ryan’s team built comprehensive CLI tools, clear documentation, and fast CI long before AI agents. Now those same investments make agents more successful—they can verify their own work, follow blessed paths, and iterate quickly. This creates a virtuous cycle: DX investments help agents, and agent infrastructure (like cloud dev environments) helps humans too.

Blog & detailed workflow walkthroughs from this episode:

How I AI: Ryan Nystrom’s 3 Notion Workflows for Engineering Velocity: https://www.chatprd.ai/how-i-ai/ryan-nystrom-notion-workflows-for-engineering-velocity

↳ Implement Features Using Spec-First Development and an AI Coding Agent: https://www.chatprd.ai/how-i-ai/workflows/implement-features-using-spec-first-development-and-an-ai-coding-agent

↳ From Notion Task to GitHub Pull Request in 20 Minutes with a Coding Agent: https://www.chatprd.ai/how-i-ai/workflows/from-notion-task-to-github-pull-request-in-20-minutes-with-a-coding-agent

↳ Automate Daily Standup Preparation with a Custom Notion AI Agent: https://www.chatprd.ai/how-i-ai/workflows/automate-daily-standup-preparation-with-a-custom-notion-ai-agent

Claude Code just got WAY more powerful

Claire breaks down the biggest announcements from Anthropic’s “Code with Claude” event and what they actually mean for builders shipping AI products today.

Listen now on YouTube • Spotify • Apple Podcasts

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.

]]><![CDATA[Spec-driven development: The AI engineering workflow at Notion | Ryan Nystrom]]>https://www.lennysnewsletter.com/p/spec-driven-development-the-ai-engineeringhttps://www.lennysnewsletter.com/p/spec-driven-development-the-ai-engineeringMon, 11 May 2026 12:03:12 GMT

Ryan Nystrom is a software engineer at Notion. He joined in December 2024 after Notion acquired Campsite, the team communication platform he co-founded with Brian Lovin. At Notion, he’s been a core builder of Notion AI and the Custom Agents feature launched in February 2026. He manages a team of six to seven engineers while still writing code himself, currently running Project Afterburner, a push to cut Notion’s CI time to a quarter of its current duration.

What you’ll learn:

  1. How to build a Notion AI custom agent that auto-generates your daily standup pre-read by pulling from Slack, GitHub, Honeycomb metrics, and yesterday’s meeting transcript

  2. How to configure subagents and MCP integrations within Notion AI

  3. How Notion’s internal “Boxy” system lets engineers @mention Codex from within Notion comments and get a full pull request with screenshots in 20 minutes

  4. The spec-first development workflow: dictate an idea into Whisper, have Codex format it as a proper spec, commit it to the repo, and let the agent implement and verify it autonomously

  5. Why fast CI is absolutely critical in the age of AI coding agents

  6. How to prompt AI coding agents to defend their reasoning under pushback

  7. Why engineering managers and even senior executives should keep writing code

Brought to you by:

WorkOS—Make your app enterprise-ready today

Orkes—The enterprise platform for reliable applications and agentic workflows

In this episode, we cover:

(00:00) Introduction to Ryan Nystrom

(02:48) How AI has upended 12+ years of the same working routine

(04:30) Project Afterburner: Notion’s push to cut CI time to a quarter

(09:00) Why high-frequency, high-quality meetings beat lower-frequency standups

(11:10) How automated context surfaces every engineer’s work equally

(12:15) Why cutting meeting prep is a burnout protection mechanism

(14:26) The case for engineering managers writing code

(16:13) Inside “Boxy”: Notion’s internal VM-based background agent system

(20:30) Old World vs. New World code review

(24:51) Prompting Codex from Notion comments

(29:20) The emotions around code review

(31:01) Quick recap

(32:00) Spec-first development: writing and checking agent specs into the repo

(35:10) The spec as changelog: version control for how a feature actually works

(37:53) How engineers’ roles are evolving

(39:00) Lightning round

(45:21) Where to find Ryan

Tools referenced:

• Notion AI: https://www.notion.com/product/ai

• Notion Custom Agents: https://www.notion.com/blog/introducing-custom-agents

• Codex (OpenAI): https://openai.com/codex

• Claude Code (Anthropic): https://claude.ai/code

• Honeycomb (observability + MCP): https://www.honeycomb.io

• Whisper (OpenAI voice transcription): https://openai.com/research/whisper

• Slack: https://slack.com

• GitHub: https://github.com

Other references:

• How Stripe built “minions”—AI coding agents that ship 1,300 PRs weekly from Slack reactions | Steve Kaliski (Stripe): https://www.chatprd.ai/how-i-ai/stripes-ai-minions-ship-1300-prs-weekly-from-a-slack-emoji

• Notion 3.3 Custom Agents launch (February 24, 2026): https://www.notion.com/releases/2026-02-24

Where to find Ryan Nystrom:

X: https://x.com/ryannystrom

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

GitHub: https://github.com/rnystrom

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.

]]><![CDATA[How to build a company that withstands any era | Eric Ries, Lean Startup author]]>https://www.lennysnewsletter.com/p/how-to-build-a-company-that-withstandshttps://www.lennysnewsletter.com/p/how-to-build-a-company-that-withstandsSun, 10 May 2026 12:03:32 GMT

Eric Ries is the author of The Lean Startup, a book that reshaped how a generation of founders think about building companies. His new book, Incorruptible, explains how successful companies are destroyed by failing to protect what makes them valuable, and how to change it.

Listen on YouTube, Spotify, and Apple Podcasts

In our in-depth conversation, we discuss:

  1. Why 80% of venture-backed founders are ousted within three years of going public

  2. The governance structures that protect companies like Anthropic, Costco, and Novo Nordisk

  3. The simple legal filing that takes two pages and could save your company

  4. Financial gravity: why successful companies predictably get corrupted into mediocrity

  5. Why mission-aligned companies like Anthropic reap major benefits from protecting their mission through governance

  6. Why success won’t protect you—it instead makes you a bigger target

Brought to you by:

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

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

Where to find Eric Ries:

• X: https://x.com/ericries

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

• Website: https://www.incorruptible.co

• Newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://news.theleanstartup.com/

• Podcast:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://ericriesshow.com

• YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@theericriesshow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Referenced:

• Reflections on a movement | Eric Ries (creator of the Lean Startup methodology): https://www.lennysnewsletter.com/p/reflections-on-a-movement-eric-ries

• How Anthropic’s product team moves faster than anyone else | Cat Wu (Head of Product, Claude Code): https://www.lennysnewsletter.com/p/how-anthropics-product-team-moves

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

• Vital Farms: https://vitalfarms.com

• BlackRock: https://www.blackrock.com

• Costco: https://www.costco.com

• Cloudflare: https://www.cloudflare.com

• “The best time to plant a tree” quote: https://quoteinvestigator.com/2021/12/29/plant-tree

• Whole Foods: http://wholefoodsmarket.com

• Marie Krogh: https://en.wikipedia.org/wiki/Marie_Krogh

• August Krogh: https://en.wikipedia.org/wiki/August_Krogh

• Martin Shkreli: https://en.wikipedia.org/wiki/Martin_Shkreli

• Novo Nordisk: https://www.novonordisk.com

• Zeiss: https://www.zeiss.com

• Philip Morris: https://www.philipmorrisusa.com

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

• Matthew Prince on X: https://x.com/eastdakota

• “AI Will Break the Internet”—Cloudflare CEO’s Big Prediction: https://www.ericriesshow.com/matthew-prince-ai

• Andrew Mason on LinkedIn: https://www.linkedin.com/in/andrewmason

• Groupon: https://www.groupon.com

• Steve Jobs: https://en.wikipedia.org/wiki/Steve_Jobs

• Yvon Chouinard: https://en.wikipedia.org/wiki/Yvon_Chouinard

• Patagonia: https://www.patagonia.com

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

• Johnson & Johnson: https://www.jnj.com

• Peter Drucker: https://en.wikipedia.org/wiki/Peter_Drucker

• How the Former U.S. CTO Built a $3B Healthcare Company Powered by Love | Todd Park: https://www.ericriesshow.com/how-the-former-us-cto-built-a-3b-healthcare-company-powered-by-love-todd-park

• Devoted Health: https://www.devoted.com

• Don’t be evil: https://en.wikipedia.org/wiki/Don%27t_be_evil

• Silicon Valley on HBO Max: https://www.hbomax.com/shows/silicon-valley/b4583939-e39f-4b5c-822d-5b6cc186172d

• Adam Smith: https://en.wikipedia.org/wiki/Adam_Smith

• Understanding public benefit corporations: Profit with a purpose: https://www.britannica.com/money/what-is-a-public-benefit-corporation

• B Corp: https://www.bcorporation.net

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

• Clayton Christensen: https://en.wikipedia.org/wiki/Clayton_Christensen

• Howard Schultz on LinkedIn: https://www.linkedin.com/in/howardschultz

• HEB: https://www.heb.com

• Mary Parker Follett: https://en.wikipedia.org/wiki/Mary_Parker_Follett

• Elon Musk on X: https://x.com/elonmusk

• Sam Altman on X: https://x.com/sama

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

• Daniela Amodei on LinkedIn: https://www.linkedin.com/in/daniela-amodei-790bb22a

• Kobayashi Maru: https://en.wikipedia.org/wiki/Kobayashi_Maru

• Mark Zuckerberg: https://en.wikipedia.org/wiki/Mark_Zuckerberg

• Larry Page: https://en.wikipedia.org/wiki/Larry_Page

• Sergey Brin: https://en.wikipedia.org/wiki/Sergey_Brin

• Rome Conference on AI, Ethics, and Governance: https://romeai.wsgrevents.com

• OpenAI: https://openai.com

• Cohere: https://www.coherehealth.com

• Palantir: https://www.palantir.com

• John Lewis & Partners: https://www.johnlewis.com/

• Mondragon: https://mondragon-corporation.com

• Alibaba: https://www.alibaba.com

• What is Evergreen?: https://www.tugboatinstitute.com/what-is-evergreen

• Berkshire Hathaway: https://www.berkshirehathaway.com

• Conway’s law: https://en.wikipedia.org/wiki/Conway%27s_law

• Ants vs. humans: Solving the piano-mover puzzle: https://arstechnica.com/science/2025/01/ants-vs-humans-solving-the-piano-mover-puzzle

• Frederick Winslow Taylor: https://en.wikipedia.org/wiki/Frederick_Winslow_Taylor

• The Principles of Scientific Management (1911): https://en.wikipedia.org/wiki/The_Principles_of_Scientific_Management

• Rowntree’s: https://en.wikipedia.org/wiki/Rowntree%27s

Recommended books:

• The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses: https://www.amazon.com/Lean-Startup-Entrepreneurs-Continuous-Innovation/dp/0307887898

• Incorruptible: Why Good Companies Go Bad... and How Great Companies Stay Great: https://www.amazon.com/Incorruptible-Good-Companies-Great-Stay/dp/B0FWZZBPZB

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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]]><![CDATA[🧠 Community Wisdom: What to do when non-PMs start shipping directly to production, thoughts on Claude Code’s pricing A/B test, the use of gen AI in games, and more]]>https://www.lennysnewsletter.com/p/community-wisdom-what-to-do-whenhttps://www.lennysnewsletter.com/p/community-wisdom-what-to-do-whenSat, 09 May 2026 18:18:35 GMT👋 Hello and welcome to this week’s edition of ✨ Community Wisdom ✨ a subscriber-only email, delivered every Saturday, highlighting the most helpful conversations in our members-only Slack community.

Read more

]]><![CDATA[Code with Claude: The 5 biggest updates explained]]>https://www.lennysnewsletter.com/p/code-with-claude-the-5-biggest-updateshttps://www.lennysnewsletter.com/p/code-with-claude-the-5-biggest-updatesThu, 07 May 2026 01:39:47 GMTClaire breaks down the biggest announcements from Anthropic’s “Code with Claude” event and what they actually mean for builders shipping AI products today. From scheduled AI routines to outcome-based agents, multi-agent orchestration, and new memory systems, Claire walks through the features she’s most excited to use immediately—and how they could reshape the future of agentic software.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

  1. How Claude Code routines let you automate recurring workflows on schedules or webhooks

  2. What “Outcomes” are and how rubric-based agent grading works

  3. How multi-agent orchestration enables specialized AI teams with different roles and tools

  4. Why Anthropic’s new “Dreams” memory system matters for long-term agent behavior

  5. Why increased Claude Code usage limits are a bigger deal than they sound

  6. How Claire thinks about building practical agentic products today

Resources:

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

• Claude Code Routines Docs: https://code.claude.com/docs/en/routines

• Define Outcomes Docs: https://platform.claude.com/docs/en/managed-agents/define-outcomes

• Dreams Docs: https://platform.claude.com/docs/en/managed-agents/dreams

• Multi-Agent Docs: https://platform.claude.com/docs/en/managed-agents/multi-agent

• Managed Agent Webhooks Docs: https://platform.claude.com/docs/en/managed-agents/webhooks#supported-event-types

• Codex (OpenAI): https://openai.com/codex

• GitHub: https://github.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.

]]><![CDATA[Quests, token leaderboards, and a skills marketplace: The elite AI adoption playbook | John Kim (Sendbird)]]>https://www.lennysnewsletter.com/p/quests-token-leaderboards-and-a-skillshttps://www.lennysnewsletter.com/p/quests-token-leaderboards-and-a-skillsWed, 06 May 2026 12:03:39 GMT

John Kim is the co-founder and CEO of Delight.ai, a customer experience platform that’s transforming how companies deploy AI. But what makes John’s story fascinating isn’t just his product; it’s how he’s turned his entire company into an AI-native organization. His marketing team built a fully functional e-commerce swag store with Stripe integration in days. His sales team built their own CRM tools. His recruiting team automated their entire workflow. And it’s all tracked, measured, and celebrated through an internal platform called Automators.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

  1. How Sendbird’s marketing team built a fully functional swag store with Stripe integration in a day (with no engineering support)

  2. How the Automators platform works—an internal marketplace where anyone can request AI tools and engineers (or AI agents) can build them

  3. How to create secure, compliant templates so non-technical teams can ship to production safely

  4. How Sendbird built a token usage dashboard with five tiers (beginner through AI God) and why tracking the smoothness of the curve matters more than the total

  5. Why visible leadership usage is the most powerful adoption signal

  6. Why Sendbird rewrote job descriptions to prioritize curiosity, agency, and energy over years of experience

  7. How John uses AI for his own learning

Brought to you by:

WorkOS—Make your app enterprise-ready today

ThoughtSpot—Build AI-powered analytics into your product

In this episode, we cover:

(00:00) Introduction to John Kim

(02:45) The Delight.ai swag store built by marketing in two days

(05:51) The before times: when fun had to earn its place on the roadmap

(07:55) Demo: The Automators platform and quest system

(13:47) The AI Engineer for Internal Operations role

(16:06) Demo: The company-wide skills marketplace

(17:19) Treating AI adoption as a product

(18:43) Real wins: team-level and campaign examples

(21:51) Why SaaS isn’t dead—it’s being rebuilt internally

(23:46) Demo: The token tracking dashboard

(26:32) Measuring without fear: setting expectations, not punishments

(28:54) Quick recap

(30:51) Personal AI use cases: endless knowledge at your fingertips

(36:15) Lightning round and final thoughts

Tools referenced:

• Claude Code: https://claude.ai/code

• Codex (OpenAI): https://openai.com/codex

• Obsidian: https://obsidian.md

• GitHub: https://github.com

• Stripe: https://stripe.com

Other references:

• Jason Levin (CEO of Memelord) on How I AI: https://www.lennysnewsletter.com/p/from-a-690-newsletter-to-3m-api-how

• Konami Code: https://en.wikipedia.org/wiki/Konami_Code

• Andrew Huberman’s podcast: https://hubermanlab.com/

• Y Combinator: https://www.ycombinator.com/

Where to find John Kim:

X: https://x.com/doshkim

Instagram: https://instagram.com/dosh

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

Company: https://delight.ai

Delight.ai Spark Conference (May 7, SF): https://delight.ai/spark

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.

]]><![CDATA[Why SaaS freemium playbooks don’t work in AI, and what to do instead]]>https://www.lennysnewsletter.com/p/why-saas-freemium-playbooks-donthttps://www.lennysnewsletter.com/p/why-saas-freemium-playbooks-dontTue, 05 May 2026 13:03:32 GMT👋 Hey there, I’m Lenny. Each week, I answer reader questions about building product, driving growth, and accelerating your career. For more: Lenny’s Podcast | Lennybot | How I AI | My favorite AI/PM courses, public speaking course, and interview prep copilot

Subscribe now

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

Today’s guest author, Vikas Kansal, leads product for Google AI, arguably the most successful consumer subscription bundle in history, with Gemini 3.1, Nano Banana, NotebookLM, Veo3, and terabytes (!) of cloud storage. Vikas has been on the front lines of figuring out how to successfully monetize AI products, balancing compute costs with sustainable growth, and in today’s in-depth guest post, he shares all the lessons he and his team have learned about paywalling AI.

Let’s get into it.

You’ve just launched an incredible AI product. Growth is exploding, and users are completely hooked on the “magic” of your free tier. Then your bills arrive. In traditional SaaS, serving an extra free user costs essentially zero. In AI, every time a free user hits “Enter,” your GPUs fire, and your cash burns. AI products need to figure out monetization quickly, or compute costs will bankrupt your company.

Most product teams still reach for the traditional SaaS freemium playbook when designing their AI freemium strategy: give away the basics, and gate the best features. But as growth expert (and four-time Lenny’s Podcast guest) Elena Verna rightly points out, when building an AI product, you have to give away a massive amount of “magic” for users to get to the aha moment. They need to actually experience the model writing a perfect email or generating that perfect image before deciding to use a product even a second time. In today’s increasingly noisy market, the time to value (TTV) must be instantaneous if you hope to keep people’s attention.

But this creates a paradox: what happens when that free magical experience becomes so good (and expensive in compute) that it cannibalizes your premium tier?

While launching Google’s AI subscriptions, we slammed right into this wall. The free tier was so good that for most tasks, it was outperforming humans. For free! It could write flawless Python, draft perfect marketing copy, and summarize long email threads instantly. We realized we were competing against ourselves, while also trying to create a sustainable business as demand for AI compute exponentially increases over time. Users rightly asked themselves, “Why should I pay $20 a month when the free version is already smarter than I am?”

The traditional SaaS playbook would suggest that we should’ve paygated some of our best features, like the viral NotebookLM podcast generator, Nano Banana images, or Gemini Deep Research. In the past, Slack successfully gated message history and Figma gated team libraries. But if we had paygated the ability to talk it out live with Gemini or Deep Research, users would never have experienced the magic needed to build a daily habit and worked up a desire to try the tools again. Plus, this may not have actually addressed the biggest sources of compute costs. The reality of AI products forced us to completely rebuild the anatomy of a paywall for AI subscriptions. Here is the framework we developed.

The anatomy of a modern AI paywall

Generated with Nano Banana Pro, a benefit of the Google AI subscription bundle 👀

The modern AI paywall should be constructed with both sides of the equation in mind: what users expect and what actually costs the company money in compute. This means moving beyond a traditional SaaS freemium model to multiple dynamic, usage-based, and outcome-driven tiers. If you are building an AI paywall, shift your upgrade triggers to these three pillars that align customer utility with costs to the company:

Pillar 1: Gate usage intensity

When we first added a paywall to Google’s AI features, our instinct was the traditional SaaS “premium” play: a single $20 Gemini Advanced tier, where users pay to access the smartest model. But we quickly hit two problems. First, as I mentioned above, the free tier was already so capable that many users felt it was already “smarter than them.” So they saw no reason to upgrade. But the second was more surprising: the power users who did upgrade were consuming such immense amounts of compute that the unit economics for the company were terrifying. We realized the real value proposition wasn’t just the quality of the model/answer, but the volume of work a user could pump through the system. And we’d have to price that access accordingly if the business model was going to work.

So we redesigned our offering into Plus, Pro, and Ultra tiers. Each bucket maps to a specific level of usage intensity, offering higher usage and larger context windows (up to 1 million tokens) as you move up. This provides users with predictable, prepaid pricing tiers and avoids the pay-per-use AWS-style bill shock that consumers hate. You get a taste of most features in every tier, and the Pro tier may be sufficient for a casual experimenter, while power users would opt for the Ultra tier. This approach aligned our unit economics and subscriber distribution to create a financially sustainable business model. We found that gating usage intensity was a more powerful monetization lever than gating model intelligence.

As another example, Midjourney does this well. They use a “Fast Mode” vs. “Relax Mode” system. Fast Mode provides instant GPU access for quick results using limited monthly hours—which are priced accordingly. Once Fast Hours are exhausted, users on Standard plans or higher can switch to unlimited Relax Mode, which operates in a queue based on demand. Users are paying for priority access to their GPUs to generate more, faster images and not necessarily for better images. And the company can keep their unit economics tight.

Pillar 2: Gate outcomes

While Pillar 1 monetizes raw compute and volume, Pillar 2 monetizes productivity. The free tier may give a user the right answer, but it requires manual effort—copying, pasting, prompting, and formatting. To get users to upgrade, you must put a paywall in front of features that collapse multi-step tasks into a single click. “Pro” users are highly sensitive to friction, and they will justify the subscription cost simply based on the hours of work it eliminates from their life.

Our original value proposition for Gemini Advanced was built entirely around providing the “best and fastest answer” a frontier model could produce. Since then, LLMs made a major transition from passive text generators to autonomous AI agents that can reason, plan, use tools, and retain memory to complete complex tasks. We embraced this shift by making Chrome auto browse exclusively for our higher tiers. Chrome’s auto browse (powered by Gemini) is an AI agent feature that autonomously navigates websites, fills out forms, compares products, adds items to carts, and manages tasks like booking appointments within the browser. We framed the paywall around the employee labor saved, by letting the agent navigate the web and execute actions on behalf of the user to complete actual work. We stopped selling “answers” and started selling “hours.”

Intercom’s Fin AI agent is the gold standard in outcome-based AI pricing. It charges $0.99 per resolution. It’s free to let the AI try to answer, but you only pay when the user confirms their problem was actually solved. Sierra also does this well.

Pillar 3: Gate the heaviest compute modalities

When we were brainstorming the go-to-market for Genie 3—our real-time interactive “world model”—we knew the compute costs would be staggering. During internal dogfooding, we joked that the TPUs were melting on every prompt. We realized that offering this to every free user wasn’t just a bad business move but was physically impossible. There simply weren’t enough TPUs to serve this model at the 100K+ QPS required for hundreds of millions of DAUs. However, we also found that consumers intrinsically understand that these heavy modalities are a premium service. They don’t expect a cinematic, photorealistic 3D world to be free in the same way they expect a 500-word email to be.

So we made Genie 3 available exclusively to our highest-tier members. By elevating this frontier world model into a premium tier, we turned a massive compute liability into a powerful incentive for users to move to the top of our pricing ladder. This also enabled us to showcase the state-of-the-art model without running into capacity constraints.

My advice is to use your most expensive, compute-heavy modalities as the ultimate upgrade trigger for your highest subscription tier. Make text and basic image generation universally accessible to drive top-of-funnel growth, but set a hard paywall the moment a user wants to render a cinematic video, run a real-time simulation, or enter a persistent 3D environment.

Craft your monetization ecosystem

Well-crafted and value-aligned tiers can capture the user’s experimental budget, but they won’t keep it. AI subscriptions have relatively higher churn compared with traditional SaaS simply because the core usage habits are still being formed. To access that lifetime user value, you have to design an ecosystem surrounding the tiers to contain and expand your customer base. Below is how you optimize conversion, retention, and cost to serve to ensure that you’re building an ecosystem that lasts.

Nail your conversion catalysts

Deciding what value to put in which tier is only half the battle. You also need to master when and how to prompt the upsell that actually drives revenue. To push a highly engaged free user over the tipping point, you need specific conversion catalysts—behavioral triggers, contextual UX nudges, and strategic packaging that transform a frustrating roadblock into a frictionless, no-brainer upgrade. Showing the upgrade prompt is more an art than a science. Driving meaningful conversions ultimately comes down to surfacing the right contextual nudge to the right user at the exact moment of high intent. With Google AI subscriptions, we’ve found a few approaches that really work:

Read more

]]><![CDATA[🎙️ This week on How I AI: The internal AI tool that’s transforming how Stripe designs products]]>https://www.lennysnewsletter.com/p/this-week-on-how-i-ai-the-internalhttps://www.lennysnewsletter.com/p/this-week-on-how-i-ai-the-internalMon, 04 May 2026 15:01:56 GMT

Demos not memos: How Stripe built their internal AI prototyping tool | Owen Williams

Listen now on YouTube • Spotify • Apple Podcasts

Brought to you by:

  • Celigo—Intelligent automation built for AI

  • Cursor—The best way to code with AI

Owen Williams is a design manager at Stripe who built Protodash, an internal AI prototyping tool that lets designers and PMs turn Stripe’s design system into clickable, production-quality prototypes in minutes. What started as a set of Cursor rules and React components has evolved into a full prototyping platform that runs in the browser, supports design reviews, and helps teams move from “memos” to working demos. In this episode, Owen shares how Protodash came together, why generic AI design tools often produce “blurple slop,” how PMs became the surprise power users, and what changes when teams can explore real product ideas before writing production code.

Biggest takeaways:

  1. Protodash started as a bundle of Cursor rules, React components, and MCP integration—then evolved into a full web-based prototyping platform. The first version was pragmatic: lower the barrier to entry so designers only need to know “npm run dev” and nothing else. Owen bundled Stripe’s design system components, created an MCP server for Sail, and wrote extensive Cursor rules that taught the AI how to use the project. If a user pastes a Figma link, check the Sail MCP server before writing code. If the MCP server is unavailable, don’t just imagine the design system. These rules came from months of iteration and pain points.

  2. The biggest mindset shift for designers is not being afraid of the terminal anymore. Owen’s background is engineering-based, and he always valued hiring technical designers who understood enough of the terminal to be dangerous. Pre-AI, that jump to technical competency was really hard. Now you can just ask Claude, “How does Git work?” and it tells you. You don’t even need to know the commands.

  3. Generic AI prototyping tools don’t know your design system, and that creates “blurple slop.” Owen was sitting in design reviews watching designers struggle with tools like v0 that produced uncanny-valley Tailwind prototypes. The nav would be off, fonts wrong, components mismatched. Stripe has a high quality bar and a well-defined design system called Sail, so Owen thought: these tools should know about our design system and construct dashboards from those building blocks reliably. That insight led to Protodash.

  4. An equal number of PMs and designers now use Protodash, and that’s actually making designer-PM relationships better. Owen’s first reaction when PMs started using it was nervousness—“Oh my goodness, PMs designing.” But it’s been thrilling to see PMs build things that look like Stripe in the right way, explore ideas earlier, and communicate better with their designers. Conversations shift from “Should we staff a designer on this project?” to “Here’s the actual work; let’s discuss how to make it better.” PMs can now unblock themselves, test ideas with users earlier, and advocate for design resources with real prototypes instead of abstract arguments.

  5. Prototyping with data in code lets you push the edges of use cases in ways Figma never could. Owen showed a dashboard with almost half a million dollars in gross volume—but what about the zero state? What about a company getting one transaction a day? What about different business models: startup versus enterprise? Before, getting all those states into a Figma file was a mess. Now you can just toggle between them. You can design more-interesting things and have them touch reality sooner.

  6. The most valuable internal tools are the ones that match your company’s specific culture and workflows. Owen points out that staffing internal-tools teams has always been hard, but now you can just build tools that evolve the way you work. Design reviews at Stripe have a tradition of sharing Google Docs with tables for feedback—so Owen built a design review mode where everyone can comment on the prototype, get an AI-generated summary, and send fixes directly to the AI.

  7. Owen’s rules for working with AI: be specific up front, and as soon as you feel like swearing, slash clear and start again. He has a content background and learned that being specific in the first prompt helps a lot. But the bigger lesson is avoiding sunk-cost fallacy. As someone who’s built a bunch of stuff, you get attached and think “surely I’ll get this to work.” Often just throwing it away and starting fresh is faster. If being loud didn’t work the first time, reset the context window.

Blog & detailed workflow walkthroughs from this episode:

Stripe’s Owen Williams on Killing ‘Blurple Slop’ with an Internal Prototyping Studio: http://chatprd.ai/how-i-ai/stripe-owen-williams-on-buildling-internal-prototyping-studio

↳ How To Connect a Design System to an AI Code Editor for High Fidelity Prototypes: https://www.chatprd.ai/how-i-ai/workflows/how-to-connect-a-design-system-to-an-ai-code-editor-for-high-fidelity-prototypes

↳ Streamline Design Reviews with an AI-Powered Prototyping Studio: https://www.chatprd.ai/how-i-ai/workflows/streamline-design-reviews-with-an-ai-powered-prototyping-studio

↳ Build a Personal AI App to Track Purchases and User Manuals: https://www.chatprd.ai/how-i-ai/workflows/build-a-personal-ai-app-to-track-purchases-and-user-manuals

]]><![CDATA[The internal AI tool that’s transforming how Stripe designs products | Owen Williams]]>https://www.lennysnewsletter.com/p/the-internal-ai-tool-thats-transforminghttps://www.lennysnewsletter.com/p/the-internal-ai-tool-thats-transformingMon, 04 May 2026 12:03:59 GMT

Owen Williams is a design manager at Stripe who built Protodash, an internal AI-powered prototyping platform that lets designers and PMs create high-quality Stripe dashboard prototypes without writing code. What started as a bundle of Cursor rules and React components evolved into a full web-based prototyping studio that runs in dev boxes, complete with design review modes, variant testing, and AI-powered iteration. Surprisingly, PMs now use Protodash just as much as designers, fundamentally changing how Stripe approaches prototyping, design reviews, and engineering handoffs.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

  1. How Stripe built an internal AI prototyping tool using Cursor rules, MCPs, and their design system

  2. Why “blurple slop” happens when designers use generic AI tools—and how to fix it

  3. The architecture behind Protodash: React router, design system components, and MCP integrations

  4. How Stripe prototypes in dev boxes so designers never have to worry about local setup

  5. Why “demos, not memos” transformed Stripe’s design review culture

  6. How Stripe built design review modes, variant testing, and AI annotation directly into your prototyping tool

  7. Why internal tools don’t need to be production-grade to be transformative

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) Welcome and intro to Owen Williams

(02:19) The “blurple slop” problem with AI design tools

(03:50) Protodash: an internal vibe-coding tool for Stripe prototypes

(05:26) Why an engineering background helped Owen lower the bar for designers

(07:55) The Cursor rules that taught the Stripe design system

(09:04) Running prototypes on dev boxes vs. locally

(10:30) “Demos, not memos” and rewiring design reviews at Stripe

(14:50) Building Protodash Studio: a browser-based wrapper for prototyping

(19:04) Live demo: variants, line charts, and remixing prototypes in browser

(21:02) Self-testing prototypes that take screenshots and check their work

(23:20) Multiple variant features

(26:08) The annotate-for-AI button for in-canvas feedback

(27:21) Design review mode: comments, summaries, and AI follow-up

(29:39) Why building internal tools beats buying off-the-shelf

(32:50) PMs as the surprise power users of Protodash

(35:20) Live demo: a Black Friday/Cyber Monday pet store dashboard

(42:03) Lo-fi modes, monospace fonts, and “Comic Sans for WIP” at Shopify

(44:45) Quick recap

(45:35) The Radar prototype that changed engineering handoff

(49:08) Lightning round and final thoughts

Tools referenced:

• v0: https://v0.app/

• Cursor: https://cursor.com/

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

• Claude Design: https://www.anthropic.com/news/claude-design-anthropic-labs

• Figma: https://www.figma.com/

• Stripe Radar: https://stripe.com/radar

• Balsamiq: https://balsamiq.com/

Where to find Owen Williams:

X: https://x.com/ow

Website: https://owenwillia.ms/

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

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.

]]><![CDATA[Why cultivating agency matters more than cultivating skills in the AI era | Max Schoening (Head of Product, Notion)]]>https://www.lennysnewsletter.com/p/why-cultivating-agency-matters-morehttps://www.lennysnewsletter.com/p/why-cultivating-agency-matters-moreSun, 03 May 2026 12:30:59 GMT

Max Schoening is head of product at Notion, where he’s been especially effective at getting designers and PMs to ship code, prototype in the terminal, and launch extremely successful AI products. He was previously a PM at Google, ran design at Heroku, was VP of Design (and a part-time engineer) at GitHub, and is a two-time founder. He’s one of the most AI-forward product leaders out there and one of the deepest thinkers on how AI changes how we build and use software.

Listen on YouTube, Spotify, and Apple Podcasts

We discuss:

  1. What’s most worked in getting designers and PMs to embrace AI

  2. Why agency—not skills—is the thing that separates people who thrive from those who fall behind

  3. How the first 10% of every project is now “free,” and what that means for product development

  4. Max’s “tiny core” theory of great products: iPhone multitouch, the GitHub pull request, Notion blocks, Dropbox’s menu bar icon

  5. Why the SaaSpocalypse is overstated

  6. Why the amount of software has exploded but the quality hasn’t, and why that gap creates opportunity

Brought to you by:

WorkOS—Make your app Enterprise Ready, with SSO, SCIM, RBAC, and more

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

Where to find Max Schoening:

• X: https://x.com/mschoening

• LinkedIn: https://www.linkedin.com/in/max-schoening

• Website: https://max.dev

Referenced:

• Esther 4:14: https://www.biblegateway.com/passage/?search=Esther%204%3A14&version=NIV

• Notion: https://www.notion.com

• Figma: https://www.figma.com

• Bret Victor—Stop Drawing Dead Fish: https://www.youtube.com/watch?v=ZfytHvgHybA

• GitHub: https://github.com

• Notion’s Brian Lovin on building a shared prototype playground, using Claude Code to transform Figma designs into working code, and why AI is changing how designers work: https://www.lennysnewsletter.com/p/i-havent-written-a-single-line-of

• Eric Liu on LinkedIn: https://www.linkedin.com/in/erictliu

• Steve Jobs’s quote: https://quotefancy.com/quote/911503/Steve-Jobs-Everything-around-you-that-you-call-life-was-made-up-by-people-that-were-no

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

• The Dieter Rams video pinned to Max’s X profile: https://x.com/mschoening/status/1725199919739519299

• Frank Gehry: https://en.wikipedia.org/wiki/Frank_Gehry

• He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor on the future of careers, coding, agents, and more: https://www.lennysnewsletter.com/p/he-saved-openai-bret-taylor

• Joanna Stern’s post on X: https://x.com/JoannaStern/status/2023529964185022898

• How Anthropic’s product team moves faster than anyone else | Cat Wu (Head of Product, Claude Code): https://www.lennysnewsletter.com/p/how-anthropics-product-team-moves

• This is the flowchart of how Slack decides to send a notification: https://blog.bytebytego.com/p/flowchart-of-how-slack-decides-to

• Tyler Cowen’s blog: https://marginalrevolution.com

• Simon Last on X: https://x.com/simonlast

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

• Why Software Is Eating the World: https://a16z.com/why-software-is-eating-the-world

• Why humans are AI’s biggest bottleneck (and what’s coming in 2026) | Alexander Embiricos (OpenAI Codex Product Lead): https://www.lennysnewsletter.com/p/why-humans-are-ais-biggest-bottleneck

• Heroku: https://www.heroku.com

• Kubernetes: https://kubernetes.io

• Kathy Sierra: Badass—Making Users Awesome, at Mind the Product San Francisco: https://www.youtube.com/watch?v=LNnv2GrlnZw

• Ivan Zhao on LinkedIn: https://www.linkedin.com/in/ivanhzhao

• Dropbox: https://www.dropbox.com

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

• Clayton Christensen: https://en.wikipedia.org/wiki/Clayton_Christensen

• The ultimate guide to JTBD | Bob Moesta (co-creator of the framework): https://www.lennysnewsletter.com/p/the-ultimate-guide-to-jtbd-bob-moesta

• Lee Sedol: https://en.wikipedia.org/wiki/Lee_Sedol

• AlphaGo: https://deepmind.google/research/alphago

• TBPN: https://www.tbpn.com

• Chris Rock: https://en.wikipedia.org/wiki/Chris_Rock

• Stack Overflow: https://stackoverflow.com

• Project Hail Mary: https://www.imdb.com/title/tt12042730

• The Handmaid’s Tale: https://www.imdb.com/title/tt5834204

• Ghostty: https://ghostty.org

• Mitchell Hashimoto on X: https://x.com/mitchellh

• Moshi: https://getmoshi.app

• YMDK Split Keyboard Corne V4.1 46 Keys Ergonomic Hotswappable 40% Layout 3D PLA RGB QMK Vial All Key Programmable Mini Independent Gaming Keyboard Kit: https://www.amazon.com/YMDK-Ergonomic-Hotswappable-Programmable-Independent/dp/B0DWSKP3ZT

• Civivi pocket knives: https://www.civivi.com/collections/folding-pocket-knives

• Marcus Aurelius’s quote: https://www.goodreads.com/quotes/762780-the-whole-universe-is-change-and-life-itself-is-but

Recommended books:

• Code: The Hidden Language of Computer Hardware and Software: https://www.amazon.com/Code-Language-Computer-Hardware-Software/dp/0137909101

• Tools for Conviviality: https://www.amazon.com/Tools-Conviviality-Ivan-Illich/dp/1842300113

• Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed: https://www.amazon.com/Seeing-Like-State-Condition-Paperbacks/dp/0300246757

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

]]><![CDATA[🧠 Community Wisdom: Claude Code tips for ADHD users, resources for managing up, going from corporate to startup, tiring of being your company’s AI evangelist, and more]]>https://www.lennysnewsletter.com/p/community-wisdom-claude-code-tipshttps://www.lennysnewsletter.com/p/community-wisdom-claude-code-tipsSat, 02 May 2026 18:02:57 GMT👋 Hello and welcome to this week’s edition of ✨ Community Wisdom ✨ a subscriber-only email, delivered every Saturday, highlighting the most helpful conversations in our members-only Slack community.

Read more

]]><![CDATA[Your Couch-to-5K for AI]]>https://www.lennysnewsletter.com/p/your-couch-to-5k-for-aihttps://www.lennysnewsletter.com/p/your-couch-to-5k-for-aiTue, 28 Apr 2026 15:31:24 GMT👋 Hey there, I’m Lenny. Each week, I answer reader questions about building product, driving growth, and accelerating your career. For more: Lenny’s Podcast | Lennybot | How I AI | My favorite AI/PM courses, public speaking course, and interview prep copilot

Subscribe now

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

I’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

Read more

]]><![CDATA[🎙️ This week on How I AI: GPT 5.5, Claude Design, and GPT Images 2.0 hands-on reviews—plus an inside look at Memelord]]>https://www.lennysnewsletter.com/p/this-week-on-how-i-ai-gpt-55-claudehttps://www.lennysnewsletter.com/p/this-week-on-how-i-ai-gpt-55-claudeMon, 27 Apr 2026 15:02:19 GMT

GPT 5.5 just did what no other model could

Listen now on
YouTube • Spotify • Apple Podcasts

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.

Blog & detailed workflow walkthroughs from this episode:

My GPT-5.5 Review—A 6-Hour Autonomous Task and the Bluetooth Hack No Other Model Could Solve: https://www.chatprd.ai/how-i-ai/openai-gpt-5.5-review

↳ Reverse-Engineer a Proprietary Hardware Protocol with AI: http://chatprd.ai/how-i-ai/workflows/reverse-engineer-a-proprietary-hardware-protocol-with-ai

↳ Perform an Autonomous Data Migration with an AI Agent: https://www.chatprd.ai/how-i-ai/workflows/perform-an-autonomous-data-migration-with-an-ai-agent

↳ Automate Security Vulnerability Remediation with AI: https://www.chatprd.ai/how-i-ai/workflows/automate-security-vulnerability-remediation-with-ai

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

Listen now on
YouTube • Spotify • Apple Podcasts

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.

Blog & detailed workflow walkthroughs from this episode:

How I Put Claude Design and GPT Images 2.0 to the Test: Building Landing Pages, Slides, and Brand Kits: https://www.chatprd.ai/how-i-ai/claude-design-and-gpt-images-2-building-landing-pages-slides-and-brand-kits

↳ How to Generate a Professional Brand Kit with GPT Images 2.0: https://www.chatprd.ai/how-i-ai/workflows/how-to-generate-a-professional-brand-kit-with-gpt-images-2-0

↳ How to Convert an Article into a Polished Slide Deck with AI: https://www.chatprd.ai/how-i-ai/workflows/how-to-convert-an-article-into-a-polished-slide-deck-with-ai

↳ How to Build a High-Fidelity Landing Page with Claude Design: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-a-high-fidelity-landing-page-with-claude-design

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

Listen now on
YouTube • Spotify • Apple Podcasts

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.

Blog & detailed workflow walkthroughs from this episode:

How I AI: Jason Levin’s Workflows for Agentic Memes, Vibe Coding, and Hardware Hacking: https://www.chatprd.ai/how-i-ai/jason-levins-workflows-for-agentic-memes-vibe-coding-and-hardware-hacking

↳ Build a Custom Bedside Keyboard for Idea Capture with Raspberry Pi and ChatGPT: https://www.chatprd.ai/how-i-ai/workflows/build-a-custom-bedside-keyboard-for-idea-capture-with-raspberry-pi-and-chatgpt

↳ Build Free Marketing Tools as Lead Magnets Using AI Code Assistants: https://www.chatprd.ai/how-i-ai/workflows/build-free-marketing-tools-as-lead-magnets-using-ai-code-assistants

↳ Automate Meme Marketing with an AI Agent and OpenClaw: https://www.chatprd.ai/how-i-ai/workflows/automate-meme-marketing-with-an-ai-agent-and-openclaw

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.

]]><![CDATA[From a $6.90 newsletter to $3M API: How a non-coder built Memelord | Jason Levin]]>https://www.lennysnewsletter.com/p/from-a-690-newsletter-to-3m-api-howhttps://www.lennysnewsletter.com/p/from-a-690-newsletter-to-3m-api-howMon, 27 Apr 2026 12:03:55 GMT

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.

Listen or watch on YouTube, Spotify, or Apple Podcasts

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.

]]><![CDATA[Snapchat CEO: Why distribution has become the most important moat | Evan Spiegel]]>https://www.lennysnewsletter.com/p/snapchat-ceo-why-distribution-ishttps://www.lennysnewsletter.com/p/snapchat-ceo-why-distribution-isSun, 26 Apr 2026 12:31:49 GMT

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.

Listen on YouTube, Spotify, and Apple Podcasts

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:

Read more

]]><![CDATA[🧠 Community Wisdom: What great product leaders do differently, Claude vs. ChatGPT, receiving hard feedback two weeks into the job, tips for joining a pre-PMF startup, and more]]>https://www.lennysnewsletter.com/p/community-wisdom-what-great-producthttps://www.lennysnewsletter.com/p/community-wisdom-what-great-productSat, 25 Apr 2026 19:51:00 GMT👋 Hello and welcome to this week’s edition of ✨ Community Wisdom ✨ a subscriber-only email, delivered every Saturday, highlighting the most helpful conversations in our members-only Slack community.

Read more

]]><![CDATA[GPT 5.5 just did what no other model could]]>https://www.lennysnewsletter.com/p/gpt-55-just-did-what-no-other-modelhttps://www.lennysnewsletter.com/p/gpt-55-just-did-what-no-other-modelThu, 23 Apr 2026 19:39:26 GMT

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.

Listen or watch on YouTube, Spotify, or Apple Podcasts

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.

]]><![CDATA[How Anthropic’s product team moves faster than anyone else | Cat Wu (Head of Product, Claude Code)]]>https://www.lennysnewsletter.com/p/how-anthropics-product-team-moveshttps://www.lennysnewsletter.com/p/how-anthropics-product-team-movesThu, 23 Apr 2026 15:01:50 GMT

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.

Listen on YouTube, Spotify, and Apple Podcasts

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:

Read more

]]><![CDATA[What Claude Design is actually good for (and why Figma isn’t dead yet)]]>https://www.lennysnewsletter.com/p/what-claude-design-is-actually-goodhttps://www.lennysnewsletter.com/p/what-claude-design-is-actually-goodWed, 22 Apr 2026 12:04:31 GMT

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.

Listen or watch on YouTube, Spotify, or Apple Podcasts

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.

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