Subscribe now

P.S. Get a full free year of Cursor, Notion, Replit, Lovable, Wispr Flow, Linear, ElevenLabs, Factory, PostHog, Granola, Brain.fm, Waking Up, and more, by becoming an Insider subscriber (while supplies last). Learn more.

Tom Orbach writes the most delightful and actionable newsletter for anyone looking to grow their product. When we began discussing a collaboration, I asked him to take the best of his newsletter and turn it into a single post. Below is the most specific and creative collection of growth ideas you’ll find anywhere.

As a bonus, Tom made it easy for you to feed this list into your agent to brainstorm growth ideas for your own product. I encourage you to share this post and the Markdown file with your team (and agents)—it’ll get their creative juices flowing.

Tom writes Marketing Ideas, a newsletter about unconventional growth tactics with over 100,000 subscribers, leads growth marketing at Wiz (now part of Google Cloud), and built the Viral Post Generator, which hit 2 million users, was covered by Business Insider, The Guardian, and BuzzFeed, and was acquired in under a week. Check out his newsletter, and find him on LinkedIn.

A few years ago, I pitched my VP a growth idea that made zero sense on paper. It had nothing to do with our product, we had no way of knowing if it would work, and there was a real chance it would make us look ridiculous. But I ran with the idea anyway, and it became one of the biggest growth wins in the company’s history:

We built a fake toy store for cybersecurity executives.

We were a startup selling cloud security software, and we just wanted chief information security officers (CISOs) to notice us in a crowd of older, well-known brands.

The store “sold” a CISO panic button and blindfolds for your legal team. It cost almost nothing to make, went viral on Reddit, and brought 46,000 visitors in 24 hours. Our goal was to get attention, but our sales team ended up with hundreds of qualified leads—and in enterprise security, a single good lead can be worth seven figures.

I don’t have to tell you that it’s almost impossible to stand out these days. Everyone seems to be running the same growth playbook: high-production-value video-led launches, paid influencers, SEO/AEO, social, email, maybe some (poorly attended) events. Your competitors are in the same channels, trying to innovate in the same ways.

But attention always goes to what people don’t see coming. The unexpected gets shared for free, remembered for months, and usually costs less than one week of ad spend.

I’ve pretty much dedicated my life to collecting unexpected growth ideas that worked. Every time I see a startup do something clever, it goes into my list. And most of the time, I dig deeper or even talk to the team behind it: How did they do it? How could I make it even better?

After four years of doing this, here are my 64 favorite creative growth tactics, organized by what you’re trying to do:

1. I want to generate buzz (without paid ads/influencers)

2. I want my launch to make noise

3. I want other people to spread the word for me

4. I want more leads and conversions

5. I want to outsmart my competitors

6. I want customers to pay more (and be happy about it)

7. I want customers to stay forever

You might wonder: Won’t a list like this quickly become stale? Not likely. Each idea has been run by one company, in one niche, usually once. There’s a lot of alpha here right now before any of these ideas are overdone. Many people will read this post; very few will actually do them. This is your opportunity.

Also, you definitely don’t need to try all 64. Treat them like lottery tickets—most will do nothing, but one can hit big. Scroll down to a problem you have right now and get inspired by what’s relevant for you. Even better, feed this Markdown-file version of this post into your favorite AI agent and brainstorm which ideas make sense for your specific product, brand, and growth goals.

1. I want to generate buzz (without paid ads/influencers) 🎉

Attention is the most expensive asset in marketing, and the traditional playbook’s answer is to buy it. Everything in this section earns it instead, with concepts that are a little outrageous by design: jokes and public stunts that people share on their own. None of them need a launch, a funding round, or any news at all.

💸 Spend your marketing budget on anything that isn’t marketing: Gumloop spent its entire marketing budget filling Vancouver with street musicians (in front of branded signs) during a conference. Sticker Mule gives $100,000 a month to an influencer who gives strangers $100 bills on camera. People are tired of ads. Put that budget toward something your audience cares about funding, and you’ll stand out.

☎️ Set up a phone number where something cool happens: Cloaked, a privacy app, set up a hotline that reads you the sensitive information data brokers are selling about you, a perfect demo for their capabilities. Bland is an AI voice platform, so their billboard lets potential customers try it out by chatting with one of their agents.

🎁 Offer an amazing prize, with a ridiculous requirement: Browserbase’s CEO posted, “The easiest way to get Browserbase equity is to join our company. The second easiest way is by getting a Browserbase tattoo.” He even shipped a joke calculator for it (face tattoo gets 1% equity!). Or, to pull from a very different industry: a Broadway show called My First Time offered free tickets . . . if you proved you were a virgin (you had to take a lie detector test 😳). Only a few people will participate, but tactics like this generate content that attracts everyone else.

🎈 Go anti-professional: One of the most viral LinkedIn posts of all time came from a manager who swapped her corporate headshot for a casual, no-makeup photo. Pick a norm of your own and break it in public. You might talk about how much roles actually pay or admit that a big launch didn’t actually meet internal expectations. People are tired of perfectly curated, glossy exteriors of the professional world, so a bit of human messiness will stand out.

🚯 Be loudly anti-AI: AI is obviously the biggest trend in the tech world, so if you’re willing to cut against the current, people will notice. Procreate’s CEO took a major stand on this (“I really f***ing hate generative AI”) and has shaped the company’s strategy around it. This level of commitment has to be company-wide, but when it works, it really works—that one video got more than 10 million views and now they’re known for it. If you’re going to avoid using AI to build your product, say it out loud and get public credit.

😂 Run a meme account for your industry: The CEO of 1up, an AI platform for sales teams, has publicly said that the company gets a third of their customer leads from memes. tl;dv has also brought in as much as a third of signups from social, like their TikTok sketch-comedy series roasting corporate meeting culture. You don’t have to be standup-comic funny to make this work, but you do need to understand the very specific ways your target audience thinks and talks.

🐵 Hire a fictional character: Trevor From Torq is a fictional junior media intern played by an actor who “works at” Torq, a cybersecurity company. “Trevor” posts memes on LinkedIn and gets stopped for photos at conferences. Especially in old-school or stuffy industries, a character can say things an official brand (or exec) can’t. Before hiring an actor, just try a new social media account that can test out a different style from your main profile.

✈️ Post a crazy job listing that doubles as a story: Rilla, an 80-person AI startup most people had never heard of, got a ton of attention (including press coverage by Fortune) because of its job listings: a $1,500/month rent stipend for anyone living within 15 minutes of the office, right alongside a blunt warning about 70-hour weeks. Anthropic made headlines for a week for telling people to not use AI assistants during their application process. Review your careers page or job description templates and add something that is startlingly unique, that only your company would say.

🎭 Give your whole team fake job titles on LinkedIn on the same day: This can’t be used too often, but LinkedIn boosts job changes with extra visibility. Tl;dv “promoted” all 50+ employees to CEO at once, leading to 50+ “new job” notifications hitting thousands of feeds. Plus, it set the stage for hilarious “I’m quitting my role as CEO” posts. 😂

📈 Use your product’s data to build an industry trend report: This is a great way to get regular press coverage. Carta used its massive collection of cap tables to create its State of Private Markets report, which tech publications love to quote. When the Ramp AI Index showed Anthropic passing OpenAI in workplace adoption, Axios built a big story around it. But you don’t have to start with a massive report. Just make a habit of publicly posting interesting (anonymized!) stats that your team uncovers.

✏️ Win those “+reddit” searches: People add “reddit” to Google searches to go straight to crowdsourced wisdom and skip the SEO trash. There are lots of ways to do this: start question threads that match what people search, be the first helpful comment when someone posts about problems you solve, host AMAs, and share free resources.

🎡 Make “the world’s first {familiar thing} for {your audience}”: This is how we got the toy store I mentioned above (I’ve also done a musical for CISOs and a meditation app for CISOs). Take something everyone recognizes—a store, a dating app, a comic book—and make it absurdly specific to your niche. Your target audience will love it and the mental mismatch will make everyone else share it. Spotify’s ad team did Spreadbeats, the world’s first music video in Excel. Their target audience (media planners) loved it and they won a bunch of advertising awards, which made them even more visible to those ad buyers.

2. I want my launch to make noise 🚀

A product announcement is rarely interesting on its own, even when the product is. Every idea in this section adds a reason to pay attention that has nothing to do with your feature list.

🕵️ Launch a rumor instead of a product: For months, Instinct (an invite-only personal AI assistant) was basically just a waitlist page: no team, no screenshots, and no price. But a few VCs and well-known founders got invites, and they started posting things like “as close to THE personal AI assistant we’ve all been waiting for” with no demo, because there was nothing public to demo. The replies were filled with people begging for an invite. By the time the founder finally introduced himself, Instinct had already raised $350 million in total funding and reached a $2.5 billion valuation. You can run the small version: launch invite-only, give early access to a few people your audience respects, and ask them to post what they think without showing the product. That’s how you seed exclusivity, hype, mystery, and a dash of FOMO.

🎪 Livestream the launch: A startup called Clicky teased their launch with a video pointing to a Luma page (yes, the IRL events app), got over 1,000 RSVPs, then went live on YouTube walking through the product to an already-stoked chat. Plus, they cut the reactions into a 25-second highlight reel, which meant a better launch day and tons of great follow-on content.

🤫 Build in not-so-public: What’s better than building in public? Building alongside a private WhatsApp or Telegram group that makes early users feel like insiders. Base44’s founder called creating early-user groups “one of my best decisions” on the way to an $80 million acquisition six months after starting. Those insiders give you critical early feedback, but they’ll also be a guaranteed source of hype when you launch.

👀 Use incomplete information to pull on people’s curiosity: Ahead of a big partnership announcement, Lovable posted “coming today…” with the other org’s logo blurred out. People jumped into the replies to share their guesses, and this teaser post ended up with 2.2 million views. By the time they made the big reveal (they’d partnered with Shopify) a few hours later, there was already buzz.

🪖 Recruit a micro-influencer army: Create an open ambassador program with no follower requirements and no content approval. Anyone can join, post about you, and get paid based on impressions. This opens things up to fans of your product who have a good instinct for generating attention, even if they don’t already have a large audience. Tl;dv pays $100 per 20,000 views (capped at $700 per post). This has helped them consistently bring in more than 10 million impressions per month. A hundred small creators can often out-distribute one big sponsorship, for a fraction of the price.

🎟️ Let waitlist members cut the line: Show each signup their exact place in line, and move them up for every friend they bring. Robinhood made this move famous a few years ago, when they landed nearly 1 million signups before launch, with each member bringing in three more on average. It scales all the way down: I ran a waitlist-cutting promo before launching my newsletter, which helped drive a bunch of my early subscribers.

🙋 Launch from the founder’s account, never only the company page: This is obvious to some, but it’s so important that I have to include it. Personal profiles get two to three times the organic reach of company pages. DuckDuckGo launched with the founder’s casual “What do you think of my new search engine?” post. If you have the budget, run thought-leader ads on LinkedIn on the founder’s post instead of boosting the corporate announcement.

🎣 Get reporters to pitch you with the “Journalist Bait” framework: I know from personal experience—I’ve been featured in Business Insider, The Guardian, and BuzzFeed without pitching anyone. Your company update is not news to a journalist. They only care about spotting the next big trend. Reframe your product release as a “proof of a cultural shift” and post it on social. Let them find you.

3. I want other people to spread the word for me 📱

Word of mouth is sometimes mistaken for luck. But it’s closer to a design problem: give your happiest users something that makes them look good when they share it, and they’ll do your marketing for you.

🏆 Ship your top users a physical trophy: YouTube sends creators Play Button plaques at 100K, 1M, and 10M subscribers. OpenAI has mailed its API users a custom metal “token” at 10 billion, 100 billion, and 1 trillion tokens processed. Everyone wants to show off their accomplishments, and you know the people who receive one of these are going to share a pic on social media (plus put it on their desk or office wall forever).

🖼️ Give certificates with their name in big letters: When a customer hits a milestone, send a digital certificate built around their name. Again: people share what makes them look good. When we started doing this at Wiz, customer social shares jumped 300%. Bonus points for sending one to their boss too (“Awarded for building the kind of team that includes [Name]”).

🎧 Ask 20 experts one tiny question, then publish the results and give them credit: Email the best-known people in your field with one small ask. Like this: “What’s your favorite song to work to?” Then build a Spotify playlist with all of them, post it, and shout out each expert. I do this every year with security celebrities, and all of them share it, every time.

📸 Put your logo where people take screenshots: X redesigned their interface so that when you screenshot any post, the “Ask Grok” button immediately changes to the logo “X.com.” Every screenshot is now branded. Find what people screenshot most in your product (a result, a score, a report) and make sure your name or brand is right there.

👀 Make using your product a public act: To save a thread on X, Readwise users reply “@readwise save thread” in public, under the thread itself. Every save is a tiny ad, posted by a happy user, shown to everyone else reading the replies. Senja’s free testimonial widgets carry a small “Powered by Senja” link, so every free user’s website automatically advertises Senja—that’s tens of thousands of views a month, and hundreds of signups. I call this word-of-sight marketing: if your product has an action that people repeat daily, move it where others can see it.

🏅 Run weird world records and halls of fame: Clay celebrates the users who used the product in the strangest place (underwater, up a mountain, on jet skis). Of course every featured winner posts about it. Duolingo’s users wanted their streaks celebrated so badly they built a hall of fame themselves, now tracking more than 373,000 members.

🧶 Offer swag that makes people feel like insiders: Most swag is so generic it’s invisible. No one is going to notice that 2mm logo on a crappy pen. We’ve been giving out an ugly Christmas sweater that people beg for (1,000 comments on one giveaway post). What’s the difference? The sweater was covered with inside jokes and Easter eggs that only cloud security engineers would get. Just like Anthropic’s “thinking” hats, the key to nailing this is to make the swag say something about the person who wears it.

🎩 Publish a “Top 25” list of leaders for your industry: At Mine (my previous startup), our target audience was data protection officers. So we built a Top DPOs hall of fame. When we told each featured person that they had made the list, every single one of them shared it.

🐙 Give your power users a mascot: GitHub used Octocat to turn one of the most boring categories imaginable (version control!) into an identity that people actively choose to put on laptop lids and hoodies.

4. I want more leads and conversions 💰

You don’t need another form or another ad campaign to get more leads. Some of the best sources are places you never think about, like your 404 page or your Zoom settings. All these ideas leverage surfaces that we all touch every day but, for some reason, don’t consider to be platforms.

⚡ Use your product on dream customers, then send them the results: Pick 8 to 10 companies that you’d love to have as customers and give away free value. Send them the output of your product (the report, the audit, the analysis, etc.), without them even asking. Much better than asking them for a demo call. Vanta’s founder sent Segment a custom SOC 2 gap-assessment analysis before Vanta even had a product, and Segment became the design partner that shaped the company. (For B2C: Try this out on target celeb users.)

🗣️ Make Zoom redirect to your testimonials page when calls end: Zoom has a buried setting (post-attendee URL) that automatically opens a URL of your choice in attendees’ browsers, in the background, the second a call ends. My team points this at our testimonials page, so every demo closes with the prospect getting some social proof to browse through. Set yours to the page you most want prospects to see next.

💌 Change your popup’s “No thanks” to “Maybe later”: Wikimedia does this on its donation popups, part of an operation that raised $184 million last year. Copy it in your own popups: swap your decline buttons for “Maybe later,” and when someone clicks, show one more small box: “Want us to remind you? Leave your email.”

🌫️ Put a blurred screenshot behind your signup form: One of the weirdest A/B tests I ever ran: adding a blurry product screenshot behind the signup form boosted conversion 25%. It triggers curiosity and a progress illusion (you feel like you’re almost inside).

❌ Put your 404 page to work: AI chatbots invent URLs all the time, so your 404 page may now be getting more traffic from hallucinating LLMs. TestResults offers these visitors a prize: “These sexy AirPods trunks are yours. With your name on them.” Pretty weird! But if someone wants them, they fill out a form that has an opt-in lead capture element. Add something like this to your 404s and turn an annoying dead page into a moment of customer delight.

🎣 Start video ads by naming who should watch: In the first three seconds of any video ad, say the viewer’s job title (or specific problem) out loud. “Hey, product managers” works much better than going straight to talking about your value prop. Deel, Gong, and HubSpot all do this.

💬 Run free office hours in your LinkedIn comments: Prove your team’s expertise by offering personalized advice for anyone who replies. I did “Drop your website URL below and I’ll reply with one marketing idea” and answered hundreds of responses. This brought me thousands of newsletter subscribers, probably because it proved I know my stuff. Figure out what value you want to be known for and offer it for free where people are already scrolling.

🛝 Replace your “Book a demo” button with a “Try it yourself” playground: No one wants to give their email for nothing or wait for a Zoom call. Set up a dummy version of your product so that people can get to an aha moment without signing up or adding their credit card info. For inspiration, check out Plausible’s live analytics dashboard for its own site metrics, or Linear’s demo workspace, where you can actually create and manage tickets without even signing up.

🗓️ Make premium free during weird windows: Pluralsight occasionally offers Free Weekends, opening its entire 7,000-course library Friday to Sunday, no card required. A timeboxed window creates urgency a permanent free tier never will. This is like freemium with a deadline.

🤖 Add an “Ask AI about us” button: At the very bottom of Youform’s homepage, they have this section: “Scrolled so much but still not sure? Let your favorite AI tell you if Youform is the right fit.” There’s a button each for ChatGPT, Claude, Perplexity, and Google AI, and clicking one opens that LLM with a pre-written prompt. Since buyers and potential customers are going to do AI-powered research for you anyway, you might as well shape those conversations. Heads up: Claude just started flagging pre-written prompts that arrive through a link—so keep yours short and neutral to avoid suspicion.

🧪 Give away a mini-tool: Take a feature of your product, strip it down to one simple interaction, and offer it to the world, 100% free (and no email or signup). The Wordware team built a tiny site that roasts your X profile using their Sauna AI. 8.1 million people tried it and more than 400,000 signed up for the real product, which helped them land one of YC’s largest seed rounds.

💼 Create your industry’s job board: People are always looking for new jobs, including your target customers. So catch their attention by collecting every open role in your niche (not just the ones your company is hiring for). DataGrail runs one for privacy professionals, and if you’re not already tracking Lenny’s Jobs, what are you even doing? Bonus: You may get some industry credibility too. When we launched cloudsecurity.jobs at Wiz, publications would cite it whenever the security job market came up.

5. I want to crush the competition 🥊

Read more

How Grok Bot designers use AI agents to build personal sites and product prototypes

Brought to you by:

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

• Vanta—Automate compliance and simplify security

John Bai and Peng Zheng are designers on the Grok Bot team at SpaceXAI. In this episode, Peng shows how he built a self-updating personal site with Grok Bot handling the entire backend, from looking up a location to generating custom 3D artwork and publishing each new check-in. John demonstrates how he uses voice memos, Grok Bot, and Figma MCP to complete design work from the gym and turn shower thoughts into working prototypes. They offer a fascinating look at how AI is eliminating the tedious parts of design and making it faster and easier to explore new ideas.

Biggest takeaways:

1. The best personal website is one that keeps updating itself. Peng did not want to build another portfolio that launches, gets a few seconds of attention, and immediately becomes outdated. Instead, he created a Grok Bot workflow that turns a photo or place name into a published check-in. The bot finds the coordinates through Google Places, generates light and dark 3D miniature versions of the image, and adds everything to the site. Peng sends a single message, then walks away.

2. People no longer need to know exactly what they are building before they begin. Peng started without a Figma file, a specification, or a roadmap. He described the initial idea to Grok Bot, built something, reacted to it, and followed the most interesting possibilities. The final check-in workflow was not planned up front. It emerged through a series of “What if it could also do this?” moments. Because the bot handled the architecture, Peng could explore the idea without defining the entire system first.

3. The best AI workflows help people leave their computers. John’s Figma Bro workflow is a perfect example. When a colleague requested community assets while John was at the gym, he took two screenshots in Figma, recorded a voice memo explaining what he needed, and went back to his workout. By the time he checked again, the bot had produced three options, adjusted their sizing and colors, and completed the task.

4. Voice memos are becoming a powerful design interface. John gives Figma Bro natural-language instructions by voice, and the bot translates them into actions inside Figma through an MCP connection. He can describe an idea casually, even somewhat messily, and let the system interpret and execute it. The key is configuring the bot’s preferences ahead of time, including artboard structure, spacing, and naming conventions. That foundation allows informal instructions to produce organized files.

5. AI is making previously inaccessible creative skills available to more people. Claire could not illustrate. John wanted to create motion he could only describe with his hands. Peng wanted to produce 3D renders. In the past, each of those gaps would have required a specialist or years of practice. Now they can begin with a prompt. As Peng points out, the cost of making things has fallen so dramatically that unusual combinations and experiments are finally worth trying.

6. The most useful bots have narrow, clearly defined responsibilities. Peng organizes his agents into life and work categories, with separate bots for email triage, calendar management, 3D-printing inventory, green-card status tracking, and other specific jobs. A chief-of-staff bot handles anything that does not have an obvious owner. Each agent knows its lane, which makes delegation feel like handing work to a capable specialist instead of transferring confusion to a general-purpose chatbot.

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.

John Bai and Peng Zheng are designers on the Grok Bot team at SpaceXAI, where they’re building one of the most talked-about AI products right now. John writes publicly about his design process (his piece “Designing Grok Bot with Grok Bot” has already made the rounds) and shares bot templates with the design community. Peng brings a product-design sensibility to personal tools, and his website doubles as a live demo of what he builds.

What you’ll learn:

1. How Peng built a self-updating personal website using Grok Bot as the entire backend pipeline, with no CMS and no Figma file

2. The exact check-in bot setup that lets Peng send a photo or a place name and have his portfolio update itself automatically

3. How John’s Figma Bro bot handles production design tasks while he’s at the gym

4. How John uses voice memos to direct Figma work through an MCP connection without opening his laptop

5. The “shower thought to prototype” workflow John uses with DevBot to test interaction ideas without first going through a product manager or engineer

6. The “trash can method” of software development

7. How both designers organize their personal bot ecosystems

8. What John and Peng actually think AI means for the future of design as a craft

Brought to you by:

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

Vanta—Automate compliance and simplify security

In this episode, we cover:

(00:00) Introducing John and Peng

(02:53) The Grok Bot hype train

(04:35) Peng’s self-updating personal website built with Grok Bot

(15:12) How AI makes design more accessible

(19:35) Website update result

(20:13) John’s Figma Bro bot

(23:35) Creating marketing materials for the bot marketplace

(26:00) DevBot: from shower thoughts to working prototypes

(28:48) The trash can method of software development

(31:19) Other bots John and Peng are using

(39:05) Practical tips for when bots don’t do what you want

Tools referenced:

• Grok Bot (xAI): https://x.ai/bot

• Figma: https://www.figma.com

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

• Google Places API: https://developers.google.com/maps/documentation/places/web-service

• Notion: https://www.notion.so

• Swarm (Foursquare): https://www.swarmapp.com

Other references:

• Designing Grok Bot with Grok Bot: https://x.ai/bot/guides/designing-grok-bot-with-grok-bot

• Figma Bro bot template (shared by John Bai): https://x.ai/bot/marketplace/bots/figma-bro

• From zero coding background to hardware hacker: How Cursor + a Raspberry Pi makes AI fun: https://www.lennysnewsletter.com/p/from-zero-coding-background-to-hardware?utm_source=publication-search

Where to find John and Peng:

John Bai on X: https://x.com/johnbai

Peng Zheng on X: https://x.com/pengzheng_

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.

Read more

Roman Ugarte helped incubate and build Grok Bot, the popular new knowledge-work agent from SpaceXAI. A small, isolated team took it from first line of code to a working internal product in four weeks, and to a hugely successful public launch just three weeks later. Before Grok Bot, Roman led Growth at Cursor, where he helped scale the company from 15 people to over 1,000 before its acquisition by SpaceX.

In our in-depth conversation, we discuss:

1. The origin story of Grok Bot

2. The key decision to build it from scratch instead of adding it to Cursor

3. Why the team personally onboarded nearly 300 of its first users

4. The two early product decisions that made Grok Bot so successful

5. Their “colleague-pilled” product philosophy

6. Roman’s advice on moats, and what has allowed Cursor to keep winning in the most competitive market in the world

Brought to you by:

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

Mercury—Radically different banking, now with Command

Where to find Roman Ugarte:

• X: https://x.com/romanugarte_

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

• Website: https://x.ai

Referenced:

• Grok Bot for iOS: https://apps.apple.com/us/app/grok-bot/id6794501026

• Grok Bot for Android: https://play.google.com/store/apps/details?id=ai.x.grok.bot&hl=en_US

• How I AI: Grok Bot + Grok 4.6—what’s great (and what’s still hype) & Lessons from spending $20,000 on Devin in one month: https://www.lennysnewsletter.com/p/how-i-ai-grok-bot-grok-46whats-great

• Codex: https://chatgpt.com/codex

• ChatGPT Work: https://chatgpt.com

• Shopify: https://www.shopify.com

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

• The playbook for building high-talent-density teams | Adam Ward, Head of Talent at Cursor: https://www.lennysnewsletter.com/p/the-playbook-for-building-high-talent

• Superhuman: https://superhuman.com

• Stripe: https://stripe.com

• OpenClaw: https://openclaw.ai

• 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

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

• From skeptic to true believer: How OpenClaw changed my life | Claire Vo: https://www.lennysnewsletter.com/p/how-openclaw-changed-my-life-claire-vo

• Cursor: https://cursor.com

• Grok Build: https://x.ai/build

• Roman’s post on X, “An AI that does 100% of the job feels categorically different from one that gets you 90% there”: https://x.com/romanugarte_/status/2087344044435505175

• Notion: https://www.notion.com

• Casablanca: https://www.imdb.com/title/tt0034583

• Monk: https://www.imdb.com/title/tt0312172

• Exa: https://exa.ai

• Desiderata - Words for Life: https://allpoetry.com/desiderata---words-for-life

Recommended books:

• Cat’s Cradle: https://www.amazon.com/dp/038533348X

• The War of Art: Break Through the Blocks and Win Your Inner Creative Battles: https://www.amazon.com/War-Art-Through-Creative-Battles/dp/1936891026

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

Grok Bot vs. OpenClaw: why I replaced my entire agent stack

Brought to you by:

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

• Hyperagent—Deploy fleets of agents that handle real work

In this solo episode, Claire explains why she replaced her entire OpenClaw setup with Grok Bot. She walks through the bots she now uses to manage six inboxes, coordinate her family, review pull requests, monitor SOC 2 compliance, support customers, audit subscriptions, and shop for clothes. She also shares how she safely gives agents permission to take action, what Grok Bot still gets wrong, and the migration process she used to move every agent without losing its personality, context, or routines.

Biggest takeaways:

1. The real moat for agent platforms may be user experience, and Grok Bot wins on simplicity. Claire is highly technical. She runs Tailscale, connects to a Mac Mini over SSH, and even built a rescue bot to manage her other bots. Still, OpenClaw eventually became too difficult to maintain. Grok Bot offers many of the same capabilities in a product that reliably stays online, which was enough to convince her to migrate everything.

2. Thinking of each bot as a new hire makes agent design much easier. Claire gives every Grok Bot a specific name, job, and scope, just as she would when hiring someone for a team. Grok Bot can even infer an agent’s role from its name. Create one called “Prody McProd,” and it already has a sense of what it should do. This framing makes it easier to build focused specialists instead of agents that gradually become unfocused generalists.

3. Multi-account support is a killer feature for entrepreneurs running multiple businesses. Grok Bot allows Claire to connect six Gmail accounts alongside multiple Slack and Linear workspaces, then gives a single agent access across all of them. Other platforms are only beginning to support this kind of setup. For someone operating across several companies or projects, it makes the difference between an agent that is merely interesting and one that is actually useful.

4. Agents built for the home should help people spend less time on their computers. Claire’s guiding principle for TradBot is simple: if she has to spend time interacting with it, something has gone wrong. Its kitchen-table newspaper succeeds because the output is printed, physical, and waiting beside her family at breakfast. Sending the same personalized digest to a Kindle offers another way to bring the information into daily life without adding another screen-bound workflow.

5. Approval gates offer the right balance between autonomy and trust for high-stakes actions. Claire’s customer support agent, Holly Helpdesk, can handle refunds, but it cannot issue one without her approval. When a refund is needed, Claire receives a button that opens the actual Stripe request. She reviews it, confirms it, and the refund goes through. Within a week of launching, Holly began receiving unsolicited five-star reviews. The lesson is not that agents need approval for everything. It is that they should act independently on low-risk work and involve a human when the consequences are meaningful.

6. Grok’s default writing style is too compressed, so it needs explicit training on the user’s voice. While Claude often writes too much, Grok Bot has the opposite problem. It trims sentences so aggressively that its writing can become fragmented and difficult to follow. Claire fixed this by having it study her sent emails and create a detailed voice profile before writing on her behalf. With that context in place, it became capable of handling lower-stakes outbound communication.

7. A good compliance agent should fix problems, not just report them. Claire’s SOC 2 agent, Lockdown, logs into the controls dashboard, scans for issues, investigates new vulnerabilities, and opens a pull request with a proposed fix before notifying her. Claire reviews and approves the change, but the agent handles the underlying work. The broader opportunity is to have AI actively improve a company’s compliance posture, not simply generate another list of tasks for someone else to complete.

8. A structured migration process makes switching agent platforms feel safe and reversible. Before Claire shut down any of her OpenClaw agents, Lifeguard exported a secrets-free archive containing each agent’s identity, scheduled tasks, and gateway configuration. She uploaded those folders into Grok Bot, which effectively performed a brain transplant. The agents returned with their personalities and responsibilities intact. Because the migration preserved everything she needed to go back, switching platforms felt far less risky.

9. Agent proactivity must be intentionally scheduled. OpenClaw’s heartbeat feature made its agents feel naturally proactive, while Grok Bot remains passive unless the user creates routines. The benefit is transparency: every recurring action is visible and easy to audit in the settings. The tradeoff is that users must consciously decide when each agent should act. Without those routines, even a highly capable agent will simply wait for someone to message it.

Build your own company brain: the enterprise AI playbook from Stripe’s engineering team | Sharadh Krishnamurthy

Brought to you by:

• DX—Engineering intelligence for the AI era

• Hyperagent—Deploy fleets of agents that handle real work

Sharadh Krishnamurthy is an engineering manager at Stripe who helped build Kai, the company’s internal AI agent, in just two weeks with a team of 1.5 engineers. In this episode, he explains how Kai grew to serve more than 10,000 employees each week, why scaling AI across a company is ultimately a governance problem, and how Stripe uses projects, skills, tool policies, and human oversight to keep agents useful and safe. He also shares what happened when agents nearly overwhelmed production systems, how Stripe manages a library of more than 2,000 skills, and why strong data and developer infrastructure matter far more than simply choosing the best model.

Biggest takeaways:

1. The hardest part of bringing AI into a large company isn’t the model—it’s governance. When Sharadh and a colleague began building Kai, they quickly realized that giving every employee access to AI was not primarily a technical challenge. The real work was teaching the system how a complex global company operates. That is why Kai’s most distinctive features, including projects, tool policies, and skill routing, are designed to manage access and behavior rather than improve the underlying model.

2. Projects are configuration layers for how teams use AI, not simply folders for organizing work. In Kai, a small group of knowledgeable employees can choose the default model, approve the available tools, and define which actions require human review for an entire team or workflow. Everyone else gets the right setup automatically. Stripe’s HR project, for example, runs on a separate secure backend with tightly controlled tool access, preventing sensitive employee data from leaking into public documents.

3. Infrastructure built before the AI boom becomes even more valuable once agents arrive. Stripe had already invested in a tiered analytics catalog, a resilient Trino query engine, and a trusted metrics layer for its human developers. That same foundation now allows agents to query data safely and reliably at enormous scale. Sharadh’s provocative advice is to double the size of the developer experience and data teams before investing heavily in agents. Without strong infrastructure underneath them, even the smartest agents will struggle.

4. Agents amplify every weakness that already exists in a company’s systems. Sharadh does not believe agents create entirely new categories of problems. Instead, they dramatically increase the volume and severity of existing ones. Stripe saw agents behave unpredictably, overwhelm core systems, and nearly take down production. Better prompts were not enough to solve those problems. Stripe needed agent-specific identities, priority signals, load shedding, and a query layer capable of absorbing the additional demand.

5. Skills are what turn an AI assistant into dependable infrastructure. Kai allows any employee to convert a successful session into a reusable skill. Each skill includes guidance on when it should run, a specification written for the Kai system, and an editor for testing it before release. Stripe now has roughly 2,000 skills, including about 50 used daily across the company. Because Kai automatically selects the right skill, employees can simply describe what they need instead of learning how the system works.

6. With skills, quantity is part of the quality problem. Every new skill adds more information to the system’s retrieval pool, and irrelevant context can reduce the quality of the results. Stripe built telemetry to distinguish between skills used across the company, highly valuable workflows used by small teams, and tools that have not been touched in weeks. That data helps the team promote, combine, or retire skills while preserving useful niche workflows.

7. A small team can build an enterprise AI platform quickly when the underlying foundation is strong. Sharadh and one colleague created the first working version of Kai in just two weeks. A company-wide demonstration triggered a steep rise in adoption, and the platform is now used weekly by more than 10,000 Stripe employees, with an 86% adoption rate. Fewer than 10 people manage it today. That scale is possible because the team is supported by coding agents and years of investment in Stripe’s internal infrastructure.

8. The best enterprise AI products hide organizational complexity from the people using them. Kai appears to employees as a single, straightforward product, but it is built from many configurable layers designed to accommodate different teams, data types, and security requirements. Employees do not have to decide which connector to enable, which model to select, or whether a tool is appropriate for the information they are handling. Removing those decisions improves adoption while reducing the temptation to bypass safeguards because the approved path feels too slow.

GPT-6 Astra is a banger—here’s everything I’ve built

In this solo episode, Claire puts GPT-6 Astra through a series of real-world tests across computer use, coding, design, QA, hardware, and 3D development. She shares how Astra finally solved a ChatPRD feature she had been stuck on for six months, navigated complex production tools without her touching the keyboard, and reverse-engineered a Bluetooth display with no public API. She also explores what the model’s leap in capability means for software interfaces, ambitious personal apps, and the kinds of projects that suddenly feel possible to build.

Biggest takeaways:

1. The biggest leap with Astra is that it expands what someone is willing to attempt. Claire had spent six months trying to build a product intelligence feature for ChatPRD, but earlier models consistently made poor architectural decisions because she could not fully explain the system in a prompt. Astra got the feature 90% of the way there on its first attempt, then finished it with a few follow-ups. When previously impossible projects start working, the real change is not just productivity; it’s ambition.

2. Computer use is finally precise enough to handle complex visual interfaces. Claire now spends roughly 90% of her day with her hands off the keyboard. Astra navigated Adio’s node-based workflow builder, added new nodes, reconnected the logic, and generated custom lead-response emails without requiring her to click anything. She had abandoned similar attempts with earlier models because they lacked the necessary precision. That gap appears to be closing quickly.

3. Browser-based QA may be one of the most valuable uses of computer-use agents. Astra spent an hour and 45 minutes testing a branch for race conditions and streaming edge cases. It inspected the console, deliberately refreshed the application, and continued probing for failures. This is exactly the kind of repetitive but important testing that humans tend to rush through or skip. An agent’s willingness to stay patient with tedious work can be as valuable as its intelligence.

4. SaaS is back, and the case for eliminating interfaces is getting weaker. The “no UI is the next UI” argument assumed that AI would need direct access through APIs or command-line tools. But if an agent can reliably navigate visual software, product teams can choose the interface that best expresses the product rather than optimizing entirely around machine access. In other words, buttons are back.

5. Hardware hacking is becoming a serious use case for frontier models. Claire had spent months trying to work with the Divoom Mini 2, a device with no public API, without making much progress. Astra reverse-engineered its pixel algorithm, built a live-streaming command-line tool, and enabled her to send episode information and images directly to the display. That is more than a clever demo. It suggests AI can unlock physical devices that were previously too difficult or poorly documented to customize.

6. One-shot 3D game development is raising the ceiling for personal software. Astra built a functional Barbie-style dress-up game in Blender from a single prompt. It also created a 3D world for Claire’s family app where her kids can walk to a library or visit a pond to check in with their emotions. These are not projects she could have built with earlier models. As the cost of creating interactive worlds falls, deeply personal software starts to feel much more practical.

7. Astra’s AIM-style Codex wrapper shows how accessible ambitious desktop development has become. In one shot, Astra built a functional 1990s-inspired AIM interface around Claire’s live Codex threads, complete with active-writer indicators and away messages. The nostalgia is fun, but the bigger lesson is that custom desktop tools and unusual personal software ideas are now within reach, even when they would never justify a traditional development team.

8. Astra produces artifacts that fit into an existing brand instead of starting over. When Claire needed thumbnails while her designer was unavailable, Astra worked inside Flora and Figma, studied the existing workflows, and matched the established aesthetic. That ability to understand and extend an existing visual system is what makes the output useful in production. A polished artifact is impressive, but one that actually looks like it belongs is far more valuable.

9. Astra’s pricing makes choosing the right tasks more important than minimizing usage. At $10 per million input tokens and $50 per million output tokens, long-running computer-use and QA sessions can become expensive, particularly on the output side. The key is to deploy Astra where its additional capability is worth the premium: stubborn coding problems, complex visual interfaces, and high-toil QA work. The question is not whether the model is expensive but whether the task is valuable enough to justify using it.

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.

Sharadh Krishnamurthy is an engineering manager at Stripe, where he helped build Kai, the company’s internal AI agent used by more than 10,000 employees every week. He’s worked across several of Stripe’s core infrastructure teams, including data and developer experience, which gives him a grounded, systems-level perspective on what it actually takes to make AI work at enterprise scale. He’s currently focused on the governance, skills, and infrastructure layers that let every Stripe employee use AI safely and effectively, regardless of their technical background.

What you’ll learn:

1. Why Stripe built Kai from scratch instead of buying, and what tipped the decision

2. What Kai knows about you by default and what you actually control

3. Why “projects” at Stripe are a governance mechanism, not just a folder

4. How Stripe structured its data layer so agents can query safely at scale

5. Why the infrastructure Stripe built for human developers turned out to be exactly what agents needed

6. How Kai’s skills platform lets any employee package a workflow, and what happens when you have 2,000 of them

7. What Sharadh learned the hard way when agents nearly took down production systems

Brought to you by:

DX—Engineering intelligence for the AI era

Hyperagent—Deploy fleets of agents that handle real work

In this episode, we cover:

(00:00) Introducing Sharadh

(02:46) Why Stripe built an AI agent (Kai) instead of buying tools

(05:18) What Kai knows about you (and what you can turn off)

(06:51) Projects as a governance layer

(10:04) Live demo: Kai builds a dashboard

(12:18) Tools, skills, and the secure sandbox

(17:22) Why Stripe has benefited so much from AI

(19:20) Agentic identity, load shedding, and rogue agents

(20:41) Iterating on the dashboard

(25:01) How they rolled out Kai across the team

(29:07) How projects work

(34:18) Bespoke agents for bespoke use cases

(35:58) The skill builder workflow

(40:40) Skill quality, evals, and telemetry

(43:01) Recap

(45:13) Lightning round

Tools referenced:

• Trino: https://trino.io/

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

• Gemini: https://gemini.google.com/

• Cursor: https://www.cursor.com/

Where to find Sharadh Krishnamurthy:

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

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.

Anish Acharya is a General Partner at Andreessen Horowitz (a16z), where he has focused on consumer investing. Anish is one of the most insightful, thought-provoking, and in-the-weeds product investors I’ve met, and this conversation will get your mind buzzing. Before joining a16z, Anish was a serial founder and operator: he founded SocialDeck, which he sold to Google, then led multiple efforts inside Google before founding Snowball, which he sold to Credit Karma. At Credit Karma he rose to VP of Product and then GM of the consumer product and the broader credit card business.

In our in-depth conversation, we discuss:

1. Why you don’t have to worry about becoming part of the “permanent underclass”

2. Why company building will now involve creating a series of loops

3. What’s happening in consumer right now

4. Why the biggest opportunity in consumer is “/loop, make me happier”

5. Why moats are discovered, not designed

6. The rising importance of distribution as a moat

7. Being a model sommelier

Brought to you by:

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

Mercury—Radically different banking, now with Command

Where to find Anish Acharya:

• Andreessen Horowitz: https://a16z.com/author/anish-acharya/

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

• X: https://x.com/illscience

• SoundCloud: https://soundcloud.com/illscience

Referenced:

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

• Codex: https://chatgpt.com/codex

• Lovable: https://lovable.dev

• Replit: https://replit.com

• Wabi: https://wabi.ai

• Hugging Face: https://huggingface.co

• Airbnb: https://www.airbnb.com

• Credit Karma: https://www.creditkarma.com

• Dilbert: https://dilbert.com

• Kavak: https://www.kavak.com

• Sundar Pichai on LinkedIn: https://www.linkedin.com/in/sundarpichai

• Claire Vo on LinkedIn: https://www.linkedin.com/in/clairevo

• How I AI podcast: https://www.youtube.com/@howiaipodcast

• Claire Vo’s post on X: https://x.com/clairevo/status/2083981963371938295

• Alejandro Maza on LinkedIn: https://www.linkedin.com/in/mazaalejandro

• Pareto efficiency: https://en.wikipedia.org/wiki/Pareto_efficiency

• Qwen3.8-Max: A New Bar for Coding and Cowork: https://qwen.ai/blog?id=qwen3.8

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

• Bespin: https://en.wikipedia.org/wiki/List_of_Star_Wars_planets_and_moons#Bespin

• MiniMax H3 on fal: https://fal.ai/minimax-h3

• ElevenLabs: https://elevenlabs.io

• GLM-5.2: https://www.together.ai/models/glm-52

• Giggle Gauge: https://gigglegauge.com/

• Limitless: https://www.limitless.ai

• Kimi K3: https://www.together.ai/models/kimi-k3

• Eugenia Kuyda on LinkedIn: https://www.linkedin.com/in/eugenia-kuyda-638a8a1b

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

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

• 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

• Anthropic’s CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next

• Anthropic’s first technical PM on token maxing, the jagged edge, and living in the future | Dianne Penn: https://www.lennysnewsletter.com/p/anthropics-first-technical-pm-on

• OpenAI’s Head of Design: This is the best time in history to be a designer | Ian Silber: https://www.lennysnewsletter.com/p/openais-head-of-design-this-is-the

• “Engineers are becoming sorcerers” | The future of software development with OpenAI’s Sherwin Wu: https://www.lennysnewsletter.com/p/engineers-are-becoming-sorcerers

• 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

• Grok Bot: https://x.ai/news/introducing-grok-bot

• ChatGPT Work: https://chatgpt.com/work

• Instinct: https://instinct.co

• Tara Seshan on LinkedIn: linkedin.com/in/tarstarr

• OpenClaw: https://openclaw.ai

• 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

• Moltbook: https://www.moltbook.com

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

• Suno: https://suno.com

• Jesse Zhang on LinkedIn: https://www.linkedin.com/in/thejessezhang

• Cursor: https://cursor.com

• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer

• Town: https://www.producthunt.com/products/town

• Granola: https://www.granola.ai

• Chris Dixon on LinkedIn: linkedin.com/in/chris-dixon-9599b127b

• Seth Godin’s best tactics for building remarkable products, strategies, brands and more: https://www.lennysnewsletter.com/p/seth-godins-tactics-for-building-remarkable-products

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

• MSCHF: https://mschf.com

• Ev Williams on LinkedIn: https://www.linkedin.com/in/evanwilliams

• Kevin Rose on LinkedIn: https://www.linkedin.com/in/kevinrose

• Card V Card: https://cardvcard.com

• Atoms: https://atoms.co

• $46B of hard truths from Ben Horowitz: Why founders fail and why you need to run toward fear (a16z co-founder): https://www.lennysnewsletter.com/p/46b-of-hard-truths-from-ben-horowitz

• Ron Conway on X: https://x.com/RonConway

• Brook Byers on LinkedIn: linkedin.com/in/brook-byers-56963a139

• Tom Perkins: https://en.wikipedia.org/wiki/Thomas_Perkins_(businessman)

• House of the Dragon on HBO Max: https://www.hbomax.com/show/c68e69d7-9317-428a-a615-cdf8fe5a2e06

• The Odyssey: https://www.imdb.com/title/tt33764258/

• The new AI growth playbook for 2026: How Lovable hit $200M ARR in one year | Elena Verna (Head of Growth): https://www.lennysnewsletter.com/p/the-new-ai-growth-playbook-for-2026-elena-verna

• Why half of product managers are in trouble | Nikhyl Singhal (Meta, Google): https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble

Recommended books:

• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319

• High Output Management: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884

• The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers―Straight Talk on the Challenges of Entrepreneurship: https://www.amazon.com/Hard-Thing-About-Things-Building/dp/0062273205

• Conquests and Cultures: An International History: https://www.amazon.com/Conquests-Cultures-International-Thomas-Sowell/dp/0465014003

• Increasing Returns and Path Dependence in the Economy: https://sites.santafe.edu/~wbarthur/increasingreturnsbook.html

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

Read more

Read more

I got early access to GPT-6 Astra: when I say this model broke through tasks I couldn’t crack with 5.6 Sol or Fable, I mean it specifically: the ChatPRD product intelligence feature, building 3D games, the hardware hack, and a handful of one-shot coding projects I’d tried and failed on repeatedly.

What you’ll learn:

1. Why Astra’s computer use feels different, and which production tools I’m trusting it with

2. The one feature I’d thrown every model at for six months, and what finally got it to 90%

3. How I’m using browser use for QA, not building, and what it found that I would’ve missed

4. Why I think UI is genuinely back, and what that means for SaaS and MCPs

5. The hardware hack I’d been chasing since GPT-5.5, and how Astra finally cracked it

6. What Astra built me in Blender in one shot, and why 3D is my new capability benchmark

7. The AIM-style Mac app Astra made in one shot, and what it signals about desktop development now

8. An honest take on speed, cost, and whether Astra is worth making your daily driver

In this episode, we cover:

(00:00) GPT-6 Astra overview

(03:44) Browser/computer use test on my CRM

(09:08) Flora thumbnail generation

(13:00) Browser use for QA

(15:20) Coding: ChatPRD product intelligence feature, finally one-shotted

(18:36) Hardware hack: Divoom MiniToo CLI and live streaming display

(22:23) Building an AIM-style Mac app

(24:24) Blender and 3D assets: Barbie Bench and the kids’ family app

(28:52) Summary: what Astra is great at and what to try first

Tools referenced:

• GPT-6 Astra: https://openai.com/index/gpt-6-astra/

• Codex: https://openai.com/codex

• Flora (node-based AI image/video editing): https://flora.ai/

• Figma: https://www.figma.com

• Blender: https://www.blender.org

• GPT Image 2: https://developers.openai.com/api/docs/models/gpt-image-2

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

• cxo.dev: https://www.cxo.dev/

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.

I’m running about 30 active agents at any given moment, and in this episode I break down my full Grok Bot setup: what it is, how it compares to OpenClaw, and the nine bots I’ve built for work and my personal life. We go deep on Chief (my chief-of-staff bot sweeping six inboxes and multiple Slack workspaces), TradBot (the family agent that prints a kitchen-table newspaper for my kids), two engineering bots handling my PR queue and SOC 2 compliance monitoring, Holly Helpdesk, and a handful of personal bots I didn’t expect to actually love. I also walk through how I migrated everything from OpenClaw, including the script I used to export and transplant each agent’s identity and schedule.

What you’ll learn:

1. The three primitives Grok Bot is built on, and why one of them changes what agents can actually do

2. How Chief, my general-purpose chief of staff, handles a scope I didn’t think a single bot could manage

3. The writing quirk I noticed immediately with the Grok model, and what I did about it before letting it near my inbox

4. Why I created a family agent, what it produces every morning, and the design principle I used that has nothing to do with a screen

5. The two engineering bots doing work I used to do myself, and how one of them handles compliance in a way that surprised me

6. How Holly Helpdesk started getting five-star reviews from customers who had no idea they were talking to a bot

7. The personal bots I built mostly on a whim, and the one I now look forward to every Monday morning

Brought to you by:

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

Hyperagent—Deploy fleets of agents that handle real work

In this episode, we cover:

(00:00) Why I migrated from OpenClaw to Grok Bot

(02:10) Grok Bot overview: the three core primitives

(07:41) Chief: my chief-of-staff bot

(11:51) OpenClaw vs. Grok Bot

(12:41) TradBot: my family agent

(19:51) LGTM the PR Closer

(22:03) Lockdown: SOC 2 control monitoring bot

(24:00) Holly Helpdesk: customer support

(26:58) Penny Pincher: subscription audit, insurance negotiation, Rolex shopping

(29:37) ShopZilla and Sylvie Style: personal shopping and wardrobe bots

(32:54) How to migrate your OpenClaws

(34:26) Final take

Tools referenced:

• Grok Bot (SpaceXAI multi-agent platform): https://x.ai/news/introducing-grok-bot

• OpenClaw (previous agent platform): https://openclaw.ai/

Where to find Claire Vo:

ChatPRD: https://www.chatprd.ai/

Website: https://clairevo.com/

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

X: https://x.com/clairevo

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Subscribe now

P.S. Get a full free year of Cursor, Notion, Replit, Lovable, Wispr Flow, Linear, ElevenLabs, Factory, PostHog, Granola, Brain.fm, Waking Up, and more, by becoming an Insider subscriber (while supplies last). Learn more.

I’d always thought AI was bad at design. But after reading this mind-blowing post by Anshu Chimala, I realize I was just doing it wrong. Anshu led software engineering and design teams at Apple for 12 years, focusing on research and prototyping for future AI products. He regularly shares design tutorials and demos on X (he’s one of my favorite follows). For deeper dives into crafting distinctive experiences with AI, check out his Substack and connect with him on LinkedIn.

Let’s get into it.

A conversational calorie tracker, built in three prompts with Claude Fable 5:

A space exploration game, built in two prompts with Claude Opus 5:

A dynamic landing page, built in three prompts with Claude Opus 5 + GPT-5.6 Sol:

I often post AI design demos like these on X. Every time I do, someone inevitably asks, “Why does the model create all this incredible stuff for you, but when I try, I only get generic slop? It’s like you’re using a completely different model.”

I’m not using a different model, but I am getting more out of the models I work with. Most people only see 1% of AI’s creative potential. I want to show you how to tap into the other 99%.

AI models are capable of amazing creativity, but that creativity gets stifled by how they’re trained. Large language models are next-token predictors: at each step, they look at a sequence of text and predict what comes next based on millions of examples. The results may be rated by humans, and those ratings fed back into the model. This teaches the model to make consistent, safe choices that fit everyone’s preferences.

This makes typical LLMs great at most tasks but poor designers. To create a design, an LLM has to build it out token by token. Whenever it needs to make a design decision—what colors to use, or how to arrange elements—the model fills in the tokens it thinks are most likely to please everyone. As a result, the design usually ends up being repetitive and bland. It’s like the ultimate case of design-by-committee.

Great design, on the other hand, starts with feeling and aims to create an emotional response. It bends the rules and delights users with memorable, unexpected choices. Great design is exactly the opposite of what an LLM does naturally, which is to make the most predictable choice at every step.

However, if we can get the model to reach beyond the most predictable choices, we can access a vast landscape of creative ideas that most people miss out on.

This is a lesson I learned from managing human designers, before I was managing AI ones. For most of my career at Apple, I led an R&D team designing exploratory future AI products. Early on, our preconceived notions about how user interfaces should work limited our creativity and kept us returning to the same old ideas. Through rigor and new processes, we learned to stop re-creating what’s comfortable and instead look to the fringes of what’s possible, to generate something new. We became experts at polishing the little details to an Apple level of quality.

Since my time at Apple, I’ve been working on applying that same process to my work with AI. In the past couple years, AI agents have become extremely capable. They can do in hours what used to take my team weeks. And with the right guidance, they can create designs that look completely unlike anything else.

Loosely inspired by the Double Diamond design process, I’ve reimagined the design process for a team of AI agents instead of human designers:

1. Discover new ideas beyond the average slop by exploring a variety of directions and creating bold, ambitious design briefs.

2. Define an individual design identity by pushing AI beyond its familiar patterns and chaining models together to fully realize the design’s potential.

3. Deliver a stunning final result by polishing away the sloppy rough edges and focusing on the key elements.

By following these stages and applying the techniques within each one, you can create an incredible design remarkably quickly—and make people ask, “Why does AI create magic for you (and not me)?”

Discover: Explore the space of possibilities

The hardest part of the design process is looking at a blank screen with infinite possibilities. The best way to tackle that moment is to start by going broad before going deep. AI is an excellent tool to explore a wide variety of potential directions.

As we know, though, models tend to overrely on familiar patterns and make conservative choices. To explore the full potential design space, we want to coax a model to do the opposite: be bold, be varied, and take risks. Below are two ways to push it out of its comfort zone.

Technique 1: Use seed strings to inject variety

The idea here is to get the model to find a new source of inspiration for designs, rather than relying on the defaults it learned from training. If you’ve tried to prompt a model to design a website or app, you’ve probably already seen what that default looks like.

As a simple example, I gave four instances of Claude Code the same prompt:

Prompt:

Build me a landing page for my productivity app.

Claude Opus 5:

Almost every time, we get a purplish gradient, text on the left, graphic on the right, and the exact same structure. It looks like every AI-designed website ever.

We didn’t ask the model to do anything unique or varied, so it makes sense that it keeps falling back on the same patterns it knows well. But just asking for variety doesn’t work:

Prompt:

Build me a landing page for my productivity app. Give me something totally unique. Make every design decision completely at random.

Claude Opus 5:

The results are different from before, but they’re still not varied. The model always uses the same color scheme, structure, and even the same awkward pottery metaphors. It’s predicting tokens that sound random but aren’t actually random.

The problem is that the model can’t inherently act randomly. It can only predict the most likely token. If we want variety, we have to bring it from outside the model. One technique for this is String Seed of Thought, published by Sakana AI. We make the AI generate a random string and use it as design inspiration. That way, the model is truly making different decisions each time.

Prompt:

I want you to build me a landing page for my productivity app.

Follow this procedure:

1. Generate a long, random alphanumeric string using a shell script.

2. Define the creative direction (color scheme, layout, typography, etc.) based on the string. Look beyond the surface for subpatterns, special numbers, anything that inspires you.

3. Use your judgment to bring this direction to life and make it look great.

Don’t reveal the string in the design. It’s only for your inspiration.

Claude Opus 5:

Suddenly the outputs are much more varied! Now we’re seeing different color schemes, fonts, and new ideas. The previous designs were ones that any Claude user could get. These designs are one-of-a-kind; no two runs ever produce the same result.

Technique 2: Be much more ambitious with your prompts

Another approach to giving a model a strong push is to get more specific and wild with your prompts. This gives the model a clear vision to base its decisions on, rather than letting it make them up on the fly. The best way to find a unique idea is by bringing your own taste into the equation. You first imagine the inspiration—a video game, an interior design trend, an art installation—and describe how you’d like that inspiration to influence the AI’s outputs. Here are some examples:

“Build me a landing page for my productivity app, with a bold pixel art theme and stunning graphics. Each section should feel like a still from a video game, yet somehow it should all function as a landing page.”

“Build me a landing page for my productivity app, set in an isometric living 3D city, where different features are somehow represented by neighborhoods or buildings.”

“Build me a landing page for my productivity app, with a radically asymmetric layout, dissonant colors and typography, and uncomfortable negative space. Break all the rules but still make it look good.”

Of course, the hard part is coming up with original ideas to ask for. AI can help with this too, but if you simply ask it for ideas, you’ll get the same average ones everyone else gets. Here’s a system I use to find unique prompt ideas with AI:

1. Ask AI to list a bunch of ideas, intentionally lacking detail. The goal is just to inspire your imagination.

I want to come up with a bold, unique design language for my product. Can you list as many ideas as you can, with short, high-level descriptions? Go broad, not deep.

2. Visualize your favorites and note how you react to different directions. Then ask AI to refine them.

Industrial Control Panel:

• I’m imagining something tactile. Clicky, satisfying buttons, nice sounds.

• Initially I pictured something cartoony or skeuomorphic, but this feels tacky to me. Avoid that.

• Instead, want consistent components and little touches that land this look without going overboard.

• Gray gradients would look boring. Need more texture. Maybe we can incorporate some color, while retaining the control panel feel?

Can you sharpen this one based on my tastes?

3. Iterate until you’re satisfied, then ask AI to write the prompt to build it.

Can you write a concise prompt that an AI agent could use to build an initial POC page with this?

If you just paste AI-generated ideas back into AI, it’s hard to get something unique. After all, anyone else could have done the same thing. However, when you actively steer the design direction, you end up with something only you could have created.

Don’t be afraid to try ideas that sound terrible. If you find yourself thinking, “There’s no way this will work,” you’re on the right track. Often, your agent will surprise you, and you’ll realize you were underestimating it. If not, just throw away those results and try something else. But save the prompts that don’t work, and test them again when newer models come out. That way, you’ll know you’re taking full advantage of what the latest models can do.

Define: Deepen your design direction

So far, we’ve looked at how to explore a broad set of ideas and hopefully land on a promising initial design. No matter how we prompt, though, our initial AI-generated designs will usually still feel generic.

For example, look at the designs we came up with using seed strings:

These have promise, but they’re still relying heavily on the same stale patterns: text on the left with a CTA button below, nav bar up top, graphic on the right.

Our next goal is to give each design an individual personality through distinct design choices. Below are my favorite techniques to do that.

Technique 3: Create positive feedback loops with subagents

We need to iterate on our designs to improve them. But simply asking our agent to look at the design and improve it won’t work, because the agent isn’t objective: it reviews its own code, past decisions, and previous rationale. AI can’t easily zoom out, look at the big picture, and “think different.”

To solve this, instead of letting the coding agent decide when the design is good enough, have it ask another agent—a “design critic.” The critic’s job is to look at screenshots of the current design and provide feedback. It doesn’t care how the current design is implemented or how much effort went into it, only if it actually hits the quality bar.

This approach has an extra benefit: we can use a big, expensive model for the critic without breaking the bank, because we’ll only use it for executive decisions. A cheap, fast model can do the grunt work, while the strong critic model provides taste.

Let’s try this on our previous designs, using Claude Fable 5 as the critic:

Prompt:

I want you to improve this design. To figure out what to focus on, use a Fable 5 subagent as a design critic.

Follow this procedure at each iteration:

• Capture a screenshot of the current design

• Invoke the critic in a fresh context, with just the screenshot, not the code, implementation details, or earlier iterations/critiques

• Ask it to evaluate the aesthetic that the design is going for, imagine how a top design studio would execute this aesthetic, then outline the biggest gaps

• Lastly, it should provide a score out of 10 indicating how close the current design is to that studio-level quality bar

Provide this guidance to the critic in its prompt:

• It should think high-level about the overall structure and composition as well as look at the fine details

• It should watch out for patterns that feel overdone, excessive, or otherwise obviously AI-generated, and penalize them

• It should provide tight, specific feedback, not vague prose

• It should be bold and opinionated, not rely on what’s safe or easy

Your work is only complete when the critic independently deems it 9/10 or higher. Do not put that criterion in the critic prompt; keep it objective in its scoring. Use the same critic prompt each time.

Claude Opus 5:

Instead of the same cookie-cutter layout over and over, each design now has its own identity—but still maintains its original high-level aesthetic.

Notably, in each case, Fable accounted for less than 10% of output tokens. Asking Fable to redesign the page directly would have cost twice as much and taken much longer.

The way you set these loops up matters a lot. Here are some tips:

• Make sure the criteria for the critic are as clear and objective as possible.

  • Bad: “Judge if our design looks beautiful, not AI-generated.” This is too subjective, and the results will vary wildly from run to run.

  • OK: “Review the aesthetic we’re going for, visualize how a top design studio would execute it, then judge our design’s quality against that bar.” The prompt is still mushy, but it provides a consistent framework and quality bar.

  • Great: “Here are 5 designs: 4 professional examples and 1 screenshot of our product. Rank them by polish and taste level.” This instruction is concrete and objective, and gives a visual baseline for judgment.

• Provide example images to demonstrate the target quality bar. You can use comparable screenshots or designs you like, or even AI-generated concept art. Instruct the critic to treat these as a baseline or a moodboard, not a target. You don’t want it to copy other designs outright.

• Set the stopping criteria carefully. Otherwise, the critic may never consider the design good enough, and your agent will helplessly burn tokens trying to please it. Prompt it to do one or two iterations first, and see if it’s converging before adding more.

• Choose the right model for each job. Consider bigger models for the critic role, since more parameters generally translate to better design sense and a wider distribution of ideas. Small models can be effective as the implementer, but don’t go too small. You still need a model that’s capable of executing a design direction well.

Technique 4: Use image generation to enrich designs

Coding agents love to write code, but they usually don’t incorporate images. Instead, they tend to use the easy code-based alternatives: gradients, shapes, and basic patterns. Those are all strong giveaways of an AI-generated design.

Some agents have image tools built in, but they underutilize them. Others don’t have image tools out of the box but can easily use the OpenAI or Gemini APIs to generate images with an API key.

Let’s try this on the designs from the last step:

Prompt:

The design is pretty plain. Add more personality using image generation. Consider shaders or 3D effects in combination with images to create more interesting visuals.

For image generation, use this OpenAI API key (only use it locally, do not store it in the code or product): sk-a1b2c3d4…

Verify that your work looks right frame-by-frame in the browser.

Claude Opus 5 (before and after):

Images and effects like these can quickly add a lot of personality and make a design less obviously AI-generated, since they demonstrate more than surface-level effort.

Depending on your setup, there are different ways to connect your agent to image generation tools:

• If you use Codex, Antigravity, or Grok Build:

  • Tell your agent to use its built-in image generation. The agent already knows how to do this but rarely does so until instructed.

• If you use Claude Code or another agent but also have a ChatGPT subscription:

  • Tell your agent, “Use the Codex CLI to generate images. Help me install it if it isn’t already present. Make sure it’s billing my subscription, not an API key.” This lets you use your ChatGPT subscription for image generation without extra costs.

• If you only use Claude, or any other tool:

  • The simplest path is to give your agent an OpenAI or Gemini API key to generate images. I recommend creating a separate API key with a tight spend limit, just for your agent. That way, your costs are controlled even if the key gets out or the agent misuses it, and you can easily revoke the key without disrupting other work.

  • If you find yourself pasting keys into chats frequently, put them in a file instead, and point your agent to it in your project. Tell your agent: “Create a gitignored file called .env.agents, store this API key in it, and note to yourself in AGENTS.md/CLAUDE.md that these keys are for you to use during development (but must not ship with the product).”

Technique 5: For more advanced motion, use video generation

Video generation models are incredibly powerful these days, but most people think of them as tools for generating UGC ads or clips of Will Smith eating spaghetti. They can work wonders for everyday design work too.

There are many video models out there, and the best ones change frequently, so I like to use an aggregator platform like fal.ai. This way, we can give our agent a single API key and let it evaluate different options and choose the best one without needing multiple integrations.

Here are two ways I love to use video models in my designs:

Create stunning animated graphics

The trick is to generate a looping clip with a solid color background, then either chroma key it out (like a green screen) or, in more complex cases, use a video matting model to remove the background. This gives you an animation that you can layer anywhere in your UI without it looking like a video.

For example, I took one of our previous designs and ran this prompt:

Prompt:

Can you replace the image on this page with a looping video clip that does something more interesting? Have the crystal splinter apart and slowly spin around. It should have awesome glassy effects that refract the page background and cast shadows and light around it.

To get convincing glass refraction effects, render the video of the glass over the page background colors first (so it bakes in the refraction effects), then remove the background with a video matting model.

Use this fal.ai API key: sk-a1b2c3d4…

Find appropriate recent models for video generation and background removal.

GPT-5.6 Sol (before and after):

This is a much richer effect than you can get with code: interesting caustic reflections, glassy refraction effects, and complex physical motion.

Create fluid transitions between states

This is a really underrated use case for video models. In addition to generating video from text, many video models can interpolate between keyframe images. This lets you take two product stills and create a transition clip between them. You can play the clip when the user takes an action (like navigating to another screen of your app) or scrub through it frame-by-frame in response to a gesture (like scrolling or swiping).

Here’s a demo page showing off a scroll effect. I built it with a single prompt using GPT-5.6 Sol in Codex:

Prompt:

Build a demo page for a suitcase that uses a video model to create interactive transitions between a couple of screens. Each screen should show the suitcase in a different state, with vertical motion that feels appropriate for scrolling:

• Initially, have the suitcase floating high up in the air

• Then have it land on the floor and pop open

• Finally, have its contents neatly land into it from the top

Generate the initial frame using your image generation skill. Then, generate a video clip that starts from that frame and animates to the next state. Use the final frame of that video to seed the next transition so that it continues seamlessly. Scrub through the transitions one by one as the user scrolls.

Use this fal.ai API key: sk-a1b2c3d4…

Use a video model with strong physics and consistency, like Seedance 2.5.

GPT-5.6 Sol:

The transitions between pages scrub fluidly with the user’s scrolling and are fun to play with. Design like this makes the user want to keep scrolling and reading more about your product. And it only took one prompt!

Deliver: Polish your design into something users will love

Once we’ve gotten to a unique, standout design, the final step is to clean up the details and get it ready for production use. AI can build amazing, striking visuals, but your judgment will be key to making sure the design makes sense, flows well, and serves its practical purpose for your users.

Technique 6: Cut out elements that don’t add value

AI loves to add more, but it rarely takes away. One of the biggest signs that a design is AI-generated is that it overexplains everything or contains elements that don’t serve any practical purpose. By contrast, a design that exercises restraint immediately looks premium and tasteful.

When polishing AI designs, most of my effort goes into removing things. For example, when I was building my calorie tracking app, this was my initial design from Claude:

I’d described the app’s functionality and specifically asked for a “clean, minimalist design.” The results weren’t bad, and were certainly impressive for being fully AI-generated. However, despite my asking for minimalism, a lot in the design wasn’t adding value:

• Pink glowy effects in the background and on the progress bar

• Random colors and highlights on text

• Extra labels and empty space when displaying all the foods for a day, when the images already communicate this

• Custom buttons and text fields that look worse than built-in iOS components

I asked Claude to dial things back:

• Simplify the layout into an image-centric grid

• Get rid of gradients, glows, and unnecessary containers

• Aim for a truly minimalist aesthetic that feels Apple-native

This was the result:

To my trained eye, the result is much better. It’s opinionated and allows the visuals to speak for themselves. It uses native iOS components, and the excessive colors and gradients are gone. The text is smaller, simpler, and tighter. This is good design.

Today’s AI models would never think to make these choices on their own. Remember, AI doesn’t like to take risks, and it’s risky to strip down a design and delete code. The model needs a push from you. Look over your design and ask yourself what really needs to be there. Often, putting less on the screen communicates more, because you can hold your users’ attention without overwhelming them with clutter.

Technique 7: Remove AI tells

Read more

How I turned Claude into a self-improving PM assistant | Daniel Blum (PM, Melio)

Brought to you by:

• Optimizely—Your AI agent orchestration platform for marketing and digital teams

• Jira AI SDLC—Get your tokens’ worth with Jira

Daniel Blum is a product manager at Melio who has built a self-improving AI system that now handles 70% to 80% of his workday. In this episode, he breaks down how Claude and Cowork manage his Notion board, prepare him for the week, scan Slack and email for important context, and learn from his edits without waiting for explicit feedback. He explains how he turned the system into a 15-minute onboarding experience for other Melio employees, why the first few weeks of building with AI can feel painfully slow, and how the payoff eventually helped him accomplish a week’s worth of PM work in a single day.

Biggest takeaways:

1. The architecture matters more than the AI tool itself. Daniel believes a system becomes genuinely powerful when it can update its own core files and connect to the tools someone already uses. Once those pieces are in place, the system can improve and become more useful over time, whether it is built in Cowork, Codex, ChatGPT, or something else. He created a transformative setup using the tools Melio had already licensed, proving that the underlying architecture matters more than choosing the perfect platform.

2. Context isn’t something you set up once; it requires an ongoing system. Daniel spent months giving Claude voice memos, links, decks, and verbal brain dumps to build detailed context files for every area of his work. He then created recurring updates that refresh those files every few weeks. This keeps the gap between what Claude knows and what is actually happening inside the company as small as possible.

3. The most impressive part of Daniel’s morning brief is that it identifies what it does not know. Each day, Claude reviews his Slack, email, and notes for unfamiliar terms, projects, or goals that do not appear in its context files. It then asks Daniel targeted questions to fill those gaps. When it encountered the phrase “settlement cap,” for example, it had already read the relevant thread and understood the general idea. It only needed Daniel to confirm the meaning before saving it.

4. The value of a personalized AI system builds slowly, then becomes enormous. Daniel is candid about how frustrating the first few weeks can feel. The system does not know enough yet, its work is slightly off, and nearly everything requires a second look. But once someone pushes through the work of centralizing information and building context, the payoff can be difficult to overstate. He can now accomplish in one focused day what previously took him an entire week.

5. Self-improvement loops learn from the difference between what the AI drafted and what the person actually sent. Daniel built a weekly skill that compares Claude’s original drafts with his final versions, then uses those differences to improve future work. It resembles Alex Lieberman’s “write like me” loop, but it relies less on explicit feedback. Instead, it observes Daniel’s actual behavior and learns from the small edits he makes instinctively.

6. Feedback telemetry turns personal AI workflows into products that can improve themselves. Every one of Daniel’s skills captures moments of friction. If he says something is not working or requests a correction during a session, the system logs that signal. Once a week, his improvement loop identifies the most common problems and recommends updates. It is essentially analytics for his internal tools, and it gives him a structured way to refine the system based on how it performs in real life.

7. The Workstation plugin addresses one of the biggest barriers to adopting AI inside a company: personalization. Daniel watched several product managers struggle with Spectacular, his spec-writing gem, because it had been designed entirely around his own working style. He responded by building an onboarding flow that connects each employee’s tools, maps their colleagues, and learns their voice in about 15 minutes. Instead of starting from scratch with a generic system, every Melio employee now begins with a strong shared foundation personalized to their needs.

8. The biggest remaining limitation of today’s AI systems is persistence, not intelligence. Daniel estimates that Claude already handles 70% to 80% of his workday. What it still cannot reliably do is continue working in the cloud while his computer is off. He is already preparing for that future by teaching Claude how to recognize the “closed state” of different tasks. If a drafted Slack message is no longer saved, for example, the system can infer that it was probably sent. When truly autonomous operation becomes available, Daniel’s system will already understand what completion looks like.

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.

Daniel Blum is a product manager at Melio, a B2B payments company, and one of the most systematic thinkers I’ve had on the show when it comes to personal AI infrastructure. He’s spent the past year building a Claude- and Cowork-based productivity system that manages his Notion board, processes his Slack and email, and runs self-improvement loops every week without needing to be prompted. Beyond his own workflow, Daniel built and scaled a “Workstation” onboarding plugin that gets any Melio employee up and running with a personalized Claude setup in about 15 minutes.

What you’ll learn:

1. Why Daniel says the two rules that make any AI system powerful aren’t about the tool you pick

2. How his weekly prep automation fills an entire Notion board from scratch every Sunday, without his touching it

3. The morning brief feature that teaches Claude new internal terms on its own, so company jargon never slows it down

4. Why he describes Notion as “read-only” now, and what that says about how PM workflows are changing

5. The self-improvement loop that watches Daniel’s edits, spots recurring friction, and suggests new skills to build

6. How he uses a skill called “Improve” to filter the endless flood of AI tips without drowning in them

7. What he built to scale his personal system to every PM at Melio, and the UX lesson he learned the hard way

8. The capability gap that’s still keeping him from running 100% of his work through Claude

Brought to you by:

Optimizely—Your AI agent orchestration platform for marketing and digital teams

Jira AI SDLC—Get your tokens’ worth with Jira

In this episode, we cover:

(00:00) Daniel’s background and the PM overhead problem he needed to solve

(03:30) His AI stack at Melio

(05:00) The two rules that make any AI system genuinely powerful

(06:00) The Notion board Cowork built for him (and manages on his behalf)

(07:30) How he contextualizes Claude with voice memos, links, and recurring updates

(09:00) His weekly prep automation

(11:00) His morning brief

(15:00) How Claude flags unknown internal terms and saves them to context

(17:30) Running 70% to 80% of his workday through Cowork

(19:00) Chrome connector vs. MCPs for tools without integrations

(20:00) The real ROI question: why the early weeks feel slow, and why you push through anyway

(25:00) Scaling the system to the team with the Workstation plugin

(26:30) The self-improvement loop

(31:00) How the Improve skill separates actually useful AI tips from the hype

(32:00) The Workstation onboarding flow, and the UX lesson from distributing “Spectacular”

(38:00) The 20% Claude still can’t do, and what changes when it can

(41:00) What Daniel spends his reclaimed time on

(42:30) Claude rage

Tools referenced:

• Claude: https://claude.ai

• Notion: https://notion.so

Other references:

• PM Co-Pilot Repo: https://github.com/IamBlum/pm-copilot

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

• How the founder of Morning Brew built a Claude content machine that never runs out of ideas and never sounds like slop | Alex Lieberman: https://www.lennysnewsletter.com/p/how-the-founder-of-morning-brew-built?utm_source=publication-search

Where to find Daniel Blum:

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

Website: https://www.imdanielblum.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.

Tara Seshan leads product for ChatGPT Work at OpenAI (alongside previous podcast guest Andrew Ambrosino, who’s her engineering manager). Before OpenAI, Tara spent over six years at Stripe, where she joined as one of the first five product managers. She went on to lead product for Watershed, which Time magazine named one of the best inventions of 2022, and she is also a founder and Thiel Fellow. Most personally meaningful to me: Tara is one of the three inaugural Lenny’s Newsletter Fellows, a program I ran a couple of years ago to spotlight the most exciting up-and-coming product leaders.

In our in-depth conversation, we discuss:

1. The shift from “rowing” to “steering,” and why human judgment and ambition will become differentiators as AI takes on execution

2. How OpenAI thinks about building for model capabilities two to three months out

3. OpenAI’s best internal memes, such as “Is this maximally accelerated?” and “Are you mainlining it yet?”

4. Why ambition is the new bottleneck for companies, and why elevating others’ ambitions is now the key part of the PM job

5. Writing as thinking vs. writing as reporting

Brought to you by:

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

Mercury—Radically different banking, now with Command

Where to find Tara Seshan:

• X: https://x.com/tarstarr

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

• Newsletter: https://substack.com/@taraseshan

Referenced:

• Codex: https://chatgpt.com/codex

• ChatGPT Work: https://openai.com/chatgpt-work

• Stripe: https://stripe.com

• Watershed: https://watershed.com

• Thiel Fellowship: https://thielfellowship.org

• Meet your Lenny’s Newsletter Fellows: https://www.lennysnewsletter.com/p/meet-your-lennys-newsletter-fellows

• The rituals of great teams | Shishir Mehrotra of Coda, YouTube, Microsoft: https://www.lennysnewsletter.com/p/the-rituals-of-great-teams-shishir

• The nature of product | Marty Cagan, Silicon Valley Product Group: https://www.lennysnewsletter.com/p/the-nature-of-product-marty-cagan

• Product management theater | Marty Cagan (Silicon Valley Product Group): https://www.lennysnewsletter.com/p/product-management-theater-marty

• Patrick Collison’s examples of fast projects: https://patrickcollison.com/fast

• Inside ChatGPT: The fastest-growing product in history | Nick Turley (Head of ChatGPT at OpenAI): https://www.lennysnewsletter.com/p/inside-chatgpt-nick-turley

• Andrew Ambrosino on X: https://x.com/ajambrosino

• Tyler Cowen’s website: https://tylercowen.com

• OpenAI’s CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai

• “Chop wood, carry water” quote: https://buddhism.stackexchange.com/questions/15921/what-is-the-meaning-of-the-zen-quote-before-enlightenment-chop-wood-carry-wat

• 4 questions Shreyas Doshi wishes he’d asked himself sooner | Former PM leader at Stripe, Twitter, Google: https://www.lennysnewsletter.com/p/shreyas-doshi-live

• Alan Kay: https://en.wikipedia.org/wiki/Alan_Kay

• Brie Wolfson on X: https://x.com/zebriez

• The playbook for building high-talent-density teams | Adam Ward, Head of Talent at Cursor: https://www.lennysnewsletter.com/p/the-playbook-for-building-high-talent

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

• Sutter Hill Ventures: https://shv.com

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

• Mike Speiser on LinkedIn: https://www.linkedin.com/in/mikespeiser

• Footnotes and Tangents: https://footnotesandtangents.substack.com

• The Power Broker Book Club: https://www.robertcaro.org/copy-of-six-books-six-ny-times-book

• The Odyssey: https://www.imdb.com/title/tt33764258

• Rashomon: https://www.imdb.com/title/tt0042876

• Akira Kurosawa: https://en.wikipedia.org/wiki/Akira_Kurosawa

• Kevin Kwok on LinkedIn: https://www.linkedin.com/in/kevinakwok

• The Work You Do, the Person You Are: https://www.newyorker.com/magazine/2017/06/05/toni-morrison-the-work-you-do-the-person-you-are

• Ari Weinstein on X: https://x.com/AriX

• Sky: https://sky.app

• Dylan Field live at Config: Intuition, simplicity, and the future of design: https://www.lennysnewsletter.com/p/dylan-field-live-at-config

Recommended books:

• Barbarian Days: A Surfing Life: https://www.amazon.com/dp/0143109391

• Anna Karenina: https://www.amazon.com/Anna-Karenina-LEO-TOLSTOY/dp/8175993421

• The Power Broker: https://www.amazon.com/dp/0394720245

• War and Peace: https://www.amazon.com/War-Peace-Leo-Tolstoy/dp/8175992832

• Wolf Hall: https://www.amazon.com/dp/0312429983

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

Read more

Read more

Subscribe now

P.S. Get a full free year of Cursor, Notion, Replit, Lovable, Wispr Flow, Linear, ElevenLabs, Factory, PostHog, Granola, Brain.fm, Waking Up, and more, by becoming an Insider subscriber (while supplies last). Learn more.

Building on last week’s very exciting launch of Lenny’s Jobs (that’s a lot of likes!), I’m thrilled to bring you a companion post that will help you clarify what job to look for in the first place.

Cliff Maxwell teamed up with the legend Bobby Moesta (co-creator of the Jobs to Be Done framework and a two-time guest on Lenny’s Podcast) to build Vocation, a career navigation platform that helps professionals figure out their next step. Cliff’s own career has spanned building CPUs, education research, product management, venture capital, and serving as Chief of Staff to the late Clayton Christensen. Based on insights from over 1,000 interviews with professionals changing jobs and hundreds of one-on-one coaching clients, this hands-on guide will help you take a step back and connect with what you truly want from your work—and life.

Let’s get into it.

It’s a disorienting time to work in tech right now. For some, the rise of AI is a thrilling adventure, but for others, it’s a source of existential dread. No matter how you’re feeling, change is coming. The World Economic Forum reports that by 2030, 39% of today’s skills will change or become obsolete, and more than 90 million jobs will be displaced even as 170 million new jobs will be created.

Every generation of workers in tech has lived through recessions, layoffs, and technological change, but AI’s impact on work will be fundamentally different. It won’t just require new skills. It is redrawing the boundaries between roles and changing the day-to-day responsibilities inside them. Work may feel unstable now, but this will likely be the most stable it feels for a long time.

With this much uncertainty on the horizon, it is imperative that you develop the ability to navigate change. As your responsibilities shift and new opportunities emerge, the durable career advantage will come from knowing what kinds of problems, people, and environments bring out your best work—and having a repeatable process for identifying your next step even when the ultimate destination is unclear.

For the past year I have been building career navigation infrastructure with Bobby Moesta, one of the creators of the Jobs to Be Done framework and a two-time guest on Lenny’s Podcast. Bobby brings a product-oriented lens to career change: you are the customer, and the right next career move is the product you are looking to purchase.

Through more than 1,000 in-depth interviews with professionals changing jobs (some by choice, some by necessity)—and hundreds more people we have coached since—we identified what helps people make a successful job transition, and built a process to support them.

This is not a laundry list of LinkedIn hacks, resume tips, or ideas for how to automate your recruiting efforts. This is a step-by-step formula for designing a system that allows you to understand yourself, your context, and your best next steps when you’re in a state of change.

In this post, I’ll help you answer six fundamental questions that will instill confidence and focus anytime you are navigating a transition at work. Each question comes with exercises and tools to help you build understanding.

1. What kind of career progress do I want?

2. What career quest am I on?

3. Which work brings out my best?

4. What possible moves should I explore?

5. Which tradeoffs am I willing to make?

6. Can I get what I need without leaving?

Once you know your answers, you’ll be equipped to find your bearings now and every time the labor market throws you a curveball.

1. What kind of career progress do I want?

About a year ago, we coached an individual who, after spending several years at a startup that was running out of funding, assumed his next step was another early role in a venture-backed company.

After evaluating his core motivations and reflecting on his growing desire for impact, he found an opportunity to lead the entrepreneurship center at a local university, a job that he previously would have avoided and that none of his peers would have recommended. When he met with the administration, they effectively hired him on the spot, telling him, “You are exactly who we were looking for.” He’s now crushing it in his new role.

This story demonstrates the difference between career “progress” and “progression,” a distinction that is critical to understand when you are at a transition point in your career.

Progression is the philosophy that underpins most people’s mental model of careers. You start as an IC, get promoted, and up and up you go on the imaginary career ladder. This mindset will become increasingly less relevant as AI continues its march through knowledge work. Job boundaries and job titles will continue to blur, new job titles will emerge, and companies will struggle to neatly map job descriptions and career pathways into their org chart or KPIs.

Progress, on the other hand, is contextual and dynamic. It is ultimately a question of what your professional Job to Be Done is, and allows you to chart your own course rather than follow a single, narrow path of progression. Depending on your context, progress may be about autonomy and flexibility, preparing for the future, respect and recognition, or simply collecting a paycheck during a time of instability. It can look like a more traditional step up, but it may also look like a lateral move or even like a step back from the perspective of others—while it solves the most important problem in your life and gives you space and time to move forward later on.

A focus on progress is an active commitment to putting yourself in environments that will bring out your best. It helps you stand out amid the army of applicants all reaching for the same rung on the career ladder. Progress gives you the freedom to see your career in chapters and create space to be emergent rather than assuming you have to write one linear story all at once. To do this, you have to define the progress you’re seeking right now.

A 10-minute progress exercise

Take a blank page (or, if you’d like a live template, we made one for you here) and complete the sentence: I want to leave or change my current situation because…

Write down every friction or negative force you’re experiencing right now, or that you last experienced at work. We call these pushes. Do not polish the language. “My manager changes priorities every three days, and it’s getting exhausting” is more useful than “I want better leadership.” Unpack each one of these, and ask yourself “why” until you get to the root cause.

Then complete this sentence: I want my next chapter to give me…

List the desired outcomes you wish were true right now. We call these pulls. Again, make them concrete. “Control over when I do focused work” is more useful than “flexibility.”

Now circle the two or three pushes and two or three pulls that carry the most emotional weight. When you see the forces together, you can start to assemble what progress means for you, not for anyone else. For example, two people may both feel overburdened by new management, but one wants to join a more supportive team while the other is seeking more autonomy to work alone. Your unique combination of pushes and pulls forms the basis for your career quest, or the core motivation guiding your desire for change.

Below is an example of how this might look for someone seeking a new role:

2. What career quest am I on?

When you are seriously considering a job change, or when change is forced upon you, it’s easy to label and hold onto your most immediate feeling, whether that’s burnout, the excitement of something new, frustration, or disrespect. But underlying these emotions, less-obvious and subconscious factors are working together in a unique combination to animate your core motivation for change. This is your career quest.

Your quest for progress should inform every aspect of your job search, but most people skip this diagnosis and go directly to job boards, answer the recruiter’s call, or ask their network about job openings. They are halfway out the door before deciding what they even need this move to accomplish.

For example, I once coached an individual who was frustrated by the lack of leadership opportunities in his product role at a large tech company. When he was recruited to lead and grow the analytics function at a nearby startup, he felt excited and flattered to finally have the opportunity to grow a team.

But as we explored other pushes and pulls acting on him, a more nuanced story emerged. Despite the lack of leadership opportunities, he fundamentally liked his job. He was also in the middle of renovating his home, he wanted to be able to spend more time with his two young kids, and he wasn’t all that excited about leaving product for analytics.

The new role solved his most immediate and apparent problem, but it wouldn’t offer him the progress he needed both personally and professionally.

In our work with professionals looking to make a change, we have identified four common quests for progress that emerge again and again.

These quests are not mutually exclusive; you can be on multiple quests simultaneously, though one usually dominates. Even if you have been laid off and the immediate goal is “I need a job,” reflecting on these quests can help you decide which opportunities deserve your attention, and which you can avoid.

Quest 1: Get out

You have hit a wall you cannot fix from where you stand. Something about the manager, culture, leadership, or environment has made it hard to succeed or remain healthy. The dominant feeling is escape, and the desire to get out is usually quick, not something you stew on for months at a time.

Common pushes:

• Feeling disrespected or not trusted to do great work

• Working for a manager who is wearing you down

• Losing trust or respect in leadership and/or your peers

• Not seeing a place to grow in your current organization

Common pulls:

• The pulls are often vague: a fresh start, a healthier environment, or simply “anywhere but here.”

• When someone is looking to get out, they often accept the first available escape but run the risk of landing in the same conditions at a different company.

Quest 2: Regain control

Work has swallowed too much of your life, even if you still enjoy the work itself.

This quest often follows a change outside work: a new child, caregiving responsibilities, a health issue, or a shift in what you want your life to look like. It can also emerge when a previously manageable role becomes chaotic, or when the way your manager or company leadership is running things no longer works for you.

Common pushes:

• Feeling worn down by management

• Work is dominating your life and you’re starting to make sacrifices

• Unpredictable demands, or feeling challenged beyond your ability

• Little control over your schedule or priorities

• A workload that no longer feels sustainable

Common pulls:

• More time to spend with others

• A job location that fits better into your personal life

• An employer who properly values your experience and credentials

• A scope of responsibility you can sustain

Quest 3: Regain alignment

Your job has drifted away from what you do best, what you value, or what you originally agreed to do. You may still be performing well. In some cases, that is part of the problem: you have become useful for work that does not fit you, and you don’t feel respected.

Common pushes:

• Being underused or miscast

• Watching your responsibilities move away from your strengths

• A growing gap between your values and the company’s

• Feeling unchallenged or bored

• Not seeing a clear place to grow inside the organization

Common pulls:

• Work that uses your full capabilities

• Greater alignment with your values

• Recognition that matches your contribution

• A role built around the things you do unusually well

Quest 4: Take the next step

You have completed something meaningful, mastered the role, or reached a point where the work no longer stretches you. Alternatively, you’ve hit a personal milestone (baby, move, etc.) and want a change. You are not necessarily unhappy. You are simply ready to be challenged and get the support you need to grow.

Common pushes:

• Hitting a personal or professional milestone

• Feeling unchallenged or bored

• Not seeing a clear place to grow

• Needing to provide more support for family or loved ones

Common pulls:

• Feeling like a job is a clear step forward

• Joining a tight-knit team you can count on

• Developing new skills as part of a stepping stone to something else

A quick quest diagnostic

Finish this sentence: More than anything, I need my next move to…

• Get me away from a situation I can no longer tolerate.

• Give me control over my time, workload, or life again.

• Let me do work that fits my strengths and values.

• Give me a new challenge or chapter.

How you finish the sentence will not capture every part of your situation, but it will usually reveal which quest you may be on—and also help you label what you don’t need right now.

3. Which work brings out my best?

Most professionals know their skills better than they know the conditions that allow them to do their best work. You take on responsibilities, become known for certain outcomes, develop valuable skills, but are often too busy doing the job to understand the circumstances you need to be successful and feel fulfilled.

Then a shiny title or compensation increase appears, and you accept a role that actually makes you miserable—an outcome common to ICs-turned-managers who realize too late how much they hate management.

You need to figure out what you need before you switch jobs (or are compelled to switch), not figure it out by switching. This is what building your energy profile is all about: it provides a blueprint for evaluating new opportunities, along with a starting point for questions you can ask a hiring manager or peer as you explore open roles.

Build your energy profile

To get started, grab some Post-it notes or spin up a Miro board, and make a simple timeline of roles you’ve held and the title, company, and duration for each role (including your current role if employed). If you want a pre-built template, we’ve got one here. The steps below will show you how to fill this out, and I’ve included an example of the beginnings of my own energy profile:

Step 1: Audit your calendar

Pull up the past month of your work calendar and list what you actually did, hour by hour.

Do not use the language of your job description. Describe the activity:

• Collaborative planning meeting

• Individual research

• Customer call

• Writing a strategy document

To help you through this, you can integrate your calendar with an AI agent of your choice (via a simple connection or MCP) and use this simple prompt:

“For the past month, make a list of core meetings and time blocks on my calendar and ask me 1-2 questions about each to uncover what I actually did during those blocks of time. For example, if you see a ‘team meeting,’ ask me briefly what my role was and how I contributed. Then produce a list of the 8-10 core activities in my work life.”

Step 2: List your energizers

Which tasks energized you?

Look for work where you lost track of time, volunteered extra effort, or kept thinking after the meeting ended. Include the tasks that left you with more energy afterward.

Step 3: List your drainers

Which tasks did you avoid, postpone, rush through, or perform adequately just to get them over with?

Include work you are good at but dislike. Competence and energy are not the same thing. Many careers get built around things a person does well but does not want to keep doing.

Step 4: Repeat the exercise across previous jobs

Review your last job, then the one before that. You can’t go hour by hour, but for each role there were likely six to eight core responsibilities or tasks you engaged in on a regular basis.

As you complete this, you will begin to see a history of your career written in the language of energy instead of titles or responsibilities.

Step 5: Identify themes among your energizers and drainers

Revisit the activities you collected in the last step and see if you can identify broader themes or clusters that emerge from tasks that energize and drain you. As you do this, clarity is key. “Working with others” is not a clear theme. Neither is “heads-down work.” Working with whom? On what? Under which conditions? Similarly, “office politics” is not a clear theme for what drains you. What relationship dynamics are problematic? What decision-making processes don’t work for you?

To help you do this, you can ask questions like:

• What is this more like?

• What is this less like?

• Which conditions need to be present?

Below is an example of unpacking a theme that emerged when I built my own energy profile: “environments that allow me to become an expert at the intersection of people and technology.”

As your themes begin to emerge, you’ll realize where you shine, where you check out, and what you want out of your next role. This is the foundational work necessary to begin prototyping potential opportunities.

4. What possible moves should I explore?

Read more

Grok Bot + Grok 4.6—what’s great (and what’s still hype)

Brought to you by:

Bolt.new—Turn your idea into a real product

Jira AI SDLC—Get your tokens’ worth with Jira

In this solo episode, Claire tests Grok Bot, Cursor Origin, and Grok 4.6 to figure out what’s genuinely useful and what’s still mostly hype. She shares the Grok Bot feature that immediately won her over, why she isn’t ready to replace GitHub with Origin, and how Grok 4.6 performed against GPT-5.6, Claude Sonnet 5, and Opus 5 in her own blind evaluations.

Biggest takeaways:

1. Grok Bot’s multi-account connectors solve a problem every other agent platform seems to ignore. Most platforms assume each person has one Gmail account and one Slack workspace. Claire has four email addresses and seven Slack workspaces. Grok Bot lets her connect all of them to a single bot, which made it genuinely useful from day one in a way that Codex and Claude still have not matched.

2. Grok Bot’s simplicity is both its greatest strength and its biggest limitation. Setup is fast, the iMessage-style interface is clean, and the built-in plugins actually work. But people who enjoy customizing their agents, choosing models, shaping personalities, and tinkering with every detail may find it almost too polished. Claire loves her OpenClaw agents partly because they are chaotic and high-maintenance. So far, Grok Bot has not given her much to wrestle with.

3. Cursor Origin is a compelling vision that is not quite ready for prime time. An agent-native alternative to GitHub makes a lot of sense, especially one where Bugbot, Cursor, and the entire pull request workflow are designed around how coding agents actually work. Today, though, Origin still feels like a more attractive version of GitHub with fewer features. Teams that rely heavily on GitHub Actions, code owners, and existing automations will need a much stronger reason to migrate.

4. Grok 4.6 is a genuine frontier-model competitor. That conclusion did not come from someone else’s leaderboard. Claire runs her evaluations blind, grades the outputs herself, and gives her own judgment 70% of the final weight. Grok 4.6 finished alongside GPT-5.6 Sol at the top of the Claire Index, ahead of both Sonnet 5 and Opus 5.

5. For sharp, enjoyable agent conversations, Sonnet 5 is still the model to beat. When Claire wants an OpenClaw agent that is concise, responsive, and fun to talk with, Sonnet 5 continues to win. It has a conversational rhythm that feels more like working with a strong collaborator than issuing commands to a tool. Grok 4.6 does not yet compete in that category.

6. Cursor and xAI are assembling a surprisingly coherent enterprise stack. Grok Bot serves knowledge workers, Origin handles code hosting, Grok 4.6 provides a capable default model, and the Cursor IDE already sits at the center of many developers’ workflows. None of the pieces is perfect on its own, but together they are beginning to look like a credible enterprise platform. Large companies often prefer one vendor that can own the entire experience, and Cursor is increasingly positioned to become that vendor.

I spent $20,000 on Devin in a month. Here’s what I learned | Ryan Carson (solo founder)

Brought to you by:

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

• Jira AI SDLC—Get your tokens’ worth with Jira

Ryan Carson is a five-time founder and the solo founder of Untangle, a B2B SaaS platform for family law firms. In this episode, he breaks down how he manages up to 15 AI agents at once, ships as many as 40 pull requests a day, and uses Devin, Codex, and Claude Code to handle everything from engineering and QA to customer success and investor updates. He also shares why more AI output doesn’t necessarily lead to a better product, how a handwritten priority list keeps his agents focused, and why talking to one real customer changed the direction of his entire company.

Biggest takeaways:

1. The most important skill for a solo founder may be managing agents, not writing code. Ryan runs 10 to 15 Devin threads at once, organized into P0, P1, P2, and Bugs folders. He treats each thread the way a good manager treats a direct report: give it a clear goal, set the right priority, and avoid unnecessary hand-holding. That organizational discipline is what separates founders who gain real leverage from AI from those who simply accumulate open tabs.

2. A piece of paper can still be the best tool for protecting attention. Despite working across eight screens, Ryan keeps a handwritten list of his weekly priorities beside him. The simplicity is intentional. When agents are constantly generating updates, questions, and decisions, that physical list keeps him anchored to the three things that matter most before he gets pulled into whatever Devin surfaced overnight.

3. Ryan built an AI playbook that functions like a customer success team. His Watchdog workflow reviews every law firm account, pulls recent activity and errors from Sentry and internal logs, identifies the three most important problems, and checks whether a recent pull request has already fixed each one. Everything appears in a single Devin thread. Whenever Ryan feels the familiar anxiety of not knowing what is happening across his customer base, he runs Watchdog and quickly gets back up to speed.

4. Producing more with AI does not automatically lead to a better product. Both Ryan and Claire are skeptical of letting agents work without constraints overnight. Frontier models can generate an enormous amount of output, but they do not know what customers actually need. Product ideas, priorities, and market judgment still have to come from a human who talks to users. Ryan found product-market fit for Untangle not by shipping more code but by landing one meeting with family law attorney Renee Bauer and listening carefully.

5. Cloud agents may handle most engineering work, but Codex still shines when the frontend requires close attention. Ryan uses Devin for cloud-based work across bugs, pull requests, investor updates, and customer triage. He turns to Codex when a feature is visually complex and he needs to stay close to the implementation. The distinction is not about loyalty to a particular tool. It comes down to latency, browser access, and the ability to inspect and refine the interface in real time.

6. Coding agents can operate far beyond the codebase. Claire uses Devin for deal desk, custom quotes, and customer triage. Ryan uses it to prepare investor updates through a reusable skill. Both approach these agents with a broader question: What could someone accomplish if they understood the entire codebase and could write software to solve almost any problem? That framing reveals business uses that would never emerge from treating an agent as a simple coding assistant.

7. The best engineering interview may be a screen recording of the actual work. Ryan asks candidates to record themselves building a feature inside an existing application, with the entire screen visible and no introductory meeting required. In the next stage, he gives them access to Devin and reviews the replay of how they worked with it. This shows him how candidates think, build, and manage an agent under realistic conditions, which he finds far more revealing than a typical behavioral interview.

8. The strongest design workflow pairs Claude’s system thinking with Codex’s implementation ability. Claire used Claude Design to break a Figma file into reusable design tokens and a design.md specification. She then gave that specification to Codex, which created shared components across a monorepo. Claude is particularly good at translating a visual system into a portable spec. Codex is better at turning that spec into working, interconnected code. The workflow succeeds because each model is assigned the part it does best.

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.

Ryan Carson is a five-time founder and the current solo founder of Untangle, a B2B SaaS platform for family law firms. Before Untangle, he co-founded Treehouse, an online coding education platform, and has spent the better part of two decades building and leading tech companies. He’s active on X, where he shares his solo founder journey in real time, including what he actually spends on AI tools each month.

What you’ll learn:

1. Why Ryan manages 15 concurrent Devin agents with a folder system and a piece of paper, not a dashboard

2. The Watchdog playbook: what he built to replace a customer success team across every law firm account

3. How his LAN PR skill closes the loop on 40 daily PRs without a QA team reviewing a single one

4. Why he moved off local agents almost entirely, and the one situation where he still reaches for Codex

5. The design workflow we’re both using: Claude Design into a Markdown spec, then Codex to build the real thing

6. What he found when he got away from his computer and met a real customer, and why it changed his entire product direction

7. How he’s hiring his first engineer without a single phone screen or interview

8. Why we both think more AI output is actually the wrong goal, and what to optimize for instead

Brought to you by:

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

Jira AI SDLC—Get your tokens’ worth with Jira

In this episode, we cover:

(00:00) Introduction to Ryan Carson

(02:55) Ryan’s update: Untangle, the divorce PMF pivot, and B2B growth

(07:35) Ryan’s current Devin stack: folders, P0 threads, and the paper list

(16:29) Watchdog playbook: account monitoring across every firm

(18:09) Managing agent decision fatigue: Ryan’s method vs. Claire’s

(20:32) Producing more output does not make a better product

(22:47) Using cloud agents for ops beyond just code

(25:14) When to use Codex vs. Devin vs. Claude Code

(27:15) Merge Mommy recap

(28:15) LAN PR skill: review loops, video walkthrough, and auto-merge

(30:20) Slack vs. Devin threads for async team communication

(35:58) Claude Design plus Codex for building a technical design system

(39:00) EA tools: Polly the OpenClaw vs. Claude Code on a Mac Mini

(42:09) Quick recap and final thoughts

Tools referenced:

• Devin (Cognition): https://www.cognition.ai/

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

• Claude Code/Claude Design (Anthropic): https://www.anthropic.com/claude

• OpenClaw (Claude-based desktop client): https://openclaw.ai

• Cursor: https://www.cursor.com/

• BugBot (Devin’s built-in PR review): https://cursor.com/bugbot

• Sentry (error monitoring referenced in Watchdog): https://sentry.io/

• Ugmonk (analog to-do system): https://ugmonk.com/

Other references:

• Devin playbooks/skills documentation: https://docs.cognition.ai/

• Jack Dorsey/Buzz (async-first communication referenced): https://buzz.new/

• Merge Mommy (Claire’s Eve agent for PR risk scoring, deployed on Vercel): https://www.lennysnewsletter.com/p/build-an-ai-code-review-bot-in-30

Where to find Ryan Carson:

X: https://x.com/ryancarson

Untangle: https://untangle.us

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.

Jen Abel is the co-founder of JJellyfish and GM of enterprise sales at State Affairs. She is widely regarded as one of the sharpest practitioners in enterprise sales, and for that reason, this is her third visit to the podcast. In our first conversation we went deep on founder-led sales; in our second we mapped the $1M–$10M playbook. This time we do something I’ve never seen on another podcast: walk step by step through the full enterprise sales cycle. Most people think it’s five steps. Jen shows it’s closer to 15.

In our in-depth conversation, we discuss:

1. Why the standard five-stage CRM pipeline is a forecasting tool, not a sales process, and what the real 15-step cycle looks like

2. The “pincer model” for landing the first meeting at the executive and N-minus-one level simultaneously

3. How to craft a two-to-three-sentence message around giving them “alpha”

4. How to run an intro call that extracts maximum intelligence before you ever show a demo

5. The two-to-three-day pilot structure, how to define success jointly, and when to charge for a longer pilot versus giving it away

6. Navigating pricing, procurement, redline negotiations, and the final signature without losing momentum

Brought to you by:

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

Mercury—Radically different banking, now with Command

Where to find Jen Abel:

• X: https://x.com/jjen_abel

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

• Website: https://www.jjellyfish.com

Referenced:

• State Affairs: https://stateaffairs.com

• The ultimate guide to founder-led sales | Jen Abel (co-founder of JJELLYFISH): https://www.lennysnewsletter.com/p/master-founder-led-sales-jen-abel

• “Sell the alpha, not the feature”: The enterprise sales playbook for $1M to $10M ARR | Jen Abel: https://www.lennysnewsletter.com/p/the-enterprise-sales-playbook-1m-to-10m-arr

• SpaceX: https://www.spacex.com

• Lemlist: https://www.lemlist.com

• Palantir: https://www.palantir.com

• Jason Lemkin on X: https://x.com/jasonlk

• We replaced our sales team with 20 AI agents—here’s what happened | Jason Lemkin (SaaStr): https://www.lennysnewsletter.com/p/we-replaced-our-sales-team-with-20-ai-agents

• Building a world-class sales org | Jason Lemkin (SaaStr): https://www.lennysnewsletter.com/p/building-a-world-class-sales-org

• Careers at State Affairs: https://stateaffairs.com/careers

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