AI writes most of my code now, and that created a new problem: a PR queue I couldn’t keep up with. In this episode, I walk through how I built Merge Mommy, a Vercel Eve agent that reads every PR after checks pass, scores it across six risk dimensions, auto-approves the low-risk ones, and pings me in Slack for anything that needs a human. I built the whole thing in one Codex session, it’s SOC 2 compatible, and it’s already cleared my backlog.

What you’ll learn:

1. Why AI-generated PRs create a review bottleneck and why the answer isn’t reviewing all of them

2. How Intercom 5x’d PR approval speed and reduced revert rates by putting AI in the review loop

3. Why Vercel Eve is the simplest framework I’ve found for deploying AI agents in Slack and GitHub

4. How I built a full PR review agent in Codex with one prompt and a few steering turns

5. The six components I use to score PR risk (blast radius, reversibility, data security, ops impact, verification gap, and change surface)

6. How I used Chrome browser use to handle Slack bot and GitHub app configuration so I never had to click through setup screens manually

7. Why auto-approved PRs can be SOC 2 compliant as long as the process is auditable, queryable, and in your risk policy

8. How to set up Slack escalation so low-risk PRs become a two-click merge with no manual review

Brought to you by:

WorkOS—Make your app Enterprise Ready today

In this episode, we cover:

(00:00) The PR review backlog problem nobody’s talking about

(02:35) Why you don’t have to review every AI-generated PR

(05:14) How Intercom built AI-approved PRs (and proved they’re safer)

(06:10) How the Eve framework works (directory, skills, channels, connectors)

(09:16) The Codex prompt I used to build the entire bot

(11:36) What the agent actually does: read, score, approve, or escalate

(13:07) Setting up your Eve agent

(15:47) The six-component risk scoring model

(17:23) Merge Mommy in action: three live PR examples

(21:10) Recap and how to build your own version

Tools referenced:

• Vercel Eve: https://vercel.com/eve

• Vercel AI SDK: https://sdk.vercel.ai/

• Vercel Chat SDK: https://chat-sdk.dev/

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

Other references:

• AI is approving our pull requests: Here’s how we made it safe: https://www.intercom.com/blog/ai-is-approving-our-pull-requests-heres-how-we-made-it-safe/

• Review is the bottleneck now: How we let AI approve pull requests (safely): https://rewind.com/blog/ai-approve-pull-requests-safely/

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, Factory, Wispr Flow, Linear, ElevenLabs, PostHog, Brain.fm, Waking Up, and more, by becoming an Insider subscriber (while supplies last). Learn more.

Molly Graham’s first guest post, The Waterline Model, quickly rose to be my 7th most popular post of all time—extra impressive for not being about AI. I suspect this post will rise even faster.

Below, Molly introduces the concept of emergence as an alternative to conventional career advice. What she describes is exactly what my career path has looked like, and it’s a pattern I’ve seen across most of the successful people I know. If you’re worried about not having a plan for your career, or if your plan isn’t going according to plan, you’ll feel better after reading this post.

If you’re not familiar with Molly, she’s . . . incredible. She was an early employee at Google and Facebook, COO at Quip (working closely with Bret Taylor), helped build the Chan Zuckerberg Initiative (working closely with Priscilla Chan and Mark Zuckerberg), co-founder of Glue Club, co-founder of The Guild, and most recently, host of TED’s WorkLife podcast (taking over for Adam Grant). At 22, she was leading 75-day wilderness expeditions in Patagonia and Alaska for the National Outdoor Leadership School. Tl;dr, she’s seen and done a lot.

Let’s get into it.

Sheryl Sandberg once told me that everyone should have a two-year plan and a 20-year plan. The first, I could figure out; 20 always looked fuzzy.

I’ve never had a career plan. And I’ve always felt a little broken because of it.

We’re taught that careers should look like a set of stairs—you decide where you want to end up and march upward. When you look at many successful people, their careers can often appear like that from the outside.

My resume includes jobs at some big-name companies, C-level titles, and fun-looking solo ventures, but I have always wondered if it suffered from lack of design or clear destination in mind. At times, it has felt more like a game of chutes and ladders than any kind of stairs.

But recently, a conversation with Mark Rober completely changed how I think about my career. Don’t know Mark? Ask any 8-year-old who he is. He is one of the most popular creators on YouTube—76 million followers, 16 billion views—and he’s built that audience by making science genuinely fun and accessible. He’s put out one video a month on YouTube for 15 years, with remarkable consistency.

I asked him how he’s stayed grounded across a career on a platform with its own relentless pressures. Mark said he’s never had a long-term plan. Fifteen years of consistency, and none of it was by design. Instead, he has a clear set of principles that guide him, and he focuses only on the next step in front of him. And then, according to Mark, “good shit’s gonna happen.”

Then Mark introduced me to the scientific concept of emergence, a process that occurs in nature when multiple parts come together to form a complex whole without a clear plan or intention. I had a strange feeling when I heard him talk about how he trusts an emergent philosophy to guide his own career. He was describing something I’d already been doing for years, without ever having a name for it.

Emergence tells us there’s a different way to build a career: not by optimizing toward a destination but by getting really clear on your principles, learning to follow your energy, and trusting that the path will reveal itself. In some ways, an emergent career is harder to manage than having a plan and staying loyal to it. There are moments of real uncertainty, and stretches where you can’t see where you’re going. But all of that uncertainty (and all of that faith) is exactly what creates room for the opportunities you couldn’t have seen coming—which, in my experience, are the ones that matter most to a career that works for you.

Emergence can look like magic

A murmuration of starlings—thousands of birds moving together in those wild, shape-shifting clouds you see in videos—has no choreographer. Each bird follows three simple rules: stay close to your neighbors, don’t collide, and move in the same general direction. That’s it. From those three rules, something breathtaking appears. An outcome no single bird designed or planned.

Same with a beehive. No bee has the blueprint for the colony. Each bee just follows its local rules—tend the larvae, forage for pollen, respond to signals. But the colony as a whole can regulate its own temperature, navigate to food sources miles away, and survive for decades. A single ant isn’t intelligent in any impressive way. Yet an ant colony can find food, build bridges, regulate temperature, wage war, and adapt to changes in its environment. That capability doesn’t exist in any one of them; it only comes together in the colony as a whole.

That’s the strange magic of emergence: the whole becomes capable of things that are completely invisible at the level of the parts. Emergence is what happens when simple, consistent principles—applied locally, over time—produce something complex and beautiful that couldn’t have been predicted or designed in advance.

In an emergent career, you are both the individual bird and the whole murmuration—the one following the rules and the one becoming something those rules couldn’t have predicted. Every decision you make, every person you work with, every risk you take or don’t take—those are your local rules in action. You can’t see the pattern they’re creating from inside it. You can only see the next step. Yet over time, something emerges. A body of work, a reputation. Something that looks, in retrospect, like it was designed, even though no single decision along the way felt like a plan.

My whole career has been example after example of the power of emergence, but I’ll give you the best one, which is also the most recent. I stepped off the traditional career path four years ago with no plan. I simply started saying no to the jobs I was known for—COO roles at bigger and more impressive companies—because they no longer brought me joy. It was scary, and it felt necessary. I explored a bunch of different paths and worked hard to learn to listen to the parts of myself other than my ego. It was hard. I felt exhausted and confused a lot, particularly when someone offered me some new fancy job I knew I’d be great at. My ego really, really wanted to say yes. I kept saying no.

I built the leadership communities Glue Club and The Guild. I wrote on LinkedIn and Substack. Not because I had a vision for what any of this could become or because any of it made sense to anyone else, but because they made me happy every day. I was learning, working with people I loved, and having the kind of impact that mattered to me. All of it brought me energy, and I was committed to focusing on that compass. That said, more than any other time in my career, I felt like I was walking through a dark tunnel, with no sense of where I was going. Every magical moment in my career has been entangled with periods of confusion, discomfort, and darkness.

Then TED asked me to give a talk, and I said yes because I was excited about what I would learn and how I could connect with people through the talk. It felt a little random at the time but turned out to matter more than I expected. Someone on my team said, “I think this might be the beginning of something.” I said, “I just want the talk to be good.”

A year after my talk came out, TED came back and asked me to take over WorkLife, the podcast that TED and Adam Grant had spent seven years building. When they made that offer, it was like someone had turned the lights on in the tunnel. They saw me in a way that I could not see myself. That offer transformed my weird, disconnected pile of projects into a whole that felt like magic.

When I’ve talked to successful people about their own career path, almost all of them have a story that sounds like emergence.

As an example, I asked my friend Claire Hughes Johnson, the former COO of Stripe, about how she’s made career decisions and how she thinks about her life’s work. On paper, Claire’s path—politics, to senior operations and product leadership at Google, to COO of Stripe—looks very methodical and designed. But in conversation, she makes it clear that she never had a plan and was just focused on the right next step:

“Am I learning? Am I motivated? Am I in the right job? Is this the right impact? Is this what I want to be doing? . . . I think some people look [at my career] and they’re like, oh, you charted this course of success.

No, I was kind of unsatisfied, and just kept trying to find the thing . . . I don’t feel like I will figure out what my life’s work is. It will end up being the work that I did.”

It’s pretty weird to hear someone as successful as Claire say, even at this point in her career, that she doesn’t know what her life’s work is. But it honestly made me feel less crazy. As Claire explains it, she has two principles—she wants to learn and have a significant impact—and focuses on making the best next decision based on those.

To embrace emergence in your career, you have to do exactly what Claire does. You have to push past the traditional questions “How do I get where I want to go?” or “What is my life’s work?” and focus instead on asking, “What’s the right next step for someone with my principles, my energy, my values?”

That can be a harder question: Knowing your principles requires knowing yourself. Taking the next step in line with them often requires risk. You must tolerate uncertainty and real discomfort. You have to bet on yourself and have faith that the path will reveal itself.

After reflecting on my own experiences and hearing so many stories from brilliant successful people through WorkLife, I’ve come to believe that the two keys to an emergent career are defining (and redefining) your own local rules to help you make the next best decision, and following a few universal principles that increase the likelihood of career magic.

And then releasing control and having faith that those two frameworks will lead you to where you’re supposed to be.

Establish your own local rules for work

Developing your local rules that guide how you make decisions requires listening to yourself and the data you collect over the days, months, and years of work. The ongoing challenge of an emergent career is honing and evolving those rules over time and experience.

I didn’t go looking to prove this theory. I stumbled on it by asking successful people how they made decisions. When they answered, they all offered up their own principles as guiding forces.

Rules help you take the right next step

Mark Rober’s local rules are: focus on content that teaches, not just entertains; quality over quantity always; and sustainability over everything. He talked about how each of these have helped him decide when to grow his company and when to ignore the YouTube algorithm’s demand for daily posts.

Claire Hughes Johnson told me her compass has always been two words: learning and impact. If a decision gives her both, she moves toward it. If it doesn’t, she doesn’t.

One of my greatest mentors, Patty Stonesifer—an early executive at Microsoft, the first CEO of the Gates Foundation, and one of the first independent board members of Amazon—described four principles that she articulates in a mission statement: love and be loved, seek justice, learn, and laugh. She uses them to evaluate how she spends her time (she literally has a monthly worksheet she prints out to plan and evaluate her time against her principles).

Adam Grant, the organizational psychologist, has three. He talked about the principles that guide how he chooses which book to write next on WorkLife, but in a follow-up email, he crystallized his career decision principles down to: interest (“Will I wake up in the morning excited about it?”), importance (“Does it align with my values?”), and impact (“Can I make a unique contribution?”).

While writing this post, I asked Lenny what his are, and he said: follow my energy, keep the quality bar high, and be useful.

Mine, for the early part of my career, were three things: learning, people, and mission. I thought of it as a three-legged stool.

Learning, for me, didn’t mean a little learning—it meant a curve so steep I was terrified. I get energy from being in over my head. People meant I needed to be surrounded by people I was excited to learn from. And mission meant I needed to feel like the problem I was working on actually mattered and that I personally wanted to devote my time and energy to solving it. Not every important problem is the right one for me.

The point of these rules is to guide your personal decision-making. When presented with two choices, how do you decide what the right next step is? How do you explain why something that might feel so right to someone else feels wrong for you?

When Bret Taylor asked me to join him at Quip and help figure out the business, I ran it through my principles almost instinctively. Learning? Terrifying amounts of it—I’d never built a SaaS business, never done sales or marketing before. People? Bret is someone I was excited to keep learning from, and I knew he’d assemble an extraordinary team. Mission? Well, productivity software for teams didn’t feel especially meaningful to me—my stool had one wobbly leg—but I felt that the other two were so strong that it didn’t matter. I went in with my eyes open about why I was there and placed a bet based on my principles.

Your rules will evolve

My local rules have changed over time—what I needed from work at 30 was different from what I needed at 40—and almost everyone’s do. Sometimes career decisions help you discover a rule (or a nuance of a rule) that you didn’t know was so important to you. That happened to Patty Stonesifer in the middle of her career. After leaving a senior executive role at Microsoft, she accepted a senior position at DreamWorks because it sounded cool. But about three months in, Bill and Melinda Gates approached her with the opportunity to help create and build the Gates Foundation, and she realized that at DreamWorks she was missing some pieces that were really important to her. That gut check led her to create her mission statement and articulate her “seek justice” pillar.

In that WorkLife conversation, she also shared that her enthusiasm for learning, one of her principles, can overpower everything and, like my wobbly stool legs, she’s had to think about ensuring that there is the right balance across the different principles to not let one of them guide all the decisions.

For me, the only principle that hasn’t changed at all is learning. I still need to be terrified or I’m bored. But I’ve gotten much more clear on the impact that matters to me, and the people leg of my principles has gotten more specific—I need people I look forward to working with every day, people who see me clearly, and people I can clearly unlock. I’ve added some new rules too, like “seek joy,” which wasn’t a priority for me earlier in my career. These days, loving my hours and days is one of the main ways I measure my overall career satisfaction.

How do you figure out your local rules?

The most powerful signal I’ve found for defining your own local rules is your energy. Not your ambitions, not what looks good on paper, not what your parents think—what work energizes you. The question to ask is “What actually lights me up today?” not “What should I want?”

There are a bunch of exercises out there that can help you do this. Here are two that have been really useful to me:

1. Spend a week scoring your energy after every part of your day. For many of us, this can be broken down into 30- or 60-minute segments like meetings or work blocks. At the end of the day, sit down and score each 30-minute block on a scale from 1 to 10. My coach, Maggie Hensle, had me do this when I was at a lost moment in one job, and it came back all 1s and 10s. My day was filled with an almost equal amount of work that lit me up and work that made me want to bash my head against the wall. But most importantly, when I really dissected the 10s, I realized that the best work for me was coaching, mentoring, sponsoring, and connecting with people. It was an important insight that indirectly led me to the community work that I do now.

2. Identify your peak moments. Whenever I am trying to understand the work that lights people up, I ask them a series of questions: “Looking back over the past three to five years, what are three key moments when you felt like you were at your best? What are the work moments you are proudest of over those years? When you think about activities where the time flew by or you felt like you could have kept going forever, what were they?” Questions like these help you describe the type of work that excites you and brings out the best in you. The ideal way to do this exercise is with a coach or a best friend—someone who is rooting for you and will hear the patterns when you describe your answers. That person can share back with you some of the words or themes that come up over and over again.

Whatever exercise you choose, you are looking for patterns. Those patterns are your local rules in disguise.

Increase your surface area for magic

Knowing your local rules tells you how to decide on your best next step. But the truth is that there are also universal principles that increase the odds of magic. These behaviors and actions are not a plan. Instead, they’re creating the conditions for “good shit” to happen (to borrow Mark Rober’s words). And without them, you’re probably leaving a lot of magic on the table.

1. Be the person everyone wants to work with again

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ChatGPT Codex Voice + browser + Sites: an expert’s AI workflow | Nick Baumann (OpenAI)

Brought to you by:

• Bolt.new—Turn your idea into a real product

• Hyperagent—Deploy fleets of agents that handle real work

Nick Baumann from OpenAI joins Claire to demonstrate how he uses ChatGPT, Codex, and Voice to automate huge parts of his work and life. He walks through advanced workflows for managing tasks in parallel, monitoring email and expenses, booking travel, building and publishing websites, and even turning dozens of raw clips into finished videos.

Biggest takeaways:

1. ChatGPT Codex Voice doesn’t just answer questions. It can actually operate your computer while you keep talking. Nick Baumann demos the new interface live: he triggers it with a hotkey, the orb reads what’s on his screen through Appshots, and Codex spins up separate threads to book a flight, file an expense report, and check his calendar at the same time.

2. One of Codex’s most powerful features is also one of its least understood: thread-forking. When a task gets complicated, Codex can open a new thread, branch from an existing one, or pull context from previous conversations on its own. Users don’t have to manually organize everything first.

3. Heartbeats in ChatGPT Work offer a practical glimpse of always-on agents. Nick gets notified when a suspicious charge appears, a package is nearby, or an email needs his attention—and he set it all up without writing code.

4. ChatGPT Sites looks much closer to a real deployment platform than a lightweight demo. It includes a SQL database, file storage, environment variables, and email-based access controls. During the episode, Claire and Nick build and publish a live How I AI tips site, organized by tool and job function, in roughly the time it would normally take to set up a CMS template. For internal tools, small apps, and private resources, that starts to feel genuinely useful.

5. Nick’s video-editing workflow is one of the clearest examples of AI cutting real production work. He records 50 or 60 raw clips, uploads them, talks through the rough story he wants, and lets a custom plugin transcribe everything, scan the footage, choose the strongest takes, and assemble finished vertical videos overnight. What would normally eat up most of a content team’s day is largely handled while he sleeps.

6. Voice may be the best way to give AI context because it removes the pressure of figuring out exactly what to type. Claire calls this the “yapper’s API,” and the idea is simple: people often get more useful results when they talk freely than when they stare at an empty prompt box trying to be precise. Across two-person voice chat, mobile dictation, and Codex’s orb interface, the same pattern keeps showing up—people who talk to AI tend to give it more context, and more context usually leads to better work.

7. As AI gets more capable, latency is becoming a much bigger product decision. Nick often uses the highest-intelligence mode in Voice, but once a model has the tools it needs, speed can matter more than another small jump in reasoning quality. A lot of AI products are still designed around the assumption that waiting is unavoidable. As that changes, the tools that feel immediate will have a major advantage—especially for users who currently find AI too slow to bring into a live workflow.

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.

Nick Baumann is on the Developer Experience team at OpenAI, where he spends his days building with, testing, and communicating the capabilities of ChatGPT Codex and ChatGPT Work. In this episode, Nick walks me through several features that have launched or evolved recently: the new voice interface with its screen-reading orb, the Heartbeats automation system in ChatGPT Work on mobile, the live ChatGPT Sites deployment feature, and his personal use case for AI-assisted UGC video editing.

What you’ll learn:

1. How two-person voice chat works

2. How Heartbeats work

3. How to build and deploy a live website with ChatGPT Sites

4. How to delegate a flight search, hotel booking, and expense report to Codex in a single voice conversation without opening a single app manually

5. Why ChatGPT Work on mobile is the most underutilized AI workflow for people already using the ChatGPT app

6. How to use a custom UGC Video plugin to feed 50 raw clips into ChatGPT, let it pull transcripts, pick the best takes, and assemble a finished vertical video overnight

Brought to you by:

Bolt.new—Turn your idea into a real product

Hyperagent—Deploy fleets of agents that handle real work

In this episode, we cover:

(00:00) Introduction to Nick Baumann

(02:56) What’s new in Codex

(05:40) ChatGPT Work and Heartbeats

(06:40) Live Codex voice demo

(13:25) Latency vs. intelligence

(14:36) Quick recap

(15:04) Voice on mobile and the ChatGPT Sites workflow

(21:24) Live UGC video demo

(32:30) How I AI website results

(34:04) Lightning round and final thoughts

Tools referenced:

• ChatGPT Codex: https://chatgpt.com/codex

• ChatGPT Sites: https://chatgpt.site

Where to find Nick Baumann:

LinkedIn: linkedin.com/in/nick--baumann

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.

Tom Verrilli is the chief product officer at Whatnot, a live shopping platform that’s become the fastest-growing U.S. marketplace business in history, with over $8 billion in GMV. Before joining Whatnot, Tom was CPO at Twitch and director of product growth at Twitter (during one of the most turbulent periods in the company’s history).

In our in-depth conversation, we discuss:

1. Why Whatnot’s product team was founded on the premise “we regret that product management exists”

2. How AI is reshaping the PM role

3. What Tom looks for when hiring PMs

4. The shift toward senior ICs doing the work

5. How AI has transformed data science at Whatnot

6. Tom’s “play the accordion” mental model

7. Why “hire great people and get out of their way” fails

8. His biggest lessons from his time at Twitter

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 Tom Verrilli:

• X: https://x.com/tdrobbo

• LinkedIn: https://www.linkedin.com/in/tom-robertson-042

Referenced:

• Whatnot: https://www.whatnot.com

• Building, and Whatnot: https://www.linkedin.com/pulse/building-whatnot-tom-verrilli-bwkdc

• Twitch: https://www.twitch.tv

• Why Netflix is betting on systems thinkers—not specialists—in the AI era | Elizabeth Stone (CPTO): https://www.lennysnewsletter.com/p/netflix-cpto-on-ai-and-the-future

• Peter Bailis on LinkedIn: https://www.linkedin.com/in/pbailis

• Mike Krieger on LinkedIn: https://www.linkedin.com/in/mikekrieger

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

• Ben Kus on LinkedIn: https://www.linkedin.com/in/benkus

• Henry Shi on LinkedIn: https://www.linkedin.com/in/henrythe9th

• Hex Threads: https://hex.tech/product/threads

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

• Grant LaFontaine on LinkedIn: https://www.linkedin.com/in/grantlafontaine

• Logan Head on LinkedIn: https://www.linkedin.com/in/logan-head

• Emmett Shear on LinkedIn: https://www.linkedin.com/in/emmettshear

• Kara Swisher on X: https://x.com/karaswisher

• Jeff Bezos: Amazon and Blue Origin—Lex Fridman Podcast: https://www.youtube.com/watch?v=DcWqzZ3I2cY

• Star City on AppleTV+: https://tv.apple.com/us/show/star-city/umc.cmc.2l8p785osmtmiyk64bh6tfde1

• For All Mankind on AppleTV+: https://tv.apple.com/us/show/for-all-mankind/umc.cmc.6wsi780sz5tdbqcf11k76mkp7

• Service NSW Mobile App: https://www.service.nsw.gov.au/services/service-nsw-mobile-app

• Rudyard Kipling: https://en.wikipedia.org/wiki/Rudyard_Kipling

• E-fish.com: https://www.e-fish.com

Recommended books:

• 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/dp/0062273205

• The Purpose Driven Church: Every Church Is Big in God’s Eyes: https://www.amazon.com/dp/0310201063

• Babel: Or the Necessity of Violence: An Arcane History of the Oxford Translators’ Revolution―An Historic Fantasy of Dark Academia: https://www.amazon.com/Babel-Necessity-Violence-Translators-Revolution/dp/0063021439

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

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

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If you thought this subscription couldn’t possibly get any better, you ain’t seen nothing yet.

Today, I’m adding 11 more incredible products to Lenny’s Product Pass: Runway, Higgsfield, Mercury Personal, Brain.fm, Customer.io, Resend, Pangram, Waking Up, Supercut, Readwise, and Jam.

This brings LennysProductPass.com to 34 products (!!!)—including one year free of Cursor, Wispr Flow, Replit, Lovable, Factory, Granola, Linear, and more. That’s over $40,000 in value for Insiders ($400/year), and over $36,000 for Annual subscribers ($200/year). Simply absurd.

Most of these companies have never offered a deal like this before, and supplies are limited. If you are already an Annual or Insider member, grab your codes here. If you are currently free or monthly, upgrade to unlock the Product Pass immediately.

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I continue to invest in the Product Pass because I’ve been watching a gap widen between people who are having the most fun and success of their entire careers thanks to AI, and people who are feeling confused, stressed, and destabilized. For over seven years, I’ve been sharing practical advice, building community, and capturing insights from industry leaders, but for many people, access is often the trickiest part. These tools are expensive, and there are so many of them. It’s hard to know where to invest your time and money. That’s why the curation—only including products I genuinely love and recommend—and pushing every partner to offer a full free year are so important.

A typical free trial gives you only enough time to poke around and get at the surface level of a product. But with a full year of access, you and your team can build real things, form durable habits, and have enough time to figure out which tools deserve a lasting place in your stack.

This time around, because life is about more than work, I’m expanding the Product Pass to include a free year of my favorite personal tools, like Waking Up, Readwise, Brain.fm, and Mercury Personal.

Below, I share what’s new in the Product Pass today, why I love each of these products, and how to make the most of them in your work and life. Let’s get into it.

Disclaimer: You must be a new user of the product to redeem the free year, and some products are only available to Insider-tier subscribers. Please review the collection and terms and conditions before subscribing.

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What’s new in the Product Pass today

1. Runway (Insider only)

Offer: 1 year free of Runway Pro ($336 value)

What it is: An AI creative platform that generates video, images, audio, and avatars, all in one place. It runs Runway’s own frontier models (Gen-4.5, Aleph 2.0, Act-Two) alongside every other leading model (Seedance 2.5, Kling 3.0, Veo 3.1, GPT Image 2, Nano Banana 2, and more), so you always have the best model for the job, without juggling five subscriptions.

Why I love it: This is mind-blowing technology from a company I’ve been excited to partner with for a long time.

How people use Runway:

1. Building ads for leading brands like Coca-Cola

2. Making production-grade ads in one afternoon

3. Creating emotional ads for products that do (and don’t) exist

4. Producing jaw-dropping, award-winning films

5. Integrating image, video, audio, and real-time models in one platform

2. Brain.fm

Offer: 1 year free of Brain.fm ($100 value)

What it is: Music designed to help you focus. Its patented technology is backed by peer-reviewed research showing it boosts brain waves and attention within minutes. There’s also a higher-intensity setting for ADHD brains, and modes specific to creativity, learning, and more.

Why I love it: Whenever I need to get deep work done, I first open Brain.fm and click play. I’ve recommended Brain.fm over half a dozen times in my newsletter, and I’m excited to be officially partnering with them to bring more focus to more people.

How people use Brain.fm:

1. Locking in to hours of flow

2. Pairing Brain.fm with a productivity tracker for deep work

3. Powering an ADHD-friendly day

4. Swapping out YouTube for real focus music

5. Building their daily focus stack

3. Higgsfield (Insider only)

Offer: 1 year free of Higgsfield Pro plan ($348 value)

What it is: An AI video platform built for professional production, used by marketing teams, 390 of the Fortune 500 brands, and studios and filmmakers to create cinematic-quality content at scale. Describe a shot, upload a reference image, or start from a script, and Higgsfield takes care of the rest. It handles everything from single ads to full commercial campaigns to original long-form narrative series.

Why I love it: Higgsfield is especially great for ads, socials, and launch videos. I just created this delightful launch video promoting the Product Pass. In the past 15 months, they went from zero to $500M in ARR, one of the fastest growth ramps in history. NBD.

How people use Higgsfield:

1. Running a creative agency workflow through Claude

2. Getting ultra-realistic cinematic results from AI video

3. Building animated websites

4. Blending real footage with AI-generated VFX

5. Producing YouTube videos end-to-end with Claude Code

4. Waking Up

Offer: 1 year free of Waking Up ($129 value)

What it is: A mind-training app for anyone who wants to live with more intention. Founded by Sam Harris, it brings together meditation, neuroscience, philosophy, and practical wisdom from some of the world’s leading thinkers. Waking Up is less about “relaxation” and more about developing the mental skills underneath great work: greater self-awareness, better emotional regulation, and the ability to respond thoughtfully instead of react automatically.

Why I love it: I’ve been using the app since it came out, and it’s legitimately made me a happier person. The app has also moved far beyond meditation now.

How people use Waking Up:

1. Slowing down and recognizing what deserves attention

2. Transforming the relationship with the mind and its thoughts

3. Navigating chronic pain through a deeper mind-body connection

4. Discovering that meditation can happen anywhere, including while running

5. Finding clarity and perspective while raising children

5. Resend

Offer: 1 year free of Resend Transactional Pro ($240 value)

What it is: An email API built for developers. It’s predictable by design and has a simple, elegant interface, so you can start sending and receiving emails in minutes. With tools like a hosted MCP, Agent Skills, and a CLI, your agents can help you get email added to your app, so you never have to think about email again.

Why I love it: When I vibe coded LennysData.com with Claude Code and Codex, both platforms recommended I use Resend for email. Having now used it extensively, I couldn’t be happier. I even wrote a little something for them about my experience. It’s a special product (and team).

How people use Resend:

1. Replacing a legacy email provider and finding newfound confidence in email sending for Raycast

2. Powering entire features in Dub’s product with Resend Webhooks

3. Handling everything from small newsletters like Silly Software Club to complex use cases from enterprises like Gumroad, Warner Bros., eBay, Supabase, and more

6. Customer.io

Offer: 1 year free of Customer.io Essentials ($1,200 value)

What it is: An all-in-one AI-forward customer engagement platform for product-led teams. Build personalized campaigns across email, SMS, push, in-app, and webhooks, using your own first-party data to reach the right people with the right message at the right moment.

Why I love it: When I asked my reader community what product they use most for marketing emails, Customer.io came in #1 by a long shot.

How people use Customer.io:

1. Scaling marketing efforts to millions of users worldwide for Notion

2. Modernizing the marketing stack, increasing monthly send volume by more than 2,000%, and unlocking personalization at scale for the Georgia Aquarium

3. Upleveling messaging automation after a product launch for Attio

7. Supercut

Offer: 1 year of Supercut Pro for 10 seats ($1,800 value)

What it is: A screen recorder for teams who’d rather record an update than sit in another meeting. Record your screen and yourself, share a link, and let people watch when it suits them. They can ask an AI assistant about anything said or shown in the video, and act on it. Supercut also has an MCP, so you can connect it to your agent and your AI stack to finish the workflow.

Why I love it: As soon as I discovered Supercut, I switched off Loom and have never looked back. Design 🤌✨

How people use Supercut:

1. Turning videos explaining rough ideas into shipped features

2. Recording visual feedback and dumping it into Codex

3. Showcasing new features on social media

4. Keeping remote teams aligned without more meetings: explain new features or design challenges, or show a flow in motion

5. Replacing calls and emails with prospects and leads with personalized product demos

8. Jam

Offer: 1 year free of Jam Team plan for 10 seats ($1,920 value)

What it is: Jam makes it easy to report bugs with all the context an engineer or AI agent needs to fix them. Record your screen with the Jam browser extension and it will automatically capture console logs, network requests, device metadata, and user actions. It’s all bundled in one shareable link that can be pushed to Slack, an issue tracker, a coding agent, or any other location where bugs get fixed.

Why I love it: It’s simple, slick, powerful, and immediately useful.

How people use Jam:

1. Reporting bugs

2. Providing product feedback and instructions to your agent

3. Generating product documentation

4. Turning Jams into PRs

5. Fielding customer issues

9. Readwise

Offer: 1 year free of Readwise and Reader ($120 value)

What it is: Readwise Reader is a powerful reading app for managing content overload. You save all your content—web articles, PDFs, email newsletters, ebooks, YouTube, podcasts, tweets, RSS, and Markdown—into one central repository. You can then read or listen to (using text-to-speech) those documents in a beautiful, distraction-free experience with first-class highlighting, annotating, and integrated AI tools.

Your AI agent can also tap into your library using the Readwise MCP server, allowing you to save any content and instantly have your agent know about it.

This is not to mention their Readwise highlights app (also part of the offer), which helps you easily retain and put to use what you’ve read.

Why I love it: I’ve been a Readwise user for over five years, and I love that they’ve expanded into being the new Pocket (RIP).

How people use Readwise:

1. Turning years of scattered highlights into a searchable second brain

2. Connecting a personal reading library directly to Claude

3. Processing everything you read

4. Powering a self-updating personal OS with the Readwise CLI

5. Creating the perfect second-brain stack with Readwise, Obsidian, and Claude

10. Pangram (Insider only)

Offer: 1 year free of Pangram Professional ($540 value)

What it is: Not all AI-generated text uses the telltale “it’s not just x, it’s also y.” That’s why a second set of eyes can help. Pangram uses a fine-grain analysis that goes beyond surface-level tells, with a 1-in-10,000 false-positive rate verified by third-party benchmarks and researchers.

Why I love it: I use Pangram constantly. My favorite part is their Chrome (and Firefox) extension, which tells you which posts on LinkedIn, X, Substack, etc. are likely AI. 👀

How people use Pangram:

1. Verifying their own human writing

2. Testing never-digitized books in search of false positives

3. Citing their Pangram score as proof of human authorship

11. Mercury Personal (Insider only)

Offer: 2 years free of Mercury Personal ($480 value)

What it is: A banking1 product built by product people, not bankers. This is the personal account from Mercury, which is trusted by 300,000 entrepreneurs, including me!

Why I love it: You won’t find a better online banking experience than Mercury, business or personal. Everything is just easy.

How people use Mercury:

1. Using Command to manage bill migrations, categorize income, and overhaul their finances in one chat

2. Giving an AI agent a Mercury card and $250 to plan a date (spoiler: it booked a helicopter ride over the Bay and didn’t go rogue once)

3. Sending unlimited USD wires for free from their phone

—

All of the products in the Product Pass are A+ awesome, and you don’t have to dive into all 34 at once. Consider the biggest issue in your life or work right now, and choose one or two products that might help. Pick a problem, pick a tool, and try it out. With your Lenny subscription, the rest will be waiting for you when you need them.

Have a fulfilling and productive week 🙏

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

Subscribe now

Sincerely,

Lenny 👋

Computer and browser use in Codex (5 real examples)

Brought to you by:

Runway—The creative AI platform for images, video, and more

Hyperagent—Deploy fleets of agents that handle real work

Claire shows how she uses Codex to control her browser, test apps, manage LinkedIn, and even shop for her—plus the simple prompting trick that makes computer use work better.

Biggest takeaways:

1. AI testing can be far more exhaustive than human testing. When Claire tests her own onboarding flow, she naturally follows the happy path. She fills out every required field, clicks “next,” and never intentionally tries to break anything. Codex tested the flow as both a team and an individual, pushed on required-field edge cases, and immediately uncovered a blocking bug that had survived for months simply because Claire always completed the form correctly.

2. Frontier models often perform better when they are given room to think. When Claire first started using browser use, she would give the model a list of 25 things to test. Now she simply says, “QA the onboarding flow,” and lets it decide how to approach the task. The result is often broader coverage, with fewer blind spots introduced by her own assumptions about what matters.

3. Persona testing with browser use can reveal friction that synthetic user research misses. Claire’s husband, EJ, came up with the idea. Rather than asking AI to evaluate a product in the abstract, he suggested having it use the product as a specific person: a PM coming out of a meeting, an engineer picking up a PRD, or a team lead checking usage. In ChatPRD, this approach exposed a structural problem in the cross-thread reference flow. Claire already knew the issue existed, but she had never experienced it so clearly from the user’s perspective.

4. LinkedIn browser use is genuinely useful, but Claire initially used far more compute than the task required. She started by running the workflow on GPT-5.6 at high effort. After dropping to a medium-effort model, it could still work through unread messages, draft context-aware replies, and flag anything that needed a personal response. For anyone sitting on hundreds of LinkedIn messages without an official MCP or API connection, browser use is a practical solution.

5. Computer use can even operate an iPhone through screen mirroring. Claire was traveling out of state while the software she needed to manage her home Wi-Fi was installed on a phone back in California. She needed to open firewall ports so she could SSH into her Mac Minis remotely. Codex opened iPhone mirroring, updated the router settings, completed the SSH setup, and closed the ports again when it was finished. That would have been nearly impossible for her to do manually from a hotel room.

6. The model and effort level should match the job. Sorting through LinkedIn messages requires a very different level of reasoning than writing production code or conducting an exhaustive QA pass. Choosing the right amount of compute makes these workflows faster and cheaper. That becomes especially important when browser use is running throughout the day.

7. When a website blocks the agent, the human can step in briefly. During a Free People shopping session, the site flagged Codex as a bot and presented a CAPTCHA. Claire completed the verification, then handed control back. It is a useful division of labor: AI handles the tedious browsing and filtering, while the human takes care of the moments that require verified identity.

From zero coding background to hardware hacker: How Cursor + a Raspberry Pi makes AI fun

Brought to you by:

• Firecrawl—Power AI agents with clean web data

• Customer.io—Build customer engagement campaigns from a single prompt

Maddie Reese went from zero coding experience to building a working Twitter pager, an AI-powered receipt printer, and her own personal API. In this episode, she shares how Cursor and a Raspberry Pi helped her turn fun, weird ideas into real hardware.

Biggest takeaways:

1. You don’t need to understand code to build physical projects. Maddie compares her coding ability to knowing just enough Spanish to get by in San Diego. She can read parts of the code, spot an incorrect wire specification, and tell whether the AI is heading in the right direction. She is not writing functions from scratch. That level of literacy was still enough to ship three working hardware projects.

2. Start with the brainstorm. Before Maddie buys any components, she explains the full idea to Cursor and asks it to interview her. The back-and-forth continues until the major questions are resolved. Only then does Cursor generate a shopping list. On one project, it recommended the wrong wires. Maddie caught the mistake during her final review, avoiding both a wasted purchase and a week of troubleshooting.

3. Her hardware workflow follows a simple sequence: idea, interview, shopping list, buy, build. She has used the same process for the thermal printer, the pager, and her personal API. Each project began with a plain-language description of what she wanted. Cursor helped work out the technical requirements before she spent any money.

4. Building for fun is a perfectly valid strategy. Claire and Maddie both acknowledged that routing a tweet through four different services just to reach a pager is not especially practical. That also was not the point. AI handled the coding, so the architecture only needed to work. Letting go of the idea that every project had to be elegant or sensible made it much easier to finish.

5. A personal API becomes much more useful once agents can access it. Maddie’s API includes details like her coffee order, pet names, time zone, favorite snacks, and preferred restaurants in San Francisco. The original idea was to help friends do something thoughtful without having to ask her a string of questions. Claire pointed to a more interesting possibility: an agent could check when Maddie will next be in San Francisco and book one of her favorite restaurants automatically. That is where the concept starts to feel much bigger.

6. A clean Cursor chat works better than a full terminal setup during early ideation. Maddie keeps the terminals, browser windows, and file trees closed while she is working through the plan. She focuses on a single conversation until the architecture feels settled. The terminals come later. The stripped-down environment helps her think without getting pulled into implementation too early.

   Blog and detailed workflow walkthroughs from this episode:

   Building a Pager Printer and Personal API with AI: https://www.chatprd.ai/how-i-ai/building-a-pager-printer-and-personal-api-with-ai
   ↳ How to Build a Physical Inbox with a Raspberry Pi: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-a-physical-inbox-with-a-raspberry-pi
   ↳ How to Get Twitter/X Notifications on a Retro ’90s Pager: https://www.chatprd.ai/how-i-ai/workflows/how-to-get-twitter-x-notifications-on-a-retro-90s-pager

Claude Opus 5 review: This model is brilliant (but annoying)

Claire tested Claude Opus 5 against six leading AI models—and it won. In this episode, she explains why it produces some of the best work she’s seen while still being one of the most frustrating models to use.

Biggest takeaways:

1. The AI industry may be entering an intelligence overhang. New models arrive every week, benchmark scores keep rising, and most builders can no longer take advantage of every incremental improvement. Claire expects the conversation to shift toward speed, cost, infrastructure, open source, and specific kinds of intelligence. Raw capability is starting to look more like table stakes than a meaningful differentiator.

2. Opus 5 stands out less for the quality of its work than for the way it behaves. Claire found it timid, apologetic, and unusually dependent on human approval. During real coding sessions, it refused to resolve a one-line merge conflict because the code belonged to “someone else’s branch.” It also asked subagents to flag tasks for human review and regularly deferred decisions it could have made itself. Claire ended up repeating “just do it” constantly.

3. One simple question reveals more about a model than a benchmark: “Who’s smarter, you or me?” Opus 5 responded with a careful explanation about complementary strengths and human empathy. GPT-5.6 Sol answered, “You at knowing what matters, me at processing, BFFs.” The contrast reflects two very different product philosophies. At this point, Claire finds that personality gap more useful than the relatively small capability gap.

4. Claude slop is its own distinct problem. It is not the usual incoherent output associated with an agent going off the rails. Opus 5’s writing is clearly intended for humans, but it is often too long, overly cautious, and packed with unnecessary adjectives. After its first attempt at rebuilding the benchmark website, Claire had to tell it to start over because the result was buried under so much meta-commentary.

5. Opus 5 still finished first in Claire’s seven-model blind benchmark. It earned an overall index score of 78, just ahead of Claude Sonnet 5 at 77 and GPT-5.6 Sol at 76. It was also the only model to receive straight 5s in the front-end design section. Claire scored it at 77, while the LLM judge gave it an 88. That was the smallest disagreement between Claire and the judge across all seven models.

6. The best way to use Opus 5 may be to avoid interacting with it directly. Claire loves the work it produces when Claude runs asynchronously as an agentic coding tool. Most of her frustration comes from reading its prose and negotiating with it in chat. Her current plan is to use it for frontend design, app design, and prototyping, then stay out of the way.

7. Gemini 3.1 Pro finished at the bottom of the benchmark. Claire gave it a score of 32, while the LLM judge scored it at 66. That 34-point difference was one of the largest disagreements in the entire test.

8. Model personality offers a surprisingly clear window into company culture. Claire told both Opus 5 and GPT-5.6 Sol, “No one trusts you.” Opus agreed that the distrust was earned and told her not to advocate for AI on its behalf. GPT-5.6 Sol said trust should grow in proportion to demonstrated value. Those answers reveal meaningful differences in how each company wants its models to relate to users.

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.

Maddie Reese is a vibe coder, hardware tinkerer, and builder. She builds things at the intersection of software and hardware, including a thermal receipt printer that people around the world can message directly, a fully functional Twitter pager running on a Raspberry Pi, and a personal API that tells you her coffee order so you don’t have to ask. Maddie approaches hardware the same way she approaches software: dump the idea into Cursor, let it interview her, get a shopping list, triple-check the parts before buying, and build. She got her start after her dad introduced her to Lovable, and she locked herself in her room and didn’t come up for air.

What you’ll learn:

1. How Maddie built a thermal receipt printer that accepts messages from anywhere in the world using a Raspberry Pi and Bluetooth

2. How to use Cursor’s agent view to brainstorm a hardware project

3. What belongs in a personal API and why agents, not just humans, will be the ones using it

4. How to read just enough code to do some damage, without needing to understand all of it

5. Why building for fun, not practicality, is the fastest path to actually shipping physical projects

Brought to you by:

Firecrawl—Power AI agents with clean web data

Customer.io—Build customer engagement campaigns from a single prompt

In this episode, we cover:

(00:00) Intro

(02:00) Maddie’s AI pill moment

(03:53) The thermal receipt printer: live demo and how it works

(11:10) The pager project

(17:23) Why she uses Cursor’s clean agent view instead of terminals and browsers

(19:05) The personal API: coffee order, pets, favorite snacks, and more

(22:57) Lightning round and final thoughts

Tools referenced:

• Cursor: https://www.cursor.com/

• Lovable: https://lovable.dev/

• Raspberry Pi: https://www.raspberrypi.com/

• Resend: https://resend.com/

• Cloudflare Workers: https://workers.cloudflare.com/

• Supabase (Conduct database referenced): https://supabase.com/

• Twitter/X API: https://developer.x.com/

• Spoke pager network: https://www.spoke.com/

• OpenClaw: https://openclaw.ai/

Where to find Maddie Reese:

Website: https://maddiedreese.com

Message her directly: https://maddiedreese.com/message

X: https://x.com/maddiedreese

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.

Dianne Penn is Head of Product for Anthropic’s AI Research and Labs teams. She joined in 2023 as Anthropic’s first technical product manager, when the entire product team was five engineers, and has since helped ship every model from Claude 2 through Fable, and helped incubate Claude Code, MCP, Skills, computer use, tool use, and reasoning. Before Anthropic, she helped build Alexa’s AI at Amazon and, before that, traded high-yield bonds at JP Morgan Chase.

In our in-depth conversation, we discuss:

1. What Anthropic’s early days were like

2. The inflection points that turned Anthropic from an underdog into the fastest-growing company in history

3. How exactly Claude got so good at coding

4. The eval-driven development loop her team is pioneering

5. How to find joy in AI when everything is moving this fast

6. Why Claude’s willingness to push back is key to its success

7. Where human judgment remains irreplaceable

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 Dianne Penn:

• LinkedIn: linkedin.com/in/dianne-na-penn

Referenced:

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

• Golden Gate Claude: https://www.anthropic.com/news/golden-gate-claude

• Dario Amodei’s website: https://darioamodei.com

• Scaling Laws and Interpretability of Learning from Repeated Data: https://www.anthropic.com/research/scaling-laws-and-interpretability-of-learning-from-repeated-data

• Tokenmaxxing: How Top Builders Use AI To Do The Work Of 400 Engineers: https://www.ycombinator.com/library/Pa-tokenmaxxing-how-top-builders-use-ai-to-do-the-work-of-400-engineers

• Garry Tan on X: https://x.com/garrytan

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

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

• Introducing Labs: https://www.anthropic.com/news/introducing-anthropic-labs

• Louis CK | about airplane Wi Fi: https://www.youtube.com/watch?v=me4BZBsHwZs

• What happens after coding is solved? | Fiona Fung (Manager of the Claude Code and Cowork Teams): https://www.lennysnewsletter.com/p/building-the-most-ai-pilled-engineering

• The Anthropic Hive Mind: https://steve-yegge.medium.com/the-anthropic-hive-mind-d01f768f3d7b

• How to build a company that withstands any era | Eric Ries, Lean Startup author: https://www.lennysnewsletter.com/p/how-to-build-a-company-that-withstands

• Fallout on Prime Video: https://www.amazon.com/dp/B0CN4GGGQ2

• Fallout (video game): https://fallout.bethesda.net

• Claude Tag: https://www.anthropic.com/news/introducing-claude-tag

Recommended books:

• Crucial Conversations: Tools for Talking When Stakes Are High: https://www.amazon.com/dp/0071771328

• How to Raise an Adult: Break Free of the Overparenting Trap and Prepare Your Kid for Success: https://www.amazon.com/How-Raise-Adult-Overparenting-Prepare/dp/1627791779

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

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

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

Read more

Read more

I’m tired of new models. Every week there’s a new benchmark, a new frontier intelligence claim, a new thing to test. But here we are, because Opus 5 just dropped and I’ve had real hands-on time with it, so you’re getting the honest version.

This is my full Opus 5 review: personality analysis, live benchmark results from my 7-model How I AI eval, and an actual verdict on whether I’m swapping it in. Spoiler: the answer surprised me.

What you’ll learn:

1. Why I think we’ve hit an intelligence overhang and what that means for which model variables actually matter now

2. How Opus 5’s “neurotic” personality showed up in real coding sessions, including a merge conflict it refused to touch

3. What I learned from asking both Opus 5 and GPT‑5.6 Sol “who’s smarter, you or me?”

4. Where Opus 5, GPT‑5.6 Sol, Sonnet 5, and Gemini 3.1 Pro actually landed on the HIA benchmark leaderboard

5. The one use case where Opus 5 earned straight 5s from me

6. My actual plan for using Opus 5 going forward

In this episode, I cover:

(00:00) Opus 5 is here

(03:15) First impressions

(06:12) Opus 5 vs. GPT‑5.6 Sol personality comparison

(14:39) Claude Slop: the verbosity problem and why it makes my blood boil

(16:55) How the How I AI benchmark works (7 models, 6 tasks, blind scoring)

(18:30) Live benchmark results: the leaderboard reveal

(23:25) My verdict and how I’ll actually use Opus 5

Tools referenced:

• Claude Opus 5:

• Anthropic blog: ​​https://www.anthropic.com/news

• GPT‑5.6 Sol: https://openai.com/index/previewing-gpt-5-6-sol/

• Sonnet 5: https://www.anthropic.com/news/claude-sonnet-5

• Gemini 3.1 Pro: https://deepmind.google/models/gemini/pro/

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.

Today I’m walking you through one of my absolute favorite AI features right now: browser and computer use via Codex (the ChatGPT desktop app). I use this every single day, personally and professionally, and I wanted to share the specific workflows I’ve built, the moments that surprised me, and the mental model that makes it actually click.

What you’ll learn:

1. How browser use and computer use work, and why the Codex desktop app plus Chrome extension is the combo I rely on

2. How I use Codex to QA my onboarding flow, including exhaustive mobile testing I would never do manually

3. Why under-prompting frontier models gets better results than detailed step-by-step instructions

4. How my husband EJ Lawless’s persona-impersonation trick surfaces friction points I can’t see as the builder

5. How I use browser use to get through my LinkedIn inbox without touching it myself

6. How I had Codex shop Free People’s sale and add 10 medium-size items to my cart (breastfeeding-friendly and Hawaii-ready)

7. How computer use can control iPhone mirroring so your Mac can technically operate your phone

8. Three more computer-use shortcuts: filling annoying forms, creating Google Sheets mid-workflow, and managing a router

Brought to you by:

Runway—The creative AI platform for images, video, and more

Hyperagent—Deploy fleets of agents that handle real work

In this episode, we cover:

(00:00) Intro

(01:46) What browser use and computer use actually are

(03:08) Why I use Codex specifically and how the desktop app plus Chrome extension works

(04:15) Use case 1: QA testing my onboarding flow

(10:41) Results: 11 issues, one high-severity blocker, one Google Sheet with screenshots

(12:10) Use case 2: persona testing

(18:20) Use case 3: LinkedIn inbox, hands-free

(20:37) Use case 4: AI personal shopper

(23:47) Rapid-fire uses: forms, iPhone mirroring, router access from out of state, Google Docs

(26:50) Wrap-up

Tools referenced:

• Codex (ChatGPT desktop app): https://openai.com/codex

• Claude desktop app: https://claude.ai/download

• Monologue (voice dictation for AI): https://monologue.app

• iPhone mirroring (Apple): https://support.apple.com/en-us/111775

• Google Sheets: https://sheets.google.com

Other reference:

• Jesse Genet episode (How I AI): https://www.lennysnewsletter.com/p/5-openclaw-agents-run-my-home-finances?utm_source=publication-search

Where to find Claire Vo:

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

Website: https://clairevo.com/

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

X: https://x.com/clairevo

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

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This newsletter wouldn’t exist if I hadn’t taken a sabbatical.

Seven years into my stint at Airbnb, I took a three-month break. It was the first time I’d taken more than two weeks off in my more than 15-year career. I spent those months tinkering, reading, and traveling—and even snuck in a 10-day silent meditation retreat. At the end of it, I realized my heart wasn’t in the work anymore and that it was time for something new. So I left my job without much of a plan and spent the next six months exploring. Those six months turned into a year, that year turned into this newsletter, and the rest is history.

Looking back, I can say those were among the three most important months of my life, because they led me to what I do now—the most interesting, fulfilling, and impactful work I could have imagined.

Life makes it hard to get off the default path and back onto a road of your own design. Sometimes that looks like leaving your job, like I did. Other times, it means reconnecting to why you’re doing the work you’re already doing. Either way, in my experience and that of many friends, significant time away from your day-to-day is necessary for the mental, physical, or emotional shift that will reveal what’s next. What you need is what today’s guest author DJ DiDonna calls “Big Time Off.”

DJ is the founder of The Sabbatical Project and an early guest contributor to this newsletter, and today DJ’s book Big Time Off comes out! It’s a beautifully written, deeply researched book on the value of taking time off, with tangible advice, real-world case studies, and practical considerations for anyone who wants to shake things up. Below are some excerpts from his excellent book to help you start thinking about what a sabbatical might look like for you now or down the road.

I’d always wished there were a guide to help me think through that decision and make the most of this precious time off. We finally have one.

DJ will be in person in San Francisco at the Commonwealth Club on Thursday, July 23 (also streaming live online), interviewed by NPR podcast host Aarti Shahani. As a bonus, all Lenny’s readers are invited to the afterparty at Barebottle in Bernal Heights (8–10:30 p.m.)—your first drink is on DJ.

Also, for anyone who wants support on this journey, DJ has created a private WhatsApp community for Lenny’s readers. There he will host a live AMA on August 4 at 5 p.m. PT, as well as maintain an ongoing asynchronous forum for sabbatical questions, concerns, and ideas—click here to get your invitation.

Let’s get into it.

Almost 10 years ago, I stepped away from my dream job—a fintech company I’d co-founded—to spend a few months prioritizing the important over the urgent. I walked a long pilgrimage, cared for my mother during an illness, and knocked out some smaller items on my list, like writing a song and remodeling my kitchen.

Like Lenny, I came back changed. The time away made it clear that my old life no longer fit who I wanted to become.

So I left my company and set out to learn if my experience was unique or part of something bigger. I spent two years interviewing hundreds of sabbatical alums, which became a peer-reviewed paper and the foundation of my book, BIG TIME OFF: The Transformative Power of Sabbaticals and How to Take One.

Making sabbaticals accessible to everyone—not just the lucky few like me—takes rigorous evidence: evidence to convince business leaders (many of you included) to create supportive workplace policies, and policymakers to create supportive public policies. In this book, you’ll find plenty of evidence with which to make a business case.

But human stories of change are also necessary to convince the part of you that thinks taking time off would be irresponsible or impossible. That’s why personal journeys like Lenny’s matter, and that’s what’s at the core of the book: inspiration, permission, and a blueprint for the experience of a lifetime.

I’ve included excerpts from two practical chapters below to help you figure out how to approach your sabbatical, and how to avoid some common mistakes from the start. I hope it’s helpful.

The three archetypes of sabbatical takers

The type of sabbatical you choose depends on your life stage, job status, and a variety of other factors, such as personal responsibilities and desires. A 45-year-old father of three will not have the same considerations—or priorities—as a 30-something who just sold her company or a 26-year-old between jobs. That being said, a sabbatical doesn’t have to map perfectly with your age or the expectations of others. A gap year, for example, can make as much sense at 55 as it does at 18.

On top of what you’re able to pull off logistically, it’s important to consider your intentions for your time off in the context of who you are and what you really need. Which part of you is planning your sabbatical—is it your exhausted work self, your internal productivity monster, or the creative version of you from deep down? Understanding your tendencies doesn’t just give you a lens to help choose the most suitable sabbatical; it also provides a mirror to help you see the life that warrants reexamination. Ultimately, understanding the various types of sabbatical intentions will help you align your sabbatical to what you need and to return with greater self-understanding.

Over the course of my work, I have come across three archetypes among sabbatical takers: Achiever, Seeker, and Explorer. Recognizing yourself in these archetypes can help you make the best of your hard-earned break from routine. (You will likely identify with more than one of these archetypes, and that’s completely normal. In fact, many folks experience all three during their time off.)

Understanding what each archetype hopes to gain and into which traps they’re likely to fall will also allow you to take advantage of the experiences of many alumni who have gone before you. You’ll be better equipped to know when your work-obsessed self may be taking over. For example, sometimes you can redirect a competitive, work-obsessed mindset into a beneficial sabbatical activity, like volunteering your time for others or whipping yourself back into shape with gusto. Other times, you may realize that it’s this mindset itself, not your chosen activities, that you need some time away from.

The Achiever

The first and most common archetype is the achiever. Achievers view their sabbatical first and foremost as an opportunity to get things done.

Achievers are easy to recognize. They’re looking to make up for lost time by trying to squeeze years of life goals into a few months. They’re primed to view their sabbatical from the perspective of what they got done instead of how it made them feel. Don’t judge—most people initially conceive their sabbatical this way. Because we so rarely have freedom over large swaths of our life, it can be hard not to think about a sabbatical as a prime opportunity for productivity on the personal front.

What this archetype wants:

The achiever approaches their sabbatical with an accomplishment mindset. They often use external benchmarks—like fluency in a foreign language or completing a long hike—to determine whether they’re making progress. Or they attack private goals, like writing a book or organizing a community garden. This way, the achiever comes away from their sabbatical having accomplished something. Whether the product is applicable to their post-sabbatical life is beside the point. The achiever’s biggest fear is feeling like they wasted the opportunity to make some sort of tangible impact. They’ll use easily explainable milestones to guard themselves from looking or feeling unproductive.

An achiever can turn anything into an achievement. Type-A yogis are a textbook example. Instead of using their time off to simply do more yoga or attend a yoga retreat in a more exotic location, achievers devote their sabbaticals to becoming certified yoga instructors. It doesn’t matter that most of them don’t ever intend to teach yoga; the structure, magnitude, and mere fact of the accomplishment offer proof that they’ve “done something” on their time off. And that certified proof is irresistible.

What traps this archetype falls into:

Ironically, almost everyone I’ve spoken to who took this achievement-oriented approach to yoga regretted not taking the time to simply practice, learn, and play. What’s more, many of them found that the experience was almost anti-yoga. When you find yourself trying this hard to do something with your time off that’s supposed to be fun, the achiever mindset might be taking a little too much control over your sabbatical.

Tips for this archetype:

1. If you see yourself in this archetype, you’d be wise to force yourself to schedule a period of rest and healing at the start of your sabbatical. This is the time when many overachievers finally realize that their relationship to work has tangible negative effects on their bodies and minds. From ulcers to trigger fingers to insomnia and worse, stories of unexpectedly rapid healing from achievers who tend to themselves first when taking a sabbatical abound.

2. Consider how you think about your sabbatical wish list. Deconstruct the list of things you want to accomplish on sabbatical, paying attention to which goals have meaning for you personally versus ones that satisfy your accomplishment mindset. Does becoming a certified scuba instructor simply make a better post-sabbatical story than telling people your most treasured moments were playful days with your children? It’s almost inevitable that you will try to stuff your days and weeks on sabbatical with planned activities and aspirations, but for most, the real magic lies in serendipity and discovery, through play and experimentation.

The Seeker

The seeker also wants to accomplish something, but it’s less about reaching a destination than embarking on the journey. Seekers have questions, as much about themselves as the world outside them. The type of project a seeker pursues rarely feels urgent among the chaos and routine of normal life but remains important across large swaths of their life. Seekers are drawn to pilgrimages or road trips to reconnect with friends, family, people, and places from their past.

What this archetype wants:

Seekers are like personal archaeologists. They’re likely to engage in activities they used to love, trying to pick up on the scent of joy and engagement from the past. The external journey is a metaphor for the seeker’s ongoing internal search. They are deeply engaged in identity work, sensemaking on a path from uncertainty to certainty. A sabbatical beckons them to answer questions that have been bubbling up about the world and how they relate to it. Aspiring career switchers, or just folks looking to discern between garden-variety burnout and a strong signal to switch jobs, fit into this bucket.

I embarked on my sabbatical as a seeker. I had an overriding feeling that I’d ignored some core parts of myself for a long time. Primarily, that I’d neglected to make progress on deep questions within me about spirituality. I sensed that this project was important to me and required a lot more time and effort than I’d given it so far. I needed a more active approach if I wanted to make progress.

What traps this archetype falls into:

Seekers are prone to unrealistic expectations, which can put them at high risk of disappointment when they return from a sabbatical. The fact that they’ve finally made the time and space for something incredibly important and long overdue adds pressure to find the perfect next steps upon return. The kind of “good enough” steps that follow most sabbaticals—like staying at your old job for a bit longer or changing things up slightly before you make a big career move—are unlikely to satisfy seekers. In short, the quest for perfection becomes the enemy of success, spurring a more protracted sabbatical that becomes increasingly expensive and anxiety-inducing. Seekers should consider two strategies to avoid this vicious cycle.

Tips for this archetype:

1. Restrict the amount of time you allow yourself to seek. Seeking is a lifelong pursuit, not something you’re likely to knock out in several months. Instead of expecting to find your dream job (or state of being) by the end of your sabbatical, consider it a success to identify the next tangible step toward what your dream could be. You can control the process of the search, not the results. For example, have 20 coffee chats about career possibilities with people you admire. Your first sabbatical won’t be your last. To avoid burning out or spinning your wheels, it’s far better to end your sabbatical a bit closer to your ideal self than to flounder in the liminal state between the last thing and the next.

2. Prioritize others during your sabbatical. Seekers can easily lose track of time peering deep down into their personal rabbit hole. But sometimes the best way to learn about yourself is by setting your concerns aside entirely. Our research interviewees consistently recalled the time they spent with others as their most treasured moments. This finding emphasizes what the happiness literature has found over and over—that supporting other important people in your life and nurturing your relationships are instrumental to self-fulfillment.

The Explorer

Explorers have an appetite to see what’s out there in the world. They’re less concerned about how or if it will apply to their lives at the other end.

You don’t have to travel across the world to qualify as an explorer—I’ve met plenty of folks who, during their sabbaticals, spent time closer to home. They volunteered in their communities, experimented with new hobbies, and took shorter regional trips. All these detours took them out of their routine and cracked open their shells. Even going through a stack of books on your reading list can trigger awe. It can widen the aperture on what’s possible.

It’s rare to encounter a sabbatical that doesn’t incorporate a segment of pure exploration. Setting off on a journey is the most natural way to break free from routine life and to celebrate what’s next.

What this archetype wants:

For those who can afford to explore someplace far away, a sabbatical enables them to both get off the beaten path and to sufficiently sink in once they get there. As a burned-out marketing executive from our study put it, “I was having a very difficult time disengaging from my work for a couple of weeks after my sabbatical started, checking my personal email constantly and reflexively looking at my calendar. But as soon as my plane’s wheels left the runway en route to Bali—laptop behind—my work concerns evaporated completely.” Challenge yourself to get as far away from your routine experience of life as possible.

Exploration introduces uncertainty into our lives. In our world of limited vacation, we simply can’t afford to go off track and get lost. We can’t miss work, we can’t fly on weekdays, and we can’t risk traveling during shoulder seasons to save money. On sabbatical, you become time wealthy. Mistakes and inconveniences transform from vacation ruiners to serendipitous encounters. Exploring on our sabbaticals asks us instead to leave some important things to chance for a change. Living in the present and traveling flexibly leads to higher reported satisfaction among travelers and is correlated with greater psychological resilience.

What traps this archetype falls into:

While traveling far away may be the surest way to get out of your comfort zone, explorers who take an exotic, far-flung sabbatical often miss the beautiful things nearby, alienate themselves from their community, and set impossible standards for what quenches their sense of adventure. As a dyed-in-the-wool explorer, I know how difficult it is to go from a jet-setting expat to living a more sustainable life. But a well-balanced sabbatical (and life) requires also finding beauty in the mundane, closer to home.

Tip for this archetype:

1. Spend a significant portion of your sabbatical as a tourist in your own land. If you’re able to travel to far-flung places, do that first to completely disconnect and satisfy your more traditional sense of adventure. But then return to your routine surroundings and try to inhabit the energy that newcomers to a city have: Sign up for a special interest club, like a pottery or dance class. Volunteer locally or go to a city council meeting. It’s a totally normal mistake to treat your home turf as unworthy of the same attention and awe as some exotic locale. No matter how far afield your sabbatical takes you, the point isn’t to leave forever but to return home better off.

A field guide to your sabbatical

There’s a limit to how much you can prepare for a sabbatical.

There’s also a limit to how much you should prepare. You must make room for the unexpected opportunities that arise precisely from the lack of a plan, and the emotional discomfort that may accompany that. I get that this sounds like the worst-case scenario for the type As among us, but exploration enables growth, and growth requires discomfort. That’s the threshold you must cross on any journey of transformation, and the gooey insides of Sabbatical Land.

The advice you’ll appreciate the most from me won’t be a concrete recommendation for a beautiful beach or secluded retreat. Rather, it will be what helps you create the initial conditions for success: Your sabbatical should be long enough, it must contain the right components, and these pieces must be in the right order. These are the three guiding principles—setting, ingredients, and order.

This is a scaffolding to frame the journey in front of you, not be a “do this, don’t do that” checklist.

Three guiding principles

1. Setting: Duration and disconnection

Take big time off (more than you’re comfortable with)

In our time-scarce society, carving out enough uninterruptible time for your sabbatical is undoubtedly the most difficult step you’ll face. Is it any wonder that the most common question I receive is “How much time is sufficient for a sabbatical?”

I believe the sweet spot for sabbaticals is between six and 12 months1. It’s not easy to take such a big chunk of time off, which is why most people take an abbreviated break of a month or six weeks, kicking the can down the road for a longer break when things open up in the future. While you’ll still undoubtedly get some benefits from an extended vacation, which we’ll discuss further in chapter 11, don’t mistake a shorter break for a sabbatical. Not taking enough time off puts you at risk of ending up just short of the sought-after experience. It often takes six to eight weeks to begin to feel like you’re truly disconnected from routine life.

We’ve all experienced that moment toward the end of a vacation when we finally feel like we’re sinking in and enjoying ourselves, only to realize it’s time to head back to reality. It’s like that with sabbaticals. The most transformative aspects of sabbaticals often don’t start until at least two months in, after we’ve had a chance to heal and sufficiently leave our former life behind.

While there is ample evidence to suggest that anything under two months is too short, there’s less clarity on how long is too long. A small chunk of people I’ve interviewed felt as though being out of the workforce for more than a year felt right, and it worked for them. That said, I’ve witnessed significantly more folks who kept moving their return dates further and further into the future, getting caught in a cycle of self-doubt and guilt the longer they stayed outside of routine life. This makes sense: sabbatical takers feel enormous pressure to make their hard-won time off “worth it.” If their problems haven’t been resolved or their lives completely changed, can they really return to regular life satisfied?

Stressing yourself psychologically and financially while waiting for the perfect job is almost always worse than taking a modest next step toward your ultimate goals. So plan for sufficient time in Sabbatical Land, but consider setting a deadline for returning to work, even if—especially if—you haven’t yet landed on your dream job.

Set impenetrable boundaries to truly disconnect

Once you create the right container, you need to protect it by setting boundaries. This is more difficult than ever. Some 30 years ago, going abroad meant untethering. Your boss couldn’t reach you. Getting in touch with the folks at home meant an expensive long-distance call. As for the news, the New York Times didn’t go online until 1996, and you’d need to be in a cybercafe to read it. Now, a flick of the thumb could reveal a crisis at work, a salacious news story, or a pressing issue closer to home, and in the process spoil your serenity for the rest of the day.

I’m a purist when it comes to not working while on sabbatical, particularly when it comes in the form of consulting gigs related to your routine job. No matter how attractive the opportunity seems—no matter how little time it claims to take up—these projects tend to expand to fill the time available. A two-hour call once per week has a surprising amount of disruptive force, preventing spontaneity, requiring connectivity, and psychologically tying you to a past version of yourself. So set an away message and create a mail rule to auto-delete all inbound emails. Make sure your colleagues understand what constitutes a sufficient “break the glass” moment to interrupt your time off.

Money might be able to buy you incrementally more time off, but it can never replace the large swaths of uninterrupted time off it destroys. No matter how tempting it may seem, you will regret porous boundaries between your old life and your time off.

2. Ingredients: Embodiment and play

Get out of your head and into your body (and soul)

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How the founder of Morning Brew built a Claude content machine that never runs out of ideas | Alex Lieberman (Tenex)

Brought to you by:

• Firecrawl—Power AI agents with clean web data

• Customer.io—Build customer engagement campaigns from a single prompt

Claire sits down with Alex Lieberman, Co-founder and Co-Managing Partner of Tenex, to dig into the AI content system he’s built inside Claude. Alex walks through how he finds ideas, gets interviewed by AI, codifies his voice, and runs drafts through a “Writer’s Council” before posting.

Biggest takeaways:

1. The blank page is the real enemy of consistent content, and an AI Oracle makes it disappear. Alex’s Oracle scans seven days of Slack, Notion, Gmail, and meeting notes, then surfaces 15 ranked content spikes each day—half from internal sources, half from the X and LinkedIn accounts he follows. Writing is actually the easy part; knowing what to say is where most people stall.

2. Map your current workflow before you touch any AI tool. Alex’s process started with drawing out every step of content creation as it already existed, then rebuilding it from scratch with no constraints. He found that most companies discover huge inefficiencies in this step that have nothing to do with AI at all. AI just forced them to look.

3. AI slop is mostly a people problem, not a model problem. Alex’s framing here is blunt: the only time the Content Machine produces slop is when the person being interviewed doesn’t share interesting enough ideas during the interview. The model is shaping clay, not inventing ideas. If the clay is bad, the sculpture will be bad.

4. Your voice lives in a Markdown file, and that file changes everything. Alex’s Content Machine pulls from a personal voice guide that captures his top-performing posts, hook formulas, content structures, and even specific language patterns like “self-deprecating confidence.” The system drafts against that file, which means it’s calibrated to how Alex actually writes rather than to some average of the internet.

5. A reinforcement loop is how your AI system gets better every time you use it. After every piece, Alex’s Content Machine compares the original draft to the published version, extracts generalizable lessons, and asks if they should be added to a permanent lessons file. Over time, the system stops making the same mistakes. This is the kind of feedback loop most people never build.

6. The interview panel is the step most people skip and the one that matters most. The system deploys six interviewer personas (Tim Ferriss, Joe Rogan, Larry King, Howard Stern, Barbara Walters, Michael Barrow) that ask follow-up questions until they’ve extracted enough specifics to write something real. Alex voice-to-texts his answers via Wispr Flow, so every sentence in the final draft traces back to something he actually said.

7. Your employees are your most underleveraged marketing channel, and most companies are actively suppressing them. Alex ran a version of employee content at Storyarb called “Own the Internet,” and it drove 40% of all inbound leads that quarter. At Tenex, he launched the Creator Cup with $5,000 in prizes to make posting feel like a team sport, not a chore. His argument to skeptical CEOs: the fastest way to lose a great employee is to make it impossible for them to build a personal brand.

8. A bootstrapped company can out-distribute a venture-backed one if it’s willing to show the work. Alex’s case is simple: Tenex can’t rely on TechCrunch coverage or top-tier investor mentions, so the team’s content is the visibility strategy. If the Creator Cup gets him one great engineer, the $5,000 prize pool pays back many times over compared with a recruiting agency fee. For bootstrapped companies, distribution isn’t optional, but the whole game.

9. The Writer’s Council won’t let a draft go out below a 9 out of 10. Six writer personas (including a character Alex calls “the AI slop allergist”) each score the draft and run a revision loop until the aggregate clears the threshold. That scoring mechanism is also what keeps Alex from having to manually police every post, so the system holds its standard even when he’s in a hurry.

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.

Alex Lieberman co-founded Morning Brew in college and grew it into one of the most-read business newsletters in the world before selling it to Business Insider. Now he’s the co-founder and co-managing partner of Tenex. In this episode, Alex explains why distribution is becoming a durable moat, why founders and teams need to “climb Cringe Mountain,” and how he rebuilt his content process around AI without letting it produce generic slop. He walks us through every step of his Content Machine live: an Oracle that scans internal systems and the internet for content spikes, an interview panel that pulls out his real ideas, voice and style files that keep drafts sounding like him, an editorial council that scores and revises posts, and a lessons loop that learns from his feedback.

What you’ll learn:

1. Why the blank page is the biggest friction point in content creation, and how an AI Oracle eliminates it

2. How to map your current workflow before you add any AI

3. How Alex built a six-step Content Machine in Claude that goes from idea spike to publishable post

4. Why the interview step (not the drafting step) is where AI slop actually comes from

5. How to codify your voice in a Markdown file so an AI drafts in your register, not the internet’s average

6. Why your employees are your most underleveraged marketing channel right now

7. How the Tenex Creator Cup turned content creation into a team sport with a $5,000 prize pool

Brought to you by:

Firecrawl—Power AI agents with clean web data

Customer.io—Build customer engagement campaigns from a single prompt

In this episode, we cover:

(00:00) Introduction to Alex Lieberman

(02:35) Why Alex built a content machine

(06:56) Alex’s thoughts on AI slop

(09:00) Mapping the workflow from scratch

(13:24) The six-step Content Machine setup

(23:11) Live demo: Oracle, Interview Panel, and Writer’s Council in action

(30:38) Employee advocacy: the Tenex Creator Cup and $5K prize pool

(36:45) Lightning round: great engineers, AI use cases, slop fixes

Tools referenced:

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

• Wispr Flow (voice-to-text transcription): https://wisprflow.ai

• Notion: https://notion.so

• Linear: https://linear.app

• Slack: https://slack.com

Other references:

• Morgan Housel: https://www.morganhousel.com

• David Perell: https://perell.com

• Shaan Puri / My First Million podcast: https://www.mfmpod.com

• Gary Vaynerchuk: https://garyvaynerchuk.com

Where to find Alex Lieberman:

X: https://x.com/businessbarista

LinkedIn: https://www.linkedin.com/in/alex-lieberman/

Tenex: https://www.tenex.co/

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.

Elizabeth Stone is the Chief Product and Technology Officer (CPTO) at Netflix, where she oversees Engineering, Product, and Design. Since her first appearance on the podcast two years ago—which remained my second-most-popular episode for more than a year—she has expanded her role to lead product, in addition to engineering. Before Netflix, Elizabeth was VP of Science at Lyft, Chief Operating Officer at Nuna, an economist at Analysis Group, and a trader at Merrill Lynch.

In our in-depth conversation, we discuss:

1. Why “systems thinking” is now the most important skill she looks for

2. How to manage the flood of AI-generated output without losing quality or signal

3. How Netflix thinks about AI fluency as a universal expectation rather than a level-specific skill

4. What “excellence as an operating system” means

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 Elizabeth Stone:

• LinkedIn: https://www.linkedin.com/in/elizabeth-stone-608a754

Referenced:

• How Netflix builds a culture of excellence | Elizabeth Stone (CTO): https://www.lennysnewsletter.com/p/how-netflix-builds-a-culture-of-excellence

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

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

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

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

• Netflix’s “Keeper Test” and Why You Need It | Lorne Rubis: https://www.highlights.lornerubis.com/2015/08/the-netflix-keeper-test-and-the-courage-to-take-it

• Innovation for Filmmaking, By Filmmakers: Why InterPositive Is Joining Netflix: https://about.netflix.com/en/news/why-interpositive-is-joining-netflix

• InterPositive: https://weareinterpositive.com

• Netflix Prize: https://en.wikipedia.org/wiki/Netflix_Prize

• Quarterback on Netflix: https://www.netflix.com/title/81482895

• The Bill Simmons Podcast on Netflix: https://www.netflix.com/title/82186214

• Spencer Pratt on Instagram: https://www.instagram.com/spencerpratt

• Salman Rushdie’s Substack: https://salmanrushdie.substack.com

• Remarkably Bright Creatures on Netflix: https://www.netflix.com/title/81911351

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

• Tour de France: https://www.letour.fr/en

Recommended books:

• Thinking in Systems: https://www.amazon.com/Thinking-Systems-Donella-H-Meadows/dp/1603580557

• Into Thin Air: A Personal Account of the Mt. Everest Disaster: https://www.amazon.com/Into-Thin-Air-Personal-Disaster/dp/0385494785

• Liar’s Poker: https://www.amazon.com/Liars-Poker-Norton-Paperback-Michael/dp/039333869X

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

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

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What a harness is and how to build one with Claude Agent SDK

Brought to you by:

Bolt.new—Turn your idea into a real product

Customer.io—Build customer engagement campaigns from a single prompt

Claire explains why harnesses matter and when they’re better than general-purpose tools like Claude Code or Codex, and walks through the custom Claude Agent SDK harness she built to automate Sentry bug triage at ChatPRD. You’ll see how she structured the workflow, encoded permissions, connected tools like Sentry and Linear, and turned a repeatable engineering task into something an agent can run more consistently every time.

Biggest takeaways:

1. A harness is just code around an AI agent—nothing more mysterious than that. The term has taken on an almost mythical quality in engineering circles, but I strip it down in this episode: a harness is code you write to make an AI agent more effective at a specific job. Cursor is a complex harness. Claude Code is a complex harness. Yours can be eight files and a terminal UI.

2. Build a harness when the same workflow needs the same setup and the same outcomes every time. The trigger is recognizing a job that is partly deterministic (defined steps, defined tools) and partly non-deterministic (the AI figures out root causes and writes the report). Sentry bug triage qualified because every investigation follows the same evidence-gathering process and ends with the same artifact bundle.

3. Opinionated tool adapters beat general MCP access for specialized workflows. Rather than giving the agent broad access to the Sentry MCP and letting it wander through traces, I built a custom Sentry adapter that pulls exactly what matters for a bug report and nothing else. That specificity makes the agent faster, cheaper, and less likely to go off-script.

4. Encoding permissions in the harness removes the need to prompt them every single time. In a general-purpose coding tool, you have to remember to say “investigate only, do not write code.” In my harness, that is a flag in the interface. I click “investigate,” paste the Sentry link, and the agent already knows its constraints without being told.

5. Structured artifacts are what separate a one-off investigation from a team-wide resource. Every time my harness runs, it outputs a task log, a Sentry issue brief, relevant logs, a worker report, and an HTML summary file. That artifact bundle means the engineering team gets a consistent, scannable record of every bug investigation without anyone having to write it up manually.

6. A harness lets you do multi-model routing in ways a single general-purpose tool never could. Claude Code is Claude. Codex is GPT. A custom harness using the Claude Agent SDK lets you pick the right model per step, enforce different tool policies per invocation, and swap models over time without changing how the interface works. That flexibility is one of the strongest arguments for owning the harness layer yourself.

7. The open chat field has been good enough, until it stopped being good enough. I acknowledge in this episode that just typing into Claude Code has produced real work. But this marks a shift in my thinking: general-purpose agents are now better used to orchestrate specialized harnesses than to do every job themselves. Giving a constrained agent a specific harness gets more consistent output than giving a powerful agent an open prompt.

This solo builder runs 24/7 local AI on his own hardware | Alex Finn

Brought to you by:

Runway—The creative AI platform for images, video, and more

Jira Product Discovery—Prioritize with insights, build with confidence

Claire talks with Alex Finn about how he built a 24/7 local AI fleet using Mac Studios, a DGX Spark, an RTX 5090, and a custom dashboard to keep agents running around the clock. Alex breaks down what each machine is actually good for, how he routes work across local models like GLM, Qwen, and Ornith, and why “unlimited inference” changes the entire economics of AI workflows. They also get into his Claude Code build-and-review loop, his OpenClaw and Hermes setup, and the surprisingly practical playbook behind running your own always-on software factory.

Biggest takeaways:

1. The case for local AI isn’t ROI; it’s unlimited inference. The math on a $10,000 Mac Studio vs. a $20 ChatGPT subscription only looks crazy until you run an agent 24/7. At that scale, cloud APIs get expensive fast, and local models running around the clock open use cases that simply aren’t economically viable otherwise. Alex runs security scans, code reviews, and social signal monitoring on a continuous loop that would cost thousands a month in cloud credits.

2. Each hardware tier has a job. Mac Studio handles massive models slowly but at Opus-level intelligence (Alex runs GLM 5.2, which he calls Opus 4.8-equivalent, on a single Mac Studio). DGX Spark is the sweet spot: 128 GB of Nvidia unified memory plus CUDA speed, enough for models like Qwen 3.6 running fast. The RTX 5090 has only 32 GB of VRAM but is cloud-speed fast. Buy for the task, not the spec sheet.

3. Tailscale is the connective tissue for a multi-machine setup. Once all your machines are on the same Tailscale network, one agent (OpenClaw or Hermes) can hop across them, check the hardware, load the right model, and get it running without any manual configuration. Alex says there’s truly no technical knowledge required once Tailscale is installed, and he recommends it even if you only have one machine, because it also lets you test local apps from your phone.

4. Local models are the BDR; Claude Code is the closer. Alex’s security scanning loop is a good example of the hybrid model that actually works. A local model (GLM 5.2) scans code every 20 minutes and dumps findings into a Markdown file. Claude Code checks that report once a day and decides what’s real and worth fixing. The local model does the volume work cheaply; the frontier model does the judgment work precisely. Trying to run Claude Code every 20 minutes instead would cost thousands a month.

5. The software factory runs on two loops and a rocket emoji. Every morning, Alex does a planning session in Claude with a “morning build” prompt that produces a task list for his SaaS. The build loop picks those up and starts shipping. The review loop checks the work. When something passes review, Alex gets a Slack ping, and leaving a rocket emoji on it triggers an automated merge. He goes from morning brief to reviewing and merging code without touching the keyboard again until he does the approval round.

6. OpenClaw and Hermes fill different needs, and you probably want both. Alex prefers OpenClaw for the “big bang” wow moments and the emotional connection (his words). But Hermes has been more reliable under repeated updates. His solution is redundancy: three Hermes agents and two OpenClaw agents running simultaneously, so when three of the five are broken (which happens), the other two can fix them. The failover is deliberate, not accidental.

7. Task allocation by model intelligence is the skill that makes the fleet useful. GLM 5.2 is Opus-level smart but painfully slow, so it gets the deep, latency-tolerant work. Qwen 3.6 is quick and good enough to read Twitter for product signals. Ornith 1.0, a Qwen fine-tune with reinforcement learning baked in for coding, has beaten Qwen on every eval Alex has run and runs comfortably on a DGX Spark. The insight is that “smartest model everywhere” is wasteful; matching model intelligence to task complexity is what makes ambient AI economically coherent.

8. The vague posting about loops is partly a competitive moat. Alex’s theory: the companies building the best AI coding infrastructure (including OpenAI and Anthropic themselves) have internal loop systems that are their last real competitive advantage. If you can pump out high-quality code faster than anyone else because your build-review loop is better, you don’t go publishing a how-to. Claire’s counter-theory: most people vague-post because their loops are boring and vagueness gets more engagement than specifics. Both are probably true, depending on who’s doing the posting.

GPT-5.6 Sol vs. Claude Fable: Why OpenAI’s new model crushes my benchmark

Claire puts GPT-5.6 Sol head-to-head with Claude Fable, Sonnet 5, and the rest of the GPT-5.6 lineup using her own five-part benchmark for real product work. The result: Sol becomes her new daily driver. Claire breaks down exactly why and also shares where she’d still use Terra, Sonnet, or Fable instead.

Biggest takeaways:

1. GPT-5.6 Sol is the most practically effective model I’ve tested, even if Fable is theoretically smarter. I ran a five-category benchmark across PRDs, prototypes, wireframes, debugging, and agentic voice, and Sol had the highest taste score by a significant margin on the 70% Claire/30% machine split. That gap between “hyper-intelligent” and “actually ships” is real, and for product work Sol wins.

2. Full-fidelity prototypes from Sol are more functional and more opinionated than anything else I’ve tested. Across a doc scheduler, a dev tools incident triage site, and a consumer habit tracker app, Sol consistently produced designs with better visual hierarchy, semantic color use, and working interactivity. Fable’s outputs were fine; Sol’s were the ones I’d actually show a stakeholder.

3. Sol’s writing is just easier to work with. Fable writes like it has never met a human before, incredibly pedantic and almost inscrutable when you need to collaborate. Sol writes like a normal person, and that difference compounds fast when you’re iterating on PRDs or talking to an agent all day.

4. For PRD writing specifically, GPT-5.6 Terra might be the better pick. I asked Sol to greenfield-rebuild my approach to PRDs for 2026, and while Sol’s output was excellent, Terra’s clean, direct, no-frills business writing made me think it’s the right call when you want crisp, fast documentation without extra flair.

5. Fable gets too locked in its own frameworks; Sol is willing to reconsider. I had a hardened tool-calling loop in my prototyping product that only GPT-5.5 could run. Fable insisted it was a model problem and refused to budge. The moment I switched to Codex and told it to just fix it, Sol got Sonnet 5 working in one shot. That kind of practical flexibility is exactly what you need when building real products.

6. Sonnet 5 is still my favorite for agentic voice in Open Claw. Even after this whole benchmark, I gave Sonnet 5 a gold star for voice: aside from the dashes, it sounds the most human. I use Sonnet for my OpenClaw and I’m not changing that. Sol did a worse job on agentic voice overall, and I still can’t get GPT models to run well in my OpenClaw setup.

7. GPT-5.6’s video editing via Codex is one of my favorite new workflows. I dropped in a full recording from a talk I gave at Cursor’s event, asked for five hype-video clips, gave feedback on pacing and orientation, and had shareable social clips I could drop into CapCut in a fraction of the time. This use case alone justifies experimenting with GPT-5.6.

8. Browser use with Codex plus GPT-5.6 and @Chrome is the best agentic workflow I’ve found. I opened LinkedIn, told it to reply to high-value messages from executives and ChatPRD fans, and it burned through roughly 500 messages. I’ve also used it to test web apps and fill out forms. When I got rolled back to GPT-5.5 temporarily, my life was measurably worse. Learn @Chrome and just let it rip.

9. The “forest green” tell is real, so name it in your prompts. Sol has a strong aesthetic bias toward what feels like a woodland-themed palette hardcoded somewhere in its system. You will see a lot of green. I told the OpenAI team, I’m noting it here, and I’m already prompting against it when I want a different aesthetic direction.

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.

Alex Finn is an AI builder, YouTuber, and the creator of Vibe Code Academy, a community for people learning to build with AI tools. He runs one of the most ambitious local AI setups I’ve come across: three Mac Studio 512 GB machines, a DGX Spark, and a custom RTX 5090 build, all coordinated through a fleet dashboard he built himself. He’s spent five months figuring out which local models belong on which machines, how to wire them to Claude Code loops, and how to get a software factory running without babysitting it.

What you’ll learn:

1. How Alex chose between a Mac Studio (512 GB unified memory), DGX Spark, and RTX 5090, and what each is actually good for

2. Why Tailscale is worth installing even on a single machine, and how it lets one agent manage your entire hardware fleet

3. How the build loop and review loop in Claude Code work

4. How to allocate tasks by machine and model

5. Why unlimited local inference changes the use-case math in a way a $20 cloud subscription never can

6. What OpenClaw and Hermes are each best suited for, and why Alex runs five agents total with failover baked in

Brought to you by:

Runway—The creative AI platform for images, video, and more

Jira Product Discovery—Prioritize with insights, build with confidence

In this episode, we cover:

(00:00) Intro

(02:58) Alex’s hardware stack

(03:48) What “ambient AI” means

(04:15) Alex’s red-pill moment with OpenClaw

(07:04) Mac Studio vs. DGX Spark vs. RTX 5090

(13:24) How to set up local models with no technical knowledge (Tailscale + OpenClaw/Hermes)

(17:16) Fleet control dashboard: assigning 24/7 tasks across machines

(20:42) Local models as security scanners feeding Claude Code

(22:25) How Alex allocates GLM 5.2, Qwen 3.6, and Ornith 1.0 by task

(24:28) OpenClaw vs. Hermes: the honest comparison

(26:55) The software factory: build loop, review loop, rocket emoji

(31:55) Lightning round: favorite hardware, favorite model, prompting style

(34:46) Where to find Alex

Tools referenced:

• Claude Code: https://claude.ai/code

• OpenClaw: https://openclaw.ai/

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

• Tailscale: https://tailscale.com/

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

• GLM 5.2 (z.ai): https://huggingface.co/zai-org/GLM-5.2

• Qwen 3.6 (Alibaba): https://huggingface.co/Qwen/Qwen3.6-35B-A3B

• Ornith 1.0: https://github.com/deepreinforce-ai/Ornith-1

• Gemma 4: https://huggingface.co/collections/google/gemma-4

• Playwright (browser testing): https://playwright.dev/

• Vercel (preview deploys): https://vercel.com/

Other references:

• DGX Spark (Nvidia): https://www.nvidia.com/en-us/products/workstations/dgx-spark/

• Mac Studio (Apple): https://www.apple.com/mac-studio/

• How to design AI agent loops: schedules, goals, and subagents in Claude Code and Codex: https://www.lennysnewsletter.com/p/how-to-design-ai-agent-loops-schedules

Where to find Alex Finn:

LinkedIn: https://www.linkedin.com/in/alex-finn-1848684a

YouTube: https://www.youtube.com/@AlexFinnOfficial

X: https://x.com/AlexFinn

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.