Subscribe now

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.

Subscribe now

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.

Subscribe now

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.

Subscribe now

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

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)

Read more

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:

Read more

Read more

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.

Noam Segal is a longtime research leader across Airbnb, Meta, Twitter, Zapier, Intercom, and Figma, a certified coach, AI builder, and my community research lead. Together, we run the annual Tech Worker Sentiment Survey, now in its second year and one of the largest of its kind: a quantitative study of how people in tech actually feel about their jobs, AI, burnout, and the future of their careers. This year’s survey captured responses from thousands of workers across product, engineering, design, research, marketing, data, and sales, and the results are striking.

In our in-depth conversation, we discuss:

1. Why AI has split the tech workforce almost exactly in half—one half that’s thriving, another that’s shaken

2. The four emotional archetypes defining tech workers right now (the Energized, the Conflicted, the Disoriented, and the Resentful)

3. Why burnout has jumped an alarming 11 points in a single year

4. Why nobody in tech would recommend their job to someone entering the industry today

5. The #1 fear in tech right now (it’s not job loss to AI)

6. Why managers are the single biggest lever for employee well-being

7. Concrete advice for what employees and leaders can do right now

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 Noam Segal:

• X: https://x.com/noamseg

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

Referenced:

• How tech workers are feeling in 2026: a workforce splitting in two: https://www.lennysnewsletter.com/p/how-tech-workers-are-feeling-in-2026

• How tech’s most resilient workers handle burnout: https://www.lennysnewsletter.com/p/how-techs-most-resilient-workers

• Please stop the AI Confidence Theater: https://www.elenaverna.com/p/please-stop-the-ai-confidence-theater

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

• NPS Is The Worst: https://www.npsistheworst.com

• The Terminator: https://www.imdb.com/title/tt0088247

• Skynet: https://terminator.fandom.com/wiki/Skynet

• Inside Devin: The world’s first autonomous AI engineer that’s set to write 50% of its company’s code by end of year | Scott Wu (CEO and co-founder of Cognition): https://www.lennysnewsletter.com/p/inside-devin-scott-wu

• Devin: https://devin.ai

• An AI state of the union: We’ve passed the inflection point, dark factories are coming, and automation timelines | Simon Willison: https://www.lennysnewsletter.com/p/an-ai-state-of-the-union

• Redeploying Fable 5: https://www.anthropic.com/news/redeploying-fable-5

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

• Inside Linear: Building with taste, craft, and focus | Karri Saarinen (co-founder, designer, CEO): https://www.lennysnewsletter.com/p/inside-linear-building-with-taste

• Building beautiful products with Stripe’s Head of Design | Katie Dill (Stripe, Airbnb, Lyft): https://www.lennysnewsletter.com/p/building-beautiful-products-with

• 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

• OpenAI Codex lead on the new shape of product work | Andrew Ambrosino: https://www.lennysnewsletter.com/p/openai-codex-lead-on-the-new-shape

• Elon Musk: ‘Chances are we’re all living in a simulation’: https://www.theguardian.com/technology/2016/jun/02/elon-musk-tesla-space-x-paypal-hyperloop-simulation

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

GPT-5.6 Sol is back, and I ran it through my full How I AI vibe benchmark against GPT-5.6 Terra, Luna, Claude Fable 5, and Sonnet 5 across five categories: PRDs, prototypes, wireframes, debugging, and agentic voice. Sol won by a meaningful margin on my Claire Weighted Index (70% my taste, 30% Terminal Bench 2.1), and I also tested two use cases I can't stop thinking about: building a gamified homework tracking app for my kids in one shot with Codex, and browser automation with Chrome that burned through 500 LinkedIn replies while I did literally nothing.

What you’ll learn:

1. How I scored five AI models (including GPT 5.6 Sol, Fable 5, and Sonnet 5) using my “Claire Weighted Index” benchmark across PRDs, prototypes, code, and agentic voice

2. The difference between GPT-5.6 Sol (Terra) and Sol for PRD writing

3. How Fable’s precision and pedantry made it harder to collaborate with, and the exact moment Sol broke through where Fable got stuck

4. Why Sonnet 5 is still my go-to for agentic voice in OpenClaw, even after this whole benchmark

5. How I used GPT-5.6 Sol in Codex to build a fully gamified homework tracking app for my kids in one shot

6. The video editing use case that saved me hours clipping a talk I gave at Cursor’s event

7. How to use Codex plus GPT-5.6 and Chrome for browser automation, and why this is my single most-loved use case right now

In this episode, I cover:

(00:00) Intro

(01:10) The three GPT-5.6 models: Sol, Terra, Luna

(02:17) Pricing: Sol vs. Fable API costs

(03:24) The How I AI benchmark

(05:03) Claire-weighted Index results

(07:00) Per-task winners: prototypes, PRDs, agentic voice

(11:59) What Claire actually rewards

(13:20) Full-fidelity prototype side-by-sides (Sol vs. Fable)

(17:45) Wireframes

(18:19) Agentic voice

(19:15) Where Sol is better than other models

(23:56) Gamified kids’ homework app, built in one shot

(28:02) Fable’s pedantry problem and how Sol broke through it

(31:49) Two bonus use cases: video editing and browser use

(35:08) Final summary and model recommendations

Tools referenced:

• GPT 5.6 (Sol, Terra, Luna): https://help.openai.com/en/articles/20001325-a-preview-of-gpt-56-sol-terra-and-luna

• Codex: https://openai.com/codex

• ChatPRD: https://www.chatprd.ai/

• CapCut: https://www.capcut.com/

• Math Academy: https://www.mathacademy.com/

Other references:

• Cursor event where Claire spoke on the future of PM: https://www.youtube.com/watch?v=4CAFK-rc26A

• ChatPRD blog (where benchmark outputs will be published): https://www.chatprd.ai/

Where to find Claire Vo:

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

Website: https://clairevo.com/

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

X: https://x.com/clairevo

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

Adam Mosseri is the Head of Instagram, where he oversees an app used by over 3 billion people. He also leads the team building Threads. Adam has run Instagram for longer than its founders did, after taking over from Kevin Systrom and Mike Krieger in 2018. A designer by training, he spent over 15 years at Meta, starting as a designer on Facebook’s mobile app, rising to lead Facebook’s News Feed, and eventually chosen to lead Instagram. During his tenure, Instagram’s user base has more than tripled.

In our in-depth conversation, we discuss:

1. How the canonical product team structure is changing in 2026, from baker’s-dozen specialist teams to lean pods of four to six generalists

2. The rise of the “product staff” role—a blending of PM, design, data science, and research into one generalist operator

3. Why Adam is bullish on designers even as functional boundaries dissolve, and which roles are most at risk

4. What the Instagram algorithm knows about you, and why it’s only now catching up to what people assumed it knew years ago

5. Why the rise of AI-generated content is a tailwind for Instagram, and how the company is thinking about creator identity in a synthetic-content world

6. The two biggest product failures of Adam’s career—Facebook Home and the first version of Reels

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 Adam Mosseri:

• X: https://x.com/mosseri

• LinkedIn: linkedin.com/in/mosseri

• Instagram: https://www.instagram.com/mosseri

• Threads: https://www.threads.com/@mosseri

Referenced:

• 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

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

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

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

• A rational conversation on where AI is actually going | Benedict Evans: https://www.lennysnewsletter.com/p/a-rational-conversation-on-where

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

• Mythos: https://www.anthropic.com/claude/mythos

• Fable: https://www.anthropic.com/claude/fable

• Pluralistic: The Reverse-Centaur’s Guide to Criticizing AI: https://pluralistic.net/2025/12/05/pop-that-bubble

• Plastic Dream Sequence on Instagram: https://www.instagram.com/plasticdreamsequence

• TikTok: https://www.tiktok.com

• Facebook–Cambridge Analytica data scandal: https://en.wikipedia.org/wiki/Facebook%E2%80%93Cambridge_Analytica_data_scandal

• Facebook Home: https://en.wikipedia.org/wiki/Facebook_Home

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

Everybody is saying, “It’s not the model, it’s the harness,” but almost nobody stops to explain what a harness actually is. So I did. I built one live on the show: a Sentry bug-debugging harness for my company ChatPRD, using the Claude Agent SDK, a custom terminal UI built with the Ink library, and opinionated adapters for Sentry, Linear, GitHub, and Vercel. The harness handles evidence gathering, root-cause analysis, and follow-up artifact creation, all without me needing to type “dear agent, please fix this bug” ever again. I also walk through the architecture, share the code structure, and give you the exact process I used so you can build your own harness for any repetitive, structured workflow in your business.

What you’ll learn:

1. What a harness actually is

2. When to build a harness versus when to stick with a general-purpose tool like Claude Code or Codex

3. How to encode specific permissions into a harness

4. The three components every harness needs

5. How I used GPT-5.5 and Claude Opus to build the harness code itself (and where they both initially resisted)

6. How to structure the artifacts your harness produces so the whole team can use the output

Brought to you by:

Bolt.new—Turn your idea into a real product

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

In this episode, we cover:

(00:00) What is an AI harness?

(03:19) When to build a harness

(04:33) Why Claire picked bug triage

(06:00) Why not just use Claude Code?

(07:48) Demo: The custom harness interface

(11:04) Architecture: runs, tasks, tools, and artifacts

(13:44) Building it with Codex and Claude

(15:08) Code map and file layout

(16:51) A look at the code

(19:18) The live investigation result

(21:01) How to build your own harness

Tools referenced:

• Claude Agent SDK (Anthropic): https://code.claude.com/docs/en/agent-sdk/overview

• Claude Sonnet 4.6 (model used inside the harness): https://www.anthropic.com/news/claude-sonnet-4-6

• Claude Opus (used to build the harness): https://www.anthropic.com/claude/opus

• GPT-5.5 (Codex, used to build the harness): https://openai.com/index/introducing-gpt-5-5/

• Ink (terminal UI library for Node.js): https://github.com/vadimdemedes/ink

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

• Linear (project management): https://linear.app/

• GitHub: https://github.com/

• Vercel: https://vercel.com/

Where to find Claire Vo:

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

Website: https://clairevo.com/

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

X: https://x.com/clairevo

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

Subscribe now

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

A year ago, we ran our first large-scale survey of how tech workers feel about their jobs and careers. We summed up what emerged in four words: burned out, but optimistic. Today we’re back with the results from our 2026 survey, and it’s a tale of two workforces.

One half feels amplified by AI—more capable, more confident, more excited than they’ve been in their entire career. The other half feels shaken by it—less sure of their value and whether there’s still a place for them. Which side of that line people fall on predicts how they feel about their career more than anything else, including their current role, seniority, company size, or any other measure we collected. The workforce is bifurcating into two realities.

But there’s more: Burnout overall jumped 11 points in a single year, and four in 10 respondents are worried about losing their job. Even those who feel optimistic about their own career may not recommend that friends follow their path. In the AI era, everyone agrees the ground is moving. No one is sure yet if it’s an earthquake or a launch.

We think these findings are important enough that we’re making this post free for everyone.

Let’s break it down.

Our biggest takeaways

1. The workforce is splitting in two. Tech workers are either amplified by AI or shaken by it, and that divide shapes their feelings about work more than any title, tenure, or company.

2. Burnout is surging, and optimism is fading. Significant burnout rose from 44.7% to 55.7% of respondents, while career optimism fell from 54.8% to 48.7%. Those who feel destabilized by AI are feeling the least optimistic and the most burned out. A worrisome trend.

3. Tech workers wouldn’t recommend their own field. More than half (53%) would steer a newcomer away from a career in their role, even though they’re optimistic about their own future.

4. Productivity is up, but quality is questionable. 82% say AI is making them measurably more productive, but many worry the gains are coming at the cost of the sharpness of the work and the worker.

5. The underlying fear is of being overworked. Only 22% worry about “losing my job to AI.” Far more worry about being expected to do more for the same pay (51%), getting trapped in an unsustainable pace (46%), and the quality of their work going down (41%).

6. Almost everyone is ambivalent. 77% of respondents picked at least one positive and one negative emotion about AI. The average person selected more than five emotions. The defining feeling about AI is ambivalence.

7. Designers and researchers are the most worried. They report the most AI anxiety, the most fear of job loss, the worst-rated managers, and the lowest willingness to recommend their field. It’s a continuation of a trend we flagged last year.

8. Founders are still the happiest people in tech, and small companies are still the best places to work. Both findings replicate from 2025, and both are statistically robust.

9. Managers are still the biggest lever for happiness. Manager quality remains the strongest driver of burnout and one of the strongest drivers of everything else.

10. The industry, in tech workers’ own words, is “chaotic.” Asked to describe the state of tech in a sentence, the most common theme by far was chaos, though the sentiment was split almost evenly between excitement and dread.

Takeaway 1: The workforce is splitting in two

To understand AI’s deeper impact on people, we asked an existential question: How has working with AI shifted how you see yourself as a professional? We gave respondents five options. Here’s how they responded:

• “Amplified (I can do more, and better)”: 49.0%

• “Redefined (My role is changing shape, but I don’t see that as clearly positive or negative)”: 27.4%

• “Destabilized (I’m less sure where I stand or what’s really mine)”: 13.9%

• “Diminished (I feel less essential or less valuable)”: 5.0%

• “Unchanged”: 3.2%

When we lined up that question against the rest of the survey, the four identity groups differed dramatically:

As you go from “amplified” to “diminished,” optimism collapses, burnout climbs, layoff fear climbs, and willingness to recommend the field falls off. The people who feel amplified by AI are thriving. Those who feel diminished by it are in distress on every measure.

To make sure this wasn’t an artifact, we ran the numbers a few different ways:

• In a regression pitting every variable against each other, AI-identity stance was the single strongest predictor of career optimism (standardized β = +0.39) and of whether someone would recommend their field (β = +0.60)—stronger than role, level, and company size combined.

• As an effect size, the gap between the “amplified” and “diminished” groups on optimism is large (Cohen’s d ≈ 1.55). For context, the famously strong “founder effect” we’ll discuss later clocks in at d ≈ 0.56. The AI divide is roughly three times as large as that. It is, by a wide margin, the biggest effect in the dataset.

The question that best predicts how a tech worker feels about their work, in 2026, is no longer “What do you do?” or “Where do you work?” It’s “What has AI done to your sense of who you are?”

Meet the four tech workers of 2026

We did one more pass on this data: instead of using a single identity question, we clustered respondents based on the full pattern of emotions they reported about AI. Four types emerged, and you almost certainly recognize them.

The Energized (41%). The all-in adopters. They lead with “excited” (91%), “curious” (83%), and “hopeful” (59%). They’re the most optimistic group, the least burned out, and the only segment with a clearly positive read on their field. For them, AI truly seems like a superpower.

“Product has become fun again! You become an explorer, you play around . . . you spend long hours full of excitement. We’re in an amusement park.” —PM, Principal IC

The Conflicted (35%). The ambivalent center of gravity—and the largest group after the Amplified. Their signature emotions are “conflicted—holding positive and negative feelings at once” (68%)—and “curious” (64%), trailed closely by “overwhelmed” (56%) and “tired” (55%). They haven’t soured on AI; they’re just exhausted by the work of keeping up with it while holding two feelings at the same time.

“I’m simultaneously having the most fun I’ve had as a product builder and also feeling the most uncertainty I’ve felt. I’m confident I’ll be able to keep my skills sharp and adapt, but I’m not yet sure what it is that I’ll need to adapt into.” —PM, Senior IC

The Disoriented (12%). Defined almost entirely by one feeling: “disoriented—my role keeps shifting,” layered with “overwhelmed” (74%) and “tired” (73%). These are people watching their job change shape beneath them faster than they can find their footing again. They still think AI is somewhat useful. They’re not “refusers.” They’re just losing the thread of their role in the workplace.

“Things are so uncertain, we’re like farmers on the cusp of the industrial revolution. We know going into farming is the wisest long-term career choice, but we don’t see a clear path. This kind of uncertainty crowds out productivity.” —VP Product

The Resentful (12%). The burned-out and checked-out. Every one of them selected “resentful—I feel pressured to use AI,” and they cluster with “tired,” “conflicted,” and “overwhelmed.” They report the lowest optimism, the lowest willingness to recommend their field, and the lowest sense that AI is helping them at all. This is AI fatigue transformed into resistance.

“Tech overall kind of sucks right now. We used to adopt new technology because we were excited about the cool new things we could do. Now all we hear is ‘Use AI or you will lose your job’—and then people get fired anyway. I hate it.” —Director of Product

Takeaway 2: Burnout is surging, and optimism is fading

Significant burnout is now the majority experience for tech workers. 55.7% of working tech professionals report significant burnout—meaning they describe themselves as “moderately,” “very,” or “completely” burned out. Last year, that number was 44.7%. More than a quarter (26.2%) are now “very” or “completely” burned out.

Career optimism is dropping. Fewer than half (48.7%) of respondents are optimistic about the future of their career (down from 54.8% being optimistic last year). We’ve gone from “burned out but optimistic” in 2025 to “significantly burned out, and not that optimistic” a year later. We’re curious (and a little scared) about how this will look in a year.

That being said, job enjoyment is holding up: 42.6% enjoy their work “very much” or “extremely”; another 36.7% rate it “moderately”; and only about one in five (20.6%) enjoy it slightly or not at all.

Why the apparent contradiction? Enjoyment, burnout, and optimism are different constructs. Enjoyment is about the work itself, and people still like the work. Burnout is about pace, and people are increasingly worn out by how much they have to do. Optimism is about where things are heading. You can love your craft, be worn out by how much of it you’re doing, and feel doubt about the future all at once.

How worried are you about layoffs?

This year, we also added a question to the survey: How worried are you about being laid off in the next year?

41.2% are at least moderately worried, including 19.9% who are “very” or “extremely” worried. 28% aren’t worried at all. So roughly four in 10 tech workers are carrying real job-security anxiety into their week—a sizable undercurrent.

What makes layoff worry worth its own question is how tightly it’s bound to everything else. Of all the things we measured, layoff worry is the single strongest correlate of career pessimism (r = –0.47). Nothing else tracks negative outlook as closely. When people are scared of losing their jobs, their optimism goes first.

We’ll come back to who is most worried later. It’s not who you might guess.

Takeaway 3: Tech workers wouldn’t recommend their own field

This year, we asked an NPS-like question about people’s careers and roles: On a scale of 0 to 10, how likely are you to recommend a career in your role to a friend starting out today?

More than half of working tech professionals would actively steer a newcomer away from the path they chose. That translates to an average NPS score of –39. Moreover, a third of the people who call themselves optimistic still wouldn’t recommend their own field.

The cleanest way to say it: “The water’s fine; don’t come in.” People have largely made peace with their own trajectory. They’ve got the skills, the relationships, and the seniority to ride it out. But they’ve lost faith that the on-ramp still works for someone behind them.

“I’m lucky I’m later in my career . . . AI can augment what I’ve built. I think I won’t be in a position to hire and mentor new PMs, but I’ll be safe. Which feels really crappy to say.”

“I’m at the point where I can just retire and choose not to, so I’m not worried about my own career. But I’m worried about the younger generations.”

The recommendation score varies enormously by role, and the spread is its own story.

Founders would (just barely) still wave you in. Designers and researchers very much would not. And the score climbs steadily with seniority: senior and staff-level individual contributors are the least likely to recommend their field (both at NPS –49), while VPs (–23) and founders (–5) are the most. The further up you’ve climbed, the more the ladder still looks worth it; the people on the rungs below are the ones telling others not to start the climb.

Takeaway 4: Productivity is up, but quality is questionable

Given the rising burnout, the layoff anxiety, the doom in the discourse, you’d expect tech workers to be rather sour on AI. They’re not.

At the individual level, the AI numbers are among the most positive in the survey. 82% say AI is already making them at least moderately better at their job, and nearly half (49.4%) say “very much” or “extremely.” 60% feel confident or ahead of their peers in AI skills, compared with just 22.5% who feel anxious or behind.

But then we looked closer at what “better at my job” means. When we asked people to describe in their own words how AI had changed their work, “better” turned out to mean producing more and faster, but not higher quality. The productivity gains are coupled with deep unease about the costs of leveraging AI.

“I can do more, faster, but not better.”

“Amplified and destabilized at the same time. We just set a new denominator for the job. And it moves higher and higher every month.”

And the cost isn’t only in the quality of outputs. A striking number of people described their focus, their judgment, and their thinking as suffering:

“I’m amplified, but my brain is rotting, and my work feels worse.”

“I feel like I don’t think hard enough anymore—I just follow Claude. I don’t fully understand what I merge.”

“I miss feeling smart and having aha moments. I miss talking [to] and brainstorming [with] humans instead of machines.”

The productivity gains are real, but the quality of the work and the sharpness of the person producing it are taking a hit. The bar keeps rising to match what AI makes possible, and a growing share of people feel that neither the output nor their own mind is keeping up.

Takeaway 5: The underlying fear is of being overworked

Respondents’ number-one worry about AI’s impact on their career is the squeeze—AI raised the bar for output, and the reward was . . . more output expected, for the same paycheck.

They’re scared that the work will get harder, faster, and cheaper, and that they’ll be expected to keep smiling through it.

It feels like the dominant narrative about AI and work has been about replacement: the robots are coming for your job. Clearly, that’s not what tech workers are most afraid of. “Losing my job to AI” came in near the bottom of the list, at 22%.

Remember the “AI is replacing parts of my job” question? Half of the respondents say it’s happening to at least a moderate extent. You’d expect that feeling to drive layoff anxiety, but it doesn’t. The correlation between “AI is taking over parts of my job” and “I’m worried about being laid off” is essentially zero (r = +0.05).

What people are actually worried about is being asked to do more for the same pay, and watching the quality of their work slip.

It shows up vividly in the open-ended answers:

“More and more work is being handed off to me because I can use AI to get it done. But that makes it impossible to keep up with quality standards and not burn out.”

“AI helps with the toil, but then it’s also an enabler to do even more toil.”

“When we automate intellectual tasks, we’ll have to do high-value creative or strategic work only—doing that eight hours a day is not realistic. I used to take rest during repetitive tasks.”

This might sound like it contradicts the layoff worry from earlier. It doesn’t. People fear layoffs, but they mostly don’t blame AI for them. What they fear from AI is being buried in more work.

Add this all up, and you get a workforce that’s more productive than ever but quietly dreading what comes next. The speed AI unlocked got plowed straight back into expectations. Every gain becomes the new baseline, and the people expected to hit it are running out of room to breathe.

Takeaway 6: Almost everyone is ambivalent

If there’s one feeling that defines tech workers’ relationship with AI in 2026, it isn’t excitement, and it isn’t fear. It’s both, at the same time.

We asked people to check off every emotion that described how they feel about AI in their work. Here’s the full list, in order:

The two leaders are unambiguously positive (curious, excited). But the next cluster (if we ignore “conflicted”) is made up of people who are overwhelmed and tired. People are curious and overwhelmed. Excited and tired. Only 33% feel “hopeful,” even though 64% feel “excited.” Excitement about the present is running well ahead of hope about where this all goes.

Nikhyl Singhal named this phenomenon “smiling exhaustion.” The burnout of a few years ago was grim—all overhead and no agency. Today’s is different. People are shipping again, compensation has climbed, and many roles seem reborn. The catch is that there’s no off-switch: the tempo is brutal, and the rules rewrite themselves every month. It’s relentless, but it can also be exhilarating.

You see this in that 51% explicitly selected “holding positive and negative feelings at once.” But that undercounts the real ambivalence. When we looked at who picked at least one positive and at least one negative emotion, the number jumped to 77%. The average respondent selected five or more emotions (one person selected 13). It’s a workforce in which three out of four individuals are carrying a complex set of emotions about work.

Takeaway 7: Designers and researchers are the most worried

If AI is dividing the workforce, the obvious question is: along what lines? Who’s getting amplified, and who’s getting left behind?

The clearest pattern is by role: designers and researchers are at the epicenter of AI anxiety across the board, while founders and executives are feeling the best. We measured the share of each role that landed in negative identity or emotional buckets, and the spread is stark:

Among researchers, 51% are “anxious about my job security,” versus 15% of founders. Among designers, 63% feel “overwhelmed by the pace of change” and 61% feel “tired,” the highest of any role. Researchers are among the most likely to fear “losing my job to AI” (36%, just behind Data/Analytics at 38%), and designers are the most likely to feel the comp squeeze (61% selected “expected to do more for the same compensation”). Both report the lowest willingness to recommend their field of any role, and designers, as we’ll see, report the worst-rated managers in the survey.

Last year, designers and researchers showed the largest negative sentiment shift of any group. A year later, they’re the most negative on nearly every measure we have.

As a researcher, I’m acutely aware of the years of insecurities plaguing the research community. The biggest discussions for us have always been about getting a seat at the table and democratizing research across other functions. Many now feel the seat is being pulled from under us, and the work is being democratized, not to other roles but to AI.

By level, the most identity-destabilized group is early-career ICs (27%). (This is a wrinkle we’ll untangle in a moment, because those same early-career folks are, paradoxically, among the more optimistic.)

And the bigger the company, the more likely its people are to feel adrift in the AI transition: 23% feel destabilized at 10,000-plus-person companies, versus 15% at companies of 1 to 10.

AI is hardest on people in creative and research roles, on the most junior people, and on people working at the largest companies.

Takeaway 8: Founders are the happiest people in tech, and small companies are the best places to work

For all the AI upheaval, some of last year’s biggest findings came back almost unchanged, and their persistence through such a turbulent year makes them all the more convincing. Founders are still the happiest people in tech, and smaller companies are still better places to work than big ones. Before you read those as good news, it’s worth saying what “best” means here. The whole industry is sitting on a high baseline of burnout and a rather negative career view, and the winners of this section are the people who feel a little less of it.

Founders aren’t just the happiest people in tech—on most measures, they’re genuinely happy.

Founders and executives top nearly every measure in the survey: the highest optimism, the highest job enjoyment, the lowest burnout, the lowest layoff worry, and the most excitement about AI. That gap between founders and execs versus everyone else holds up statistically. On career optimism, it measures d ≈ 0.56, a medium-size effect and the second-largest in the entire dataset, behind only the AI divide.

As we wrote last year, the likeliest explanation is ownership: founders have the most control over their own destiny, and control turns out to be one of the best buffers against everything else. 71% are optimistic about their careers, they enjoy their work more than any other role, and they’re the least worried about layoffs of any group.

Ownership has limits, though. Nearly half of founders (47%) are still at least moderately burned out, with 18% very or completely burned out, even with the most control and the most upside of anyone in tech. And when we asked whether they’d recommend their path to a newcomer, founders landed at an NPS of –5. That is far healthier than the field’s –39, but it’s still bad. Even the happiest people in tech come out slightly net-negative on telling someone to follow their path.

One caveat: we only surveyed people who are founders today. The ones whose startups failed aren’t represented, and most startups don’t make it. Keep that in mind before you quit to go start something!

Smaller companies are still better places to work than big ones.

Company size predicts sentiment with almost eerie consistency. Walk from the smallest companies to the largest, and every measure of well-being gets steadily worse as the company grows:

People at small companies are more optimistic, less burned out, less worried about layoffs, and even feel AI is helping them more, likely because they have more freedom to actually use it. The “big-company blues” we described last year have settled in.

Look at the absolute numbers, though, not just the slope. Even at the smallest companies, 42% of people are at least moderately burned out, and the would-recommend score never climbs out of the red, sitting at –28 at 1-to-10-person shops. Small companies are winning a race to the least bad.

Two smaller echoes of 2025:

Where you physically work still hardly matters. There are barely any differences between how fully remote, hybrid, and in-office workers feel. Hybrid workers come out marginally the happiest (and in-office workers rate their managers the worst), but the gaps are small, just as we found last year. Employment type tells a familiar story with one twist. Founders and the self-employed are the happiest and least burned out, while contractors and freelancers are an interesting split—they are among the least burned out (less of the grind) but the most worried about layoffs (no job security).

One wrinkle you may have noticed: the largest companies (10,000+) tick up slightly in optimism and down in burnout compared with the 5,001–10,000 tier, breaking the smooth gradient. But neither difference is statistically significant (5,001–10,000 is our smallest sample), so the line flattens at the top rather than reversing. The one measure that does keep climbing to the very top is layoff worry. Workers at 10,000-plus-person companies are the most worried of anyone in the survey.

Takeaway 9: Managers are still the biggest lever for happiness

One more finding held firm from last year, and it may be the most actionable of all. Manager effectiveness remains the strongest driver of burnout in the entire dataset (it beats role, company size, and AI sentiment), and one of the strongest drivers of everything else. The gradient is dramatic:

Workers with an extremely effective manager report roughly 65% higher job enjoyment and dramatically lower burnout than those with an ineffective one. Yet only 25.5% of tech workers rate their manager as highly effective, while 36.5% rate theirs as ineffective, numbers that have barely budged since last year. The most powerful retention lever in tech is also the most neglected. (Notably, the worst-rated managers cluster in Data/Analytics and Design. The latter is a double blow, since designers are also among the most AI-anxious.)

Takeaway 10: The industry is “chaotic”

We asked, “In a sentence, how would you describe the state of the tech industry right now?” About 70% of respondents answered, and the single most common theme, by a wide margin, was chaos: roughly three in 10 explicitly used words like change, chaotic, uncertain, unstable, and in flux. Another one in six described an industry moving too fast to keep up with—treadmills, hamster wheels, hurricanes, “drinking from a firehose.” After that came AI hype and bubble talk (12%) and then, finally, a note of excitement and opportunity (11%).

A few responses capture the sentiment better than any percentage can:

“We’re in the 2nd inning of a massive shift, and no one knows how it will end, but all you can do is keep taking at-bats.”

“It feels like working on pure software is like picking up pennies in front of a steamroller.”

“The industry feels like it has lost its center of gravity—replacing curiosity about customers with an obsession over AI, automation, and efficiency.”

The chaos plus hype is well-described in this quote from a senior PM:

“Manic. Half are out of touch, clinging to the bandwagon, making the problem worse by pouring into the overhype. The other half are exhausted by the first half.” —Senior IC PM

When we ran sentiment analysis on the chaos-related quotes, the split was nearly even: 37% positive, 37% negative, and 26% neutral. The dominant theme is disorientation, but the emotional charge is truly bimodal. The same churn reads as thrilling to one person and terrifying to the next.

We confirmed this by splitting the responses by who wrote them. Career optimists and career pessimists describe the same industry in opposite terms. Optimists reach for “exciting,” “transforming,” “opportunity,” “fast-moving.” Pessimists reach for “chaos,” “layoffs,” “greed,” “dystopia.” Same disruption, opposite forecasts: half the room is anxiously bracing for AI’s impact; the other half is eagerly leaning into the AI era.

Where do we go from here?

The 2026 workforce is more burned out and less optimistic than a year ago, splitting along the fault line of AI into those who are thriving and those who are struggling, and a large, ambivalent middle caught between. Tech workers are mostly afraid of being squeezed by their jobs and increasing productivity expectations, privately convinced the field is no longer worth recommending to newcomers, while individually still finding real power and even joy in the tools. It’s a complicated moment. It’s also not a hopeless one.

So here’s what the data suggests you can actually do about it.

If you’re an employee:

Read more