Lenny RachitskyModify

2026-09-30 22:45:15

I spent the day at OpenAI’s DevDay in San Francisco, and I have good news and bad news: OpenAI released a lot of stuff.

In this episode, I break down the announcements worth paying attention to - and show you what happened when I tested some of them early. We’ll meet my Dot, explore why Spaces and Sites could matter for how teams work, and get into the model and API updates I’m most excited about as a developer.

I use the Decisions API to find podcast thumbnails where nobody looks awkward, build a collaborative sketchpad with Astra ultrafast, and let my kids redesign a 3D world in real time. That last experiment cost about $97. My wallet has thoughts.

These are my early impressions: what’s promising, what still feels rough, and what I think you should try first.

What you’ll learn:

1. What OpenAI’s Dots can do, how I’ve been using mine, and why I’m waiting to give a full verdict

2. Why Spaces might be one of the most underhyped announcements for collaboration between humans and agents

3. How Sites with connectors and plugins could help teams share internal tools with the right data permissions

4. Where GPT-6.1 Sol fits in my model stack—and why speed and cost matter

5. What vision adds to the Decisions API, including my thumbnail-selection and hot dog demos

6. What Astra ultrafast makes possible for interactive AI apps, from collaborative drawing to a changing 3D game

7. Where the speed feels magical, where the experience still needs work, and what it costs

In this episode, we cover:

(00:00) OpenAI DevDay recap—and pressing the Codex reset button

(00:58) Dots: early impressions and rough edges

(06:57) Spaces: working with humans and agents

(10:37) Sites, connectors, and sharing internal tools

(13:06) Models and platform: GPT-6.1 Sol

(14:36) Decisions API: fast decisions with vision

(15:27) Finding better podcast thumbnails with AI

(16:29) Hot dog or not hot dog?

(17:17) Astra ultrafast: speed, pricing, and possibilities

(18:50) The Other Pencil: drawing alongside AI

(19:45) Little Starship: a 3D world you can change with a prompt

(21:24) The $97 AI game—and what it makes possible

(22:25) Agents API, computer use, plugins, and plan updates

(23:03) What I’d try first

Tools referenced:

• ChatGPT: Dots, Spaces, and Sites: https://chatgpt.com/

• Codex: https://openai.com/codex/

• OpenAI API — GPT-6.1 Sol, Decisions API, and Astra ultrafast: https://platform.openai.com/

• Jev: https://typesafe.ai/

Other references:

• OpenAI DevDay 2026: https://devday.openai.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 [email protected].

2026-09-30 20:04:27

John Lindquist created egghead.io, a developer education platform used by hundreds of thousands of working engineers. These days he’s building mega.dev, a hands-on program specifically for developers who want to do real work with AI agents, not just prototype them.

What you’ll learn:

1. Why Jev is a decision engine, not a chatbot, and what that distinction actually changes about how you build

2. How John built a real-time voice to-do app that classifies and executes commands with no visible pause

3. The data deduplication pattern that merges messy records in milliseconds using confidence scores

4. Why Jev works best as a router, and how a single text input can navigate users deep into an app

5. What a chess match between Jev and a low-reasoning LLM reveals about speed, cost, and when to use which

6. The multi-step classification pattern John reaches for when one Jev pass isn’t enough

7. Where Jev falls short, and when you should still reach for a full generative model

Brought to you by:

Vanta—Automate compliance and simplify security

In this episode, we cover:

(00:00) John Lindquist returns for Jev week

(04:32) What Jev actually outputs

(06:15) Demo: real-time voice to-do app

(08:17) How sequential Jev calls chain together

(10:38) Demo: plain English to function name (grocery cart)

(11:50) Demo: data deduplication and record merging

(13:45) Confidence scores and multi-model validation

(15:06) Demo: Jev as a multi-level app router

(18:23) Architecting around Jev

(19:35) Demo: Jev vs. traditional LLM at chess (speed and cost benchmarks)

(24:29) DOM interactions as a decision set, not an infinite canvas

(28:21) Demo: Wikipedia “path to philosophy” route mapper

(30:28) Demo: multi-agent coordination and collision avoidance

(33:36) Demo: real-time presentation coach

(36:56) Quick recap

(39:54) Lightning round and final thoughts

Tools referenced:

• Jev (TypeSafe AI decision model): https://typesafe.ai/blog/introducing-system-one-models-and-jev

• Vercel AI Gateway: https://vercel.com/docs/ai-gateway

• OpenRouter: https://openrouter.ai

• Opus 5.5 (mentioned in context of iterative demo building): https://www.anthropic.com/claude-opus-5-5

Where to find John Lindquist:

LinkedIn: linkedin.com/in/john-lindquist-84230766

X: https://x.com/johnlindquist

Mega.dev: https://mega.dev/

Egghead.io: https://egghead.io/

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 [email protected].

2026-09-29 21:15:57

👋 Hey there, I’m Lenny. Each week, I share deeply researched product, growth, and career advice. For more: Lenny’s Jobs | Lennybot | Become an AI-Native Builder and other favorite AI/PM courses

Subscribe now

I’m excited to share that every main-stage talk from the recent Lenny & Friends Summit is now online. Here’s the full list (in no particular order):

1. Raise the ceiling: how to scale intent, quality, and artistry with AI (Katie Dill, Head of Design at Stripe)

2. How to build products on a moving frontier (Dan Shipper, CEO of Every)

3. The last roadmap (Claire Vo, How I AI)

4. The limiting factor: how to design an AI software factory for speed (Geoff Charles, CPO at Ramp)

5. Roles aren’t converging—they’re expanding (Tamar Yehoshua, Chief Product and AI Officer at Atlassian)

6. The new playbook: what it takes to be a top PM today (Robby Stein, VP Product, Google Search)

7. The high-impact IC era (Elena Verna, Head of Growth at Lovable)

8. Strong opinions, loosely held (Marty Cagan, SVPG)

9. Context is now the product: what product leadership looks like when software can build itself (Karri Saarinen, CEO of Linear)

10. Anthropic panel with Ami Vora, CPO, and Mike Krieger, Head of Labs

11. OpenAI panel with Tara Seshan, ChatGPT Work, and Nan Yu, Codex

For easy viewing, here’s the entire main-stage talks playlist.

To get a taste of what the day was like, I’ve pulled my favorite messages from attendees. Also, a little slideshow. It’ll warm your heart.

A few reflections

Now that we’re a few weeks out, I’ve had a chance to reflect on the day. Here are the top 5 trends that have stayed with me:

1. There’s a huge need for (curated) IRL events right now. It’s wild. I’ve rarely felt this much PMF for anything I’ve done. My sense is that the more time we spend working with agents, the lonelier the work becomes, and the more we need to find a place to connect with other humans. I heard this theme more than anything else from attendees:

   • “Things are certainly different now, but underneath all this technology, we’re still just ancient humans looking for connection.” —Charlie Koch

   • “I left filled with gratitude for the experience, the conversations, and the reminder that even in a role that can sometimes feel lonely, we don’t have to figure everything out alone.” —Hemanth Babu Shekar

   • “I left feeling genuinely sparked by what’s possible, and also a little less alone in figuring out what comes next.” —An attendee

   • “I left this conference having fallen in love with product all over again.” —Jenny Zhao

2. Nobody has it figured out. Should we be building software factories? Are roadmaps over? Should PMs be shipping to production? We had speakers take both sides of these debates throughout the day. But what everyone agreed on is that there’s no one way to do it. My takeaway is we’re all still figuring it out, and the best way to do that is together. We heard this theme from both speakers and attendees:

   • “The answer that we have come to is: it depends. It depends what type of product it is. It depends on what phase in the product it is.” —Tamar Yehoshua, Atlassian

   • “We don’t know the answer. We don’t think anyone knows the answer. We’re all going to discover it. And we’re all fundamentally on the same team.” —Ami Vora, Anthropic

   • “This is just one way. There’s so many ways to build products. You probably heard 10 other ones today that were just as valid as this one. You should decide on what makes sense for you.” —Robby Stein, Google Search

   • “The best practices have not been written yet. We get to do that.” —Katie Dill, Stripe

3. It’s never been easier to build something nobody wants. As building gets easier, choosing the right thing to build, knowing what good looks like, and caring enough to fix it are becoming increasingly important for successful product teams. Almost every speaker touched on this theme:

   • “And now we can ship all our bad ideas, like congratulations to us.” —Claire Vo, How I AI

   • “If almost anything you can think of can get built, it’s more clear to me than ever that the actual true value of PM is around judging. It’s around taste. It’s around doing something extremely well.” —Robby Stein, Google Search

   • “They’re not impressed if we animated something with Three.js and Blender in 30 minutes. They are impressed by us solving their problems and clever touches in the details that show we anticipated their needs.” —Katie Dill, Stripe

   • “The output is not the product. Customers are not buying the lines of code.” —Karri Saarinen, Linear

   • “Is this useful or is it just new? It’s a big thing you need to differentiate.” —Dan Shipper, Every

4. The PM job is expanding, not disappearing. Instead of the PM, eng, and design circles of the Venn diagram collapsing into one “builder,” each role is instead evolving and expanding.

   • “Roles are overlapping, but they are also expanding. . . . Everyone can do so much more than they could do before.” —Tamar Yehoshua, Atlassian

   • “PMs will absolutely evolve and expand their role to not just the product organization but the marketing organization, the sales organization, the growth organizations, the operational organizations. PMs will become GMs, and they will own the actual business outcome and lead the entire function.” —Geoff Charles, Ramp

   • “I build and change the pricing page. I build prototypes, I deploy to production myself. I do the user research, I do the analysis, I do all of the optimizations.” —Elena Verna, Lovable

5. It’s important to be reminded of what’s possible—and to raise our ambitions. One of the most inspiring parts of the experience for me, and for a lot of people at the Summit, was to be surrounded by people at the top of their craft, demonstrating what great looks like. This is especially important now that AI allows us (and forces us) to be a lot more ambitious.

   • “If we only use AI to make the things that we already make just faster, then we are definitely missing out on the most interesting part. AI can help us raise the ceiling, not just the floor.” —Katie Dill, Stripe

   • “The biggest constraint is honestly people’s understanding and ability to absorb what you’re giving them.” —Nan Yu, OpenAI

   • “I think next year is the ambition game. . . . What experiments can I run in two weeks, three weeks, that would have taken us a year last time?” —Claire Vo, How I AI

   • “Other conferences collect a bunch of people seeking validation that what they were doing in product wasn’t too off the mark. Whereas this group set the bar for what’s possible.” —An attendee

Also, a key lesson: every event needs a guitar guy.

A huge, huge thank-you to all of the attendees, speakers, and partners for making the day what it was. I’m so grateful you took a chance on spending the day with us.

And finally, an extra-special thank-you to the Stripe team. Their events team produced the entire Summit, and their design team made it so friggin’ cute and unique. Bringing together over 1,000 product managers and creating such a warm and inviting vibe is a special skill. Stripe nailed it. Serious high talent density over there—it’s no coincidence that I’ve had more people from the company on my podcast than any other. Thank you, Stripe.

Till next time!

P.S. Here’s the entire main-stage talks playlist for easy viewing. Enjoy!

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 👋

2026-09-28 23:03:13

Jev for beginners: how to use it and what to build

Brought to you by:

• OpenArt—An all-in-one AI creation platform for images, videos, music, audio, and more

In this solo episode, Claire tests Jev, TypeSafe AI’s new decision model that returns structured choices, scores, and probabilities instead of generated text. She uses it to analyze 1,700 pull requests for 9 cents, map her Claude and Codex usage, triage email, search 4,500 YouTube comments, and process 200,000 classifications for about $4. She also explains why Jev works best alongside a frontier model and how its speed and pricing make entirely new kinds of real-time apps and large-scale analysis practical.

Biggest takeaways:

1. Jev is a decision model, not a language model, and that distinction can make many tasks dramatically cheaper. Instead of generating text, it returns predefined values such as a category, score, or probability. Claire believes this covers roughly 90% of what many software workflows actually need, at 4 cents per million input tokens with no output-token fee.

2. It cost Claire 9 cents to understand where two years of engineering work went. She used Jev to compare 1,700 ChatPRD pull requests across 17,000 pairs, then had Gemini Flash Lite label the resulting clusters. In about two minutes, she learned that nearly 30% of the company’s engineering work had gone toward platform, security, and infrastructure.

3. Some of the most useful analysis is already sitting on a local computer. Claude Code and Codex store past sessions locally, allowing Jev to classify them in minutes. Claire discovered that engineering had fallen from nearly 100% of her AI usage in January to less than 40% by September, with agents and media publishing filling the gap.

4. Jev becomes far more powerful when paired with a frontier model. Claire uses Jev to classify, cluster, filter, and route large datasets, then sends only the most important groups to GPT-6 Astra for deeper reasoning. For ChatPRD’s product insights graph, this approach processed 1,100 signals and completed 200,000 operations for about $4 on the Jev side.

5. Jev’s pricing changes which ideas are worth building. Because it returns small predefined values instead of generating long responses, TypeSafe charges nothing for output tokens. Claire spent less than $10 on Jev during the week, making classification workloads that would normally be expensive at scale feel almost free.

6. Jev makes real-time AI loops practical. Claire built a voice app that turns a spoken phrase into a color, matches it with a quote based on sentiment, and displays everything almost instantly. Jev made its decisions so quickly that the quote API became the slowest part of the workflow.

7. YouTube comment analysis is an immediate use case for any podcast team. Claire classified 4,500 How I AI comments by sentiment, identified 58 containing episode ideas, and built a keyword search that scans the full dataset in under a second. The results showed strong demand for a Grok versus Muse comparison and an 80% positive response to the “Claude Code for product managers” episode.

8. The real skill is recognizing where a pipeline only needs a decision. Jev will not write documentation or design an interface, but it can sort, route, rank, and filter enormous datasets quickly and cheaply. Claire now asks one question before every build: Where does this workflow simply need to make a decision? That is where Jev belongs.

I left Claude for months. Opus 5.5 is why I’m back.

Claire tests Claude Opus 5.5 after months of leaving Claude out of her daily workflow. She puts it through long-running agentic tasks, frontend prototyping, writing, SVG illustration, computer use, and video editing to see where it earns a place back in her stack. She also shares why she is pairing it with Codex for cross-model code review, where Claude’s safety limits still get in the way, and which tasks remain firmly in Codex territory.

Biggest takeaways:

1. A model’s personality can matter just as much as its intelligence. Claire stopped using Claude for months because its rambling, preachy, and overly verbose replies made it unpleasant to work with. Opus 5.5 is the first model in the family that no longer makes her blood boil, which is a meaningful improvement even if no benchmark captures it.

2. Opus 5.5’s lower price and faster performance make long-running agent work more practical. It is 40% cheaper than Opus 5, and Claire found it noticeably faster. It successfully completed four complex tasks spanning inbox triage, backend development, research, and computer use, including runs of up to 82 steps from a single prompt.

3. Silence during long-running tasks creates its own user experience problem. Opus 5.5 sometimes remains quiet for eight or nine minutes, leaving users unsure whether it is still working. It is a reminder that perceived latency matters alongside actual latency, especially when agents run for extended periods.

4. Opus 5.5 is the strongest frontend designer Claire has tested so far. Its ChatPRD homepage redesign was bold and polished enough that she plans to ship it. The model handles hierarchy, white space, and visual rhythm exceptionally well, though it still struggles with consumer-app aesthetics and defaults to “Claude orange” without direction.

5. SVG illustration is an unexpected strength of Opus 5.5. It was the only model Claire tested that produced clean, charming, and animatable character SVGs with consistent styling across multiple expressions. The characters remained visually coherent, and their anatomy mostly made sense.

6. Opus 5.5 has a clear safety posture, and sometimes that means saying no. It refused when Claire asked it to skip testing and push directly to production, and it may route cybersecurity work to Opus 4.8. Whether that feels reassuring or frustrating depends on the workflow, but its boundaries are consistent.

7. The best use of Opus 5.5 may be as an adversarial reviewer for another model. Claire now has Codex and Opus review each other’s work rather than using one to replace the other. This cross-model loop catches issues either model might miss alone, making the additional cost worthwhile when quality matters.

8. Computer use and video editing still belong to Codex in Claire’s workflow. Opus 5.5’s ElevenLabs MCP video test produced weak color grading, too few jump cuts, and sloppy overlays. Codex also remains stronger at computer use in her current setup, giving her no reason to shift either category to Claude.

9. Claude is back, but it has not replaced Codex as Claire’s daily driver. Opus 5.5 has earned a role in pull-request reviews, architecture questions, and frontend development. Codex’s desktop experience, computer use, and workflow integration still keep it in the primary position.

Opus 5.5 vs. GPT-6 Sol: which model won my blind taste test?

Claire takes the How I AI bench live to compare GPT-6 Astra, GPT-6 Sol, Claude Opus 5.5, and more across the work she actually does. She blind-scores writing, frontend prototypes, agent personality, and SVG illustrations, with an AI judge helping evaluate backend work, long-running agents, and computer use. She also checks video edits and a 3D Barbie game build. Along the way, she explains why Astra won her heart, Opus 5.5 won her week, and Sol delivered mixed results while remaining a favorite for everyday work.

Biggest takeaways:

1. Expanding the benchmark from two categories to eight changed what Claire could see. The original How I AI Vibe Review focused on PRDs and frontend prototypes. Adding personal productivity tasks like inbox triage, along with backend development, long-running agent tasks, computer use, SVGs, and video editing exposed clear differences between the models Claire preferred for design and those she enjoyed interacting with.

2. Opus 5.5 returned to Claire’s workflow because of ergonomics, not benchmarks. After repeatedly asking Claude to communicate like a normal person, she found Opus 5.5 concise, clear, and far less irritating. At one point, Claire thought the old frustration had returned, then realized she had accidentally selected Opus 5. The difference was that obvious.

3. Making Opus 5.5 quieter also made it feel slower, even when it was not. Long stretches of silence can make users wonder whether the model is still working. GPT-6 Sol found a better balance in Claire’s testing, narrating enough to feel responsive without creating additional noise.

4. GPT-6 Sol’s lower price changes how teams should think about model selection. Learning that Sol costs roughly half as much as Opus 5.5 immediately changed how Claire thought about routing work. She also believes teams should optimize caching before obsessing over model choice, since ChatPRD has seen significant savings when its caches are configured properly.

5. Dash-heavy writing is an immediate warning sign in Claire’s benchmark. Two models received a 1 out of 5 for agent personality because nearly every message contained an em dash. It may sound overly specific, but Claire sees it as a reliable signal that a customer-facing agent will sound like generic AI writing instead of a natural collaborator.

6. Claire and the AI judge disagree, which makes the benchmark more useful. The judge favored Fable and rated Sol lower, while Claire preferred Astra. The difference reflects two definitions of quality: the judge rewards correctness and structure, while Claire measures how much she actually wants to use the model.

7. The blind SVG comparison changed Claire’s earlier verdict. In her standalone Opus 5.5 review, Claire favored its character illustrations. But in this live blind comparison, Astra and Sol came out ahead on character SVGs, surprising her after she had predicted a Claude win.

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.

2026-09-28 20:03:04

Jev is TypeSafe AI’s new decision model. It returns type-safe structured values (a choice, a score, a probability) instead of generated text, at 4 cents per million input tokens with no output charge. This week I ran it on five real projects: PR categorization, a meta-analysis of my own Claude and Codex sessions, Gmail triage, the ChatPRD product insights graph, and a live audience dashboard built from 4,500 YouTube comments.

What you’ll learn:

1. What makes Jev fundamentally different from every other model I’ve used

2. How I analyzed 1,700 PRs for 9 cents and what I found out about where my engineering effort actually went

3. The personal meta-analysis you can run on your own Claude and Codex sessions right now

4. Why I stopped using Jev alone, and what I pair it with now

5. How I turned 4,500 YouTube comments into a searchable audience dashboard for almost nothing

6. The real-time app I built in an afternoon that shows something surprising about Jev’s speed

7. Why Jev’s pricing model is different from any LLM I’ve used, and what it makes practical to build

8. The ChatPRD product insights project: 1,100 signals, 200,000 classifications, and what it cost me

Brought to you by:

OpenArt—An all-in-one AI creation platform for images, videos, music, audio, and more

In this episode, we cover:

(00:00) Jev launch and what makes it different from every other model

(02:49) Type-safe values explained

(05:28) Understanding Jev outputs

(07:39) Use case 1: PR categorization and pairwise clustering

(11:12) Use case 2: analyzing your own local Claude Code and Codex sessions

(13:00) Use case 3: Gmail triage with Jev scoring and LLM follow-up

(14:30) Use case 4: ChatPRD’s product insights graph

(18:17) Demo: How I AI audience signal dashboard

(22:14) Demo: voice-to-color emotion-mapping app

(25:16) Jev week recap and what’s coming in episode 2

Tools referenced:

• Jev (TypeSafe AI): https://typesafe.ai

• Vercel: https://vercel.com/ai

• GitHub API: https://docs.github.com/en/rest

• YouTube Data API v3: https://developers.google.com/youtube/v3

• OpenAI Realtime Voice API: https://platform.openai.com/docs/guides/realtime

• Gemini 3.5 Flash-Lite: https://ai.google.dev/gemini-api/docs/models/gemini-3.5-flash-lite

• API Ninjas Quotes API: https://api-ninjas.com/api/quotes

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 [email protected].

2026-09-27 20:32:18

Molly Graham is back for round two, and this one is even more powerful. Molly has spent more than 20 years helping organizations and the humans inside them navigate growth and change. She’s held leadership roles at Google, Facebook, Quip, and the Chan Zuckerberg Initiative and is the host of TED’s WorkLife podcast (which she took over from Adam Grant). She also runs Glue Club, a leadership community for senior operators, and writes a popular newsletter called Lessons.

In our in-depth conversation, we discuss:

1. Why Molly’s famous “give away your Legos” career advice no longer holds true in an AI world

2. The grief, loneliness, and burnout sweeping through the tech industry right now

3. Why delegating to AI is fundamentally different from delegating to a human

4. The fear narrative around AI job displacement, and why it’s overblown

5. Which Legos you should never give to AI

6. What the best managers are doing right now

Brought to you by:

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

DX—Engineering intelligence for the AI era

Where to find Molly Graham

• X: https://x.com/molly_g

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

• Substack: https://mollyg.substack.com

• Website: https://glueclub.com

Referenced:

• The high-growth handbook: Molly Graham’s frameworks for leading through chaos, change, and scale: https://www.lennysnewsletter.com/p/the-high-growth-handbook-molly-graham

• Myspace: https://myspace.com

• ‘Give Away Your Legos’ and Other Commandments for Scaling Startups: https://review.firstround.com/give-away-your-legos-and-other-commandments-for-scaling-startups

• Safeway: https://www.safeway.com

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

• 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

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

• How to step confidently into the unknown with Manoush Zomorodi: https://mollyg.substack.com/p/new-worklife-episode-how-to-step

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

• “I Know It When I See It” Doesn’t Scale with Hilary Gridley: https://mollyg.substack.com/p/worklife-hilary-gridley

• Foo Camp: https://en.wikipedia.org/wiki/Foo_Camp

• Tim O’Reilly on LinkedIn: linkedin.com/in/timo3

• Adam Mosseri: AI is a tailwind for authenticity: https://www.lennysnewsletter.com/p/adam-mosseri-ai-is-a-tailwind-for

• 10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey): https://www.lennysnewsletter.com/p/10-growth-tactics-that-never-work-elena-verna?utm_source=publication-search

• Waymo: https://waymo.com

• AI’s third era: the rise of persistent AI coworkers | Tara Seshan (Product Lead ChatGPT Work): https://www.lennysnewsletter.com/p/ais-third-era-the-rise-of-persistent

• Airbnb: https://www.airbnb.com

• Booking.com: https://www.booking.com

• Brian Chesky’s secret mentor who died 9 times, started the Burning Man board, and built the world’s first midlife wisdom school | Chip Conley (founder of MEA): https://www.lennysnewsletter.com/p/chip-conley

• Hugging Face: https://huggingface.co

• You’re closer to an AI expert than you think with Max Mullen: https://mollyg.substack.com/p/worklife-max-mullen

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

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

• Why Clay Has an AI Writing Policy: https://www.clay.com/blog/ai-writing-policy

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

Recommended book:

• The Reverse Centaur's Guide to Life After AI: https://www.amazon.com/Reverse-Centaurs-Guide-Life-After/dp/037462156X

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

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