Podcast
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Hosted by Martin Rosén-Lidholm / Listen Notes · EN
A curated podcast playlist by Martin Rosén-Lidholm.
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Episodes
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Engineering Management In The Age Of AI
Apr 3000:42:50Tap to summarizePodcast: Stellar WorkEpisode: Engineering Management In The Age Of AIPub date: 2026-04-27Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationMost engineering teams using AI are about 2x faster. Not 10x. The bottleneck moved, but nobody optimized the rest.Jeff Lee-Chan spent 10 years at Google working on YouTube, then seven years at Snapchat. He went from IC to staff engineer to engineering manager. Now he spends 20 to 30 extra hours a week experimenting with AI tools outside his day job.In this conversation, you'll hear:Why a 30-minute AI side project convinced him the world had changedWhat the actual AI stack looks like inside Big Tech right now (it's mostly Claude Code, Cursor, and Codex)Where the real bottleneck sits when coding speed isn't the problem anymoreWhy he thinks 4-5 custom code review bots beat a single default oneHow AI is shrinking the junior engineering pipeline and what that meansThe one mistake he made early in management that he'd warn every new manager aboutJeff Lee-Chan on LinkedIn: https://www.linkedin.com/in/jeffrey-lee-chan/Mentoring with Jeff: https://mentorcruise.com/mentor/jeffreylee-chan/More episodes & all platforms: https://stellarwork.start.pageNewsletter: https://substack.com/@stellarworkHost: Ben, founder of Stellar WorkThe podcast and artwork embedded on this page are from Benjamin Igna, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
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#94 - Exploiting Your Unfair Advantages - Shirley Wu (Shirley Wu Studio)
Apr 2501:00:47Tap to summarizePodcast: The Work Item - Real Talk on Tech's Toughest Career Choices (LS 27 · TOP 10% what is this?)Episode: #94 - Exploiting Your Unfair Advantages - Shirley Wu (Shirley Wu Studio)Pub date: 2025-10-20Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationShirley Wu is back-to-back on The Work Item - we previously talked about her unexpected career in data visualizations, but for this episode we're switching things up a little bit and instead focus on building long-term career advantages based on Shirley's experience. This is an especially important topic in the era of AI, where folks have a lot of uncertainty about their career tracks and what it means to build durable moats that can survive the industry being upended by new tools and approaches to getting things done. Shirley's experience is particularly relevant here as an independent studio owner - she's someone who has years of experience to lean on flying solo and seeing how one can establish their own reputation and image in the space. You can find Shirley on the following sites: 🎨 Shirley Wu Studio 🦋 Bluesky 💼 LinkedIn 📸 Instagram The podcast was produced by Den Delimarsky. Feedback If you haven't already, make sure to subscribe to the show and leave a review or a rating, wherever you are getting your podcast. I really appreciate your feedback and am working to make this podcast more useful for you, the listener, with every episode. Ratings and feedback make it so others can easily discover and enjoy the insights you listen to here!The podcast and artwork embedded on this page are from Den Delimarsky, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
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272. Vad händer när man tappat gnistan?
Apr 2500:35:23Tap to summarizePodcast: Developers! - mer än bara kodEpisode: 272. Vad händer när man tappat gnistan?Pub date: 2026-04-23Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationEn lyssnare har kodat sedan högstadiet, programmering var hans grej – men nu är gnistan borta. Han undrar om det är AI som tagit något ifrån honom, småbarnsåren, eller om han helt enkelt växt ifrån det.Vi pratar om vad som händer med motivationen när verktygen börjar lösa problemen åt dig, om "kod som personlighet" och vad som händer med självbilden när den identiteten rubbas, och om det faktiskt är okej att bara… inte vara en sådan där som älskar kod längre.Vi tittar också på färsk data från Gallup om hur AI-adoption faktiskt ser ut bland Gen Zs och på Gallerix-skandalen där AI-genererade motiv lades ut för försäljning utan att konstnärerna visste om det.🤓 Svårighetsnivå: 2/5🔗 Länkar:Gallup: Gen AI Adoption Steady, Skepticism ClimbsSVT: Efter AI-kritik – Gallerix tar bort 50 motiv💬 Ställ en anonym fråga eller insändare som vi kan ta upp i podden!💌 Håll kontakten med oss:DiscordInstagramFacebookhello@developerspodcast.comhttps://www.developerspodcast.comOm du gillar podden får du gärna stötta oss genom att köpa vår merch, bli en Patreon, subscriba till podden eller skriva en recension! ★ Support this podcast on Patreon ★ The podcast and artwork embedded on this page are from Madeleine Schönemann och Sofia Larsson, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
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How to Become a "Builder PM" with n8n, Claude Code, and OpenClaw | Mahesh Yadav (ex-Google, AWS, Meta, Microsoft; Founder LegalGraph AI)
Apr 2501:36:50Tap to summarizePodcast: The Growth Podcast (LS 37 · TOP 2.5% what is this?)Episode: How to Become a "Builder PM" with n8n, Claude Code, and OpenClaw | Mahesh Yadav (ex-Google, AWS, Meta, Microsoft; Founder LegalGraph AI)Pub date: 2026-04-20Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationToday’s episodeLinkedIn just changed the title of its product managers to product builders.What does it even mean to be a “builder PM”?Well, tools only get you so far. Learning Claude Code is helpful, but means nothing if you don’t have an understanding of the underlying first principles.That’s today’s episode.Mahesh Yadav created one of our most popular episodes, with over 35K views on YouTube, and now he’s back. Earlier, he taught you AI agents. Today, he’s touching you how to become a builder PM:If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.I’m giving a free talk on how to get interviews at the top AI PM companies on Thursday, April 23rd 2026 @ 9:00AM PDT. Grab your seat.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Maven - Build cohort-based courses that scale* Amplitude - The market leader in product analytics* Jira Product Discovery - Prioritize what matters with confidence* NayaOne - Airgapped cloud-agnostic sandbox to validate AI tools faster* Product Faculty - Get $550 off their #1 AI PM Certification with my link----Key Takeaways:1. Builder PM defined - A builder PM talks to customers, figures out what to build, and ships the first version to 10 customers without talking to any developer. The skill is knowing what to build, not knowing how to code.2. Four agent components - Every agent that works has intelligence (model), tools (actions), memory (session context), and knowledge (your company data). Every agent that disappoints is missing at least one.3. n8n for foundations - n8n is the best learning tool because you visually see every component of the agent architecture as separate nodes. Build your first multi-agent system and evaluation pipeline here.4. Claude Code ate three company types - Context companies, action companies, and evaluation companies all got replaced by one agentic loop inside Claude Code. The three pieces collapsed into one tool.5. Computer control is the real unlock - File system access plus bash commands equals full laptop capability. This is why Claude Code went from coding tool to work operating system.6. Long-horizon jobs changed the game - AI agents went from 3-minute tasks to 3-6 hour sustained jobs in six months. This turns Claude Code from assistant to autonomous worker.7. Continuous learning loops - Build a second agent that watches your corrections to the first agent's work. After five repeated patterns, it proposes a skill update. Your tools get better every day.8. OpenClaw pattern - Delegation through existing channels, full machine sandboxing, model-agnostic. Not a product but a pattern that Google and AWS will copy inside their ecosystems.9. AI PM interviews changed - At L5 and L6, product sense questions are being replaced with live building exercises and system design for AI architectures. Pull out Claude Code during the interview or you are already out.10. Compensation trajectory - From $120K at Microsoft to $1.3M at Google over 13 years, doubling every 18 months through AI-focused switches. Left because big companies kill innovation with six-week approval cycles.----Where to find Mahesh Yadav* LinkedIn* Maven CourseRelated contentPodcasts:* Claude Code Team OS with Carl Vellotti* OpenClaw + Claude Code with Naman Pandey* Claude Code OS with Dave KilleenNewsletters:* The complete context engineering guide* How to use Claude Code like a pro* Practical AI agents for PMs----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribeThe podcast and artwork embedded on this page are from Aakash Gupta, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
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Marc Andreessen introspects on The Death of the Browser, Pi + OpenClaw, and Why "This Time Is Different"
Apr 1601:16:20Tap to summarizePodcast: Latent Space: The AI Engineer Podcast (LS 44 · TOP 1% what is this?)Episode: Marc Andreessen introspects on The Death of the Browser, Pi + OpenClaw, and Why "This Time Is Different"Pub date: 2026-04-03Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationFresh off raising a monster $15B, Marc Andreessen has lived through multiple computing platform shifts firsthand, from Mosaic and Netscape to cofounding A16z. In this episode, Marc joins swyx and Alessio in a16z’s legendary Sand Hill Road office to argue that AI is not just another hype cycle, but the payoff of an “80-year overnight success”: from neural nets and expert systems to transformers, reasoning models, coding, agents, and recursive self-improvement. He lays out why he thinks this moment is different, why AI is finally escaping the old boom-bust pattern, and why the real bottleneck may be less about models than about the messy institutions, incentives, and social systems that struggle to absorb technological change.This episode was a dream come true for us, and many thanks to Erik Torenberg for the assist in setting this up. Full episode on YouTube!We discuss:* Marc’s long view on AI: from the 1980s AI boom and expert systems to AlexNet, transformers, and why he sees today’s moment as the culmination of decades of compounding technical progress* Why “this time is different”: the jump from LLMs to reasoning, coding, agents, and recursive self-improvement, and why Marc thinks these breakthroughs make AI real in a way prior cycles were not* AI winters vs. “80-year overnight success”: why the field repeatedly swings between utopianism and doom, and why Marc thinks the underlying researchers were mostly right even when the timelines were wrong* Scaling laws, Moore’s Law, and what to build: why he believes AI scaling laws will continue, why the outside world is messier than lab purists assume, and how startups can still create durable value on top of rapidly improving models* The dot-com crash and AI infrastructure risk: Marc’s comparison between today’s AI capex boom and the fiber/data-center overbuild of 2000, plus why he thinks this cycle is different because the buyers are huge cash-rich incumbents and demand is already here* Why old NVIDIA chips may be getting more valuable: the pace of software progress, chronic capacity shortages, and the idea that even current models are “sandbagged” by supply constraints* Open source, edge inference, and the chip bottleneck: why Marc thinks local models, Apple Silicon, privacy, trust, and economics all point toward a major role for edge AI* American vs. Chinese open source AI: DeepSeek as a “gift to the world,” why open models matter not just because they’re free but because they teach the world how things work, and how open source strategies may shift as the market consolidates* Why Pi and OpenClaw matter so much: Marc’s claim that the combination of LLM + shell + filesystem + markdown + cron loop is one of the biggest software architecture breakthroughs in decades* Agents as the new “Unix”: how agent state living in files allows portability across models and runtimes, and why self-modifying agents that can extend themselves may redefine what software even is* The future of coding and programming languages: why Marc thinks software becomes abundant, why bots may translate freely across languages, and why “programming language” itself may stop being a salient concept* Browsers, protocols, and human readability: lessons from Mosaic and the web, why text protocols and “view source” mattered, and how similar principles may shape AI-native systems* Real-world OpenClaw use: health dashboards, sleep monitoring, smart homes, rewriting firmware on robot dogs, and why the most aggressive users are discovering both the power and danger of agents first* Proof of human vs. proof of bot: why Marc thinks the internet’s bot problem is now unsolvable via detection alone, and why biometric + cryptographic proof of human becomes necessaryTimestamps* 00:00 Marc on AI’s “80-Year Overnight Success”* 00:01 A Quick Message From swyx* 01:44 Inside a16z With Marc Andreessen* 02:13 The Truth About a16z’s AI Pivot* 03:29 Why This AI Boom Is Not Like 2016* 06:33 Marc on AI Winters, Hype Cycles, and What’s Different Now* 10:09 Reasoning, Coding, Agents, and the New AI Breakthroughs* 12:13 What Founders Should Build as Models Keep Improving* 16:33 AI Capex, GPU Shortages, and the Dot-Com Crash Analogy* 24:54 Open Source AI, Edge Inference, and Why It Matters* 33:03 Why OpenClaw and PI Could Change Software Forever* 41:37 Agents, the End of Interfaces, and Software for Bots* 46:47 Do Programming Languages Even Have a Future?* 54:19 AI Agents Need Money: Payments, Crypto, and Stablecoins* 56:59 Proof of Human, Internet Bots, and the Drone Problem* 01:06:12 AI, Management, and the Return of Founder-Led Companies* 01:12:23 Why the Real Economy May Resist AI Longer Than Expected* 01:15:53 Closing ThoughtsTranscriptMarc: Something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. Having said that, I think what’s actually happened is an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years where that was controversial. And so, so the way I think about what’s happening is basically, I think, I think about basically the, the, the period we’re in right now is it’s, I call it 80 year overnight success, right?Which is like, it’s an overnight success ‘cause it’s like bam, you know, chat GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they’re drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it’s not just that it’s all brand new, it’s that it’s an unlock of all of these decades of like very serious, hardcore research.If I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough.swyx: Before we get into today’s episode, I just have a small message for listeners. Thank you. We will not be able to bring you the ai, engineering, science, and entertainment contents that you so clearly want if you didn’t choose to also click in and tune into our content.We’ve been approached by sponsors on an almost daily basis, but fortunately enough of you actually subscribed to us to keep all this sustainable without ads, and we wanna keep it that way. But I just have one favor to ask all of you. The single, most powerful, completely free thing you can do is to click that subscribe button.It’s the only thing I’ll ever ask of you, and it means absolutely everything to me and my team that works so hard to bring the in space to you each and every week. If you do it, I promise you will never stop working to make the show even better. Now, let’s get into it.Alessio: Hey everyone, welcome to the Lidian Space Pockets. This is CIO, founder Kernel Labs, and I’m joined by s Swix, editor of Lidian Space.swyx: Hello. And we’re in a 16 Z with a, uh, mark G and welcome.Marc: Yes, yes. A and what, half of 16? Something like that. A one. Exactly,swyx: exactly. Uh, apparently this is the, the final few days in your, your current office.You’re moving across the road.Marc: Uh, we’re, yeah. We have a, we have some, we have some projects underway, but yeah, this is actually, oh, this is the original. We’re in actually the original office. We’re in the, we’re in the, we’re, we’re in the whole thing.swyx: It’s beautiful. Yeah. Great.Marc: Thank you.swyx: So I have to come out, uh, this is a, you know, I wanted to pick a spicy start in October, 2022.I ju...
Transcribe →Extreme Harness Engineering for Token Billionaires: 1M LOC, 1B toks/day, 0% human code, 0% human review — Ryan Lopopolo, OpenAI Frontier & Symphony
Apr 1601:12:43Tap to summarizePodcast: Latent Space: The AI Engineer Podcast (LS 44 · TOP 1% what is this?)Episode: Extreme Harness Engineering for Token Billionaires: 1M LOC, 1B toks/day, 0% human code, 0% human review — Ryan Lopopolo, OpenAI Frontier & SymphonyPub date: 2026-04-07Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationWe’re proud to release this ahead of Ryan’s keynote at AIE Europe. Hit the bell, get notified when it is live! Attendees: come prepped for Ryan’s AMA with Vibhu after.Move over, context engineering. Now it’s time for Harness engineering and the age of the token billionaires.Ryan Lopopolo of OpenAI is leading that charge, recently publishing a lengthy essay on Harness Eng that has become the talk of the town:In it, Ryan peeled back the curtains on how the recently announced OpenAI Frontier team have become OpenAI’s top Codex users, running a >1m LOC codebase with 0 human written code and, crucially for the Dark Factory fans, no human REVIEWED code before merge. Ryan is admirably evangelical about this, calling it borderline “negligent” if you aren’t using >1B tokens a day (roughly $2-3k/day in token spend based on market rates and caching assumptions):Over the past five months, they ran an extreme experiment: building and shipping an internal beta product with zero manually written code. Through the experiment, they adopted a different model of engineering work: when the agent failed, instead of prompting it better or to “try harder,” the team would look at “what capability, context, or structure is missing?”The result was Symphony, “a ghost library” and reference Elixir implementation (by Alex Kotliarskyi) that sets up a massive system of Codex agents all extensively prompted with the specificity of a proper PRD spec, but without full implementation:The future starts taking shape as one where coding agents stop being copilots and start becoming real teammates anyone can use and Codex is doubling down on that mission with their Superbowl messaging of “you can just build things”.Across Codex, internal observability stacks, and the multi-agent orchestration system his team calls Symphony, Ryan has been pushing what happens when you optimize an entire codebase, workflow, and organization around agent legibility instead of human habit.We sat down with Ryan to dig into how OpenAI’s internal teams actually use Codex, why the real bottleneck in AI-native software development is now human attention rather than tokens, how fast build loops, observability, specs, and skills let agents operate autonomously, why software increasingly needs to be written for the model as much as for the engineer, and how Frontier points toward a future where agents can safely do economically valuable work across the enterprise.We discuss:* Ryan’s background from Snowflake, Brex, Stripe, and Citadel to OpenAI Frontier Product Exploration, where he works on new product development for deploying agents safely at enterprise scale* The origin of “harness engineering” and the constraint that kicked off the whole experiment: Ryan deliberately refused to write code himself so the agent had to do the job end to end* Building an internal product over five months with zero lines of human-written code, more than a million lines in the repo, and thousands of PRs across multiple Codex model generations* Why early Codex was painfully slow at first, and how the team learned to decompose tasks, build better primitives, and gradually turn the agent into a much faster engineer than any individual human* The obsession with fast build times: why one minute became the upper bound for the inner loop, and how the team repeatedly retooled the build system to keep agents productive* Why humans became the bottleneck, and how Ryan’s team shifted from reviewing code directly to building systems, observability, and context that let agents review, fix, and merge work autonomously* Skills, docs, tests, markdown trackers, and quality scores as ways of encoding engineering taste and non-functional requirements directly into context the agent can use* The shift from predefined scaffolds to reasoning-model-led workflows, where the harness becomes the box and the model chooses how to proceed* Symphony, OpenAI’s internal Elixir-based orchestration layer for spinning up, supervising, reworking, and coordinating large numbers of coding agents across tickets and repos* Why code is increasingly disposable, why worktrees and merge conflicts matter less when agents can resolve them, and what it really means to fully delegate the PR lifecycle* “Ghost libraries”, spec-driven software, and the idea that a coding agent can reproduce complex systems from a high-fidelity specification rather than shared source code* The broader future of Frontier: safely deploying observable, governable agents into enterprises, and building the collaboration, security, and control layers needed for real-world agentic workRyan Lopopolo* X: https://x.com/_lopopolo* Linkedin: https://www.linkedin.com/in/ryanlopopolo/* Website: https://hyperbo.la/contact/Timestamps00:00:00 Introduction: Harness Engineering and OpenAI Frontier00:02:20 Ryan’s background and the “no human-written code” experiment00:08:48 Humans as the bottleneck: systems thinking, observability, and agent workflows00:12:24 Skills, scaffolds, and encoding engineering taste into context00:17:17 What humans still do, what agents already own, and why software must be agent-legible00:24:27 Delegating the PR lifecycle: worktrees, merge conflicts, and non-functional requirements00:31:57 Spec-driven software, “ghost libraries,” and the path to Symphony00:35:20 Symphony: orchestrating large numbers of coding agents00:43:42 Skill distillation, self-improving workflows, and team-wide learning00:50:04 CLI design, policy layers, and building token-efficient tools for agents00:59:43 What current models still struggle with: zero-to-one products and gnarly refactors01:02:05 Frontier’s vision for enterprise AI deployment01:08:15 Culture, humor, and teaching agents how the company works01:12:29 Harness vs. training, Codex model progress, and “you can just do things”01:15:09 Bellevue, hiring, and OpenAI’s expansion beyond San FranciscoTranscriptRyan Lopopolo: I do think that there is an interesting space to explore here with Codex, the harness, as part of building AI products, right? There’s a ton of momentum around getting the models to be good at coding. We’ve seen big leaps in like the task complexity with each incremental model release where if you can figure out how to collapse a product that you’re trying to.Build a user journey that you’re trying to solve into code. It’s pretty natural to use the Codex Harness to solve that problem for you. It’s done all the wiring and lets you just communicate in prompts. To let the model cook, you have to step back, right? Like you need to take a systems thinking mindset to things and constantly be asking, where is the Asian making mistakes?Where am I spending my time? How can I not spend that time...
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Building a new engineering team by turning another one around - Tips from Tinder
Mar 3100:54:52Tap to summarizePodcast: Level-up Engineering (LS 33 · TOP 5% what is this?)Episode: Building a new engineering team by turning another one around - Tips from TinderPub date: 2024-10-16Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationTransforming teams doesn’t go without its challenges.Let’s look at Tinder’s example. In this episode, Chris O'Brien, Director of Engineering at Tinder, shares his insights on building and leading engineering teams, particularly focusing on turning around existing teams. He discusses transforming teams, transitioning into a leadership role, Tinder’s culture and hiring process and a lot more.Sign up to the Level-up Engineering newsletter!In this interview we're covering:Building a new team by turning another one aroundTransitioning into a leadership roleTinder’s cultureKeeping business, customer and team needs alignedTinder’s hiring processExcerpt from the interview:“Change isn't easy for anyone, especially in the workplace where stability and predictability matter. Switching teams suddenly can be unsettling, and it takes time for people to adapt and build trust with their new colleagues. That's why I've always believed in prioritizing relationship-building. It's something my mentor taught me early on, and it's proven to be invaluable. When there's already a foundation of trust and camaraderie, transitions become smoother, and teams become stronger.”The podcast and artwork embedded on this page are from Apex Lab, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
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Episode 265: How Marketplace Teams Decide What to Build
Mar 3100:30:11Tap to summarizePodcast: Product Thinking (LS 48 · TOP 1% what is this?)Episode: Episode 265: How Marketplace Teams Decide What to BuildPub date: 2026-03-25Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationCreating great product organizations takes more than setting roadmaps. It requires clear priorities, shared decision-making, and a strong sense of what makes the business uniquely valuable. In this episode, Melissa Perri brings together insights from three product leaders on how teams can create focus, alignment, and clarity as they scale.You’ll hear from Kristin Dorsett, Chief Product Officer at Viator at the time, on balancing top-down priorities with bottom-up autonomy and why doing fewer things at once leads to more meaningful progress. Craig Saldanha, Chief Product Officer at Yelp, explains how explicit product principles help teams make better decisions and stay aligned, especially in a two-sided marketplace.Mauricio Monico reflects on lessons from eBay and Wish, including the risks of copying competitors, the importance of explaining strategy clearly across the organization, and why turnarounds often begin by fixing marketplace fundamentals before chasing growth. Together, these perspectives offer a practical look at how product leaders create alignment without losing adaptability.You’ll hear us talk about:Balancing strategy and team autonomyKristin Dorsett explains how her organization combines top-down company priorities with team-level ownership. Some teams are aligned to a small number of company-wide big bets, while others are given lightweight charters and room to define their own roadmap. The conversation shows how strategic direction and local autonomy can work together when expectations are clear.Why doing fewer things leads to better outcomesA major theme in Kristin’s segment is the discipline of focus. She describes the company’s evolution from trying to pursue dozens of major initiatives at once to narrowing that list down to just three. The result was stronger alignment across departments and better progress on the work that mattered most.Product principles and marketplace decision-makingCraig Saldanha shares how Yelp codified its product culture into a set of decision-making tenets. He discusses how those principles help teams handle trade-offs, move faster on reversible decisions, and stay thoughtful on harder-to-reverse choices. He also explains how Yelp thinks about marketplace dynamics, consumer and business needs, and the flywheel that drives sustainable growth.Why companies lose their way when they copy competitorsMauricio Monico reflects on how eBay struggled when it tried to imitate Amazon instead of leaning into its own value proposition. He also walks through Wish’s turnaround, where the initial focus was not growth but restoring marketplace health through better merchant standards, product quality, and delivery performance. His examples show why clarity, differentiation, and strong fundamentals matter more than reactive strategy.Episode resources:Try Granola today: http://granola.ai/productinstituteCheck our courses: https://productinstitute.com/Episode 221: Balancing Strategy and Execution at Scale with Kristin Dorsett:https://www.produxlabs.com/product-thinking-blog/episode-221-kristin-viator-strategy-experimentationEpisode 162: Product Roadmap: Building a Platform for the Next Decade with Craig Saldanha, Chief Product Officer at Yelp:https://www.produxlabs.com/product-thinking-blog/2024/3/13/episode-162-product-roadmap-building-a-platform-for-the-next-decade-with-craig-saldanha-chief-product-officer-at-yelpEpisode 158: Turning the Tide with Mauricio Monico’s Lessons from eBay, Facebook, and Google:https://www.produxlabs.com/product-thinking-blog/2024/2/14/episode-158-turning-the-tide-with-mauricio-monicos-lessons-from-ebay-facebook-and-googleKristin Dorsett on LinkedIn:https://www.linkedin.com/in/kristindorsett/Craig Saldanha on LinkedIn:https://www.linkedin.com/in/craigsaldanha/Mauricio Monico on LinkedIn:https://www.linkedin.com/in/mspmonico/The podcast and artwork embedded on this page are from Melissa Perri, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
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123. Bonusepisode: Første kapitel af Afdelingen for Magisk tænkning
Mar 3100:21:18Tap to summarizePodcast: AdfærdEpisode: 123. Bonusepisode: Første kapitel af Afdelingen for Magisk tænkningPub date: 2025-11-25Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarization*Find podcastens nyhedsbrev lige her: https://mortenmunster.com/podcasts/*Så er der en lille tidlig julegave til dig:-)Bonusepisoden i dag er nemlig første kapitel af lydbogsudgaven af Afdelingen for Magisk Tænkning. Så hvis du ikke har købt bogen endnu, kan du høre første kapitel kvit og frit her.Og hvis du er en af de smukke mennesker, der allerede har læst den, kan du få fornøjelsen af at genbesøge første kapitel i lyd.Der er i øvrigt en del, der har spurgt, hvornår lydbogen udkommer. Svaret er, at den allerede er her. I forhold til streaming på Mofibo og den slags, så er meldingen, at den burde komme engang til næste år. Hvornår ved jeg ikke.Find bogen lige her:Papirudgave:SAXOBog&idéLydbog:Bog&idéSAXOThe podcast and artwork embedded on this page are from Morten Münster, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
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From skeptic to true believer: How OpenClaw changed my life | Claire Vo
Mar 3101:46:35Tap to summarizePodcast: Lenny's Podcast: Product | Career | Growth (LS 62 · TOP 0.1% what is this?)Episode: From skeptic to true believer: How OpenClaw changed my life | Claire VoPub date: 2026-03-29Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationClaire Vo is the host of our sister podcast, “How I AI,” a former product executive and engineer, and founder of an AI startup called ChatPRD. Claire now runs her business, podcast, and family life with the help of nine OpenClaw agents running on multiple Mac Minis and old laptops. In this episode, Claire shares her journey from OpenClaw skeptic (it deleted her family calendar the first time she tried it) to true believer, and gives a masterclass in using AI agents in real life.We discuss:1. The exact step-by-step process to install and set up OpenClaw (it’s easier than you think)2. How to avoid the biggest OpenClaw mistakes (don’t install it on your main computer)3. Actual use cases that have changed Claire’s life (e.g. family scheduling, inbound sales, podcast prep, and course management)4. Why multiple specialized agents beat one general-purpose agent5. The security risks everyone worries about—and how to handle them6. Browser limitations, memory issues, and practical workarounds—Brought to you by:Mercury—Radically different bankingOmni—AI analytics your customers can trustOrkes—The enterprise platform for reliable applications and agentic workflows—Where to find Claire Vo:• X: https://x.com/clairevo• LinkedIn: https://www.linkedin.com/in/clairevo• Podcast: https://www.youtube.com/@howiaipodcast• Website: https://clairevo.com• ChatPRD: https://www.chatprd.ai—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Claire and OpenClaw(08:00) The journey from OpenClaw skeptic to believer(11:50) What OpenClaw actually does that’s useful(13:35) OpenClaw vs. other AI agent products(17:05) How to actually install OpenClaw: the basics(18:49) Setting up like you’d onboard a real assistant(20:41) Security and privacy considerations(24:53) Live demo: Installing OpenClaw step-by-step(28:47) Setting up Q: an agent for her kids’ homework(34:08) Understanding “soul,” “identity,” and “memory”(40:40) The unlock: multiple agents, not just one(45:02) How to run multiple agents on one machine(47:28) Jesse Genet’s homeschooling use case(49:58) Real examples and use cases(56:41) Finn, Claire’s family agent(1:00:05) Sage the Course Bot(1:02:15) Common issues and workarounds(1:08:08) The Exa/Perplexity web search workaround(1:09:29) Memory management and context overload(1:12:09) Pro tip: Screen sharing to manage Mac Minis(1:14:18) Using Google Workspace for agent collaboration(1:16:24) What makes OpenClaw special(1:20:15) The “yappers API” and ramble mode(1:22:04) Using Claude Code as your OpenClaw brain surgeon(1:25:16) Bringing management skills to AI agents(1:29:32) Why this matters(1:32:37) Lightning round and final thoughts—Referenced:• OpenClaw: https://openclaw.ai• Claude Cowork: https://claude.com/product/cowork• Fry’s Electronics: https://en.wikipedia.org/wiki/Fry%27s_Electronics• Peter Steinberger on LinkedIn: https://www.linkedin.com/in/steipete• Telegram: https://telegram.org• WhatsApp: https://www.whatsapp.com• Fin: https://fin.ai• Why OpenClaw feels alive even though it’s not (this AI has a heartbeat but not a brain): https://x.com/clairevo/status/2017741569521271175• 5 OpenClaw agents run my home, finances, and code | Jesse Genet: https://www.youtube.com/watch?v=96Vl8s3EQhk• Executive Playbook for AI in Engineering, Product, and Design: https://maven.com/clairevo/ai-native-epd-org• Zach Davis on LinkedIn: https://www.linkedin.com/in/zach-m-davis/• ChatGPT Atlas: https://chatgpt.com/atlas• Perplexity Comet: https://www.perplexity.ai/comet• Browser (OpenClaw-managed): https://docs.openclaw.ai/tools/browser• Buffer: https://buffer.com• Brave: https://brave.com/search/api/• Exa: https://exa.ai• Hilary Gridley on X: https://x.com/yourgirlhils• How to become a supermanager with AI: https://www.lennysnewsletter.com/p/how-to-become-a-supermanager-with• How custom GPTs can make you a better manager | Hilary Gridley (Head of Core Product at Whoop): https://www.youtube.com/watch?v=xDMkkOC-EhI• How to debug a team that isn’t working: the Waterline Model: https://www.lennysnewsletter.com/p/how-to-debug-a-team-that-isnt-working• Jensen Huang on LinkedIn: https://www.linkedin.com/in/jenhsunhuang• How I built a 1M+ subscriber newsletter and top 10 tech podcast | Lenny Rachitsky: https://www.lennysnewsletter.com/p/how-i-built-a-1m-subscriber-newsletter• Age of Attraction on Netflix: https://www.netflix.com/title/81779095• Oura Ring: https://ouraring.com/• Eight Sleep: https://www.eightsleep.com• Hoopsalytics: https://hoopsalytics.com• DJI Osmo smartphone gimbal: https://www.amazon.com/DJI-Stabilizer-Tracking-Extension-Stabilization/dp/B0FJ2L67HJ?ref_=ast_sto_dp• Silent basketball: https://www.amazon.com/Rzkipdy-Silent-Basketball-Size-27-5/dp/B0FHFSQWPP/ref=sr_1_9• Marc Andreessen: The real AI boom hasn’t even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom—Recommended books:• Treasure Island: https://www.amazon.com/Treasure-Island-Robert-Louis-Stevenson/dp/1505297400• Alice’s Adventur...
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