Podcasts about devtools

This months panel dives into Remix v3 without React, exploring its DIY VDOM framework and manual reactivity approach. We discuss the latest React Foundation governance changes and what React 19.2 brings, from the Activity component to useEffectEvent and server streaming support. The conversation also covers how the proposed H-1B $100,000 fee could affect tech hiring, thoughts on Firefox, the Perplexity and Washington Post paywall, and a spicy Tailwind vs CSS debate. Links Paige Niedringhaus Website: https://www.paigeniedringhaus.com X: https://x.com/pniedri GitHub: https://github.com/paigen11 TJ Van Toll Website: https://www.tjvantoll.com X: https://x.com/tjvantoll GitHub: https://github.com/tjvantoll LinkedIn: https://www.linkedin.com/in/tjvantoll Jack Herrington Website: https://jackherrington.com YouTube: https://www.youtube.com/channel/UC6vRUjYqDuoUsYsku86Lrsw X: twitter.com/jherr Github: github.com/jherr Noel Minchow LinkedIn: https://www.linkedin.com/in/noel-minchow Resources Remix v3 Dumps React for Pure Web Standards: The JS Rebellion That's Freeing Devs from Framework Hell!: https://bybowu.com/article/remix-v3-dumps-react-for-pure-web-standards-the-js-rebellion-thats-freeing-devs-from-framework-hell Remix Jam 2025 Recap: https://remix.run/blog/remix-jam-2025-recap Wake up, Remix!: https://remix.run/blog/wake-up-remix Introducing the React Foundation: https://react.dev/blog/2025/10/07/introducing-the-react-foundation useEffectEvent: https://react.dev/blog/2025/10/01/react-19-2#use-effect-event Trump's $100,000 H-1B visa shock: https://www.bbc.com/news/articles/ce3yy58lj79o We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey (https://t.co/oKVAEXipxu)! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com (mailto:elizabeth.becz@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Check out our newsletter (https://blog.logrocket.com/the-replay-newsletter/)! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. (https://logrocket.com/signup/?pdr) Chapters 0:00 Intro 1:10 Remix v3 Breaks from React 4:40 Manual Reactivity Debate 7:45 Docs, Demos, and Developer Confusion 9:00 Framework Future and Web Standards 13:00 Shopify and Remix 14:00 React 19.2 + Foundation Shift 17:00 New React Features Discussion 20:00 React's Backward Compatibility Wins 21:00 Why Meta Let Go of React 27:00 The $100K Visa Shock 32:00 Global Impact and Legal Fallout 36:00 What Companies Should Do Next 38:00 Hot Takes Begin 39:00 The Witcher 4 Trailer Debate 40:00 Firefox vs Chrome 43:00 Perplexity & Washington Post Drama 45:00 Dev Tools, Paywalls, and Browsers 46:00 Paige vs Tailwind 48:00 AI Writing Bad CSS 49:00 Outro Special Guest: Jack Herrington.

Victor, VP of Marketing at Strapi, walks us through how AI can be used in content creation—what tools work, what to watch out for, and how you can try some of these techniques yourself. This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links:   •  Victor's X   •  Victor's Linkedin   •  Strapi   •  GrowthX   •  Kapa   •  Octolens   •  Semrush

This week's episode dives into all the major announcements from React Conf 2025—from the upcoming changes to React Native DevTools and React Foundation, to long-awaited features like CSS support and Hermes V1. Plus, I share updates on my latest projects, including the release of my Pocket Clone and progress on the Wolt Clone.⚛️ React Native Radar:

Ben Dicken is a developer educator at PlanetScale, he's an incredible writer and teacher, who's made some amazing technical articles that developers actually love reading. We get into his reasons for working so hard on these articles, his process, and how he makes content that genuinely helps engineers understand complex ideas.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links:   •  Ben's X   •  B-trees and database indexes article   •  IO devices and latency article

In this episode, we explore Adam Frankl's concept of a Technical Advisory Board, and how it helps DevTools founders learn directly from potential users. I share personal experience organizing one-on-one interviews to find out real customer problems and gives tips for recruiting members. We explore how to set up the meetings, analyse feedback, and get the most value from the process.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links:   •  The first tab call   •  How to recruit TAB members   •  After the first set of TAB calls   •  Adam's Linkedin   •  Adam's book

What happens when AI stops being a product and starts being a platform?With OpenAI's latest Dev Day changing the game, we might find out sooner rather than later. In this episode, Chris and Yaniv explore OpenAI's Dev Day and the huge announcements made (including the Sora 2 launch), as well as their apparent transformation from product to platform. In other tech news, the guys dive into Anthropic's open-source counterpunch with Claude's Agent SDK, along with Apple's controversial censorship call - all while analyzing what all of this means for startup founders building in the AI era.In this episode, you will:Understand how SDK, OpenAI's new ChatGPT App, turns AI into a full developer ecosystemLearn about “platform risk”,and how founders can avoid being crushed by itCompare OpenAI's walled-garden strategy to Anthropic's open, Unix-inspired approachExamine the launch of Sora, OpenAI's new AI video social network, and why it mattersDebate whether large tech companies can ever succeed in building new social platformsAnalyze Apple's ICE Block takedown and what it says about censorship in curated ecosystemsThe Pact Honor the Startup Podcast Pact! If you have listened to TSP and gotten value from it, please:Follow, rate, and review us in your listening appSubscribe to the TSP Mailing List to gain access to exclusive newsletter-only content and early access to information on upcoming episodes: https://thestartuppodcast.beehiiv.com/subscribe Secure your official TSP merchandise at https://shop.tsp.show/  Follow us here on YouTube for full-video episodes: https://www.youtube.com/channel/UCNjm1MTdjysRRV07fSf0yGg Give us a public shout-out on LinkedIn or anywhere you have a social media followingKey linksGet your question in for our next Q&A episode: https://forms.gle/NZzgNWVLiFmwvFA2A The Startup Podcast website: https://www.tsp.show/episodes/Learn more about Chris and YanivWork 1:1 with Chris: http://chrissaad.com/advisory/  Follow Chris on Linkedin: https://www.linkedin.com/in/chrissaad/  Follow Yaniv on Linkedin: https://www.linkedin.com/in/ybernstein/Producer: Justin McArthur https://www.linkedin.com/in/justin-mcarthurIntro Voice: Jeremiah Owyang https://web-strategist.com/

Anthropic dropped a bunch of good Claude Code updates. There's a new a native VS Code extension, a v2 of the terminal version of Claude, code checkpoints, and it's all powered by Sonnet 4.5, Anthropic's best coding model yet.On top of Claude's glow up, the Google Chrome team launched a Chrome DevTools MCP. AI coding assistants will be able to debug web pages directly in a Chrome browser, including using DevTools to review network requests, console output and page structure, simulate user interactions, and even automate performance audits.In a poorly planned move, the CEO of Vercel tweeted a picture of himself meeting with the controversial Israeli Prime minister, Benjamin Netanyahu. This sparked a massive online backlash, some Vercel employees quit, and many customers are leaving the hosting site and vowing to stop using Next.js, as well. Timestamps:00:46 - Claude Sonnet and Claude Code updates7:55 - Chrome DevTools MCP13:50 - Vercel Drama19:03 - State of JS survey is open20:21 - GitHub's plan to make npm more secure25:30 - Meta builds data center the size of 70 football fields29:04 - What's making us happyLinks:Paige - Chrome DevTools MCPJack - Vercel's in hot water after a selfie with Benjamin NetanyahuTJ - Claude Code levels upState of JS survey is openGitHub's got a plan to make npm more secureMeta builds AI data center the size of 70 football fieldsPaige - Star Trek: Strange New Worlds season 3Jack - reactnorway.comThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. You can join us in our Discord channel, explore our website and reach us via email, or talk to us on X, Bluesky, or YouTube.Front-end Fire websiteBlue Collar Coder on YouTubeBlue Collar Coder on DiscordReach out via emailTweet at us on X @front_end_fireFollow us on Bluesky @front-end-fire.comSubscribe to our YouTube channel @Front-EndFirePodcast

Matt Carey from AI Demo Days, shares his experience of organizing developer events in London and San Fransisco. He discusses the real costs involved and how creating fun, community-driven events makes all the difference - plus a spicy take on Hackathons!This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links:   •  AI Demo Days   •  Matt Carey's links

Gitpod has rebranded to Ona and shifted its focus to building AI tools for enterprise teams. This episode digs into why they made the leap, how they're standing out in a crowded AI space, and what it's been like rethinking developer workflows from the ground up. We talk about dev environments, differentiating in the AI space, forward-deployed engineers and more. This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links:   •  Ona   •  Christian's X   •  Matthew's X

Creating docs that actually work means knowing what to write, how to write it, and where it belongs. In this episode, we break down the diataxis documentation framework—a simple but powerful system that splits docs into four clear types: tutorials, how-to guides, explanations, and reference. We look at examples of tools that have implemented diataxis to write their documentation with clarity and purpose.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links:   •  Diataxis   •  Sequin   •  Layercode   •  Logdy

Karen from Composio shares how developers are using MCP to connect tools like Slack, Notion, and Gmail with AI agents, growing from nearly zero to 100,000 users in 6 months. They capitalized on key moments when new AI tools, such as Grok versions and Claude releases, came out, creating examples and demos that resonated strongly across social media and got them retweeted by Elon Musk. Hear how the team learns to use these tools better over time, helping each new release work smarter than the last.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links:   •  Composio    •  Composio's X   •  Karan's X   •  Launch Video

En este episodio de DevTalles repasamos las novedades del Angular Summer Update 2025, donde el equipo de Angular presentó mejoras clave como el soporte estable para aplicaciones sin Zone.js, nuevas formas de trabajar con animaciones más simples, optimizaciones en las plantillas y herramientas pensadas para integrarse con la inteligencia artificial. Además, DevTools ahora permite visualizar señales y rutas de forma más clara, se añadieron utilidades al test harness, y hasta el componente Mat Menu puede usarse como menú contextual. Todo esto apunta a un Angular más ligero, moderno y preparado para el futuro.https://blog.angular.dev/angular-summer-update-2025-1987592a0b42

Lee Robinson helped Vercel grow to $200M+ in ARR and scaled the Next.js community to over 1.3 million active developers. I dive into his blog posts to uncover valuable insights and lessons about how he achieved this success, covering topics like docs, community building, developer education, marketing, and product development.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links:   •  Lee Robinson's blog   •  Lee Robinson's X   •  Peter Yang's interview   •  swyx's interview   •  Gonto on Scaling DevTools   •  Developer Marketing CommunityP.s. this is a new style of episode, let me know what you think. 

Welcome back to another episode of the EUVC Podcast, where we gather Europe's venture family to share the stories, insights, and lessons that drive our ecosystem forward. Today's conversation takes us on a global journey from Croatia to San Francisco to uncover how one founder caught lightning in a bottle and is now racing to harness it.Our guest: Ivan Burazin, founder of Daytona. With a career spanning Toronto, Croatia, Infobip, Shift Conference, and now Daytona, Ivan brings a rare, global perspective on how Europe can lead in DevTools and AI infrastructure. Alongside him, our dear friend Enis Hulli from E2VC joins to spotlight Daytona's story, the lessons from its dramatic pivot, and what it means for founders and investors navigating this new AI wave.Ivan has spent two decades at the intersection of infrastructure and developer communities. From racking servers in the early 2000s to launching one of the first browser-based IDEs in 2009 to scaling the Shift Conference to thousands of attendees, his career has consistently circled around enabling developers.Daytona's first act was a cloud IDE provider for enterprises — “one-click setup for secure developer environments.” With Fortune 500 customers onboard, revenue flowing, and a healthy pipeline, Daytona 1.0 showed promise. But something was missing.Six months ago, Ivan and his team made a bold decision to pivot. Daytona 2.0 is no longer about provisioning dev environments for humans — it's about powering AI agents with the computers they need.“Agents are not computers themselves. They need access to computers to run browsers, clone repos, analyze data. Daytona gives them that — an isolated sandbox with machine-native interfaces built for agents.” – IvanThe differences between human and agent runtimes turned out to be massive:Humans tolerate 30 seconds of spin-up; agents need milliseconds.Humans solve problems sequentially; agents branch into parallel “multiverse” solutions.Humans parse terminal output; agents require clean APIs.By recognizing this, Daytona carved out a new category: the computer for agents.The pivot coincided with a deliberate move to San Francisco. Ivan recalls how Figma embedded with designers at Airbnb, or how Twilio found adoption among early Valley startups. To own mindshare in a new category, location mattered.“From San Francisco outwards, adoption flows naturally. From Europe inwards, it's like pushing uphill.” – IvanSo Daytona went all-in: presence at AI meetups, team members flying in and out, and early product evangelism on the ground.HAfter the pivot, Daytona saw extraordinary pull from the market:Customer conversations ended with “send me the API key”.Infrastructure demand showed power-law dynamics: just a handful of fast-growing customers could drive scale.Instead of polished decks, Ivan shared raw revenue dashboards with authenticity.The momentum was immediate and tangible.Ivan admits he hadn't explicitly asked permission to pivot. He hinted at it in updates, tested the idea with a hackathon, and only later informed his cap table. The response? Overwhelmingly positive.“Almost half the angels replied. Go f***ing go. Let's go. I should've told them sooner.” – IvanEnis highlights this as a key distinction: experienced angels with broad portfolios encourage bold swings, while less diversified angels may fear the risk.Catching lightning is one thing. Harnessing it is another. Ivan's current focus:Hiring deliberately: keeping the team small and ownership-driven.White-glove onboarding: every serious customer gets a Slack channel with the whole team.Balancing speed and reliability: ship daily, but solve today's scale problems without over-engineering.Enis introduces a new term: seed-strapping — raising a seed, skipping A and B, and scaling straight to unicorn status.Ivan is cautious. Infra is capital-intensive, and while Daytona could raise a Series A today, he's committed to doing it on his terms.

Web development is constantly evolving, and so are the tools we use to build. In this episode, Amy and Brad chat with the organizers of Squiggle Conf about the future of web dev tooling, how conferences shape the developer experience, and why community matters just as much as code.Chapters0:00 - Intro0:34 - Meet the Guests: Squiggle Conf OrganizersSquiggle Conf1:19 - What Makes Squiggle Conf Unique3:19 - Tooling and Developer Experience3:30 - Penguins, IMAX, and the Conference Venue4:18 - Who Should Attend Squiggle Conf5:31 - How Talks Are Selected and Curated6:51 - Social and Community Aspects of the Conference12:19 - Behind the Scenes of Organizing a Conference17:46 - Lessons Learned from Running Events23:30 - The Role of Tooling in Modern Development27:21 - Browser-Based Tools and Their Impact28:51 - Shoutout to Astro and Other FrameworksAstroStarlight - Astro's template for documentation33:51 - Comparing Different Conference Experiences38:55 - Building Momentum in the Developer Community40:45 - Looking Ahead: The Future of Squiggle Conf42:02 - Final Thoughts from the Organizers43:43 - Picks and PlugsAre the Types Wrong? — a package & CLI tool by Andrew Branch from the TypeScript teamThe Harry Potter movie seriesCloudflareOne Switch - Mac Menu Bar AppRedwoodSDK

Carter Rabasa, head of DevRel at Langflow, talks about organizing and participating in hackathons, how these events enable developers to break free from routine work, and how they can help accelerate tool development.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links:   •  Carter's X   •  Carter's LinkedIn   •  Cascadia AI Hackathon (and Luma)   •  AI Tinkerers   •  Bolt Virtual Hackathon 

Rita Kozlov is the VP of Developers and AI at Cloudflare. We talk about how Cloudflare focuses on building disruptive, efficient technologies like their Workers platform to gain long-term competitive advantages. They use their own developer platform to ship fast, and hire people who deeply care, with a culture of curiosity and transparency that drives continuous innovation.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links:Rita's X Rita's LinkedInCloudflareswyx article on cloudflare Stratechery article on Cloudflare's disruption 

You just can't keep TanStack out of the news for more than a few weeks before a new product appears. This week, it's TanStack Devtools, which provides a centralized devtools panel of all the Tanstack libraries for streamlined DX and custom devtools support.The State of CSS 2025 survey results are in, and highlights include: devs love the new `:has()` feature, Tailwind CSS continues to be the most popular CSS framework, and over 60% of respondents are still using Sass or SCSS in their web apps.Continuing the CSS topics, Panda CSS, a CSS-in-JS library that debuted in 2023, just hit v1. Panda gained traction by being a CSS-in-JS library built for the server-first era (meaning RSC support), and it adds new features like static analysis, type safety, and support for modern CSS like cascade layers, JSX style props, and a `createStyleContext` API for cross-framework design systems.Timestamps:0:56 - TanStack Devtools6:28 - State of CSS 2025 survey results15:23 - Panda CSS v123:19 - Perplexity wants to buy Chrome from Google25:52 - Google Gemini is having a mental breakdown30:50 - Bolt.new unveils Bolt Cloud35:14 - The dialog element's closedby attribute39:20 - What's making us happyLinks:Paige - Panda CSS v1 Jack - TanStack DevtoolsTJ - State of CSS 2025 survey resultsPerplexity wants to buy Chrome from GoogleGoogle Gemini's having a mental breakdownBolt.new unveils Bolt CloudThe dialog element's `closedby` attributePaige - Express VPNJack - A Psalm for the Wild Built bookTJ - The Retrievals podcast and The Savannah Bananas baseball teamThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. You can join us in our Discord channel, explore our website and reach us via email, or talk to us on X, Bluesky, or YouTube.Front-end Fire websiteBlue Collar Coder on YouTubeBlue Collar Coder on DiscordReach out via emailTweet at us on X @front_end_fireFollow us on Bluesky @front-end-fire.comSubscribe to our YouTube channel @Front-EndFirePodcast

In this episode of JavaScript Jabber, we sit down with Vinicius Dallacqua, a seasoned software engineer with a passion for performance and developer tooling. Vinicius shares his journey from coding in central Brazil with limited connectivity to building cutting-edge tools like PerfLab and PerfAgent. We dive into the intersection of AI and DevTools, exploring how artificial intelligence is transforming performance debugging, web development workflows, and even the future of browsers.We also tackle the big questions: How do developers avoid bias when building in high-performance environments? What role will agentic browsers play in the evolution of the web? And how can AI-powered DevTools lower the barrier for developers intimidated by performance profiling? If you're curious about the future of frontend performance, DevTools, and AI-driven development, this conversation is packed with insights.Links & ResourcesPerfLab – Performance tooling platformPerfAgent – AI-powered DevTools assistantVinicius Dallacqua on X (Twitter)Paul Kinlan's AI Focus – Essays on AI and the webPerfNow Conference – Leading performance conference in AmsterdamBecome a supporter of this podcast: https://www.spreaker.com/podcast/javascript-jabber--6102064/support.

Matt Palmer from Replit shares how the company scaled to $100M in ARR from ~$10M in under a year. We talk about the importance of video for teaching the non-linear process of working with AI, the challenge of rewriting documentation for a broader audience using the Diátaxis framework, and how they support a diverse community of users navigating this new AI-driven development landscape.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links:ReplitReplit Agent documentationDiátaxis documentation frameworkMatt's YouTubeMatt's XReplit's Youtube

Zach Lloyd, CEO and founder of Warp, joins The Tech Trek to unpack what it really takes to build tools that transform the developer experience. From rethinking the terminal to balancing product focus with user growth, Zach shares hard-earned lessons from scaling products that developers actually want to use. This is a conversation about building with empathy, understanding workflows, and making deliberate trade-offs that move the needle.Key Takeaways• Why deep focus on the developer workflow leads to products that stick• The importance of balancing big-picture vision with small, iterative improvements• How to make trade-offs between growth experiments and core product quality• Why some of the most powerful product ideas come from rethinking “old” tools• The role of design and speed in shaping developer adoptionTimestamped Highlights[03:15] The inspiration behind Warp and why the terminal needed rethinking[09:42] Balancing user requests with long-term product vision[14:10] How small quality-of-life improvements can have outsized impact[21:55] Deciding when to invest in growth versus core product work[28:30] Lessons from building for an audience of highly opinionated users[36:05] Why the future of dev tools will blend speed, design, and collaborationQuote of the Episode“The best products come from understanding the real workflow pain and then removing it in a way that feels almost invisible to the user.”Resources MentionedWarp: https://www.warp.devIf you enjoyed this conversation, follow The Tech Trek on your favorite podcast platform and connect with me on LinkedIn for more insights from the leaders shaping the future of technology.

Logan Kilpatrick shares how DeepMind's organizational changes helped their resurgance in AI. What needs to happen to reach 100m developers. And why the next six months are more exciting than ever.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links:Google DeepMind  Logan Kilpatrick Logan Kilpatrick podcast NotebookLM Gemini CLI Veo 

Sam Lambert is the CEO of PlanetScale - a cloud database provider.Sam shares:- Why dropping the free tier was one of PlanetScale's best decisions. But is not for every startup.- People solving serious problems appreciate serious content and if you can create meaningful content, that's a big advantage.- CEOs should be transparent and collaborative but assertive. Don't let your company die while enacting someone else's decision - Express hard-to-convey-benefits via your customers' experiencesThis episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links:- Sam's Twitter- Sam's LinkedIn - PlanetScale - PlanetScale Postgres - Caching blog post - Ted Nyman - Snowflake - Vitess 

Sam Bhagwat is the CEO of Mastra - a typescript AI agents framework. Sam is also the cofounder of Gatsby, the popular React framework that was acquired by Netlfiy. Sam shares what he learned building Gatsby and how they're applying those lessons to Mastra. Why they're building in TypeScript, not Python. Why 20% of their users are in Japan. And why they're distributing 1,500 physical books per week on AI agents. This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links:- Mastra - Sam Bhagwat  - Gatsby  - Principles of Building AI Agents 

In this episode of Founded & Funded, Madrona Partner Vivek Ramaswami sits down with Jared Palmer — designer, developer, and founder of Turborepo (acquired by Vercel), and former VP of AI at Vercel. Jared walks through his unique path from Goldman Sachs to Vercel, and how he combined finance, design, and engineering to create beloved developer tools like Formik, TSDX, and Turborepo, and v0. The two dive deep into: Why vertical integration is the future of AI-native dev platforms The founding and launch of Vercel's v0.dev How Vercel is positioning for a world with 700M code-generators, not just 28M developers What makes teams and products move fast Why “text-to-app” will soon become “text-to-business” Whether you're a founder building dev tools, a product leader thinking about AI-native apps, or a developer curious about the future of your craft — this episode is packed with lessons and foresight. Subscribe and listen now! Transcript: https://bit.ly/4kQWVig Chapters:  (00:00) Introduction (01:40) Jared Palmer's Early Career in Finance (04:40) Transition to Design and Freelancing (07:12) Building a Career in Open Source (11:46) Creating TurboRepo (13:47) Joining Vercel (15:27) Adjusting to Corporate Life (17:37) The Power of Transparency in Teams (17:50) Building a Thriving Team Environment (19:08) Origins of v0 (19:29) Early Development and Challenges (21:38) Key Innovations and Prototypes (22:58) Launch and Rapid Growth (25:32) Navigating a Competitive Landscape (30:02) Future of AI and Software Development

This is the first time I'm turning the mic around. This is the story of StreamPot. A DevTool I launched about a year ago.It was just acquired by ittybit so I thought I'd bring ittybit's founder Paul on to basically interview me about what went right and what went wrong.Hopefully you enjoy learning a bit more about the guy usually asking the questions.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links:- Jack Bridger- StreamPot - StreamPot GitHub- Announcement  - Paul Anthony Williams - ittybit- FFmpeg  - Hetzner

Jake Cooper is the Founder of Railway.This conversation explores how AI accelerates the need for strong backend infrastructure, when to build vs buy in AI software, and why there are only two moats: solving hard problems and doing hard things.We also unpack Railway's bold product bets, like enabling creators to earn revenue with backend templates, building their own data centers, and not building their own AI models.Jake also talks about their four week new hire onboarding, how they build a problem roadmap, why operators should be managers, and why you should almost never work weekends.Thank you to Angelo Saraceno @ Railway and Erica Brescia Bacon @ Redpoint for help brainstorming topics for the conversation.Thanks to Ramp for supporting this episode. It's the corporate card and expense management platform used by over 40,000 companies, like Shopify, CBRE and Stripe. Time is money. Save both with Ramp. Get your $250 here.Timestamps:(3:33) Solving the hardest problems in dev tools(8:16) Starting with the hardest thing(11:18) How AI accelerated the need for Railway(12:50) Importance of backend in AI-native software(16:52) Jake's angel fundraise strategy(20:51) Resisting AI for so long(25:32) Using AI to get leverage(29:57) Build vs buy in AI software(33:22) When Jake knew Railway was working(34:27) Creating infrastructure templates(38:04) Building data centers and a cloud service(40:27) Two moats: Hard problems and hard things(46:25) Hitting 8-figures in revenue(48:47) Railway's four week onboarding(54:25) Building a problem roadmap(56:16) You can't set your own culture(1:01:58) Railway's viral “How We Work” post(1:08:39) Using Discord instead of Slack(1:11:25) How hypergrowth companies mess up org design(1:14:03) Why you shouldn't work weekends(1:19:45) Not betting big on AI models(1:21:53) Lessons from Zuck, Martin ScorseseReferencedRailwayCareers at RailwayThe Inward Draw of CapitalismHow We Work Volume 1Volume 2Volume 3Volume 4Follow JakeTwitterLinkedInSubstackFollow TurnerTwitterLinkedInSubscribe to my newsletter to get every episode + the transcript in your inbox every week.

Paul Copplestone is the CEO of Supabase, the Postgres development platform. He talks about the discipline needed to cross the enterprise chasm without isolating your original community. This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links:- Paul's LinkedIn - Paul's X - Paul's website- Supabase - Enterprise Sales vs Product-led Growth - Friction logs - Ant Wilson - Multigres: Vitess for Postgres 

Quinn Favret is the founder of Tavus. They do AI video research and products. They saved a Scaling DevTools episodes with their lipsync feature. This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. https://workos.com/Links:- Tavus - Tavus lipsync API - Quinn Favret - Scaling DevTools episode saved by Tavus 

Mark Fussell is the CEO of Diagrid, a developer platform that provides tools and services for building cloud native applications. They've raised $24.2M from Amplify and Norwest. He is also the co-creator of Dapr, an open source tool used by 40,000 companies. Mark's favorite books: - Crossing the Chasm (Author: Geoffrey A. Moore)- Good to Great (Author: Jim Collins)- The Dispossessed (Author: Ursula K. Le Guin) (00:01) Opening and Introduction(00:09) The Origins of Dapr: Solving Developer Pain(01:53) Why Launch Diagrid After Building Dapr at Microsoft(03:36) Why Dapr Gained Traction Among Developers(05:30) Open Source Commercialization: What to Charge For(07:51) When Do Companies Turn to Diagrid for Help?(09:53) Key Features: PubSub, Workflow, and Catalyst(11:48) North Star Metrics and Innovation Philosophy(13:17) Pricing Strategy for Infra and Dev Tools(15:28) Competing Against Hyperscalers Like AWS & Azure(17:32) Who Diagrid Competes With and Role of Platform Engineering(19:29) The Agentic Shift in Microservices(21:28) How AI Is Changing Microservices Design(22:59) What's Coming Next at Diagrid: Roadmap and AI Features(24:51) Lessons from the First Five Customers(26:59) Rapid Fire Round--------Where to find Mark Fussell: LinkedIn: https://www.linkedin.com/in/mfussell/--------Where to find Prateek Joshi: Newsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-infinite X: https://x.com/prateekvjoshi 

Andrew Filev is the founder of Zencoder. Zencoder is building AI coding agents. In this episode, we explore the evolution from simple code completion AI to more sophisticated software engineering agents. While tools like GitHub Copilot revolutionized code suggestions, the next frontier involves AI agents that can handle complex engineering tasks and collaborate with each other through emerging protocols.The discussion dives into agent-to-agent protocols, which enable AI systems to work together autonomously on software development tasks. This advancement suggests a future where AI agents could manage entire development workflows, from requirements gathering to testing and deployment. We also touch on the importance of using slower summer periods strategically - making it an ideal time for engineering teams to evaluate their tooling, processes, and prepare for upcoming development cycles.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links- Zencoder - Andrew Filev - Wrike- Powered by Claude- Vercel- Perplexity AI - Scale AI 

In this episode we talk about Wordware, programming with LLMs, and what it now means to be a developer. Robert and Filip explain how they're building tools that let non-engineers create AI workflows, why the definition of 'developer' is changing in the AI era, and their vision for background agents that automate your work while you focus on creative tasks.Links:- Wordware  - Wordware Sauna Waitlist - Wordware is hiring - Filip Kozera - Robert Chandler This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. P.s. thanks to Oana Olteanu for making it happen  

Dev Tools Guild launches its dev tool funding initiative. Coinbase launches Coinbase Payments. Ethereum developers add four more EIPs to Fusaka. And L2Beat implements its new classification framework. Read more: https://ethdaily.io/724 Disclaimer: Content is for informational purposes only, not endorsement or investment advice. The accuracy of information is not guaranteed.

Tony Holdstock-Brown is the CEO and founder of Inngest, a tool to run AI and backend workflows at scale.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links:- Inngest - Tony's (inactive) LinkedIn - Traction book Note: the studio lost video footage about 20 minutes in. Sorry about that. Audio is fine though. 

Stack Overflow has long been the go-to platform for developers to learn, collaborate and solve coding challenges. In this Tech Disruptors episode, Bloomberg Intelligence senior analyst Sunil Rajgopal speaks with Stack Overflow CEO Prashanth Chandrasekar on how generative AI is reshaping developer workflows and platform strategy. They discuss the company's pivot toward enterprise use cases, including private knowledge sharing, agentic AI integration and data licensing. The conversation also explores major partnerships with AI and cloud providers and Stack Overflow's evolving role in a rapidly changing developer ecosystem.

Utpal Nadiger is the cofounder of Digger.dev. Digger built a popular open source IaC orchestration tool. Their new product Infrabase is an AI DevOps agent that scans IaC code in your pull requests.We talk about SF, resiliency and pivoting.Links:Utpal Digger Tavus (lipsync)This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Why/how we lipsynced: The  (amazing) studio accidentally had Utpal's camera switched off for the first 20 minutes. So I lipsynced the audio onto the latter part of the video. You can probably notice if you look closely. And also his gestures don't always look congruent because of the lipsyncing. But overall, incredible tech from Tavus - much better than a blank screen in my opinion!

Scott and Wes break down the state of web browsers in 2025, from the rise and fall of Arc and the fate of Firefox to hot takes on Opera GX, Raycast, and why power users might not be profitable. They compare rendering engines, rant about dev tools, and reveal what browser stats say about Syntax listeners. Show Notes 00:00 Welcome to Syntax! 01:37 Rendering Engines. 02:11 Arc Browser. 02:41 Microsoft Edge. 03:45 Why not Brave? 05:25 Brought to you by Sentry.io. 05:50 Google Manifest v2. 07:32 Opera. OperaGX. 10:13 Vivaldi. 11:23 The death of Arc Browser. 11:44 Dia? 14:43 No revenue from power-users. Letter to Arc Members. 15:38 Arc's transition to a new browser. 17:02 Browser companies need to lock users fast! 19:42 Gecko. 19:45 Firefox. 21:08 Zen. 22:38 Webkit. There Still Arent Any iPhone Browsers With Custom Engines 29:18 Wtf is Ladybird? 34:14 Usage statistics. StatCounter.com. 39:32 Dev Tools experience ranked. 42:06 Tab experience. 43:37 Containers and profiles. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

V 71. epizóde som sa rozprával s Jurajom Masárom, spoluzakladateľom Better Stack – jednej z najrýchlejšie rastúcich DevTools firiem v Európe, ktorá získala investície od fondu, za ktorým stojí aj zakladateľ Heroku. Juraj dnes riadi firmu v New Yorku z Prahy a v podcaste otvorene hovorí o tom, čo naozaj znamená budovať globálny biznis z Európy.V podcaste sme sa rozprávali o tom, prečo sa podľa neho neoplatí budovať globálnu firmu na Slovensku a aké sú najväčšie výhody i nevýhody podnikania v USA. Juraj otvorene priznal, že Better Stack musel otočiť produktový smer, hoci predtým získali investíciu. Zároveň priblížil, ako sa im darí škálovať tím – napríklad tým, že výber nových ľudí trvá aj 5 mesiacov, ale výsledok stojí za to. V podcaste nájdete konkrétny postup výberu, položené otázky a aj spôsob eliminácie uchádzačov.Prebrali sme aj tému CTO a CMO vo firmách – kto má kedy čo robiť, čo patrí do ich zodpovedností a ako udržať oba svety – produkt aj marketing – v rovnováhe. Juraj popísal aj svoj názor na AI vo vývoji softvéru a tiež to, prečo podľa neho senior programátorov tak skoro nenahradí. Zaujímavý bol aj pohľad na ich marketing – prečo v Better Stacku nevytvárajú tradičný marketingový tím, ale namiesto toho stavajú výkonný obsahový tím, ktorý má za cieľ pomáhať developerom po celom svete?Tento diel je plný skúseností, užitočných a hlavne praktických rád pre podnikanie, vďaka ktorým dokážete zlepšiť svoj biznis aj v prípade, že nie ste technologická firma. Užívajte!---------------------------------------------------------------------------Kapitoly:00:00:00 – Predstavenie hosťa00:01:16 – Prečo začať podnikať mimo Slovenska?00:04:51 – Ako sa podniká v USA a čo to prináša?00:11:02 – Ako škálovať firmu aj bez investorov?00:15:31 – Príbeh Better Stack00:22:59 – Ako získať špičkových vývojárov do tímu?00:35:15 – Kedy startup potrebuje pivot?00:45:01 – Vymení AI developerov?00:53:24 – Marketing, ktorý funguje01:08:50 – Ako rozdeliť zodpovednosť medzi CTO a CMO?01:11:09 – Founder mindset01:17:41 – Zmysel života podľa Juraja Masára---------------------------------------------------------------------------Viac z podcastov nájdete na:https://www.truban.sk/podcast/---------------------------------------------------------------------------Všetky spomenuté knihy a podcasty nájdete v článku na blogu:https://wp.me/p5NJVg-QQ---------------------------------------------------------------------------Podcast si môžete vypočuť aj na streamovacích platformách:● Spotify ▸ https://spoti.fi/31Nywax ● Apple podcast ▸ https://apple.co/3n0SO8F---------------------------------------------------------------------------● Najlepšie z podcastu na Instagrame ●https://www.instagram.com/truban.podcast/● Truban.sk ●https://bit.ly/3r1vYQJ ● Instagram ●https://www.instagram.com/truban/● Facebook ●https://www.facebook.com/miso.truban● LinkedIn ●https://sk.linkedin.com/in/truban

Steve Ruiz is the founder of tldraw - a whiteboard SDK / infinite canvas SDK. We talk creativity, taste and obsession. And marketing to developers.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links:Steve Ruiztldraw 

While the AppJS is about to kick off, we talk about some cool new packages and updates around Reanimated and a new solution for local-first apps!Also in this episode:- New React Native Essentials - Apple still doesn't like my app- New Expo Router v5 Course- Are you VibeCoding?

Luke Harries leads growth at ElevenLabs. Eleven Labs builds incredible AI voice models. Luke dives into why launches matter so much, the origin story of ElevenLabs and why a hackathon can change your life.Links:Luke Harries ElevenLabs   This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. P.s. I used Eleven Labs without any edits for the transcript/subtitles.

Elston Baretto is the founder of Tiiny.host - the simplest place to put your work online. In this episode we talk about how Elston has been able to grow Tiiny to 70,000+ sign ups per month with content marketing. This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links:- Tiiny.host - Elston Baretto - Ramen Club - Charlie Ward - Sabba - Veed 

News includes Hex 2.2.0 with the new :warnifoutdated option for keeping dependencies updated, Honeybadger's APM with built-in Elixir traces for major components, José Valim demonstrating Tidewave with Zed's AI coding agents, LiveDebugger v0.2.0 with DevTools integration and component highlighting, Dave Lucia's new Elixir "Lua" library for embedding Lua scripting, Paulo Valente's "handoff" library for distributed function graph execution, a PhD thesis on Elixir code smells becoming a finalist for a prestigious award, and more! Show Notes online - http://podcast.thinkingelixir.com/254 (http://podcast.thinkingelixir.com/254) Elixir Community News https://paraxial.io/ (https://paraxial.io/?utm_source=thinkingelixir&utm_medium=shownotes) – Paraxial.io is sponsoring today's show! Sign up for a free trial of Paraxial.io today and mention Thinking Elixir when you schedule a demo for a limited time offer. https://github.com/hexpm/hex/releases/tag/v2.2.0 (https://github.com/hexpm/hex/releases/tag/v2.2.0?utm_source=thinkingelixir&utm_medium=shownotes) – Hex releases 2.2.0 introducing the :warnifoutdated option to help keep dependencies updated. Taking a week off - no episode next week, but returning the following week. https://www.honeybadger.io/blog/elixir-performance-monitoring (https://www.honeybadger.io/blog/elixir-performance-monitoring?utm_source=thinkingelixir&utm_medium=shownotes) – Honeybadger now offers APM with built-in Elixir traces, including default dashboards for Ecto, Phoenix/LiveView, Oban, Absinthe, Finch, and Tesla. https://x.com/josevalim/status/1920062725394243640 (https://x.com/josevalim/status/1920062725394243640?utm_source=thinkingelixir&utm_medium=shownotes) – José Valim demonstrates Tidewave being used with Zed editor's AI coding agents. https://zed.dev/agentic (https://zed.dev/agentic?utm_source=thinkingelixir&utm_medium=shownotes) – Zed's agentic features used with Tidewave to code a pricing plan component. https://www.reddit.com/r/elixir/comments/1kgyfhb/livedebuggerv020is_out/ (https://www.reddit.com/r/elixir/comments/1kgyfhb/livedebugger_v020_is_out/?utm_source=thinkingelixir&utm_medium=shownotes) – LiveDebugger v0.2.0 released with Chrome DevTools extension, component highlighting, callback trace filtering, and dark mode. https://podcast.thinkingelixir.com/249 (https://podcast.thinkingelixir.com/249?utm_source=thinkingelixir&utm_medium=shownotes) – Previous podcast episode discussing LiveDebugger with Krzysztof. https://blog.swmansion.com/whats-new-in-livedebugger-v0-2-0-4543d3af5486 (https://blog.swmansion.com/whats-new-in-livedebugger-v0-2-0-4543d3af5486?utm_source=thinkingelixir&utm_medium=shownotes) – Blog post covering the new features in LiveDebugger v0.2.0. https://hexdocs.pm/luerl/readme.html (https://hexdocs.pm/luerl/readme.html?utm_source=thinkingelixir&utm_medium=shownotes) – Luerl v1.4.1 released with Hex docs - an implementation of Lua 5.3 in Erlang/OTP. https://github.com/rvirding/luerl (https://github.com/rvirding/luerl?utm_source=thinkingelixir&utm_medium=shownotes) – The GitHub repository for Luerl, which Dave Lucia worked on with Robert Virding. https://www.lua.org/about.html (https://www.lua.org/about.html?utm_source=thinkingelixir&utm_medium=shownotes) – Information about Lua, a lightweight, embeddable scripting language. https://bsky.app/profile/davelucia.com/post/3lozadtvqtc2m (https://bsky.app/profile/davelucia.com/post/3lozadtvqtc2m?utm_source=thinkingelixir&utm_medium=shownotes) – Dave Lucia's announcement of his new Elixir "Lua" library. https://davelucia.com/blog/lua-elixir (https://davelucia.com/blog/lua-elixir?utm_source=thinkingelixir&utm_medium=shownotes) – Blog post explaining Dave's new Elixir Lua library. https://github.com/tv-labs/lua (https://github.com/tv-labs/lua?utm_source=thinkingelixir&utm_medium=shownotes) – The GitHub repository for the new Elixir Lua library, providing an ergonomic interface to Luerl. https://hexdocs.pm/handoff/ (https://hexdocs.pm/handoff/?utm_source=thinkingelixir&utm_medium=shownotes) – Documentation for "handoff", a new Elixir library for distributed function graph execution. https://bsky.app/profile/polvalente.social/post/3louqxeegrs2u (https://bsky.app/profile/polvalente.social/post/3louqxeegrs2u?utm_source=thinkingelixir&utm_medium=shownotes) – Paulo Valente's announcement of the handoff library, which enables distributed Nx computations. https://github.com/polvalente/handoff (https://github.com/polvalente/handoff?utm_source=thinkingelixir&utm_medium=shownotes) – GitHub repository for the handoff library created by Paulo Valente and sponsored by TvLabs. https://bsky.app/profile/lucasvegi.bsky.social/post/3lke2pt2zws2e (https://bsky.app/profile/lucasvegi.bsky.social/post/3lke2pt2zws2e?utm_source=thinkingelixir&utm_medium=shownotes) – Lucas Vegi's PhD thesis "Code Smells and Refactorings for Elixir" is a finalist for the SBC Dissertation Award. https://hexdocs.pm/elixir/code-anti-patterns.html (https://hexdocs.pm/elixir/code-anti-patterns.html?utm_source=thinkingelixir&utm_medium=shownotes) – Elixir's code anti-patterns guide, a practical resource related to code smells and refactoring in Elixir. Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Find us online - Message the show - Bluesky (https://bsky.app/profile/thinkingelixir.com) - Message the show - X (https://x.com/ThinkingElixir) - Message the show on Fediverse - @ThinkingElixir@genserver.social (https://genserver.social/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen on X - @brainlid (https://x.com/brainlid) - Mark Ericksen on Bluesky - @brainlid.bsky.social (https://bsky.app/profile/brainlid.bsky.social) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - David Bernheisel on Bluesky - @david.bernheisel.com (https://bsky.app/profile/david.bernheisel.com) - David Bernheisel on Fediverse - @dbern@genserver.social (https://genserver.social/dbern)

For this podcast, Jon Westfall recorded our discussion in parallel and had it create a detailed summary and a kind of mind map. I fed PLAUD's detailed summary into Google NotebookLM and had it create the condensed summary below: They discuss various technological tools and their applications, beginning with their experiences using a wearable transcription device, the Plaud NotePin, for capturing ideas during meetings. The discussion expands to the potential benefits and privacy considerations of recording interactions, touching on the limitations of inexpensive body cams and the potential of smartphones for video evidence. The hosts then explore how AI-powered transcription and summarization services can enhance content consumption and creation, citing examples of using these tools with podcasts and historical audio. They anticipate future AI advancements, particularly in video editing with tools like Google Flow and potential new extended reality (XR) glasses announced at Google I/O. The conversation also covers practical Python scripting for tasks like downloading YouTube transcripts, using development tools, and navigating file-sharing challenges, as well as integrating calendars with an Outlook plugin. Finally, they touch on the capabilities of AI assistants like Microsoft Copilot Vision and the intersection of AI with media and entertainment, referencing the Apple TV+ "Murder Bot" TV series.

Eric Allam is the cofounder of Trigger.dev. Trigger gives you open source background jobs. We talk about how Trigger iterated different versions until landing on something developers really want. And now the growth is crazy. And also, I use Trigger and it's genuinely a great product.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links:Eric Allam Trigger Trigger Open Source project 

In this episode, we talk with James Garbutt about e18e, a community-driven initiative focused on improving the performance of JavaScript packages across the ecosystem.We discuss: • The goals and vision behind e18e • What's slowing down the JS ecosystem • Why performance work is often invisible—and how to fix that • The importance of community coordination in open source • How developers can get involved in improving the packages they rely onIf you care about build times, bundle sizes, and the health of the JavaScript ecosystem, this episode is for you.This episode is sponsored by WorkOS (https://workos.com) and Mailtrap (https://l.rw.rw/devtools_4)

Kyle is the cofounder of Depot.  Depot accelerates your Docker image builds and GitHub Actions workflows. Kyle shares how Depot were able to grow to $1M ARR and beyond with a very lean team.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. Links:Depot Kyle Galbraith 

This episode is a deep dive into DevTools marketing with Jason Lengstorf, founder of CodeTV.Links:Jason on XCodeTVJason's YouTubeThis episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. 

If you're in SF: Join us for the Claude Plays Pokemon hackathon this Sunday!If you're not: Fill out the 2025 State of AI Eng survey for $250 in Amazon cards!We are SO excited to share our conversation with Dharmesh Shah, co-founder of HubSpot and creator of Agent.ai.A particularly compelling concept we discussed is the idea of "hybrid teams" - the next evolution in workplace organization where human workers collaborate with AI agents as team members. Just as we previously saw hybrid teams emerge in terms of full-time vs. contract workers, or in-office vs. remote workers, Dharmesh predicts that the next frontier will be teams composed of both human and AI members. This raises interesting questions about team dynamics, trust, and how to effectively delegate tasks between human and AI team members.The discussion of business models in AI reveals an important distinction between Work as a Service (WaaS) and Results as a Service (RaaS), something Dharmesh has written extensively about. While RaaS has gained popularity, particularly in customer support applications where outcomes are easily measurable, Dharmesh argues that this model may be over-indexed. Not all AI applications have clearly definable outcomes or consistent economic value per transaction, making WaaS more appropriate in many cases. This insight is particularly relevant for businesses considering how to monetize AI capabilities.The technical challenges of implementing effective agent systems are also explored, particularly around memory and authentication. Shah emphasizes the importance of cross-agent memory sharing and the need for more granular control over data access. He envisions a future where users can selectively share parts of their data with different agents, similar to how OAuth works but with much finer control. This points to significant opportunities in developing infrastructure for secure and efficient agent-to-agent communication and data sharing.Other highlights from our conversation* The Evolution of AI-Powered Agents – Exploring how AI agents have evolved from simple chatbots to sophisticated multi-agent systems, and the role of MCPs in enabling that.* Hybrid Digital Teams and the Future of Work – How AI agents are becoming teammates rather than just tools, and what this means for business operations and knowledge work.* Memory in AI Agents – The importance of persistent memory in AI systems and how shared memory across agents could enhance collaboration and efficiency.* Business Models for AI Agents – Exploring the shift from software as a service (SaaS) to work as a service (WaaS) and results as a service (RaaS), and what this means for monetization.* The Role of Standards Like MCP – Why MCP has been widely adopted and how it enables agent collaboration, tool use, and discovery.* The Future of AI Code Generation and Software Engineering – How AI-assisted coding is changing the role of software engineers and what skills will matter most in the future.* Domain Investing and Efficient Markets – Dharmesh's approach to domain investing and how inefficiencies in digital asset markets create business opportunities.* The Philosophy of Saying No – Lessons from "Sorry, You Must Pass" and how prioritization leads to greater productivity and focus.Timestamps* 00:00 Introduction and Guest Welcome* 02:29 Dharmesh Shah's Journey into AI* 05:22 Defining AI Agents* 06:45 The Evolution and Future of AI Agents* 13:53 Graph Theory and Knowledge Representation* 20:02 Engineering Practices and Overengineering* 25:57 The Role of Junior Engineers in the AI Era* 28:20 Multi-Agent Systems and MCP Standards* 35:55 LinkedIn's Legal Battles and Data Scraping* 37:32 The Future of AI and Hybrid Teams* 39:19 Building Agent AI: A Professional Network for Agents* 40:43 Challenges and Innovations in Agent AI* 45:02 The Evolution of UI in AI Systems* 01:00:25 Business Models: Work as a Service vs. Results as a Service* 01:09:17 The Future Value of Engineers* 01:09:51 Exploring the Role of Agents* 01:10:28 The Importance of Memory in AI* 01:11:02 Challenges and Opportunities in AI Memory* 01:12:41 Selective Memory and Privacy Concerns* 01:13:27 The Evolution of AI Tools and Platforms* 01:18:23 Domain Names and AI Projects* 01:32:08 Balancing Work and Personal Life* 01:35:52 Final Thoughts and ReflectionsTranscriptAlessio [00:00:04]: Hey everyone, welcome back to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Small AI.swyx [00:00:12]: Hello, and today we're super excited to have Dharmesh Shah to join us. I guess your relevant title here is founder of Agent AI.Dharmesh [00:00:20]: Yeah, that's true for this. Yeah, creator of Agent.ai and co-founder of HubSpot.swyx [00:00:25]: Co-founder of HubSpot, which I followed for many years, I think 18 years now, gonna be 19 soon. And you caught, you know, people can catch up on your HubSpot story elsewhere. I should also thank Sean Puri, who I've chatted with back and forth, who's been, I guess, getting me in touch with your people. But also, I think like, just giving us a lot of context, because obviously, My First Million joined you guys, and they've been chatting with you guys a lot. So for the business side, we can talk about that, but I kind of wanted to engage your CTO, agent, engineer side of things. So how did you get agent religion?Dharmesh [00:01:00]: Let's see. So I've been working, I'll take like a half step back, a decade or so ago, even though actually more than that. So even before HubSpot, the company I was contemplating that I had named for was called Ingenisoft. And the idea behind Ingenisoft was a natural language interface to business software. Now realize this is 20 years ago, so that was a hard thing to do. But the actual use case that I had in mind was, you know, we had data sitting in business systems like a CRM or something like that. And my kind of what I thought clever at the time. Oh, what if we used email as the kind of interface to get to business software? And the motivation for using email is that it automatically works when you're offline. So imagine I'm getting on a plane or I'm on a plane. There was no internet on planes back then. It's like, oh, I'm going through business cards from an event I went to. I can just type things into an email just to have them all in the backlog. When it reconnects, it sends those emails to a processor that basically kind of parses effectively the commands and updates the software, sends you the file, whatever it is. And there was a handful of commands. I was a little bit ahead of the times in terms of what was actually possible. And I reattempted this natural language thing with a product called ChatSpot that I did back 20...swyx [00:02:12]: Yeah, this is your first post-ChatGPT project.Dharmesh [00:02:14]: I saw it come out. Yeah. And so I've always been kind of fascinated by this natural language interface to software. Because, you know, as software developers, myself included, we've always said, oh, we build intuitive, easy-to-use applications. And it's not intuitive at all, right? Because what we're doing is... We're taking the mental model that's in our head of what we're trying to accomplish with said piece of software and translating that into a series of touches and swipes and clicks and things like that. And there's nothing natural or intuitive about it. And so natural language interfaces, for the first time, you know, whatever the thought is you have in your head and expressed in whatever language that you normally use to talk to yourself in your head, you can just sort of emit that and have software do something. And I thought that was kind of a breakthrough, which it has been. And it's gone. So that's where I first started getting into the journey. I started because now it actually works, right? So once we got ChatGPT and you can take, even with a few-shot example, convert something into structured, even back in the ChatGP 3.5 days, it did a decent job in a few-shot example, convert something to structured text if you knew what kinds of intents you were going to have. And so that happened. And that ultimately became a HubSpot project. But then agents intrigued me because I'm like, okay, well, that's the next step here. So chat's great. Love Chat UX. But if we want to do something even more meaningful, it felt like the next kind of advancement is not this kind of, I'm chatting with some software in a kind of a synchronous back and forth model, is that software is going to do things for me in kind of a multi-step way to try and accomplish some goals. So, yeah, that's when I first got started. It's like, okay, what would that look like? Yeah. And I've been obsessed ever since, by the way.Alessio [00:03:55]: Which goes back to your first experience with it, which is like you're offline. Yeah. And you want to do a task. You don't need to do it right now. You just want to queue it up for somebody to do it for you. Yes. As you think about agents, like, let's start at the easy question, which is like, how do you define an agent? Maybe. You mean the hardest question in the universe? Is that what you mean?Dharmesh [00:04:12]: You said you have an irritating take. I do have an irritating take. I think, well, some number of people have been irritated, including within my own team. So I have a very broad definition for agents, which is it's AI-powered software that accomplishes a goal. Period. That's it. And what irritates people about it is like, well, that's so broad as to be completely non-useful. And I understand that. I understand the criticism. But in my mind, if you kind of fast forward months, I guess, in AI years, the implementation of it, and we're already starting to see this, and we'll talk about this, different kinds of agents, right? So I think in addition to having a usable definition, and I like yours, by the way, and we should talk more about that, that you just came out with, the classification of agents actually is also useful, which is, is it autonomous or non-autonomous? Does it have a deterministic workflow? Does it have a non-deterministic workflow? Is it working synchronously? Is it working asynchronously? Then you have the different kind of interaction modes. Is it a chat agent, kind of like a customer support agent would be? You're having this kind of back and forth. Is it a workflow agent that just does a discrete number of steps? So there's all these different flavors of agents. So if I were to draw it in a Venn diagram, I would draw a big circle that says, this is agents, and then I have a bunch of circles, some overlapping, because they're not mutually exclusive. And so I think that's what's interesting, and we're seeing development along a bunch of different paths, right? So if you look at the first implementation of agent frameworks, you look at Baby AGI and AutoGBT, I think it was, not Autogen, that's the Microsoft one. They were way ahead of their time because they assumed this level of reasoning and execution and planning capability that just did not exist, right? So it was an interesting thought experiment, which is what it was. Even the guy that, I'm an investor in Yohei's fund that did Baby AGI. It wasn't ready, but it was a sign of what was to come. And so the question then is, when is it ready? And so lots of people talk about the state of the art when it comes to agents. I'm a pragmatist, so I think of the state of the practical. It's like, okay, well, what can I actually build that has commercial value or solves actually some discrete problem with some baseline of repeatability or verifiability?swyx [00:06:22]: There was a lot, and very, very interesting. I'm not irritated by it at all. Okay. As you know, I take a... There's a lot of anthropological view or linguistics view. And in linguistics, you don't want to be prescriptive. You want to be descriptive. Yeah. So you're a goals guy. That's the key word in your thing. And other people have other definitions that might involve like delegated trust or non-deterministic work, LLM in the loop, all that stuff. The other thing I was thinking about, just the comment on Baby AGI, LGBT. Yeah. In that piece that you just read, I was able to go through our backlog and just kind of track the winter of agents and then the summer now. Yeah. And it's... We can tell the whole story as an oral history, just following that thread. And it's really just like, I think, I tried to explain the why now, right? Like I had, there's better models, of course. There's better tool use with like, they're just more reliable. Yep. Better tools with MCP and all that stuff. And I'm sure you have opinions on that too. Business model shift, which you like a lot. I just heard you talk about RAS with MFM guys. Yep. Cost is dropping a lot. Yep. Inference is getting faster. There's more model diversity. Yep. Yep. I think it's a subtle point. It means that like, you have different models with different perspectives. You don't get stuck in the basin of performance of a single model. Sure. You can just get out of it by just switching models. Yep. Multi-agent research and RL fine tuning. So I just wanted to let you respond to like any of that.Dharmesh [00:07:44]: Yeah. A couple of things. Connecting the dots on the kind of the definition side of it. So we'll get the irritation out of the way completely. I have one more, even more irritating leap on the agent definition thing. So here's the way I think about it. By the way, the kind of word agent, I looked it up, like the English dictionary definition. The old school agent, yeah. Is when you have someone or something that does something on your behalf, like a travel agent or a real estate agent acts on your behalf. It's like proxy, which is a nice kind of general definition. So the other direction I'm sort of headed, and it's going to tie back to tool calling and MCP and things like that, is if you, and I'm not a biologist by any stretch of the imagination, but we have these single-celled organisms, right? Like the simplest possible form of what one would call life. But it's still life. It just happens to be single-celled. And then you can combine cells and then cells become specialized over time. And you have much more sophisticated organisms, you know, kind of further down the spectrum. In my mind, at the most fundamental level, you can almost think of having atomic agents. What is the simplest possible thing that's an agent that can still be called an agent? What is the equivalent of a kind of single-celled organism? And the reason I think that's useful is right now we're headed down the road, which I think is very exciting around tool use, right? That says, okay, the LLMs now can be provided a set of tools that it calls to accomplish whatever it needs to accomplish in the kind of furtherance of whatever goal it's trying to get done. And I'm not overly bothered by it, but if you think about it, if you just squint a little bit and say, well, what if everything was an agent? And what if tools were actually just atomic agents? Because then it's turtles all the way down, right? Then it's like, oh, well, all that's really happening with tool use is that we have a network of agents that know about each other through something like an MMCP and can kind of decompose a particular problem and say, oh, I'm going to delegate this to this set of agents. And why do we need to draw this distinction between tools, which are functions most of the time? And an actual agent. And so I'm going to write this irritating LinkedIn post, you know, proposing this. It's like, okay. And I'm not suggesting we should call even functions, you know, call them agents. But there is a certain amount of elegance that happens when you say, oh, we can just reduce it down to one primitive, which is an agent that you can combine in complicated ways to kind of raise the level of abstraction and accomplish higher order goals. Anyway, that's my answer. I'd say that's a success. Thank you for coming to my TED Talk on agent definitions.Alessio [00:09:54]: How do you define the minimum viable agent? Do you already have a definition for, like, where you draw the line between a cell and an atom? Yeah.Dharmesh [00:10:02]: So in my mind, it has to, at some level, use AI in order for it to—otherwise, it's just software. It's like, you know, we don't need another word for that. And so that's probably where I draw the line. So then the question, you know, the counterargument would be, well, if that's true, then lots of tools themselves are actually not agents because they're just doing a database call or a REST API call or whatever it is they're doing. And that does not necessarily qualify them, which is a fair counterargument. And I accept that. It's like a good argument. I still like to think about—because we'll talk about multi-agent systems, because I think—so we've accepted, which I think is true, lots of people have said it, and you've hopefully combined some of those clips of really smart people saying this is the year of agents, and I completely agree, it is the year of agents. But then shortly after that, it's going to be the year of multi-agent systems or multi-agent networks. I think that's where it's going to be headed next year. Yeah.swyx [00:10:54]: Opening eyes already on that. Yeah. My quick philosophical engagement with you on this. I often think about kind of the other spectrum, the other end of the cell spectrum. So single cell is life, multi-cell is life, and you clump a bunch of cells together in a more complex organism, they become organs, like an eye and a liver or whatever. And then obviously we consider ourselves one life form. There's not like a lot of lives within me. I'm just one life. And now, obviously, I don't think people don't really like to anthropomorphize agents and AI. Yeah. But we are extending our consciousness and our brain and our functionality out into machines. I just saw you were a Bee. Yeah. Which is, you know, it's nice. I have a limitless pendant in my pocket.Dharmesh [00:11:37]: I got one of these boys. Yeah.swyx [00:11:39]: I'm testing it all out. You know, got to be early adopters. But like, we want to extend our personal memory into these things so that we can be good at the things that we're good at. And, you know, machines are good at it. Machines are there. So like, my definition of life is kind of like going outside of my own body now. I don't know if you've ever had like reflections on that. Like how yours. How our self is like actually being distributed outside of you. Yeah.Dharmesh [00:12:01]: I don't fancy myself a philosopher. But you went there. So yeah, I did go there. I'm fascinated by kind of graphs and graph theory and networks and have been for a long, long time. And to me, we're sort of all nodes in this kind of larger thing. It just so happens that we're looking at individual kind of life forms as they exist right now. But so the idea is when you put a podcast out there, there's these little kind of nodes you're putting out there of like, you know, conceptual ideas. Once again, you have varying kind of forms of those little nodes that are up there and are connected in varying and sundry ways. And so I just think of myself as being a node in a massive, massive network. And I'm producing more nodes as I put content or ideas. And, you know, you spend some portion of your life collecting dots, experiences, people, and some portion of your life then connecting dots from the ones that you've collected over time. And I found that really interesting things happen and you really can't know in advance how those dots are necessarily going to connect in the future. And that's, yeah. So that's my philosophical take. That's the, yes, exactly. Coming back.Alessio [00:13:04]: Yep. Do you like graph as an agent? Abstraction? That's been one of the hot topics with LandGraph and Pydantic and all that.Dharmesh [00:13:11]: I do. The thing I'm more interested in terms of use of graphs, and there's lots of work happening on that now, is graph data stores as an alternative in terms of knowledge stores and knowledge graphs. Yeah. Because, you know, so I've been in software now 30 plus years, right? So it's not 10,000 hours. It's like 100,000 hours that I've spent doing this stuff. And so I've grew up with, so back in the day, you know, I started on mainframes. There was a product called IMS from IBM, which is basically an index database, what we'd call like a key value store today. Then we've had relational databases, right? We have tables and columns and foreign key relationships. We all know that. We have document databases like MongoDB, which is sort of a nested structure keyed by a specific index. We have vector stores, vector embedding database. And graphs are interesting for a couple of reasons. One is, so it's not classically structured in a relational way. When you say structured database, to most people, they're thinking tables and columns and in relational database and set theory and all that. Graphs still have structure, but it's not the tables and columns structure. And you could wonder, and people have made this case, that they are a better representation of knowledge for LLMs and for AI generally than other things. So that's kind of thing number one conceptually, and that might be true, I think is possibly true. And the other thing that I really like about that in the context of, you know, I've been in the context of data stores for RAG is, you know, RAG, you say, oh, I have a million documents, I'm going to build the vector embeddings, I'm going to come back with the top X based on the semantic match, and that's fine. All that's very, very useful. But the reality is something gets lost in the chunking process and the, okay, well, those tend, you know, like, you don't really get the whole picture, so to speak, and maybe not even the right set of dimensions on the kind of broader picture. And it makes intuitive sense to me that if we did capture it properly in a graph form, that maybe that feeding into a RAG pipeline will actually yield better results for some use cases, I don't know, but yeah.Alessio [00:15:03]: And do you feel like at the core of it, there's this difference between imperative and declarative programs? Because if you think about HubSpot, it's like, you know, people and graph kind of goes hand in hand, you know, but I think maybe the software before was more like primary foreign key based relationship, versus now the models can traverse through the graph more easily.Dharmesh [00:15:22]: Yes. So I like that representation. There's something. It's just conceptually elegant about graphs and just from the representation of it, they're much more discoverable, you can kind of see it, there's observability to it, versus kind of embeddings, which you can't really do much with as a human. You know, once they're in there, you can't pull stuff back out. But yeah, I like that kind of idea of it. And the other thing that's kind of, because I love graphs, I've been long obsessed with PageRank from back in the early days. And, you know, one of the kind of simplest algorithms in terms of coming up, you know, with a phone, everyone's been exposed to PageRank. And the idea is that, and so I had this other idea for a project, not a company, and I have hundreds of these, called NodeRank, is to be able to take the idea of PageRank and apply it to an arbitrary graph that says, okay, I'm going to define what authority looks like and say, okay, well, that's interesting to me, because then if you say, I'm going to take my knowledge store, and maybe this person that contributed some number of chunks to the graph data store has more authority on this particular use case or prompt that's being submitted than this other one that may, or maybe this one was more. popular, or maybe this one has, whatever it is, there should be a way for us to kind of rank nodes in a graph and sort them in some, some useful way. Yeah.swyx [00:16:34]: So I think that's generally useful for, for anything. I think the, the problem, like, so even though at my conferences, GraphRag is super popular and people are getting knowledge, graph religion, and I will say like, it's getting space, getting traction in two areas, conversation memory, and then also just rag in general, like the, the, the document data. Yeah. It's like a source. Most ML practitioners would say that knowledge graph is kind of like a dirty word. The graph database, people get graph religion, everything's a graph, and then they, they go really hard into it and then they get a, they get a graph that is too complex to navigate. Yes. And so like the, the, the simple way to put it is like you at running HubSpot, you know, the power of graphs, the way that Google has pitched them for many years, but I don't suspect that HubSpot itself uses a knowledge graph. No. Yeah.Dharmesh [00:17:26]: So when is it over engineering? Basically? It's a great question. I don't know. So the question now, like in AI land, right, is the, do we necessarily need to understand? So right now, LLMs for, for the most part are somewhat black boxes, right? We sort of understand how the, you know, the algorithm itself works, but we really don't know what's going on in there and, and how things come out. So if a graph data store is able to produce the outcomes we want, it's like, here's a set of queries I want to be able to submit and then it comes out with useful content. Maybe the underlying data store is as opaque as a vector embeddings or something like that, but maybe it's fine. Maybe we don't necessarily need to understand it to get utility out of it. And so maybe if it's messy, that's okay. Um, that's, it's just another form of lossy compression. Uh, it's just lossy in a way that we just don't completely understand in terms of, because it's going to grow organically. Uh, and it's not structured. It's like, ah, we're just gonna throw a bunch of stuff in there. Let the, the equivalent of the embedding algorithm, whatever they called in graph land. Um, so the one with the best results wins. I think so. Yeah.swyx [00:18:26]: Or is this the practical side of me is like, yeah, it's, if it's useful, we don't necessarilyDharmesh [00:18:30]: need to understand it.swyx [00:18:30]: I have, I mean, I'm happy to push back as long as you want. Uh, it's not practical to evaluate like the 10 different options out there because it takes time. It takes people, it takes, you know, resources, right? Set. That's the first thing. Second thing is your evals are typically on small things and some things only work at scale. Yup. Like graphs. Yup.Dharmesh [00:18:46]: Yup. That's, yeah, no, that's fair. And I think this is one of the challenges in terms of implementation of graph databases is that the most common approach that I've seen developers do, I've done it myself, is that, oh, I've got a Postgres database or a MySQL or whatever. I can represent a graph with a very set of tables with a parent child thing or whatever. And that sort of gives me the ability, uh, why would I need anything more than that? And the answer is, well, if you don't need anything more than that, you don't need anything more than that. But there's a high chance that you're sort of missing out on the actual value that, uh, the graph representation gives you. Which is the ability to traverse the graph, uh, efficiently in ways that kind of going through the, uh, traversal in a relational database form, even though structurally you have the data, practically you're not gonna be able to pull it out in, in useful ways. Uh, so you wouldn't like represent a social graph, uh, in, in using that kind of relational table model. It just wouldn't scale. It wouldn't work.swyx [00:19:36]: Uh, yeah. Uh, I think we want to move on to MCP. Yeah. But I just want to, like, just engineering advice. Yeah. Uh, obviously you've, you've, you've run, uh, you've, you've had to do a lot of projects and run a lot of teams. Do you have a general rule for over-engineering or, you know, engineering ahead of time? You know, like, because people, we know premature engineering is the root of all evil. Yep. But also sometimes you just have to. Yep. When do you do it? Yes.Dharmesh [00:19:59]: It's a great question. This is, uh, a question as old as time almost, which is what's the right and wrong levels of abstraction. That's effectively what, uh, we're answering when we're trying to do engineering. I tend to be a pragmatist, right? So here's the thing. Um, lots of times doing something the right way. Yeah. It's like a marginal increased cost in those cases. Just do it the right way. And this is what makes a, uh, a great engineer or a good engineer better than, uh, a not so great one. It's like, okay, all things being equal. If it's going to take you, you know, roughly close to constant time anyway, might as well do it the right way. Like, so do things well, then the question is, okay, well, am I building a framework as the reusable library? To what degree, uh, what am I anticipating in terms of what's going to need to change in this thing? Uh, you know, along what dimension? And then I think like a business person in some ways, like what's the return on calories, right? So, uh, and you look at, um, energy, the expected value of it's like, okay, here are the five possible things that could happen, uh, try to assign probabilities like, okay, well, if there's a 50% chance that we're going to go down this particular path at some day, like, or one of these five things is going to happen and it costs you 10% more to engineer for that. It's basically, it's something that yields a kind of interest compounding value. Um, as you get closer to the time of, of needing that versus having to take on debt, which is when you under engineer it, you're taking on debt. You're going to have to pay off when you do get to that eventuality where something happens. One thing as a pragmatist, uh, so I would rather under engineer something than over engineer it. If I were going to err on the side of something, and here's the reason is that when you under engineer it, uh, yes, you take on tech debt, uh, but the interest rate is relatively known and payoff is very, very possible, right? Which is, oh, I took a shortcut here as a result of which now this thing that should have taken me a week is now going to take me four weeks. Fine. But if that particular thing that you thought might happen, never actually, you never have that use case transpire or just doesn't, it's like, well, you just save yourself time, right? And that has value because you were able to do other things instead of, uh, kind of slightly over-engineering it away, over-engineering it. But there's no perfect answers in art form in terms of, uh, and yeah, we'll, we'll bring kind of this layers of abstraction back on the code generation conversation, which we'll, uh, I think I have later on, butAlessio [00:22:05]: I was going to ask, we can just jump ahead quickly. Yeah. Like, as you think about vibe coding and all that, how does the. Yeah. Percentage of potential usefulness change when I feel like we over-engineering a lot of times it's like the investment in syntax, it's less about the investment in like arc exacting. Yep. Yeah. How does that change your calculus?Dharmesh [00:22:22]: A couple of things, right? One is, um, so, you know, going back to that kind of ROI or a return on calories, kind of calculus or heuristic you think through, it's like, okay, well, what is it going to cost me to put this layer of abstraction above the code that I'm writing now, uh, in anticipating kind of future needs. If the cost of fixing, uh, or doing under engineering right now. Uh, we'll trend towards zero that says, okay, well, I don't have to get it right right now because even if I get it wrong, I'll run the thing for six hours instead of 60 minutes or whatever. It doesn't really matter, right? Like, because that's going to trend towards zero to be able, the ability to refactor a code. Um, and because we're going to not that long from now, we're going to have, you know, large code bases be able to exist, uh, you know, as, as context, uh, for a code generation or a code refactoring, uh, model. So I think it's going to make it, uh, make the case for under engineering, uh, even stronger. Which is why I take on that cost. You just pay the interest when you get there, it's not, um, just go on with your life vibe coded and, uh, come back when you need to. Yeah.Alessio [00:23:18]: Sometimes I feel like there's no decision-making in some things like, uh, today I built a autosave for like our internal notes platform and I literally just ask them cursor. Can you add autosave? Yeah. I don't know if it's over under engineer. Yep. I just vibe coded it. Yep. And I feel like at some point we're going to get to the point where the models kindDharmesh [00:23:36]: of decide where the right line is, but this is where the, like the, in my mind, the danger is, right? So there's two sides to this. One is the cost of kind of development and coding and things like that stuff that, you know, we talk about. But then like in your example, you know, one of the risks that we have is that because adding a feature, uh, like a save or whatever the feature might be to a product as that price tends towards zero, are we going to be less discriminant about what features we add as a result of making more product products more complicated, which has a negative impact on the user and navigate negative impact on the business. Um, and so that's the thing I worry about if it starts to become too easy, are we going to be. Too promiscuous in our, uh, kind of extension, adding product extensions and things like that. It's like, ah, why not add X, Y, Z or whatever back then it was like, oh, we only have so many engineering hours or story points or however you measure things. Uh, that least kept us in check a little bit. Yeah.Alessio [00:24:22]: And then over engineering, you're like, yeah, it's kind of like you're putting that on yourself. Yeah. Like now it's like the models don't understand that if they add too much complexity, it's going to come back to bite them later. Yep. So they just do whatever they want to do. Yeah. And I'm curious where in the workflow that's going to be, where it's like, Hey, this is like the amount of complexity and over-engineering you can do before you got to ask me if we should actually do it versus like do something else.Dharmesh [00:24:45]: So you know, we've already, let's like, we're leaving this, uh, in the code generation world, this kind of compressed, um, cycle time. Right. It's like, okay, we went from auto-complete, uh, in the GitHub co-pilot to like, oh, finish this particular thing and hit tab to a, oh, I sort of know your file or whatever. I can write out a full function to you to now I can like hold a bunch of the context in my head. Uh, so we can do app generation, which we have now with lovable and bolt and repletage. Yeah. Association and other things. So then the question is, okay, well, where does it naturally go from here? So we're going to generate products. Make sense. We might be able to generate platforms as though I want a platform for ERP that does this, whatever. And that includes the API's includes the product and the UI, and all the things that make for a platform. There's no nothing that says we would stop like, okay, can you generate an entire software company someday? Right. Uh, with the platform and the monetization and the go-to-market and the whatever. And you know, that that's interesting to me in terms of, uh, you know, what, when you take it to almost ludicrous levels. of abstract.swyx [00:25:39]: It's like, okay, turn it to 11. You mentioned vibe coding, so I have to, this is a blog post I haven't written, but I'm kind of exploring it. Is the junior engineer dead?Dharmesh [00:25:49]: I don't think so. I think what will happen is that the junior engineer will be able to, if all they're bringing to the table is the fact that they are a junior engineer, then yes, they're likely dead. But hopefully if they can communicate with carbon-based life forms, they can interact with product, if they're willing to talk to customers, they can take their kind of basic understanding of engineering and how kind of software works. I think that has value. So I have a 14-year-old right now who's taking Python programming class, and some people ask me, it's like, why is he learning coding? And my answer is, is because it's not about the syntax, it's not about the coding. What he's learning is like the fundamental thing of like how things work. And there's value in that. I think there's going to be timeless value in systems thinking and abstractions and what that means. And whether functions manifested as math, which he's going to get exposed to regardless, or there are some core primitives to the universe, I think, that the more you understand them, those are what I would kind of think of as like really large dots in your life that will have a higher gravitational pull and value to them that you'll then be able to. So I want him to collect those dots, and he's not resisting. So it's like, okay, while he's still listening to me, I'm going to have him do things that I think will be useful.swyx [00:26:59]: You know, part of one of the pitches that I evaluated for AI engineer is a term. And the term is that maybe the traditional interview path or career path of software engineer goes away, which is because what's the point of lead code? Yeah. And, you know, it actually matters more that you know how to work with AI and to implement the things that you want. Yep.Dharmesh [00:27:16]: That's one of the like interesting things that's happened with generative AI. You know, you go from machine learning and the models and just that underlying form, which is like true engineering, right? Like the actual, what I call real engineering. I don't think of myself as a real engineer, actually. I'm a developer. But now with generative AI. We call it AI and it's obviously got its roots in machine learning, but it just feels like fundamentally different to me. Like you have the vibe. It's like, okay, well, this is just a whole different approach to software development to so many different things. And so I'm wondering now, it's like an AI engineer is like, if you were like to draw the Venn diagram, it's interesting because the cross between like AI things, generative AI and what the tools are capable of, what the models do, and this whole new kind of body of knowledge that we're still building out, it's still very young, intersected with kind of classic engineering, software engineering. Yeah.swyx [00:28:04]: I just described the overlap as it separates out eventually until it's its own thing, but it's starting out as a software. Yeah.Alessio [00:28:11]: That makes sense. So to close the vibe coding loop, the other big hype now is MCPs. Obviously, I would say Cloud Desktop and Cursor are like the two main drivers of MCP usage. I would say my favorite is the Sentry MCP. I can pull in errors and then you can just put the context in Cursor. How do you think about that abstraction layer? Does it feel... Does it feel almost too magical in a way? Do you think it's like you get enough? Because you don't really see how the server itself is then kind of like repackaging theDharmesh [00:28:41]: information for you? I think MCP as a standard is one of the better things that's happened in the world of AI because a standard needed to exist and absent a standard, there was a set of things that just weren't possible. Now, we can argue whether it's the best possible manifestation of a standard or not. Does it do too much? Does it do too little? I get that, but it's just simple enough to both be useful and unobtrusive. It's understandable and adoptable by mere mortals, right? It's not overly complicated. You know, a reasonable engineer can put a stand up an MCP server relatively easily. The thing that has me excited about it is like, so I'm a big believer in multi-agent systems. And so that's going back to our kind of this idea of an atomic agent. So imagine the MCP server, like obviously it calls tools, but the way I think about it, so I'm working on my current passion project is agent.ai. And we'll talk more about that in a little bit. More about the, I think we should, because I think it's interesting not to promote the project at all, but there's some interesting ideas in there. One of which is around, we're going to need a mechanism for, if agents are going to collaborate and be able to delegate, there's going to need to be some form of discovery and we're going to need some standard way. It's like, okay, well, I just need to know what this thing over here is capable of. We're going to need a registry, which Anthropic's working on. I'm sure others will and have been doing directories of, and there's going to be a standard around that too. How do you build out a directory of MCP servers? I think that's going to unlock so many things just because, and we're already starting to see it. So I think MCP or something like it is going to be the next major unlock because it allows systems that don't know about each other, don't need to, it's that kind of decoupling of like Sentry and whatever tools someone else was building. And it's not just about, you know, Cloud Desktop or things like, even on the client side, I think we're going to see very interesting consumers of MCP, MCP clients versus just the chat body kind of things. Like, you know, Cloud Desktop and Cursor and things like that. But yeah, I'm very excited about MCP in that general direction.swyx [00:30:39]: I think the typical cynical developer take, it's like, we have OpenAPI. Yeah. What's the new thing? I don't know if you have a, do you have a quick MCP versus everything else? Yeah.Dharmesh [00:30:49]: So it's, so I like OpenAPI, right? So just a descriptive thing. It's OpenAPI. OpenAPI. Yes, that's what I meant. So it's basically a self-documenting thing. We can do machine-generated, lots of things from that output. It's a structured definition of an API. I get that, love it. But MCPs sort of are kind of use case specific. They're perfect for exactly what we're trying to use them for around LLMs in terms of discovery. It's like, okay, I don't necessarily need to know kind of all this detail. And so right now we have, we'll talk more about like MCP server implementations, but We will? I think, I don't know. Maybe we won't. At least it's in my head. It's like a back processor. But I do think MCP adds value above OpenAPI. It's, yeah, just because it solves this particular thing. And if we had come to the world, which we have, like, it's like, hey, we already have OpenAPI. It's like, if that were good enough for the universe, the universe would have adopted it already. There's a reason why MCP is taking office because marginally adds something that was missing before and doesn't go too far. And so that's why the kind of rate of adoption, you folks have written about this and talked about it. Yeah, why MCP won. Yeah. And it won because the universe decided that this was useful and maybe it gets supplanted by something else. Yeah. And maybe we discover, oh, maybe OpenAPI was good enough the whole time. I doubt that.swyx [00:32:09]: The meta lesson, this is, I mean, he's an investor in DevTools companies. I work in developer experience at DevRel in DevTools companies. Yep. Everyone wants to own the standard. Yeah. I'm sure you guys have tried to launch your own standards. Actually, it's Houseplant known for a standard, you know, obviously inbound marketing. But is there a standard or protocol that you ever tried to push? No.Dharmesh [00:32:30]: And there's a reason for this. Yeah. Is that? And I don't mean, need to mean, speak for the people of HubSpot, but I personally. You kind of do. I'm not smart enough. That's not the, like, I think I have a. You're smart. Not enough for that. I'm much better off understanding the standards that are out there. And I'm more on the composability side. Let's, like, take the pieces of technology that exist out there, combine them in creative, unique ways. And I like to consume standards. I don't like to, and that's not that I don't like to create them. I just don't think I have the, both the raw wattage or the credibility. It's like, okay, well, who the heck is Dharmesh, and why should we adopt a standard he created?swyx [00:33:07]: Yeah, I mean, there are people who don't monetize standards, like OpenTelemetry is a big standard, and LightStep never capitalized on that.Dharmesh [00:33:15]: So, okay, so if I were to do a standard, there's two things that have been in my head in the past. I was one around, a very, very basic one around, I don't even have the domain, I have a domain for everything, for open marketing. Because the issue we had in HubSpot grew up in the marketing space. There we go. There was no standard around data formats and things like that. It doesn't go anywhere. But the other one, and I did not mean to go here, but I'm going to go here. It's called OpenGraph. I know the term was already taken, but it hasn't been used for like 15 years now for its original purpose. But what I think should exist in the world is right now, our information, all of us, nodes are in the social graph at Meta or the professional graph at LinkedIn. Both of which are actually relatively closed in actually very annoying ways. Like very, very closed, right? Especially LinkedIn. Especially LinkedIn. I personally believe that if it's my data, and if I would get utility out of it being open, I should be able to make my data open or publish it in whatever forms that I choose, as long as I have control over it as opt-in. So the idea is around OpenGraph that says, here's a standard, here's a way to publish it. I should be able to go to OpenGraph.org slash Dharmesh dot JSON and get it back. And it's like, here's your stuff, right? And I can choose along the way and people can write to it and I can prove. And there can be an entire system. And if I were to do that, I would do it as a... Like a public benefit, non-profit-y kind of thing, as this is a contribution to society. I wouldn't try to commercialize that. Have you looked at AdProto? What's that? AdProto.swyx [00:34:43]: It's the protocol behind Blue Sky. Okay. My good friend, Dan Abramov, who was the face of React for many, many years, now works there. And he actually did a talk that I can send you, which basically kind of tries to articulate what you just said. But he does, he loves doing these like really great analogies, which I think you'll like. Like, you know, a lot of our data is behind a handle, behind a domain. Yep. So he's like, all right, what if we flip that? What if it was like our handle and then the domain? Yep. So, and that's really like your data should belong to you. Yep. And I should not have to wait 30 days for my Twitter data to export. Yep.Dharmesh [00:35:19]: you should be able to at least be able to automate it or do like, yes, I should be able to plug it into an agentic thing. Yeah. Yes. I think we're... Because so much of our data is... Locked up. I think the trick here isn't that standard. It is getting the normies to care.swyx [00:35:37]: Yeah. Because normies don't care.Dharmesh [00:35:38]: That's true. But building on that, normies don't care. So, you know, privacy is a really hot topic and an easy word to use, but it's not a binary thing. Like there are use cases where, and we make these choices all the time, that I will trade, not all privacy, but I will trade some privacy for some productivity gain or some benefit to me that says, oh, I don't care about that particular data being online if it gives me this in return, or I don't mind sharing this information with this company.Alessio [00:36:02]: If I'm getting, you know, this in return, but that sort of should be my option. I think now with computer use, you can actually automate some of the exports. Yes. Like something we've been doing internally is like everybody exports their LinkedIn connections. Yep. And then internally, we kind of merge them together to see how we can connect our companies to customers or things like that.Dharmesh [00:36:21]: And not to pick on LinkedIn, but since we're talking about it, but they feel strongly enough on the, you know, do not take LinkedIn data that they will block even browser use kind of things or whatever. They go to great, great lengths, even to see patterns of usage. And it says, oh, there's no way you could have, you know, gotten that particular thing or whatever without, and it's, so it's, there's...swyx [00:36:42]: Wasn't there a Supreme Court case that they lost? Yeah.Dharmesh [00:36:45]: So the one they lost was around someone that was scraping public data that was on the public internet. And that particular company had not signed any terms of service or whatever. It's like, oh, I'm just taking data that's on, there was no, and so that's why they won. But now, you know, the question is around, can LinkedIn... I think they can. Like, when you use, as a user, you use LinkedIn, you are signing up for their terms of service. And if they say, well, this kind of use of your LinkedIn account that violates our terms of service, they can shut your account down, right? They can. And they, yeah, so, you know, we don't need to make this a discussion. By the way, I love the company, don't get me wrong. I'm an avid user of the product. You know, I've got... Yeah, I mean, you've got over a million followers on LinkedIn, I think. Yeah, I do. And I've known people there for a long, long time, right? And I have lots of respect. And I understand even where the mindset originally came from of this kind of members-first approach to, you know, a privacy-first. I sort of get that. But sometimes you sort of have to wonder, it's like, okay, well, that was 15, 20 years ago. There's likely some controlled ways to expose some data on some member's behalf and not just completely be a binary. It's like, no, thou shalt not have the data.swyx [00:37:54]: Well, just pay for sales navigator.Alessio [00:37:57]: Before we move to the next layer of instruction, anything else on MCP you mentioned? Let's move back and then I'll tie it back to MCPs.Dharmesh [00:38:05]: So I think the... Open this with agent. Okay, so I'll start with... Here's my kind of running thesis, is that as AI and agents evolve, which they're doing very, very quickly, we're going to look at them more and more. I don't like to anthropomorphize. We'll talk about why this is not that. Less as just like raw tools and more like teammates. They'll still be software. They should self-disclose as being software. I'm totally cool with that. But I think what's going to happen is that in the same way you might collaborate with a team member on Slack or Teams or whatever you use, you can imagine a series of agents that do specific things just like a team member might do, that you can delegate things to. You can collaborate. You can say, hey, can you take a look at this? Can you proofread that? Can you try this? You can... Whatever it happens to be. So I think it is... I will go so far as to say it's inevitable that we're going to have hybrid teams someday. And what I mean by hybrid teams... So back in the day, hybrid teams were, oh, well, you have some full-time employees and some contractors. Then it was like hybrid teams are some people that are in the office and some that are remote. That's the kind of form of hybrid. The next form of hybrid is like the carbon-based life forms and agents and AI and some form of software. So let's say we temporarily stipulate that I'm right about that over some time horizon that eventually we're going to have these kind of digitally hybrid teams. So if that's true, then the question you sort of ask yourself is that then what needs to exist in order for us to get the full value of that new model? It's like, okay, well... You sort of need to... It's like, okay, well, how do I... If I'm building a digital team, like, how do I... Just in the same way, if I'm interviewing for an engineer or a designer or a PM, whatever, it's like, well, that's why we have professional networks, right? It's like, oh, they have a presence on likely LinkedIn. I can go through that semi-structured, structured form, and I can see the experience of whatever, you know, self-disclosed. But, okay, well, agents are going to need that someday. And so I'm like, okay, well, this seems like a thread that's worth pulling on. That says, okay. So I... So agent.ai is out there. And it's LinkedIn for agents. It's LinkedIn for agents. It's a professional network for agents. And the more I pull on that thread, it's like, okay, well, if that's true, like, what happens, right? It's like, oh, well, they have a profile just like anyone else, just like a human would. It's going to be a graph underneath, just like a professional network would be. It's just that... And you can have its, you know, connections and follows, and agents should be able to post. That's maybe how they do release notes. Like, oh, I have this new version. Whatever they decide to post, it should just be able to... Behave as a node on the network of a professional network. As it turns out, the more I think about that and pull on that thread, the more and more things, like, start to make sense to me. So it may be more than just a pure professional network. So my original thought was, okay, well, it's a professional network and agents as they exist out there, which I think there's going to be more and more of, will kind of exist on this network and have the profile. But then, and this is always dangerous, I'm like, okay, I want to see a world where thousands of agents are out there in order for the... Because those digital employees, the digital workers don't exist yet in any meaningful way. And so then I'm like, oh, can I make that easier for, like... And so I have, as one does, it's like, oh, I'll build a low-code platform for building agents. How hard could that be, right? Like, very hard, as it turns out. But it's been fun. So now, agent.ai has 1.3 million users. 3,000 people have actually, you know, built some variation of an agent, sometimes just for their own personal productivity. About 1,000 of which have been published. And the reason this comes back to MCP for me, so imagine that and other networks, since I know agent.ai. So right now, we have an MCP server for agent.ai that exposes all the internally built agents that we have that do, like, super useful things. Like, you know, I have access to a Twitter API that I can subsidize the cost. And I can say, you know, if you're looking to build something for social media, these kinds of things, with a single API key, and it's all completely free right now, I'm funding it. That's a useful way for it to work. And then we have a developer to say, oh, I have this idea. I don't have to worry about open AI. I don't have to worry about, now, you know, this particular model is better. It has access to all the models with one key. And we proxy it kind of behind the scenes. And then expose it. So then we get this kind of community effect, right? That says, oh, well, someone else may have built an agent to do X. Like, I have an agent right now that I built for myself to do domain valuation for website domains because I'm obsessed with domains, right? And, like, there's no efficient market for domains. There's no Zillow for domains right now that tells you, oh, here are what houses in your neighborhood sold for. It's like, well, why doesn't that exist? We should be able to solve that problem. And, yes, you're still guessing. Fine. There should be some simple heuristic. So I built that. It's like, okay, well, let me go look for past transactions. You say, okay, I'm going to type in agent.ai, agent.com, whatever domain. What's it actually worth? I'm looking at buying it. It can go and say, oh, which is what it does. It's like, I'm going to go look at are there any published domain transactions recently that are similar, either use the same word, same top-level domain, whatever it is. And it comes back with an approximate value, and it comes back with its kind of rationale for why it picked the value and comparable transactions. Oh, by the way, this domain sold for published. Okay. So that agent now, let's say, existed on the web, on agent.ai. Then imagine someone else says, oh, you know, I want to build a brand-building agent for startups and entrepreneurs to come up with names for their startup. Like a common problem, every startup is like, ah, I don't know what to call it. And so they type in five random words that kind of define whatever their startup is. And you can do all manner of things, one of which is like, oh, well, I need to find the domain for it. What are possible choices? Now it's like, okay, well, it would be nice to know if there's an aftermarket price for it, if it's listed for sale. Awesome. Then imagine calling this valuation agent. It's like, okay, well, I want to find where the arbitrage is, where the agent valuation tool says this thing is worth $25,000. It's listed on GoDaddy for $5,000. It's close enough. Let's go do that. Right? And that's a kind of composition use case that in my future state. Thousands of agents on the network, all discoverable through something like MCP. And then you as a developer of agents have access to all these kind of Lego building blocks based on what you're trying to solve. Then you blend in orchestration, which is getting better and better with the reasoning models now. Just describe the problem that you have. Now, the next layer that we're all contending with is that how many tools can you actually give an LLM before the LLM breaks? That number used to be like 15 or 20 before you kind of started to vary dramatically. And so that's the thing I'm thinking about now. It's like, okay, if I want to... If I want to expose 1,000 of these agents to a given LLM, obviously I can't give it all 1,000. Is there some intermediate layer that says, based on your prompt, I'm going to make a best guess at which agents might be able to be helpful for this particular thing? Yeah.Alessio [00:44:37]: Yeah, like RAG for tools. Yep. I did build the Latent Space Researcher on agent.ai. Okay. Nice. Yeah, that seems like, you know, then there's going to be a Latent Space Scheduler. And then once I schedule a research, you know, and you build all of these things. By the way, my apologies for the user experience. You realize I'm an engineer. It's pretty good.swyx [00:44:56]: I think it's a normie-friendly thing. Yeah. That's your magic. HubSpot does the same thing.Alessio [00:45:01]: Yeah, just to like quickly run through it. You can basically create all these different steps. And these steps are like, you know, static versus like variable-driven things. How did you decide between this kind of like low-code-ish versus doing, you know, low-code with code backend versus like not exposing that at all? Any fun design decisions? Yeah. And this is, I think...Dharmesh [00:45:22]: I think lots of people are likely sitting in exactly my position right now, coming through the choosing between deterministic. Like if you're like in a business or building, you know, some sort of agentic thing, do you decide to do a deterministic thing? Or do you go non-deterministic and just let the alum handle it, right, with the reasoning models? The original idea and the reason I took the low-code stepwise, a very deterministic approach. A, the reasoning models did not exist at that time. That's thing number one. Thing number two is if you can get... If you know in your head... If you know in your head what the actual steps are to accomplish whatever goal, why would you leave that to chance? There's no upside. There's literally no upside. Just tell me, like, what steps do you need executed? So right now what I'm playing with... So one thing we haven't talked about yet, and people don't talk about UI and agents. Right now, the primary interaction model... Or they don't talk enough about it. I know some people have. But it's like, okay, so we're used to the chatbot back and forth. Fine. I get that. But I think we're going to move to a blend of... Some of those things are going to be synchronous as they are now. But some are going to be... Some are going to be async. It's just going to put it in a queue, just like... And this goes back to my... Man, I talk fast. But I have this... I only have one other speed. It's even faster. So imagine it's like if you're working... So back to my, oh, we're going to have these hybrid digital teams. Like, you would not go to a co-worker and say, I'm going to ask you to do this thing, and then sit there and wait for them to go do it. Like, that's not how the world works. So it's nice to be able to just, like, hand something off to someone. It's like, okay, well, maybe I expect a response in an hour or a day or something like that.Dharmesh [00:46:52]: In terms of when things need to happen. So the UI around agents. So if you look at the output of agent.ai agents right now, they are the simplest possible manifestation of a UI, right? That says, oh, we have inputs of, like, four different types. Like, we've got a dropdown, we've got multi-select, all the things. It's like back in HTML, the original HTML 1.0 days, right? Like, you're the smallest possible set of primitives for a UI. And it just says, okay, because we need to collect some information from the user, and then we go do steps and do things. And generate some output in HTML or markup are the two primary examples. So the thing I've been asking myself, if I keep going down that path. So people ask me, I get requests all the time. It's like, oh, can you make the UI sort of boring? I need to be able to do this, right? And if I keep pulling on that, it's like, okay, well, now I've built an entire UI builder thing. Where does this end? And so I think the right answer, and this is what I'm going to be backcoding once I get done here, is around injecting a code generation UI generation into, the agent.ai flow, right? As a builder, you're like, okay, I'm going to describe the thing that I want, much like you would do in a vibe coding world. But instead of generating the entire app, it's going to generate the UI that exists at some point in either that deterministic flow or something like that. It says, oh, here's the thing I'm trying to do. Go generate the UI for me. And I can go through some iterations. And what I think of it as a, so it's like, I'm going to generate the code, generate the code, tweak it, go through this kind of prompt style, like we do with vibe coding now. And at some point, I'm going to be happy with it. And I'm going to hit save. And that's going to become the action in that particular step. It's like a caching of the generated code that I can then, like incur any inference time costs. It's just the actual code at that point.Alessio [00:48:29]: Yeah, I invested in a company called E2B, which does code sandbox. And they powered the LM arena web arena. So it's basically the, just like you do LMS, like text to text, they do the same for like UI generation. So if you're asking a model, how do you do it? But yeah, I think that's kind of where.Dharmesh [00:48:45]: That's the thing I'm really fascinated by. So the early LLM, you know, we're understandably, but laughably bad at simple arithmetic, right? That's the thing like my wife, Normies would ask us, like, you call this AI, like it can't, my son would be like, it's just stupid. It can't even do like simple arithmetic. And then like we've discovered over time that, and there's a reason for this, right? It's like, it's a large, there's, you know, the word language is in there for a reason in terms of what it's been trained on. It's not meant to do math, but now it's like, okay, well, the fact that it has access to a Python interpreter that I can actually call at runtime, that solves an entire body of problems that it wasn't trained to do. And it's basically a form of delegation. And so the thought that's kind of rattling around in my head is that that's great. So it's, it's like took the arithmetic problem and took it first. Now, like anything that's solvable through a relatively concrete Python program, it's able to do a bunch of things that I couldn't do before. Can we get to the same place with UI? I don't know what the future of UI looks like in a agentic AI world, but maybe let the LLM handle it, but not in the classic sense. Maybe it generates it on the fly, or maybe we go through some iterations and hit cache or something like that. So it's a little bit more predictable. Uh, I don't know, but yeah.Alessio [00:49:48]: And especially when is the human supposed to intervene? So, especially if you're composing them, most of them should not have a UI because then they're just web hooking to somewhere else. I just want to touch back. I don't know if you have more comments on this.swyx [00:50:01]: I was just going to ask when you, you said you got, you're going to go back to code. What

This week on No Priors, Sarah and Elad talk with GitHub CEO Thomas Dohmke about the rise of AI-powered software development and the success of Copilot. They discuss how Copilot is reshaping the developer workflow, GitHub's new Agent Mode, and competition in the developer tooling market. They also explore how AI-driven coding impacts software pricing, the future of open source vs. proprietary APIs, and what Copilot's success means for Microsoft. Plus, Thomas shares insights from his journey growing up in East Berlin and navigating rapidly changing worlds. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ThomasDohmke Show Notes: 0:00 Introduction 0:37 GitHub Copilot's capabilities 4:12 Will agents replace developers? 6:04 Copilot's development cycle 8:34 Winning the developer market 10:40 Agent mode 13:25 Where GitHub is headed 16:45 Building for the new challenges of AI 21:50 Dev tools market formation 29:56 Copilot's broader impact 32:17 How AI changes software pricing 39:16 Open source vs. proprietary APIs 48:01 Growing up in East Berlin