Podcasts about cpu

Der Markt der Notebookprozessoren ist in Bewegung: Intel und AMD erreichen mit ihren x86-Prozessoren nur noch kleine Performancesprünge, Qualcomm hängt sie mit seinen Snapdragon-X2-CPUs meinstens ab und Apples M-Chips laufen sowieso allen davon. Hinzu kommt im Laufe des Jahres die Firma Nvidia, die mit dem RTX Spark ein SoC für (zunächst recht teure) Notebooks bringt. In dieser Folge des c't uplink sprechen wir über die kommenden CPUs von Nvidia, die aktuellen von Qualcomm und was für ARM-CPUs noch kommen könnten (und welche eher nicht). Außerdem: Warum die Situation bei Linux-Treibern bei Nvidia erquicklicher werden könnte als bei Qualcomm, was AMD so plant, was Intels Panther-Lake-Chips doch ganz gut hinbekommen und mehr.

At 66 years old, instead of heading towards retirement, former Cadence CEO and legendary investor Lip Bu Tan decided to take on the hardest job in tech: turning Intel around. Elad Gil and Sarah Guo sit down with Intel CEO Lip Bu Tan to talk about why he took the job and what “saving” Intel actually looks like. Tan explains how his experience in startup culture informed his decisions to drive Intel's culture towards faster decisions, focus on customer satisfaction, and engineer accountability. He also discusses his strategy to strengthen Intel's balance sheet by welcoming investments from Jensen Huang's Nvidia, Softbank, and the US government. Tan also shares his product roadmap that centers the CPU for agentic AI and inference, the collaboration with Elon Musk on Terafab, his investing framework for semiconductors, and his views on how AI is reshaping design and operations at, as he puts it, a ‘legacy spreadsheet' tech company.         Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @LipBuTan1 | @intel Chapters: 00:00 – Cold Open 01:01 – Lip Bu Tan Introduction 01:24 – Why Lip Bu Took the Reins at Intel 03:00 – Fixing Culture 04:08 – Intel's 10-Year Vision 07:57 – Working with Elon Musk on Terafab 09:59 – Shifting Supply Chain for Semiconductors 15:34 – Limits to Scaling and Packaging 18:30 – Physical Limits to Engineering and Design 20:33 – Challenges in Semiconductor Investing 26:29 – Lessons from Cadence 28:02 – Scaling and Investment Decisions 32:03 – Rethinking Teams in AI Era 34:31 – Industrial Policy and Funding 37:25 – What Investors Misunderstand About Intel 41:10 – Where Compute Will Live 44:59 – Conclusion

NuNet is building a decentralised compute and orchestration network where people can contribute spare CPU, GPU, RAM and other resources, while developers and organisations can deploy workloads across available infrastructure. In this episode, Peter talks with Jennifer from NuNet about the new NuNet Appliance and why it matters for making decentralised compute more practical for everyday users.The conversation covers how NuNet matches the right compute to the right job, how the Appliance lowers the barrier to onboarding devices, and why use cases like n8n automations, private AI agents, edge AI, Cardano SPO infrastructure and web deployment workflows are a natural fit for the network. Jennifer also explains NuNet's zero-trust security model, pricing approach, organisations, ensembles, deployment templates, and how NTX fits into orchestration fees.If you have spare compute, want to run private AI workloads, or are building in the DePIN and Cardano ecosystem, this episode gives a practical look at how NuNet is moving from concept to usable infrastructure.Key Takeaways:- NuNet is a decentralised compute and orchestration platform that lets people contribute spare compute and lets workloads find suitable resources automatically.- The NuNet Appliance is designed to make onboarding CPUs, GPUs, RAM and other compute resources much easier for non-expert users.- NuNet can support broad workloads, including n8n automation, private AI agents, Qwen-based LLM deployments, edge AI, web builds and Cardano SPO infrastructure.- The network uses a zero-trust model where machines are cryptographically identified and verified at each interaction.- Compute pricing is designed around stable currency values, with automatic conversion into NTX rather than forcing users to price workloads directly in a volatile token.- NuNet organisations can let other DePIN projects bring their own communities and native tokens while still using NuNet's orchestration layer.- Ensembles and templates are intended to simplify deployments so users do not need to manually understand every YAML configuration detail.- NuNet is open source, with docs, GitLab, Discord, Medium and X available for people who want to try the network or contribute.Links & References:- NuNet — Compute Orchestration for a Decentralized World: https://link.learncardano.io/eGKGuZ- What is NuNet? | NuNet Documentation: https://link.learncardano.io/rHu2E4- x.com: https://link.learncardano.io/NIhPKR- https://link.learncardano.io/Tlu7wNWebsite: https://link.learncardano.io/bQ68RcX/Twitter: https://link.learncardano.io/3a1QtvDisclaimer: This content is for educational purposes only. Nothing constitutes financial advice.DISCLAIMER: This content is for informational and educational purposes only and is not financial, investment, or legal advice. I am not affiliated with, nor compensated by, the project discussed—no tokens, payments, or incentives received. I do not hold a stake in the project, including private or future allocations. All views are my own, based on public information. Always do your own research and consult a licensed advisor before investing. Crypto investments carry high risk, and past performance is no guarantee of future results. I am not responsible for any decisions you make based on this content.

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The race to build superintelligence is producing models that keep getting better at objective problems, but not at behaving like actual people. Joon Sung Park, founder and CEO of Simile and creator of Stanford's "Smallville" generative agents study, argues that simulating human society requires a fundamentally different kind of model. He frames today's frontier models as the "CPU of intelligence"—rational, superhuman at problems with right answers—and Simile as creating the "GPU of intelligence," built to encode the diversity of people's values, preferences, and tastes. It simulated 1,000 Americans and predicted their behavior 85% as accurately as people reproduce their own answers. CVS uses it for concept testing; some customers simulate their own earnings calls. Joon's larger bet: a "CERN of human society" that could one day model bank runs, climate cooperation, or the early signals of a collapsing democracy. Hosted by Sonya Huang, Sequoia Capital

Joseph S. Harrington, CPU, an independent business researcher and writer specializing in property/casualty insurance, joins Deepak Puri, Founder of The Democracy Labs, to discuss the real costs of gun violence and who is most likely to use a gun for violence. Joseph explains that most gun deaths are suicides, and most shootings are intentional, which makes gun owners uninsurable. This leaves the victims of gun violence to shoulder the burden of a shooting. A focus on the gun sellers is a possible path to protect gun rights, better manage safety programs, and provide compensation to victims. Deepak and Joseph talk about Why traditional liability insurance is not a workable solution for gun violence How gun manufacturers and dealers are shielded from liability Costs of violence include medical care, rehabilitation, and societal costs  Creating financial incentives for the gun industry to engage in gun safety and monitoring #TheDemLabs #StateTreasurers #PoliticalActivism #Democracy #Accountability #GunRights #GunControl #GunViolence #GunPolicy #CampaignStrategy #PublicSafety #RIFLAct #Liability TheDemocracyLabs.org  

The back episodes finally make it online, this is number 4 in a series of episodes - wait - this IS the current episode!Brett is back, so the episodes are getting posted almost on time!  Windows is starting to support ARM more, RAM pricing hysterics, Windows 11 CPU boost, Intel improves with iBOT, Microsoft 365 brings the CoPilot, and bots surpass humans on the network.  On with the show, enjoy!0:00 Intro1:15 Patreon2:09 Food Stories with Josh (just words, no photos)3:54 3DMark adds native Arm Windows support6:55 Josh talks about the latest Arm developments9:43 Memory prices may double this year (and related discussion)16:45 Windows 11 performance boost?19:05 Intel expands iBOT with 7 more games23:31 AMD reaches almost 45 percent CPU share on Steam25:23 Office 365 Copilot auto-install returns33:59 Bots take over the Internet36:59 Apple iOS 27 has an "agentic" solution for compromised passwords39:42 (in)Security Corner54:15 Gaming Quick Hits1:01:32 Picks of the Week1:10:02 Outro ★ Support this podcast on Patreon ★

In this episode of The Circuit, Ben and Jay dive deep into Apple's WWDC announcements, unpacking the "applied AI" strategy behind the newly indexed Siri, system-level CPU scheduling updates, and Apple's surprising embrace of Nvidia for private cloud compute. Ben also shares his takeaways from the Nebius conference in San Francisco, analyzing their unique custom server racks and their positioning as a highly capable neo-cloud infrastructure provider. Finally, Jay reports back from his recent trip to China, sharing ground-level observations on why US entity list restrictions are losing their impact, how IoT chipmaker Espressif creatively markets globally via Reddit and YouTube despite domestic blocks, and the real-world manufacturing bottlenecks facing humanoid robot actuators.

Brendan Burke says now is the time for tech, and Intel (INTC) has a growing role in the AI buildout. He believes current CEO Lip-Bu Tan will turn Intel into a core collaborator with AI hyperscalers that serves as a compelling foundry alternative to TSMC (TSM). Brendan also expects Intel to serve as a strong inferencing and CPU manufacturer as spending for AI accelerates. ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Apple's Worldwide Developers Conference happened this week, and there was enough going on that we wanted to unpack the whole thing, primarily due to the company's uncharacteristic backpedaling on its... controversial Liquid Glass UI language, not to mention the unusual focus on CPU scheduling and numerous other performance refinements across the board in this year's OS updates, rather than the more typical long list of new features. It was enough to get us saying the words "Snow Leopard," which is always a good feeling. We also consider new broader parental controls, the apparently final state of Apple Intelligence and Siri AI features, and more. Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

The back episodes finally make it online, this is number 1 is a series of episodes that did not get posted, but are now!Brett is out (which is why these didn't get posted) - but the show is great!  So much insecurity, so much Linux and retro, enjoy!Timestamps:0:00 Intro1:04 Patreon1:26 Food with Josh3:10 New Ryzen X3D already?5:16 NVIDIA just made more money than ever11:52 RTX 5090 price increase16:41 EFI partitions and Windows Update19:57 Noctua Home - adding fans to everything in your life22:57 Linux drops more vintage CPU support26:35 Vintage style ROG motherboard30:39 (In)Security Corner44:30 Gaming Quick Hit47:20 Jeremy explores the Lepro OE1 RGB floor lamp52:12 Picks of the Week1:00:29 Outro ★ Support this podcast on Patreon ★

Johnny Mac shares five stories, including that Karl Lagerfeld's famed cat Choupette has not received the reported $1.5 million inheritance seven years after his death due to a dispute involving the estate and French tax authorities; under French law pets can't inherit directly, so money would go to her caretaker, who says she has received nothing and works part-time to support the cat. In Miami, 500+ soccer players and 5,000+ people broke a Guinness World Record by juggling a soccer ball in unison for 10 seconds to fund grassroots soccer upgrades in the US and Mexico. Apple says iOS 27 will bring an advanced CPU scheduler to older iPhones. A metal detectorist found a rare late 16th/early 17th century gold and diamond ring expected to sell for £15,000–£20,000 and recovered a missing diamond by sieving the soil. Arsenal donates worn player socks to Redwings Horse Sanctuary for equine medical and protective uses.00:11 Choupette Inheritance Drama01:31 Soccer Juggling Record01:55 Apple Boosts Old iPhones02:21 Rare Ring Treasure Find03:25 Arsenal Socks Help Horses04:20 Wrap Up and Sign Off5 Good News Stories is a daily podcast with five positive, uplifting news stories to brighten your day. New episodes every day. Follow on Apple Podcasts, Spotify, or wherever you listen. Part of the Caloroga Shark Media networkJohn also hosts Daily Comedy NewsUnlock an ad-free podcast experience with Caloroga Shark Media!  For Apple users, hit the banner which says Uninterrupted Listening on your Apple podcasts app. Subscribe now for exclusive shows like 'Palace Intrigue,' and get bonus content from Deep Crown (our exclusive Palace Insider!) Or get 'Daily Comedy News,' and '5 Good News Stories' with no commercials! Plans start at $4.99 per month, or save 20% with a yearly plan at $49.99. Join today and help support the show!Get more info from Caloroga Shark Media and if you have any comments, suggestions, or just want to get in touch our email is info@caloroga.com

In this episode of the AppleVis Extra podcast, Dave Nason is joined by Thomas Domville and Tyler Stephen for an in-depth discussion of Apple's WWDC 2026 keynote. The team examines Apple's new presentation format, the company's focus on refinement, trust and safety, and artificial intelligence, and what these changes mean for blind and low-vision users.The conversation begins with overall impressions of the keynote, which the hosts describe as a departure from previous WWDC events. Rather than organizing announcements by operating system, Apple focused on three major themes: refinement, trust and safety, and AI. The hosts discuss why this approach reflects Apple's increasing platform convergence and why this year's event felt more like a “Snow Leopard” release focused on improvements and stability rather than a long list of new features.A significant portion of the discussion centers on Apple's expanded family safety and parental control features. The hosts explore improvements to Screen Time, website access controls, contact approval requests, and age-verification technologies. They also discuss how Apple's new Declarative Age Range API could potentially reduce accessibility barriers while helping companies comply with growing age-restriction requirements worldwide.The podcast then shifts to Apple Intelligence and the newly announced Siri AI experience. Thomas, Tyler, and Dave discuss Apple's renewed effort to deliver the AI-powered Siri capabilities first previewed several years ago. Topics include contextual awareness, world knowledge, app actions, improved dictation, more expressive voices, and Apple's continued rollout strategy. The hosts also discuss concerns about device compatibility, regional availability, and the growing fragmentation between supported and unsupported devices.A major accessibility highlight is Apple Intelligence image description. The hosts explain how blind users can now quickly describe images anywhere in the operating system without relying on third-party services such as Be My AI or PiccyBot. They discuss the new image description rotor actions, follow-up questioning, screen-level descriptions through the Dynamic Island, and the potential future benefits of AI-powered contextual understanding for unlabeled interface elements.Other Apple Intelligence features covered include Visual Intelligence, AI-powered web monitoring, custom Safari extension generation, natural language Shortcut creation, password management automation, and AI-assisted productivity improvements.The discussion also covers operating system compatibility changes across Apple's platforms. The hosts review iOS 27 device support, the end of Intel Mac support in macOS 27 Golden Gate, Rosetta's remaining lifespan, Apple Silicon requirements, and changes to Apple Watch compatibility.Additional accessibility-related improvements discussed include pronunciation dictionary import and export, enhanced VoiceOver verbosity controls, Braille Screen Input improvements, predictive text support for Braille users, and settings related to VoiceOver cursor visibility during screen recordings.The team also reviews Apple's claims regarding system performance improvements, including faster app launches, improved AirDrop transfers, better networking transitions, Spotlight indexing enhancements, CPU scheduling improvements, and the overall goal of making Apple devices feel more responsive…

A Computex 2026 trouxe uma série de anúncios importantes para o mercado de tecnologia, mas poucos chamaram tanta atenção quanto o RTX Spark, a nova plataforma da NVIDIA voltada para computação acelerada por IA em dispositivos locais. Neste episódio do Diocast, discutimos o que exatamente é o RTX Spark, quais problemas ele pretende resolver e como ele se posiciona em um mercado que já conta com soluções como Snapdragon X Elite, Ryzen AI e os novos processadores Intel com aceleração dedicada para inteligência artificial.Mais do que simplesmente lançar um novo chip, a NVIDIA parece estar ampliando sua presença para além das placas de vídeo tradicionais. O RTX Spark combina CPU baseada em arquitetura ARM, GPU com tecnologias derivadas do ecossistema RTX e recursos dedicados para cargas de trabalho envolvendo inteligência artificial. Na prática, isso pode abrir espaço para computadores mais eficientes, capazes de executar modelos de IA localmente, reduzindo a dependência de serviços em nuvem e melhorando aspectos como privacidade, latência e disponibilidade.---https://diolinux.com.br/podcast/lancamento-da-nvidia-rtx-spark.html

Adeniyi Abiodun has been in crypto since 2012, built trading and risk systems at investment banks, and led R&D on Facebook's Project Libra at Meta before co-founding Mysten Labs. So when he says every other L1 has a "skill issue" baked in at architecture time, it's worth listening.David Sencil sits down with Adeniyi at Consensus 2026 to walk through how Sui solved horizontal-scale consensus, why a famous L1 founder said it was impossible, and what comes next — native stablecoins, private payments by default, Walrus storage, and the agentic payment rails Stripe is pricing at a billion TPS.We cover:- Why every other L1 is capped by a single CPU and Moore's Law- The Project Libra story — "way too early" and what survived into Sui- 300ms finality vs Solana's 12 seconds- SuiUSD: $63M in a month and a half, free stablecoin transfers- Protocol-level private stablecoin transactions launching this year- Walrus storage outgrowing Arweave in a year- Why "AI doesn't care about your tribe"Filmed at Consensus 2026.Host: David Sencil

Dans cet épisode crossover Apple Différemment x Tech Café, on revient sur la WWDC 2026 d'Apple et sur la réception de la conférence. Une première impression de déception puis, avec une lecture plus attentive, Apple a finalement présenté plusieurs évolutions concrètes sur ses appareils.  Me soutenir sur Patreon Me retrouver sur YouTube On discute ensemble sur Discord Les annonces phare sur les OS Finies les séquences par OS, on optimise partout et on nettoie les vitres. Les apps se chargent plus vite, la recherche enfin pertinente et à une vitesse décente, recherche des plus beaux sourires de vos photos et vidéos, planificateur de processeur (CPU) optimisé (y compris anciens iPhone), gestion du JavaScript accélérée, switch wifi / connexion cellulaire Contrôle parental : refonte complète On est en 2026 : IA, agents et vibe coding Siri AI, app dédiée, des promesses mais pas partout (la bêta et l'Apple Watch est notre amie) L'édition de photos se met à niveau (vraiment), la génération d'images aussi Vibe coding de raccourcis ou l'aveu de l'échec du nocode Changement des mots de passe par agent IA 5 nouveaux Apple Foundation Models, : plus complexe et complet qu'il n'y paraît Ce qui sera disponible ou pas Mais aussi… Entre politique et annonces ringardes Des annonces sans les citer : un Mac tactile (dessin dans Notes, pull down to refresh, affichage des bordures des fenêtres pour l'accessibilité), l'iPhone pliant, un MacBook Neo avec 12 Go de RAM Compatibilité des OS : M pour le Mac, comme iOS 26 pour l'iPhone et c'est plus critique sur l'Apple Watch (SE 3, 9, ou Ultra 2 minimum), et quelques iPad abandonnés (il faut au moins un processeur A13 ou plus exactement un A12Z (iPad Pro de 2020) Quelques évolutions dans Notes (format markdown, liens entre les sections) Albums vraiment partagés avec autres OS Râle d'agonie ? Des annonces visionOS (fenêtres courbées, centre de contrôle revus, panoramas d'après vos photos, Siri au regard) Adieu Hidden Bar, les services de création de cartes Pas Apple Divorce acté entre les chargeurs et leurs câbles soudés USB-C en Europe Baisse de prix de l'abonnement Google Gemini AI Plus Les prix des forfaits en France vont-ils augmenter ? La voiture électrique sauve des vies (enfin évite des morts) Jeux vidéo La conclusion du Joy-Con Drift en France Participants Avec Mat Fabrice Neuman Une émission présentée par Guillaume Vendé

Your iPhone might be running hot and draining fast — and it’s not just you. Dave and Pilot Pete break down the battery chaos introduced by iOS 26.5, which brought overheating, accelerated drain, and even blocked wired charging on iPhone 17 and Air models. The fix that’s working for most people: disable iCloud Keychain first, run Reset All Settings, then carefully re-enable iCloud sync — otherwise you’ll nuke your Wi-Fi passwords across every device. iOS 26.5.1 is out and should help, but until you’ve updated, your electrons deserve better. You’ll also learn why Apple ID passkeys are locked to Apple’s own keychain with no known path to third-party managers like 1Password or Keeper, and why editing a contact on a modern Mac can somehow peg every CPU core — in 2026, no less. From there, Dave and Pete tackle the full listener mailbag: how to rescue missing contact names from Messages, the right way to boot a MacBook with a broken display into clamshell mode so it actually uses the external monitor, and a deep dive on 5K vs. 4K displays where Dave argues your eyes may not care as much as the pixel-per-inch math suggests. You’ll get smart ideas for repurposing a 2015 iPad Pro that can’t run modern apps — including Dave’s Claude Code-built weather dashboard running off a headless iMac as a web interface. A crashing 2021 MacBook Pro turns out to have been felled by a single bad SD card, and the lesson is golden: feed your crash reports to an LLM and let it do the digging. And Don’t Get Caught with outdated OpenAI macOS apps — update ChatGPT, Codex, Atlas, and Codex CLI before June 12th to stay ahead of a code-signing rotation triggered by a compromised open-source library. 00:00:00 Mac Geek Gab 1145 for Monday, June 8th, 2026 June 8th: National Best Friends Day MGG Monthly Giveaway – Win a license to SaneBox Quick Tips 00:00:01 Dan-QT-Multi-select on iPhone with a quick drag 00:04:31 Tim-QT-Have iOS 26.5 Battery Drain? Reset All Settings, but be careful! 00:13:32 Kent-QT-1144-Collapse stacks by clicking the down-facing carat in the menu 00:14:15 Mark-QT-Match Frame Rate on your Apple TV for smoother experiences 00:17:58 What are the differences between refresh rates and frame rates and…why? 00:21:09 KiwiGraham-QT-Apple Account Passkeys vs. Third Party Password Apps Sponsors 00:23:09 SPONSOR: Keeper. Right now, Keeper is offering our listeners 60% off personal and family plans at https://Keepersecurity.com/MGG. This offer is only for podcast listeners! 00:24:50 SPONSOR: Helix Sleep makes premium mattresses and bedding that are customized to fit your personal needs, and conveniently shipped to your door. Go to https://helixsleep.com/MGG for 20% Off Sitewide. 00:26:23 SPONSOR: NordLayer Browser. The business browser built for how modern work actually happens — giving IT the visibility and control to secure SaaS, stop phishing, and prevent data leaks right at the source. Your Questions Answered and Tips Shared! 00:28:09 VaShaun-How can I restore lost Contacts on my Mac? 00:37:36 Si-What to do with an 11-year-old iPad? Claude Code 00:46:40 Michael-Why do we have to pull-to-refresh for updates? 00:50:04 Blake-1144-Damaged displays, external monitors, and MonitorControl 00:55:48 Joe & Michael-CSF-1144–RetinaDesk.com for reviews of 5K and 6K monitors BenQ MA270UP 27” 4K Display Reviews 01:02:50 Hog fan and Cowboy fan-MGG Review–Favorite Tech podcast Don't Get Caught 01:04:14 Father John-DGC-Investigate those crash reports before you replace your Mac 01:09:26 Update your ChatGPT Apps ChatGPT Desktop Codex App Codex CLI Atlas 01:11:06 Andy-DGC-When Troubleshooting, Don’t Get Caught asking the wrong questions or assuming the wrong facts 01:19:36 MGG 1145 Outtro MGG Monthly Giveaway Bandwidth Provided by CacheFly Pilot Pete's Aviation Podcast: So There I Was (for Aviation Enthusiasts) The Debut Film Podcast – Adam's new podcast! Dave's Business Brain (for Entrepreneurs) and Gig Gab (for Working Musicians) Podcasts MGG Merch is Available! Mac Geek Gab iOS app Mac Geek Gab YouTube Page Mac Geek Gab Live Calendar This Week's MGG Premium Contributors MGG Apple Podcasts Reviews feedback@macgeekgab.com 224-888-GEEK Active MGG Sponsors and Coupon Codes List BackBeat Media Podcast Network

Computex happened this week, and there was enough to talk about to devote this week's episode to rounding up the high points, including Nvidia's attempt to dominate the consumer Windows market with RTX Spark, the first RGB mini-LED monitors, 8GB laptops becoming common again, PC hardware production shifting back to DDR4 and old CPU sockets, Intel's entry into the handheld gaming market, the (unsurprising) absence of any news about Zen 6 and Nova Lake, and other stuff! Show notes and links: https://tinyurl.com/techpod-342-computex-26 Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

This is a recap of the top 10 posts on Hacker News on June 06, 2026. This podcast was generated by wondercraft.ai (00:30): S&P 500 rejects SpaceX, also blocking entry for OpenAI and AnthropicOriginal post: https://news.ycombinator.com/item?id=48421442&utm_source=wondercraft_ai(01:59): Meta confirms 1000s of Instagram accounts were hacked by abusing its AI chatbotOriginal post: https://news.ycombinator.com/item?id=48427643&utm_source=wondercraft_ai(03:29): Pentagon raised threat of Israeli spying on U.S. to highest level, sources sayOriginal post: https://news.ycombinator.com/item?id=48427523&utm_source=wondercraft_ai(04:59): GrapheneOS user reported to authorities for using GrapheneOSOriginal post: https://news.ycombinator.com/item?id=48422798&utm_source=wondercraft_ai(06:28): Ask HN: Why is the HN crowd so anti-AI?Original post: https://news.ycombinator.com/item?id=48420827&utm_source=wondercraft_ai(07:58): Ntsc-rs – open-source video emulation of analog TV and VHS artifactsOriginal post: https://news.ycombinator.com/item?id=48428025&utm_source=wondercraft_ai(09:28): Pokemon Emerald Ported to WebAssembly (100k FPS)Original post: https://news.ycombinator.com/item?id=48423762&utm_source=wondercraft_ai(10:57): Moving beyond fork() + exec()Original post: https://news.ycombinator.com/item?id=48425528&utm_source=wondercraft_ai(12:27): Nvidia is proposing a beast of a CPU system for Windows PCsOriginal post: https://news.ycombinator.com/item?id=48424605&utm_source=wondercraft_ai(13:57): The intracies of modern camera lens repair (2024)Original post: https://news.ycombinator.com/item?id=48420148&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Google acaba de lanzar una herramienta que puede cambiar la forma en la que usamos inteligencia artificial: Google AI Edge Gallery, una app que permite ejecutar modelos Gemma directamente en tu dispositivo, sin depender de la nube. En este episodio pruebo modelos locales en Mac, analizo cómo funcionan con imágenes, texto y audio, comparo el rendimiento usando GPU y CPU, y te muestro algo clave: esta IA puede trabajar offline, de forma privada y gratis. Pero no todo es perfecto. También aparecen limitaciones importantes: respuestas inconsistentes, problemas de contexto, diferencias frente a Gemini en la nube, límites en transcripción de audio y una experiencia que todavía parece estar en desarrollo. La gran pregunta es:¿Estamos viendo el futuro de la inteligencia artificial personal, corriendo directamente en nuestros celulares, iPads y computadoras?¿O todavía es apenas un experimento prometedor? En este video te muestro pruebas reales, resultados concretos y mi opinión sincera sobre lo que Google acaba de poner sobre la mesa.

Show 314: The meeting focused on discussing the proper placement of D-Feedback in audio mixing chains, with participants sharing their different approaches and experiences. Bruce, John, and Gregory debated whether to place D-Feedback as the first or last plugin in the chain, with Bruce using it last to clean up audio before applying it, while John and Gregory preferred it first to avoid overworking the AI. The conversation also covered various technical topics including CPU requirements, noise reduction versus feedback elimination, and specific use cases in different venues like stadiums and corporate events. Additionally, the group shared personal stories about past audio engineering experiences, including equipment failures, famous acts they've worked with, and the evolution of audio technology over the years.Stop guessing and start growing. Join Jan Landy and his knowledgeable, affable panel of friends and colleagues for a no-filter discussion on mastering the professional life—with more laughs than a comedy club.Our ZoomCast isn't just a fountain of industry knowledge; it's also an opportunity to laugh. Think of it as therapy, but with more jokes and fewer couches. Stay updated on life and world events, share your thoughts, and enjoy multiple good chuckles along the way. -JOIN US LIVE EVERY WEDNESDAY:- 4:45 PM Pacific (UTC-7) / 7:45 PM EasternHow to Assist:Offer your support by giving us a Like, opinions in the comments on Facebook, LinkedIn, and YouTube and remember to share the show with your industry friends.Creators & Guests Jan Landy - Host D-Feedback, audio signal chain, audio mixing, feedback suppression, live sound engineering, CPU load, noise reduction, gain staging, D-Feedback placement, live sound tips, audio engineering, monitor mixing, foldback setups, professional audio processing

Industrial network protocols decide whether a machine talks or stays silent. Chuck from Horner Automation breaks down how they win, fade, and converge.Chuck has spent 36 years at Horner Automation and lived through what the industry once called the fieldbus wars. Before Horner became known for its all in one controllers, it spent a decade building specialty IO modules for GE Fanuc during the era of DeviceNet, SDS, InterBus S, PROFIBUS, and CANopen. His core argument is that most of those early protocols were technically fine. The ones that became standards won on the commercial weight of the companies backing them, not on superior specifications, with EtherCAT a rare exception that succeeded largely on technical merit.Trust is the recurring theme. Industry adopts slowly, and for years Ethernet was dismissed as too unreliable and not deterministic enough for control until Ethernet/IP, PROFINET, and Modbus TCP proved themselves. Today the market has settled around a big four set of protocols, and Chuck does not expect it to narrow further. For high speed motion he points to EtherCAT and PROFINET IRT as the implementations he most respects, since both step away from standard Ethernet at the device level to reach submillisecond timing.The episode is also a reality check on building your own hardware. Chuck and Dave describe how custom development routinely costs teams hundreds of thousands to millions of dollars, and how the real trap is obsolescence and maintenance rather than the first build. On the product side, the standout is FPD-Link, a serialization technology borrowed from automotive that carries video, touch, and power over one coaxial cable. Working with Safe Fleet, a maker of ambulances and fire trucks, Horner now mounts rugged displays up to seven meters from the PLC while still programming everything as one device.Looking ahead, Chuck argues that every PLC should now be treated as a data device first, because digitizing the process is the prerequisite for doing anything useful with AI. He also flags cybersecurity as the next burden for application engineers, with new mandates forcing both manufacturers and integrators to implement protections that were once optional. At Automate, Horner is showing HMI Connect and a 300 dollar CPU 151 that packs 18 IO points, wireless connectivity, and edge capability into a micro PLC.About Chuck and Horner AutomationChuck is a technical brand ambassador at Horner Automation, where he has spent 36 years across applications, product management, and education. An electrical engineer who started in the automotive industry, he now produces in depth tutorials on industrial protocols for the Horner APG YouTube channel. Horner Automation is a privately held controls manufacturer best known for its all in one PLC and HMI controllers, edge ready PLCs, and rugged hardware for industrial and mobile applications.Timestamps0:00 Introduction2:20 Chuck's Background and 36 Years at Horner Automation9:20 End User Engineer vs OEM Manufacturer Perspective13:20 New at Automate: HMI Connect and the CPU 151 Edge PLC21:30 The Fieldbus Wars and the History of Industrial Protocols24:20 What It Takes to Implement a Protocol Stack29:30 Why Protocols Win: Commercial Force vs Technical Merit32:40 Will Industrial Protocols Ever Converge?40:30 High Speed Motion: EtherCAT, PROFINET IRT, and Ethernet/IP44:40 FPD-Link: Rugged Remote HMI for Ambulances and Fire Trucks55:00 PLCs as Data Devices and the Push Toward AI1:02:40 Cybersecurity Mandates Coming for Application EngineersReferencesHorner Automation: https://www.hornerautomation.comAbout Your HostsVladimir Romanov is a co-host of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to de-risk modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Understanding Plant Networks: https://www.joltek.com/blog/understanding-plant-networks-how-industrial-connectivity-evolvedIndustrial Ethernet Reliability: https://www.joltek.com/blog/industrial-ethernet-reliabilityDave Griffith is a co-host of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub

Another good month – investors are giddy. Oil – CRITICALLY LOW inventory (Inside Baseball). Fed governor admits inflation is hard to control. A major name says they are reducing stocks – but are they really? Announcing the Winner of the CTP for Salesforce (CRM). PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? PayPal.Donation.Button({ env:'production', hosted_button_id:'JJJHP2GDEJC7J', image: { src:'https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif', alt:'Donate with PayPal button', title:'PayPal - The safer, easier way to pay online!', } }).render('#donate-button'); Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter Warm-Up - Another good month - investors are giddy - Oil - CRITICALLY LOW inventory (Inside Baseball) - Fed governor admits inflation is hard to control - A major name says they are reducing stocks - but are they really? - Announcing the Winner of the CTP for Salesforce Markets - Huge reversal in Software stocks - A few names on the move - and moving BIG! - SpaceX IPO - could drain markets - More AI valuations through the roof Pizza Mouth ! Reversal - Software stocks bounced this week on strong results from Snowflake and Okta, which both recorded their best days on record. - The results signal that investors may have been too quick to declare the end of software with the emergence of artificial intelligence. - Even as AI displaces certain tools and job functions, many software companies continue to show growth, assisted by their own AI products. - The iShares Expanded Tech-Software exchange-traded fund rose 8% this week and closed May up 21%, the best monthly performance for the ETF since October 2001. - With this month's rally, the iShares software ETF is only down 3.8% for the year, still badly trailing the Nasdaq, which has gained 18% in 2026. Snowflake - Amazon said Wednesday that its cloud division has landed a $6 billion spending commitment from Snowflake, which includes the use of the company's custom silicon and chips for artificial intelligence. - Snowflake's purchase of services and technology from Amazon Web Services will occur over five years, according to a press release about the agreement. - Snowflake intends to expand its use of Amazon's Graviton general-purpose chips, as well as cloud-based graphics processing units for AI. - Snowflake and Amazon are frenemies - they compete but also partner with each other. - Stock up 36% on this news DELL!!!!!!!!!!!! - Dell Technologies Inc. shares surged due to an outlook for annual sales that far surpassed expectations on demand for servers that power artificial intelligence work. - Revenue in the fiscal year ending in January 2027 will be about $167 billion, including $60 billion from the sale of AI servers, topping analysts' average estimate of $142.1 billion. - The company booked $24.4 billion in AI orders and generated $16.1 billion in AI server sales in the quarter ended May 1, with Chief Operating Officer Jeff Clarke saying “The AI opportunity shows no signs of slowing.” - The shares surged 33% to $420.91 at the close Friday in New York, the biggest single-day increase in the more than seven years since the hardware maker returned to the public markets after a five-year hiatus as a private firm. - Up 150% YTD More Dell - New XPS 13 at $699 targets price-sensitive market - Aims to compete with MacBook Neo, lower-end Windows devices - Launch amid global memory chip crunch to gain market share - WINING OVER JCD: -- 13.4-inch screen (very compact footprint) Options: 2K / 2.5K LCD (120Hz) OLED touchscreen (higher contrast)| - Very thin bezels ? almost edge?to?edge screen - Weighs 2.2 lbs - one of the lightes out there and a rival to Apple's Macbook Neo Infighting - OpenAI may release multi-chip AI software, challenging Nvidia's (NVDA) ecosystem advantage, according to The Information - Oh, and NVDA is now releasing a CPU for PCs that is aggrevating Intel and AMD Kaboom! - Blue Origin's New Glenn rocket exploded in a massive fireball while undergoing a test on a Florida launchpad, dealing a major setback to the company. - The explosion is the latest blow to New Glenn's reputation as a reliable alternative to SpaceX's Falcon 9, and Blue Origin's launch schedule is certain to suffer significant delays. - The incident will also affect Amazon's ambitions to build out its Leo satellite network and may delay Blue Origin's role in NASA's Artemis program, which aims to send humans back to the moon. - As important as it will be for Blue Origin to diagnose the cause of the rocket explosion, it could take many months to repair its launchpad in Florida. Taking Down - Really? - BlackRock Inc. is trimming its bet on stocks across its model-portfolio business as US equities surge to record highs following a strong earnings season. - The firm cut its overweight position in equities from 3% to 1%, triggering billions of dollars of flows between BlackRock's exchange-traded funds. - BlackRock remains confident in equities and will maintain positions that bet on growing corporate profits, artificial intelligence and government spending, but is rotating away from longer-dated US debt in favor of global fixed-income and liquid alternatives. Slight - SpaceX is targeting a valuation of at least $1.8 trillion in its initial public offering, according to people familiar with the matter. - The company is seeking to raise as much as $75 billion, which would make it the biggest IPO of all time, and is expected to start formal marketing of its IPO as soon as June 4. -SpaceX had $18.7 billion in revenue in 2025, and the company's pitch to investors shows its evolution into an AI services and infrastructure giant with a total addressable market of $28.5 trillion. - 3-5% of the shares will be floated (TIGHT) Strategy: keep supply constrained, which: supports price discovery maintains founder control creates early scarcity dynamics - - - SpaceX has reserved 5% of the shares ?in its planned initial public offering for certain employees and individuals selected by its executive officers, exempting them from post-IPO lock-up restrictions AND.. Even more Valuations - AI giant Anthropic is now worth more than OpenAI. - Anthropic announced a $65 billion Series H financing at a $965 billion valuation, a round led by Altimeter Capital, Dragoneer, Greenoaks and Sequoia Capital. - The financing puts its valuation above that of rival AI lab OpenAI. - The valuation has TRIPLED since February Let's GO! - Shares of LG Electronics surged as much as 24% after the company announced a series of automotive innovations built with technology from Alphabet Inc.'s Google. - The company said its new range of solutions is built on Android automotive operating systems. Its system can control multiple displays with different aspect ratios at the same time by using a single-on-chip, which is different from other conventional in-vehicle display systems, LG said. - But 24% on this news? - More reason that the KOSPI is moving higher No One Care - But... - Inflation has been above the 2% target for 5 years now - Minneapolis Federal Reserve President Neel Kashkari said Thursday that bringing down inflation in the U.S. remains his top priority, warning that consumer prices are still “much too high.”| - Speaking to CNBC's Kaori Enjoji at the Bank of Japan-IMES Conference, Kashkari said that the U.S. central bank would continue taking a “balanced approach” to its dual mandate of price stability and full employment. - 5 YEARS! ---- What that tells us is that the Fed is totally unable to do anything about inflation .... Are we the only ones that see that? Inside Baseball - From a colegie that will go un-named. --- Let's just say he is someone who knows what they are talking about and runs BIG money ----- This is what he said to me..... - Apparently, oil execs were opining with POTUS in meetings yesterday that oil inventories are at alarmingly low levels and oil prices could soon skyrocket (I might soften that language a bit but they know the oil biz better than me) if SoH does not open soon. - I ran a few numbers on total oil inventories including and excluding the SPR. - Total supplies are 10th percentile vs history (although that includes a period when the SPR ramped from 0 to 600mln barrels in the 1980's). - Today it is 4th percentile if you start from 1990 when the SPR was basically full. - The 4 week net and % draw the last 3 weeks are the largest draws of all time. - And not surprising the 1 week net and % draw of the SPR are also the 2 largest draws of all time the last 2 weeks. Surprised - No.... --- This is another story similar to what we saw a few months ago - Taiwan prosecutors suspect that three individuals smuggled at least one shipment of Nvidia Corp. AI chips to China after first exporting them to Japan. - The trio was detained for allegedly falsifying documents related to exports of Super Micro Computer Inc. servers containing advanced Nvidia chips, which the US has barred from sale to China without a license. - Taiwan authorities seized about 50 servers for which they accuse the trio of preparing fraudulent export documents, but at least one shipment had already gone through Taiwan customs and made it to Hong Kong. Under/Over? - Tesla will be somehow folder/merged or taken over by SpaceX in an all stock deal - Tesla market cap is $1.6 Trillion so that will be a tough one to take on as SpaceX is about equal in size. ---- If this happens, when ? Mini Retirement - Is this a THING? - A mini retirement is when you take a planned break from working, usually for a few months to a couple of years, instead of waiting until age 65+ to fully retire. - Tim Feerris popularized this... (4 day workweek dude) Step 1: Work & save aggressively 2–10+ years Build a specific “freedom fund” Step 2: Take time off 3 months to 2 years Travel, recharge, pursue interests, or experiment with new ideas Step 3: Return to work Same career… or pivot to something new Then repeat if desired.   Love the Show? Then how about a Donation? Announcing the THE CLOSEST TO THE PIN for SALESFORCE (CRM)   Winners will be getting great stuff like the new "OFFICIAL" DHUnplugged Shirt!     FED AND CRYPTO LIMERICKS   See this week's stock picks HERE Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter

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I'm excited to work with Microsoft once again as the presenting sponsors of the AI Engineer World's Fair! We'll streaming live from MS Build today for a special crossover pod with our friends at No Priors and the one and only Satya Nadella. However we did not hold back with this interview - we asked all the burning questions about uptime and Copilot that we know you have in your minds. Lets go!For almost two decades, GitHub has been the home of software, where both open source and closed flow, through commits, pull requests, reviews, actions, etc.This ecosystem flourished as open-source maintainers and contributors would continue shipping code for the benefit of the community. However as coding agents began to ship mass quantities of code - growing 1400% in 2026, it marked a new era that was both extremely exciting and challenging for GitHub.While these agents help more people ship more projects, they also significantly increase the floor of how much code is shipped, how often it is shipped, how many people commit code, and basically orders of magnitude multiples in every dimension of GitHub infrastructure:Now GitHub inevitably experiences more pressure on their infrastructure which was originally designed around human developers moving at human speed. This has resulted in a very publicly notable uptime story:So it begs the question of whether current systems around code can absorb what AI produces. Can CI/CD keep up when every idea becomes a build? Can open source maintainers survive floods of AI-generated slop contributions? Can GitHub preserve the human social contract of software while becoming the operating layer for agents?Which brings us to the perfect person to answer these questions: GitHub COO Kyle Daigle. In this episode, he joins swyx to unpack what happens when AI doesn't just autocomplete code, but starts changing how companies operate, how open source works, how pull requests get reviewed, and how GitHub itself has to scale. We go deep on GitHub's internal AI workflows: micro-skills, WorkIQ, MCP, Slack, Teams, email, Copilot workflows, the new Copilot desktop app, CLI, cloud agents, and how Kyle uses agents to look backwards across company context before deciding what to do next. Kyle also reflects on GitHub's history building webhooks, APIs, Actions, npm, Dependabot, and Semmle, why the AI era is breaking GitHub in new ways, how Actions became a general-purpose compute layer, and what Copilot becomes after code completion.Full Video PodWe discuss:* Kyle's expanded role across GitHub* How AI got Kyle coding again after years in leadership* Why GitHub rolls out AI through existing workflows instead of forcing new tools* WorkIQ, MCP, Slack, Teams, email, and GitHub as company context* Why massive “mega-skills” are giving way to small, atomic micro-skills* How AI changes summarization, communications, marketing, and analyst work* Why former developers in leadership may have a unique advantage in the AI era* Kyle's “15 agents on Saturday” workflow* How Kyle built an AI-generated executive presentation for CRO/CFO teams* Why AI changes the chief of staff role without removing the human work* GitHub Actions, webhooks, arbitrary code execution, and secure agent compute* The npm acquisition, supply-chain security, 2FA, and token invalidation* Slop forks, vendoring, and whether AI agents change dependency management* What pull requests become when most PRs come from agents* Prompt requests, vouching, AI review, and trust in open source* What counts as a “developer” when AI lowers the barrier to building* GitHub Spark, low-code, and why GitHub refuses to hide the code* 14x commit growth, Actions load, databases, monorepos, and availability* Copilot's evolution from completion to CLI, desktop app, cloud agents, and SDK* Context, memory, rules, and making GitHub “act like Kyle wants it to act”* Ambient AI, OpenClaw, enterprise security, and the new operating system for agents* What swyx should ask Satya Nadella about Microsoft's AI futureKyle Daigle* LinkedIn: https://www.linkedin.com/in/kyledaigle* X: https://x.com/kdaigleTimestamps00:00:00 Introduction00:03:36 Why AI Got Kyle Coding Again00:07:04 Running GitHub with AI: WorkIQ, MCP, Slack, Teams, and Skills00:15:39 The Golden Age for Former Developers in Leadership00:17:31 15 Agents on Saturday and AI-Generated Executive Work00:20:20 How AI Changes the Chief of Staff Role00:21:45 GitHub's History: Actions, npm, Webhooks, and Open Source00:28:45 Slop Forks, Vendoring, and AI Dependency Management00:33:57 Pull Requests, Prompt Requests, and Trust in Agent-Generated Code00:41:21 GitHub Stars, 200M+ Developers, and the New AI Builder Wave00:45:15 GitHub Spark, Low-Code, and Why GitHub Still Shows the Code00:47:38 GitHub's Hardest Era: 14x Growth, Reliability, and Scale00:59:21 Actions as the Compute Layer for CI/CD and Automation01:02:04 The State and Future of GitHub Copilot01:08:24 Ambient AI, Background Agents, and the Future of the SDLC01:13:09 OpenClaw, Enterprise Security, and the New OS for Agents01:18:03 Build Announcements, WorkIQ, FoundryIQ, and Microsoft Context01:21:41 What Should swyx Ask Satya?TranscriptIntroduction: Kyle Daigle's Expanded Role at GitHub and MicrosoftSwyx [00:00:00]: We're here with Kyle Daigle, COO of GitHub. Welcome.Kyle [00:00:07]: Hey, thanks for having me.Swyx [00:00:08]: You're not just CEO of GitHub. People know you as that. You have a new role.Kyle [00:00:11]: So I have an expanded role now. I've been working at GitHub for thirteen years and doing all things developer. Joined as a developer myself. And now, I'm also responsible as the CMO of Developer for Microsoft. And so all the kind of learnings and passion for developers and how we work with them and how we communicate and how we bring our products to market, we're also bringing that expertise to the broader Microsoft ecosystem and helping every developer that uses a Microsoft product or would like to have a sort of similar experience that they've had with GitHub over the years. So it's a different role in some ways, but it's also just building on the experience that I've had at GitHub of just sort of tell the truth, be authentic, show people how to use it and then let the products speak for themselves. Now just doing that with, all of Microsoft.Swyx [00:01:09]: We'll be releasing this in conjunction with Build. You got lots of stuff planned, and we can sort of touch on that whenever it's appropriate. I think one of the interesting things is I rarely meet a COO who's also a CMO. I think you're a very outward facing and you're very confident publicly. That's rare. Do you actually view yourself as COO? What's What is your thing?From GitHub Developer to COO/CMO: Building the Platform and Operating GitHubKyle [00:01:33]: I think for me, it's been funny. The titles have always been, a— have always felt a little strange to me. I joined GitHub as a developer? I wrote so much of theSwyx [00:01:46]: Let's bring that up. You wrote the back ends?Kyle [00:01:48]: I was going through, I was going through, some old photos, when folks were talking about how things were being built or how there was a build GitHub. I built, webhooks and worked with teams building the API, built the platform layer. Anything that integrated with GitHub, up until really twenty eighteen, I built or ran the engineering teams. And that's kind of where my the beginning of my passion always was helping people build things, deliver them to, their customers. And so being a developer, building for developers was always super unique. In a— I think as my role expanded, it became my ability to talk to not just developers, but also enterprise customers or business leaders and have this translation layer. And then through all those years, GitHub has always operated pretty uniquely. Post-pandemic, working remotely was not as novel as it was when GitHub started in two thousand and eight. But all that expertise of running remote teams, doing it well, became this sort of bigger role, ultimately turning into the COO role of how do we operate GitHub in the way that GitHub's always operated after the Microsoft acquisition. And kind of so on from there. So like for me, I think the— I've, I still code. I love coding but the problem has always been, people. It's a much harder problem to both support our own employees, a harder problem to communicate to developers and enterprise buyers what we're building why it matters, ‘cause those are two very different messages. And so getting to work in the mix of COO, CMO, also just being a dev, I think is what's kept me at GitHub for so long.AI Workflows for Leadership: Commits, Retrospectives, and ContextSwyx [00:03:40]: Apparently, you have— your commits have gone up. What's this? What's going on?Kyle [00:03:45]: Rui's called me out pretty aggressively. So I think— as you can imagine, right, you can see my normal era of being a dev In the twenty thirteen, twenty fourteen era, and then moving into management, and then ultimately the COO role. I think what you see there is me, really getting back to coding thanks to AI. I— similar to, attaching problems between how to market and how to operate a business and how to code, I find, building agents and workflows that are connecting very disparate problems to be what's driving this. So that's, some of it's writing software. A lot of it is, connecting a ton of a different data sources to, help me out. But that is completely me really diving in on the AI side in trying out our tools, trying out everyone's tools, But building for me, building for the non-technical leader, though I'm technical and how we're, able to use these tools more than just the simple, call and response that I think a lot of the non-technical, your employers, you have to get— you have to use AI, and so everyone uses, ChatGPT or Copilot or Claude or whatever. To really get into, how is this going to help me out, it— I find that it's not the I need to write a blog post, I need to those simple examples. Helping people find the workflows of, “Okay, I need you to go through all the PRs today. I need you to go through everything that we've posted online. I need you to go through what we did the last three months. Go through all of my Obsidian notes for any mentions of this then go through my transcripts at work.” We use, Teams, so, using WorkIQ, go call that MCP server, grab all the transcripts, go through all the Slack, and then build me out the plan of, what this week's messaging actually was. That's something that was, impossible because for me, I find AI in a what most of this launch here is actually, less building forward. It's actually, a recursive loop backwards. I'm always looking at what had happened first. Go back through the week and tell me what we did, what worked, what didn't work? And then tell me in the next three or four days-What would you tweak based on this sort of like looking backwards and then looking ahead a little bit? I find that to be so much more valuable, especially for like non-technical, because that retrospection is actually LLMs are very good at that. Like finding all the patterns, pulling them out, and then applying that retrospection to just a couple of days or just like a short period of time. Is all a bunch of apps that I've built and launched a bunch of, internal tools. I use the new, GitHub Copilot app, the desktop app with workflows. Every time I crack open my laptop, it's running workflows for me. It's just a ton of different stuff and of course, it all ends up on, it all ends up on GitHub.Swyx [00:06:47]: Of course. That's where, that's where, stuff is hosted. Man, there's so much to ask you. I was going to leave the how do you run a company with AI thing at the end. I have to ask one— double click one thing. You said, you are looking back at the week. You're, you're understanding what happens. When you say we That's three thousand people. How?Rolling Out AI Internally: Skills, CLIs, and Company ContextKyle [00:07:09]: I think when we started rolling out AI internally beyond engineering, right? One of the things that I was really, passionate about is like we have to do this in a way where no one has to change how they work. I don't want to have to teach you a tool. I don't want to have to teach you something new. And so for us, we tried out a few tools. Most of them don't work because I got to get you on board? I got to teach you how to use it. What we've actually ended up doing is we've built like a set of skills internally. We have we each have our set of skills, and we've just been distributing even to the non-technical folks, the CLI. And then effectively, we're just giving it access to like read about everything that we're writing. So that's for us, that's usually GitHub, Teams, Email, and Slack. So Teams for, video chat, generally speaking.Swyx [00:08:03]: Teams and Slack?Kyle [00:08:04]: so we use Teams for video communication, but we don't use it for chat. W-we— GitHub for a long history, right? We're alwaysSwyx [00:08:13]: Also SlackKyle [00:08:14]: Talking about ChatOps and like everything is built into Slack. Like every command, every flow.Swyx [00:08:18]: So even though you have been acquired for I don't know, eight years nowKyle [00:08:22]: we stillSwyx [00:08:23]: You still use Slack?Kyle [00:08:23]: it's a purpose-built tool for us, and I think the reality is that moving off of it would be so bluntly expensive? Simply because all the tooling is, baked in with that paradigm. And they both have their pros and cons but they don't work the same way at all. We still use a bunch of different tools Because it's the purpose-built tools that We need. And thenSwyx [00:08:47]: Well, the same doesn't go for the rest of Microsoft, presumably.Kyle [00:08:50]: like the like various teams like operateSwyx [00:08:53]: They make their own decisionsKyle [00:08:54]: Various ways. I think it just matters what you're trying to what you're trying to do. But we do we do work across kind of every tool that we use, and then by giving everyone access to all of that context and the new WorkIQ MCP server, which is quite cool if you do live in the M365 like world. I can ask it all these backwards-facing questions, and it's incredibly important for our teams that are working remotely. There's a lot of stuff you miss when you're not in an office, and we are spread out all over the world. So most of that is looking back. And then we post, we post either auto-automatically into GitHub issues or discussions, these sorts of like findings or like our industry reports. Like what's happening this morning, today, yesterday. A little automation gets run. We'll use the app. We might use GitHub Actions like with, our agentic workflows just to go do that run, and then we push it into GitHub, and w-we keep having a conversation. So usually for us, it's about that sort of like looking back, looking forward on the non-technical side. And then of course for a lot of those folks, it's also building an app, pushing it to GitHub pages or pushing it somewhere to host it et cetera. But it's just like enabling everyone with that power of it's going to take me a week to figure this out. Instead, we're going “Okay I built a skill. Let's put it into a repo. We'll all share that skill together, and then we'll use the CLI or now the app-” “just to run it.”Micro Skills vs. Mega Skills: How GitHub Uses AI at WorkSwyx [00:10:26]: All right. I think, I think we're going straight into like the team management and productivity thing. I think a lot of people are getting various levels of LLM psychosis. How do you manage the bloat of skills? Like everyone Has their thing, and they're Like trying to promote it to the rest of their peers in their org, right? And obviously, whoever becomes a skill influencer internally becomes like an AI leader, right? Of sorts. I assume you have those.Kyle [00:10:50]: like I think we haveSwyx [00:10:52]: And I assume it's a mess a Yeah.Kyle [00:10:54]: there's like I— like I think the reality is there's two pieces. Like first is I think that we're ending the era of these like massive, beautiful, perfect skills that are just like not any of those things. ‘cause for a while, right every tweet every day is like go download the skills, the perfectly managed thing to do this entire workflow. And I think that like what we've found and what— I was just with my team, this week, and we were talking about the skill side, and we're really talking about these like incredibly micro skills that are just doing one thing for us very well Versus a skill that's going to do I said, that full report. That doesn't really exist on our side anymore. It's usually how do— like a single skill that's going to identify the most important marketing information given any MCP server. Like this is the most important thing. Less about stitch a bunch of tools together and have it produce this mega output because then weeks go by, months go by, things change, and you want to tweakSwyx [00:11:58]: It's brittleKyle [00:11:58]: Your mega skill and you're screwed? You can't do that. And so now we're really just talking about the Legos we're using and just letting the instruction book be something we're all putting together. Whereas I think a lot of AI skills for a while have been that mega instruction book style.Swyx [00:12:15]: I've, thought a lot about Postel's law. I don't know if that's a term that is, means things to folks. It's the idea that you should be liberal in what you accept and strict in what you output, right? And I think that's like a good framing principle for skills. This is my skills, obviously on GitHub. I feel like everyone should have like how like some repos In GitHub are special repos? I feel like we should sort of reify the slash skills and everyone like give it some kind of special presentation. Anyway, so, yeah, this is one of those like download Download anything, transcribe anything, and then you can string together the atomic skills that do one thing well Into like some kind of orchestration skill that calls other skills. I assume, does that match?Kyle [00:12:56]: I like I think so. I think that theSwyx [00:13:00]: Summarize anything.Kyle [00:13:01]: Like I think the- For me, summarizing something for I do communications and PR and analyst relations and marketing and customer activities, and so my summarize everything is very different for each one of those like Contexts. What ‘Cause if I'm summarizing something for an analyst, that's a very different thing than, probably how I'm going to summarize something for like a customer meeting or an engagement. So that's I think like the difference when we're talking about the like the tools I might use on Saturday or the skills I might use on a Saturday when it's just for Kyle. Yeah, those are kind of like they have an atomic actual tool underneath or maybe skill, and then Kyle cares about X. But I think when we're talking about work and enabling the the marketers, communicators there, it's the atomic, this is what good summarization is, and then this is what I care about as for marketing for communications For whatever. And that I think is like the interesting matrix problem when we go from like a developer set of concerns to all kinds of different professions, is that what that word means to me is different than it means to you is different than it means to the analyst or the salesperson, and that's where I think the matrix mess is that we're starting to like still starting to find. It's about these mega skills but they're all just slight permutations, but those permutations are really important. It's the difference between someone reading this and going “Did AI make this?” what Or “This makes total sense, and I would expect this when I'm giving a briefing to Gartner,” or like whatever else.Swyx [00:14:37]: I think the beauty of it maybe is that you don't have to be that careful about what goes in there. It doesn't have to exactly fit as long as it like roughly is contained in there. I used to complain about plugin hell, basically. Like when you have a framework and then you have a hundred things that you need to integrate, everyone does like the GitHub used to be bloated full of these things. And now we don't need them anymore ‘cause now you just use skills.Former Developers in Leadership: AI as a Creation MultiplierKyle [00:15:00]: And like I think the most magical thing is the just that like I can just also crack it open. Like Like yes, I could go like change the how the plugin is coded, or like I could go do that now with AI, but I think there's just something more magical about getting a response back and being “That's not right,” and then you just crack the skill open, you just type English words and it's different. That building block is just, I think very unique. Once I get everyone to kind of understand how to best how to best make those changes to get the most power out of them.Swyx [00:15:36]: Is there a— you have a your peer group that Of people like you. Is there a common framing for Something I'm feeling is, which is true, is that is this a golden age for former developers who are now in leadership? Because you can wield the tools, you would know the right words, you're maybe not too close to the details. Doesn't matter. But like you're more effective than someone who doesn't come from that background.Kyle [00:15:59]: I think that like the secret has always been your ability to identify patterns and solve problems, and I think that for folks that like myself that don't code day to day anymore, that has made me successful as a developer, made me successful as a COO and now CMO. And so now that I have access to get and write code, I'm now applying that sort of like pattern finding and problem solving, and I know enough still about how to then go and say, “Oh, I want to make an app, but I don't want to break into jail or create something that's not going to be able to work or to be deployed scale or whatever.” that ability to apply all that additional business knowledge and still code I think is what makes that so interesting to me. Slightly different than I think some of the other like technical leaders that became business leaders and now are going back to their apps and updating them. Good for them? But I think the more, much more interesting thing is, well, now I have this whole new set of expertise over ten plus years. Why not take that and use that as a developer with these AI tools? So I definitely think that makes me more powerful, but I think that's true for like every dev as well. Most of the dev friends I still have also have some other underlying skill and passion. There's really talented, very kind of linear computer science software devs, absolutely. I just find that the folks that came from a different career, went to school for something else, went off and did this random thing, and then became a software dev, or were a dev, did a random thing, came back. Learning that extra set of information, learning those extra skills, and now having the power of an AI where I can crank up fifteen agents on Saturday while my kids are doing lacrosse, That's like really powerful. And I think it gets me back to that feeling of like creation, and it's very hard to replicate that in most other senses? That first time you build an app and you click it and you show someone that's magical. And so being able to do that not just in code, but across all kinds of different assets that's, that's huge. We were doing we're doing our every year we do our revenue planning. We talk about okay, what is it going to look like for next year? And of course as you imagine, there's, slideshows everywhere talking about what are we going to talk about, what's the narrative, et cetera. And so as you said I'm “Okay, well, I could probably just like build something to build this and then that way I don't have to go build the whole spreadsheet or I have to pass it to my team.” So we went through this process, and I got all the information and used the skills I mentioned. I built like a little app just to make it so I could look at some of the information in a SQLite database, more easily. And I ultimately built this entire presentation without touching any of it and I was “Okay, I'm just going to present this to our CRO, the CFO, their teams,” without mentioning I'd built it with AI. I like built a skill to make it look very much not AI driven. Just not pretty.AI-Generated Presentations, Human Taste, and the Changing Chief of Staff RoleSwyx [00:19:03]: Like a design. Yeah.Kyle [00:19:03]: Not pretty. But just like very clearly not AI. Kind of like don't do anything interesting.Swyx [00:19:08]: That's, yeah, that is valuable.Kyle [00:19:08]: Just go Exactly. We did the whole thing through. It used my notes from Obsidian, it used all the context I mentioned before, the plans, and Never came up once that it was AI generated.Swyx [00:19:20]: It didn't matter.Kyle [00:19:20]: Never once. D It didn't matter. And so now I takeSwyx [00:19:23]: This is a toolKyle [00:19:23]: I can take that tool and go, “Look, I don't want you to go build slideshows.” They're just helping us share information with each other. If this thing can do it With a little bit of crafting from you and then we can look at it together, awesome. There's no value in all that extra work. I think that the ability to, make it look humanly bad and and build a little app to, manipulate the data I think is part of, that upside for devs that are now in leadership roles. Because, the thing that I feel like I said before, this that's all a people, that's all a people problem. I know if you've used a coworker or not to build a slide deck, unless you spent a bunch of time to not do it.Swyx [00:20:07]: I know, but like it was so, I think there's a certain charm to just being blatantly AI. ‘Cause I think that you're well, you're just honest about There may be mistakes here that I cannot vouch for. So how much value is there? But anyway I think, actually the real question I want to ask is, there's a— You were a chief of staff To Thomas. And in the pre-AI world, the that job would've been a chief of staff job of like Can you prep me these slides and all that? And now you do it yourself.Kyle [00:20:35]: I still, I still have a chief of staff. Because, the difference is it's sort of the discussion every time we have some sort of technology evolution is it's not that the jobs the roles don't all go away, they just change? And so yeah, I don't have someone spending all their time building out slides for me and presentations ‘cause I don't need that anymore. But now I need that person that is able to go and find all the different connections between humans in those discussions to help me find out, okay, I should be meeting with this group and this team, and they have an opportunity, and I'm going to be in San Francisco today, I'm going to be in Seattle tomorrow. Those sorts of human connection aspects are still incredibly valuable and has always been a big part of that chief of staff role. But now just like chiefs of staff are not opening up, letters to process, they're doing emails. What It's the same thing. And now they're, they're not building out as many of these presentations because they have the the ability to have a AI take it on for, and share that with me and great. Let's keep moving ‘cause it's allowing us to go faster and make better decisions more quickly.Swyx [00:21:45]: Awesome. Well, so we can dive into more sort of, Productivity insights as you go. I did want to do a little bit of a brief history of colleague and hub. Because, we started here. And then you also involved the NPM acquisition. I did, I do want to touch upon that. And then more recently, I just want to bring up to present day where we're having uptime issues Which transparently we've already Addressed publicly, but we'll, we'll discuss in the pod. Did I miss anything? Like what, any other major highlights? Obviously, it's, it's a lot of years to cover.A Brief History of GitHub: Webhooks, Actions, Acquisitions, and Platform EvolutionKyle [00:22:15]: No the I think one of one highlight was right before the acquisition closed in twenty eighteen, I got to launch the first version of ActionsSwyx [00:22:27]: OhKyle [00:22:27]: At GitHub Universe. So it was OSwyx [00:22:29]: They're that young?Kyle [00:22:30]: It was October of twenty eighteen, I think. Yeah. Yeah.Swyx [00:22:33]: Gee, Jesus.Kyle [00:22:34]: I got to I was the engineering leader on that project and got to launch that. And then, yeah, we did acquisitions of NPM you said, Semmle, Dependabot Pul Panda a whole bunch of things. That was a bigSwyx [00:22:47]: Pul Panda.Kyle [00:22:48]: Abi is doing well.Swyx [00:22:51]: DX. Holy crap.Kyle [00:22:52]: Did well on DX. I and like that was a that was the big shift, after the acquisition. I had to join the sort of business side.Swyx [00:23:00]: So I need to hit you on some of these things ‘cause you were there. Right? And how often do I get to talk to someone who was there? But yeah, Actions. Is that the number one source of security issues on GitHub?Kyle [00:23:11]: Oh, sh I think that the number one source of, security issues is probably like all, the literal code in everyone's like underlying repositories. I would say back further than that is, if you remember I had to show in this graph was this is, I'm, didn't say this before, this is ultimately webhooks.Swyx [00:23:30]: You yeah.Kyle [00:23:31]: Like circa whatever it was.Swyx [00:23:32]: It says Hookshot in there.Kyle [00:23:32]: I forget. Yeah. Yeah, Hookshot's in there. And so like back then, it says GitHub Services. Do you see, it says Hookshot FE for front end, and then it says GitHub Services. GitHub Services back in the old days, right? You we had a repository that was Ruby code, and you could write any Ruby code in there, and then we would execute that On your behalf As a service, and then that way if an if you were trying to integrate with something, it didn't we would run it for you.Swyx [00:23:57]: And of course no containers ‘causeKyle [00:23:58]: No, ‘cause it wasSwyx [00:23:59]: Well, no containersKyle [00:24:00]: Twenty fourteen. And so there was some isolation obviously, but it was mostly the separations on the server level. That's like an example as long as the very old version of Pages, which ran on its own containerization infrastructure, not on Actions.Swyx [00:24:15]: Which like all-time great product.Kyle [00:24:16]: Pages powers the internet at this point to some degree. Those were places where like clearly there were no like issues like to my knowledge. But it was those things where I'm looking at and going “Okay, well we can't be running arbitrary Ruby code,” like on everyone's behalf. Then containerizing all of that up intoUh into actions now where yeah the containerization, is r-really good. The pinning most folks aren't pinning it the like to a particularSwyx [00:24:48]: ImagesKyle [00:24:48]: Sha, et cetera like their workflows, and so that's a big that's a big place Of pain for folks if they're just doing similar to any dependency management, just V1 or newest or latest, I think. But, that journey from that day to “Okay, we're just going to run all this arbitrary code, and, it'll basically be okay,” to now, no, we have, really good containerization. We have a new, underlying, ag-agent, containerization, service. It's like we're using it under the hood. It's through Azure. They recently announced it. The Azure, Dev Compute, but it's, very fast, very fast compute to be able to, spin up your own cloud agents, or whatnot. We're using it under the hood for some parts of the new,Swyx [00:25:36]: Microsoft Dev Box?Kyle [00:25:37]: No. Dev Compute, yeah.Swyx [00:25:41]: Hmm. Not finding it just yet.Kyle [00:25:44]: Oh, it's, it's in there somewhere.Swyx [00:25:46]: All right. Well, we'll cut that out.Kyle [00:25:47]: Sorry. But with, Dev Compute, you can, run, really fast, spin up really, small VMs really quickly, so you're doing a tool callSwyx [00:25:58]: Same conceptKyle [00:25:58]: Just do it containerize exact-exactly. So we're using that so definitely moving that direction to protect us from every every piece of code that we're ultimately running.Swyx [00:26:07]: look, that grows into the full SDLC? Code hosting was just the start and and then it's grown beyond that. Let's talk about NPM may-maybe ‘cause I think that's also, a very major point in the industry. I do think, it was looking for a home. It was, kind of struggling as a business, right? I don't know, I don't know how you would characterize that whole acquisition and how itNPM, Package Security, and Keeping the Internet RunningKyle [00:26:33]: like when we were talking to the team, I think the big thing for the both of us was to find a way to keep NPM, which was basically powering the internet then and way more so now to some degree running. Keep it going keep continuing to scale. It was having scaling problems, if I recall, back at that time. They were doing some rewrites. ItSwyx [00:27:00]: that's cute compared to now.Kyle [00:27:01]: Well, that's the thing is like when I'm talking to folks now, there's there's so many more underlying uses of NPM than there were back when we had them join in with GitHub. But that was ultimately the goal. It was really okay, we used to have pages. We have, the world's code. Let's make sure that we can keep NPM running well for the world. And we put a bunch of time and investment into fixing some of the underlying backend, changes, some of which we talked about some of the manifest work, et cetera. And then now, really trying to bring the the security posture of NPM up to speed. But, it is a unique challenge in that every move that we make to make it more secure will break a lot of people. And security is paramount. And also, we take it very seriously. We're, the any time that we have a problem with GitHub or we make a change that makes us more secure but hurts, there's, a snow day for developers or a really bad fire that they have to go put out. And so we've, have changed the 2FA policies. We've changed the way the tokens work. When we find tokens that have been exposed or potentially, exposed, we invalidate them, andSwyx [00:28:22]: I love that feature in GitHub. Yeah, it's greatKyle [00:28:23]: That creates issues, but, the but that's the thing is we're trying to push the community, forward without necessarily, doing something that is going to break the contract that's been for 15 years or close to it or some amount of years on NPM.Slop Forks, Vendoring, and the Future of Open Source Supply ChainsSwyx [00:28:43]: I think the— So now we're talking about, open source and publishing. And I think there's something here with what people are calling slop forks, which, I think Malta from Vercel is doing. And, part of me thinks, well, the way to get past any vulnerabilities, we just, let's just get rid of the concept of NPM. And we only publish source code. And anytime you want to import it you have your coding agent look at it and then adapt whatever subset you're going to use into your vendor it. But, the AI vendor it. Is that realistic? I don't know. Is it— Will that solve all our security issues? I don't know.Kyle [00:29:24]: I don't think it'll solve I so Mitchell was just talking Mitchell Hashimoto Was just talking about this today, and I think that I-in some ways, it's all all things, old or new again? Yeah, absolutely vendoring everything. Like I do I do remember twenty thirteen, twenty fourteen.Swyx [00:29:42]: This is Yeah. Let's, we must return toKyle [00:29:43]: That's what is We were vendoring everything. We were having actual discussions around, or at least I remember we were “Should we take this full thing?” “Why is this so big? We only need this one file.” And so I do think there's something true there where having either taking only what you need or the dependencies just getting incredibly small over time, I think will help to some degree, but it's not going to solve the fundamental problem, I don't think, because the vulnerabilities in an agent looking at them, there's time and time again, there's a million different ways in which we can convince an agent that this thing is, secure or not and pull it in. Or we can do static code analysis or runtime testing to say whether the code works or not. That is, I think, the step that needs to continue to be, invested in. The question is just on, how much scope. Should it be this enormous project that I'm pulling down, or should it be this piece? Either most companies are running some amount of security checking on the on the packages that they're bringing in or vendoring. That I think won't change. That's like what advanced security does to some degree, Socket does some degree. Like everyone is doing a piece of that. How we each do that like especially when we're talking to enterprise customers, is just like very different. No there's no one wants one single way to do it. And I think that's always been GitHub's, unique position in the world. I talk a lot to maintainers, I talk a lot to folks about this. It's we're— we rarely start like a process and a practice and like push it onto the community. We usually wait for the sort of like RFC process socially or literally, everyone agreeing, and then we'll cement something in. Because otherwise we'reMaintainers, RFCs, Vouching, and the Social Layer of TrustSwyx [00:31:35]: That fits your role in the ecosystem, yeahKyle [00:31:36]: We're GitHub. Yeah, we don't want to shape the whole thing. We want it to be figured out. But like how do you balance that like sort of Role in the industry to keep everything as secure as is possible and make sure that you're you're not going to be compromised as a human, ‘cause that's usually how it all happens. And Not not create a process or lock us into a flow that you're not going to or like Mitchell's not going to or other open source projects aren't going to like. That's always been a tricky balance for us, and I think that's something that we haven't talked about enough is we're not going to be able to fix everything for everyone in a way that everyone is going to like. So tell, help us, tell us what is working. When Mitchell was talking about, the Upvote, the upSwyx [00:32:22]: I was going to bring up his thing. Yeah.Kyle [00:32:23]: I forget what it Yeah. When he's talking to us, I was chatting with him and talking to him about this and I put it on Twitter and we talked to, also over DM, was “We're going to keep working.” but I think the important thing is I do actually want to hear what isn't working for you. And as, be as specific and clear for your project as is possible. And to every piece of credit over the many years that we've known each other through the industry, he's always done that and I appreciate that ‘cause there are places that we need to fix up, and we hear from him, and we'll fix up just like we do all other kinds of maintainers. But that that process between making those types of improvements and being more secure and like creating, I forget what he calls it's not the proof process, not the claims process. Do what I'm talking about? He has that he his projects have a way for you to kind of like,Swyx [00:33:13]: VouchKyle [00:33:13]: Vouch. Thank you. Yeah. He has like the vouch system for saying, “Hey, you should accept my PRs.” That's beenSwyx [00:33:20]: I just built this into GitHub. I don't know.Kyle [00:33:22]: Well, see, but that's the thing is that you say that and like he and his community really likes this and then I'll go talk to other maintainers and other maintainers, globally, and they're “No, this doesn't work for me.” And that is the tension, but also the kind of beauty of GitHub, depending on which way you look at it is we want to help maintainers, so we create all these tools to let you have more control over how much you take in from AI and PRs. But you can also use this. What You can go use this project, and if it takes off and becomes the kind of mostly standard, then yeah, we probably wouldn't enforce it but we would add it in because that's the flow that we tend to do?Swyx [00:34:02]: I hear a lot of people don't know the history of the pull request. And like like that's how, that's something that GitHub standardized basically.Kyle [00:34:08]: Yeah. It was a very messy process Like beforehand, and now the we have the benefit of it being the process? And now we have to go and Figure out the next best process or what adaptations change, or what does a pull request look like when eighty percent of your PRs are just coming from your agents and not From other devs?Swyx [00:34:31]: Do you like the prompt request idea from Peter?Kyle [00:34:34]: like I think that for each like each idea I think has its merits. I'm not, I'm not avoiding saying anything good or bad, but I feel like I've seen a version of we have that we have entire Thomas' store. Take all the assets of what you've built and put that in. I think that's got great ideas. There's all these various permutations of the PR flow, but I think the reason why there's not a single answer is ultimately we're trying to codify trust. We're trying to say “Okay, if Sean reviews this I'm going to trust it because you're Sean or you're the senior dev or you're the whatever.” And right now, when we are working in a flow where an agent writes code and another agent reviews code and then Kyle goes and looks at it the trust is kind of diffuse. And most of the tools that we're talking about are talking more about verification flows. We have more assets to look at, so I can probably say whether this is a good PR or not. But that still doesn't solve, I think, the human problem of I'm looking at a PR and I want to know if I can trust it. And we're still, we still tend to use human signals for that? Mitchell approving it or Kyle approving it or whatever. And so I think that's, I think that's why most of these options haven't really solved it is because, it's a social problem ultimately. It's a it's a human problem to review it and agree. Or you fully trust the tool and you're imbuing that tool with full trust Which I think in some cases that absolutely exists.AI-Generated PRs, Trust, and the Waymo AnalogySwyx [00:36:08]: And so like in the same way that there will be a tipping point in society when we don't allow humans to drive anymore Because machines are measurably better than Than humans. I'm looking for that tipping point, right? Like Mythos is ridiculously expensive. Someday we'll have Mythos on a desktop. I don't know. Will, does that change the equation?Kyle [00:36:30]: I think it's more I took a Waymo here, and I was on my phone and not looking around at all. There are other, self-driving, vehicles that I would not trust while, staring at the road. And I think that trust is something that isSwyx [00:36:48]: Is this a Zoox thing? What is itKyle [00:36:50]: I think that is both. I think that is both. LikeSwyx [00:36:53]: There's Zoox in this robo taxi. That's it. It'sKyle [00:36:56]: Well, depending on what level Of self-driving. But, my point is sort of that I think part of that is I strongly believe that's, a mixture of verifiable proof. Like how many accidents, how much data, and so on, and the human aspect of how I feel when I'm in this car, what it tells me, et cetera. And so that's why I think some of the like Some of these some of our AI tools tend to, imbue me with more of that feeling of trust, even if the data says this is 100% accurate. I feel like it takes more time for us to go, “Should I trust this or not?” And that's in the soft sense of, startups with high agency, weekend projects, and open source. And then there's enterprises and regulated industries and everything else, and that is an even harder problem to go solve because even when it is fully verified, not only do you have to have trust from the humans on the team, you probably have to have trust from multinational,Swyx [00:37:55]: Oh my GodKyle [00:37:55]: Multi governments around the world and regulating agencies. And so that's where I feel like until we tip over to your point on the sort of like human EQ side of it. I feel okay this feels okay I've been proven enough. Then the ball will start to roll a lot faster, where we'll end up getting to the “Okay, we can trust this,” and feel good about it in the Most difficult of cases.Reputation, Sponsors, Stars, and Bot Activity on GitHubSwyx [00:38:18]: If human trust is the thing that matters, I feel like GitHub as the developer social network could maybe do more there. Like vouchers are one system But, we have star counts, and then we have Contributor rights, and that's it. And I feel like there should be more in that space. I don't know if there's any other design decisions there.Kyle [00:38:37]: I think that one of the places that we don't really expose right now in this sort of way is, some degree of like hard trust and support, which would like for me is like sponsors is a good example of that.Swyx [00:38:49]: Ah.Kyle [00:38:49]: It like costs you something. To prove that I believe in your project and I trust you To some degree or I want to support you at the very least.Swyx [00:38:56]: Solve payments for open source. Why not?Kyle [00:38:58]: I think that I think that like as we keep moving forward, right, there's more and more projects where I'm, adding more and more dollars into sponsors personally because I want to like support them, but I also like know of I've probably never met them in person, but, I know of enough of their work that I want to support them. I think the thing that I don't love about stars or commit counts or anything else is ultimately, even with all of the various, abuse and de-spamming and deduplication work that we do or anti-abuse work that we do, these are all, not active social signals. They're passive ones that are ultimately gamifiable. And you may trust me, but another open source maintainer may not. And on what heuristic should you be, trusting me? That I think, is kind of where some of our thinking is right now. What signal from me is most important to you? You— If you can define that potentially, honestly in an agentic workflow that's what we see some of these open source projects do, where you have GitHub actions, and then you have like an agentic workflow that's calling AI, and you're setting these rules. Like if Kyle has submitted and gotten accepted PRs across any given project and has a social handle tied to his account in GitHub, and that social account's older than a certain amount. Really complex measures that matter to you ‘cause most open source projects have that heuristic built into their heads, if not written down in the contributing guidelines. You could take that and then go apply that and then just say, “Oh, we're not going to accept this PR.” Building something that is, I think, malleable to everyone's needs, is a little bit better, rather than going “Hmm, this account's too young.” Because what happens? The attackers just go and go and create a multitude of accounts, and they wait Until it ages up. Needs to have a certain amount of stars. That's how star inflation happens. Need to have a certain amount of reposSwyx [00:40:46]: Oh my God. YeahKyle [00:40:47]: With PRs. They all just create repos and submit PRs to each other, and then they come in and do something nefarious. And so, it's hard. It's hard to find the measure. So I think we're, we're looking more at how can we provide you tools so you can kind of choose what's best for you. And of course, we'll give you some standards. But the trust vector, gets down to I don't know, some version of like human digital ID like everyone's been talking about. Like how do I prove that it's meSwyx [00:41:13]: Give me your eyeballsKyle [00:41:14]: On the internet. Give me your eyeballs. Exactly.Swyx [00:41:18]: The I got to keep moving on Topics, but obviously I can go all day on this stuff because, I've been involved in GitHub and open source My entire professional career. Stars. Very superficial. Everyone knows it. But I think time to one hundred thousand stars is the fastest I've ever seen. Like people just reached that in I don't know, months. And then like at the same time I don't trust it right? Like how many of these are real or bot or like whatever. I don't know how to ask this but like what can we do about it? LikeKyle [00:41:49]: JustSwyx [00:41:49]: Is stars broken? Is stars fine?Kyle [00:41:51]: I think that there's kind of two, there's like two pieces. Obviously we're constantly like trying to find ways in which like your users are producing spam, which would, I would include like be like only doing star gamification. When we find them, we pluck ‘em out and we,Swyx [00:42:08]: But it's like a Whac-A-MoleKyle [00:42:10]: It's a hundred percent like a Whac-A-MoleSwyx [00:42:11]: There's no wayKyle [00:42:11]: Now, powered by AI to be helpful. But I think more so what I'm seeing is, a lot of the like fastest time to X tends to be because we're now inviting so many more people into like software development on GitHub That like the zeitgeist is just swarming? And it'sSwyx [00:42:32]: It's not just developers anymoreKyle [00:42:33]: And it's not you and I. Like like however you want to say like what a developer is it's not just folks who have been coding for a very long time. It's folks that have maybe started coding or only joined in since the AI era. And nowSwyx [00:42:44]: what's the latest Octoverse number? I know eighty million was my lastRem- member that a number of developers on GitHubKyle [00:42:50]: Oh, we're over 200 million now.Swyx [00:42:53]: Okay. Well, so you see?Kyle [00:42:55]: Like over 200 million developers now.Swyx [00:42:56]: But it's not developers, right? It's, it's people with a GitHub account.What Counts as a Developer in the AI Era?Kyle [00:43:00]: So, so this is, this is the biggest debate that I would say, everyone loves to have at GitHub at this point. From my perspective, right, I think that there's, there's clearly a difference between, professional enterprise developer and then developers. But I think that I think that the idea that we should be I don't know, splitting hairs or segmenting developers in the early era of software development is, not worth our not worth the time. SoSwyx [00:43:29]: When you get into gatekeepingKyle [00:43:31]: 100%Swyx [00:43:31]: What is a developer?Kyle [00:43:31]: 100%. ‘Cause I wasn't a developer when I started writing code? I was going toSwyx [00:43:36]: Oh, no. I made— I cloned a thing, seven years before I learned to code. And then I and then I wrote about my learning to code journey, and people Just called me a fraud ‘cause I had a GitHub account. And I'm “Well, no, I just use GitHub, but I don't know-” “I didn't know what I was doing.”Kyle [00:43:49]: I I remember that. I remember those sets of posts, and like that's, that's b******t. So I fight very clearly on the line of, if you create code, if you have an idea and you create it into some way of, I'm, I'm going to run it and use the app right now, you may still use AI in that moment, but that's okay. At some point you're going to do the next thing. You're going to create a big— You're going to have to learn about this database. You're going to fix a bug, whatever. We're all on some same journey, and those people are also hearing about the great new agent skill package or a new CLI tool or a new whatever. And those projects are going up because you want to be a part of this moment, just like I wanted to be a part of the Ruby community when Ruby was popping off when I started becoming a developer, and now I can just click the star button. And so I think that yes, there's clearly some amount of like spamming and game gamification that we're working against, but I really think we're just seeing this whole new cohort of folks that are moving from technology to technology because they're not working on a 20-year-old software application. They're working on a side app that they built on the weekend for their friends or for their new idea or whatever. And that's how you see these enormous charts going up and to the right with With stars.Swyx [00:44:59]: I think something that's remarkable is the persistence or, that GitHub extends to those folks. Usually when I see platforms go into a new audience, they usually have to, have like a second platform with a different name that wraps the main platform. But somehow GitHub has been able to sort of persist and extend, and it's friendly and whatever? So it's, it's nice.Spark, Low-Code, and Always Showing the CodeKyle [00:45:19]: I that's partially why I think as we've tried to move into I don't know, more like low-code-y things. We so we started working on Spark as like a way to, build an app and run it. I think that the reality is that we anytime we try to, kind of put even a veneer on top of it without when we put a veneer on top of something, we still always show you the code. That's kind of like a tenant. We're never going to, hide the code from you ever, because whatSwyx [00:45:52]: Why would you?Kyle [00:45:52]: That's, yeah, that's the whole point? However, I think that what we learned with things like Spark is that really the value of Spark for most devs is, easy runtime. And you may have a runtime or a host that you're going to use for that or you just build something and run it but, the package of making that even more simple isn't really needed for folks that are trying to build software and not just trying to build, an app, which is, slightly different, a slightly different goal. So I want to get you in, I want to get you comfortable. I think the best thing for me as, someone that did not traditionally come into software dev way back, I want anyone to be able to breach that chasm and not be in the I don't know, I feel like we're, we're still in an era of, STEM. I've got a 12-year-old and an eight-year-old, and it's “We got to get ‘em into STEM,”? Over and over. And I like I do, I do the things that good parents do. I was “Oh, you want to do coding?” “Yes, I want to do coding.” Do coding classes. But now they're just not afraid of doing software. And that's, I think, the thing that's honestly kept me at GitHub for so long. Anyone should be able to go and build a thing, just like I can go change a light switch in my house. I'm not going to go into the breaker box ‘cause I'll probably kill myself? But, I can go change that light switch. Everyone should be able to go and say, “This fricking app doesn't do what I want. I want it to work like this.” And that I think, is what's kind of kept us all connected with GitHub through the years and some and during the easiest of times or in the hard times because of that opportunity of, we're the home for all developers, and we want everyone to be able to have that feeling that we've had of, had an idea, I created it and holy s**t here it is.Swyx [00:47:37]: Here it is. All right, I'm going to try to do more spicy questions.GitHub's Hardest Scaling Moment: Growth, Agents, and UptimeKyle [00:47:42]: Great.Swyx [00:47:42]: Is it an easy time now or a hard time?Kyle [00:47:45]: Oh at GitHub? It's a hard time. Like, it's a hard time and also, I was just with my team and I said, “This is also, the best and most exciting time that I think I can remember at GitHub.” BecauseSwyx [00:47:57]: Best of times, worst of times. It's never oneKyle [00:47:59]: ‘cause we've we were talking about Octoverse reports and, usually we do an Octoverse report once a year, and we look at the numbers, and we say, “Oh my goodness.” I was at Universe in October saying, “This was the fastest year of growth that we've ever had,” right? And now we're doing more in a month than we did in a year last year.Swyx [00:48:20]: You're talking about PRs.Kyle [00:48:21]: Commits.Swyx [00:48:21]: Commits, yeah.Kyle [00:48:22]: PRs. Kind of like you name it by roughly every measure that we're looking at, there's some amount of sort of growth that is much bigger, and that is breaking our system in new ways, not old ways. Like webhooks were always notoriously, unreliable over the years?Swyx [00:48:38]: Whose fault is that?Kyle [00:48:39]: not anymore mine, but for a period of time, I'm sure you could pull up a tweet that was “It was me. I'm sorry.” but, now, that got rewritten at a scale level that is still working and is not having problems today. Now what we're finding isn't just the isn't the-The simple stuff that folks are on the sometimes on Twitter or on the internet are “Hey, why is this like this?” Sure. There's absolutely silly problems that we shouldn't exist. But now we're talking about, unique, novel permission problems that happen only at a scale across all different objects or whatever, that now we have to go rewrite this underlying system. And so it's, there are problems that yeah, caught us off guard, which I think I said. Like the growth is astronomical, but also we're making such material progress in that I'm excited once we're once we've kind of like reimagined the underlying foundation layer, or pieces of it at least, what's going to be possible when it's not just all of us and all the new people that are being developers and all of their agents and all the tools like working together. Because that'll still happen in that in that GitHub tool, that GitHub community. But it's a it's a hard day anytime we can't give you what you're looking for. We have the same problem internally. We operate through github. Com. Of course, we have backups when things go down and whatnot for our own operations but we feel it too. If it's not working it's not working for us, and that's kind of like the promise of dogfooding for GitHub. It's always been true. We're using the same tool you're using. We're not using a super secret version. We and so we also need it to be great for us for our customers of course for open source. And now an exponential growth of agents, Doing it too.Swyx [00:50:32]: I wanted to load for audio listeners who maybe haven't seen your tweets, whatever. So one billion commits in twenty-five. Now it's two hundred and seventy-five million per week on pace for fourteen billion this year, if growth remains linear. Is that still the pace? I don't know. It's been aKyle [00:50:48]: it's, it's speedingSwyx [00:50:50]: Roughly.Kyle [00:50:50]: It's still speeding up.Swyx [00:50:51]: It's, it's April, so yeah.Kyle [00:50:51]: Exactly. This was in April.Swyx [00:50:53]: All right. So basically you have fourteen x growth, right? Year on year on year. And I think that's a scaling issue. I think, I'm going to like try to really steel man this thing. People have experienced fourteen x growth. They haven't had your downtime. And that's like— C-can we go dig into that? Why? Like what's the— what broke? What are we doing to fix it? Like just anything for the community to reassure them.Why GitHub Reliability Is Breaking in New WaysKyle [00:51:18]: so there's a Like I was saying, there's a couple different places that we've seen the growth issues. Some of the growth issues, which is why we're t— I was talking about pushing hard on more CPUs is in actions in particular. More tools, more agents, more PRs mean more builds, more builds mean more CPUs. And so we are expanding through not just our data center, but obviously we were talking about moving to Azure and moving to, adding an additional cloud compute because we simply need more CPUs. Not as much GPUs. We definitely need GPUs too, but now CPUs are becoming a factor.Swyx [00:51:53]: It's very CPU heavy.Kyle [00:51:54]: Underneath the hood when it comes to some of the underlying services, we've been breaking up over the years our database infrastructure, so that way we have, more cognitive separation between our the various services. The place that we continue to have pain is in, permissioning. And so right now m-many of our permissioning layers sit into a database that we like internally call MySQL One, and old Hubbers will know what I'm talking about. And so we've been pulling things out of MySQL One for many years, because like and we use we use Vitess and we use other technologies to shard and we do it as one bigSwyx [00:52:31]: Famous thing, PlanetScale was born from this andKyle [00:52:32]: A hundred percent. Sam Old Hubber and friend. And so finding these opportunities to like break this out and then do that globally. The other thing that I think is interesting and both a unique opportunity and tricky is we also run everything I just talked about in a black box container with GitHub Enterprise Server for people that work on-prem. So we take everything I just said, and we also do it on-prem, and we also do all of that and we do it in a data residence setup for customers that need to have their data in a single location. Each of these has the unique characteristic around how we're sort of storing that data in MySQL or in a permissioning setup. That's where some of these outages have oc-occurred, where you're seeing it more like across the board rather than just like the one pieceSwyx [00:53:17]: Filling the databaseKyle [00:53:17]: Isn't quite working. Exactly. And so part of it is that. I think there's been some other places where agents are much more or more projects appear to be moving towards monorepo versus we were going the other direction for many years in the industry. Repos were smaller, but there were more of them, and now we're seeing the opposite. Repos are bigger, and there's, not fewer of them per se ‘cause there's new growth, but, we're just seeing many more big repos. Big repos, big monorepos have always had, a unique performance problem. Because each one, is slightly different if, particularly if the underlying blobs are incredibly big Inside the repos. And so we've done a ton of work that you pro— like most people haven't probably experienced, unless you're in this case of the monorepo. But that Git, infrastructure layer improvement does help the overall, system because, many of the improvements that make monorepos work better make all repo infrastructure work better. And so, I could kind of keep going down the line where it's another thing where we're moving out of, We're changing how we do j I'll just say job queuing for lack of a better, explanation changing the underlying technologies there.Swyx [00:54:32]: I spent two years being a job queuing guy, so.Kyle [00:54:34]: And so it's kind of a little bit of a little bit of piece by piece, and it's mostly because as we were— as it was built, we built everything in a way that assumed, I guess in some ways that the size of the pipe of work was going to remain the same. There's just going to be more people coming through each of those pipes. But instead now in places whereA git push was, generally a certain size for example, is now, no longer true.Swyx [00:55:03]: Oh, yeah.Kyle [00:55:03]: OrSwyx [00:55:05]: I push a thousandKyle [00:55:06]: On the average. 100%Swyx [00:55:06]: A thousand line commits like dailyKyle [00:55:07]: Same thing with PRs. Like PRs same thing. And like we've talked about optimizing that and making changes where, and there were technology choices that did not work there? And it got slow, and it didn't It was not fast. It did not do what the users wanted. And so we've been reeling that all out and going “Okay, that's just not right. Let's stop putting good money after bad and do it the do it the right way or the right way now.” So there's It's a it's a lot of things, not quite when I've experienced scale at GitHub historically, it's almost always two options that we've used. We go vertical scaling, particularly with databases, right? And we go horizontal scaling. Oh, we just have more people using this service. Great. We're going to add more servers, and we rack them in our data center, or we use it in a cloud. And now we're sort of in a like diagonal, where like vertical doesn't really work anymore. Horizontal isn't work either because we're all We all have some CPU or GPU constraints in the world now, and now we have to go in and like crack open services that have been running for 10 or 15 years and go, “Okay, the rules of this service have legitimately changed, and now we have to rewrite them.” None of this is an excuse. This is like we're We have to do the work. We have to make it better.Swyx [00:56:22]: actually as an infra guy, I'm “This is like one of the most fascinating scaling challenges I've ever seen.”Kyle [00:56:26]: That's that's, that's the thing that's the thing that it's hard for Like when we weren't talking about it publicly, and I was like I came out, and I was “Hey, I just want to explain what's going on.” Part of it comes from a very old GitHub ethos, which is it's our it's our uptime. It's down. W What I know you're a developer, so you're, you're inclined to want to understand more what's going on. But at the same time us going “Hey, this service didn't, perform the way we expected, and now we have to go change it,” we weren't We're not trying to hide anything from you i

Nvidia announced its new CPU at an event in Taipei and Jon, Rachel, and Matt talked about why potential customers may be interested in buying as well as the potential impacts to primary CPU players such as Intel and AMD. The team also talks about Berkshire Hathaway's homebuilder acquisition before closing with a question regarding passive investing trends. Jon Quast, Matt Frankel, and Rachel Warren discuss: -Nvidia's new Vera CPU -The potential fallout in the CPU markout -Berkshire Hathaway's latest acquisition -Passive investing's impact on the stock market Companies discussed: Nvidia (NVDA), AMD (AMD), Intel (INTC), Qualcomm (QCOM), Berkshire Hathaway (BRK.A)(BRK.B), Taylor Morrison (TMHC) Host: Jon Quast Guests: Matt Frankel, Rachel Warren Engineer: Dan Boyd Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement.We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode.Learn more about your ad choices. Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices

NVIDIA puts out a CPU meant for Windows machines, while Dell joins the laptops gunning for the MacBook Neo. And its chips, chips, chips as Computex kicks off.Starring Tom Merritt and Robb Dunewood.Show notes can be found here. Hosted on Acast. See acast.com/privacy for more information.

June is here so guess what? It's officially Hot AI Summer.

The Game Deflators break down new pickups, big gaming news, PS6 backwards compatibility rumors, a Steam Deck price jump, and a retro review of Woody Woodpecker on PS2. Chapters: 00:00 Intro 03:34 Game Pickups and Retro Game Books 06:45 Magic Card Collection and Trading Insights 09:44 Valkyrie Profile Gameplay Experience 12:40 Saros Game Review and Gameplay Mechanics 15:33 Upcoming Game Releases and Personal Gaming Plans 18:51 Microsoft's Exclusive Titles and Banjo Kazooie Nostalgia 35:39 Nostalgia and Character Development in Gaming 37:38 The Future of Xbox Exclusives 40:46 Lenovo's Controversial Handheld Console 47:45 Steam Deck Price Hike and Market Impact 54:43 PlayStation 6 and Backward Compatibility 59:42 Woody Woodpecker Game Review and Nostalgia 01:11:12 Outro John and Ryan return with a fresh round of gaming talk, starting with new pickups from RetroGameBooks.com and a look at what each host has been playing. John is closing in on the finale of Valkyrie Profile, while Ryan celebrates completing Saros and shares thoughts on its final stretch. The conversation shifts to community sentiment as Xbox players voice a growing desire for a revival of Banjo Kazooie. The guys break down why the franchise still resonates and whether Microsoft might finally listen. From there, the episode takes a sharp turn into hardware drama. Lenovo has pulled a handheld device that shipped preloaded with Nintendo and Sega games, raising questions about licensing, oversight, and how something like this makes it to market. The Steam Deck also enters the spotlight after a major price hike that has players debating value, timing, and Valve's long‑term strategy. PlayStation rumors heat up as reports suggest the PS6 could support PS3 titles thanks to a new CPU design. John and Ryan explore what this could mean for backward compatibility and how it might reshape Sony's next generation. To wrap up the show, the Inflation Deflation Challenge features a retro review of Woody Woodpecker: Escape from Buzz Buzzard Park on the PS2. The guys revisit its chaotic platforming, oddball charm, and current market value to decide whether it still holds up.   Find us on TheGameDeflators.com   Twitter - www.twitter.com/GameDeflators Facebook - www.facebook.com/TheGameDeflators Instagram - www.instagram.com/thegamedeflators   The views and opinions expressed on this channel are solely those of the author. The content within these recordings are property of their respective Designers, Writers, Creators, Owners, Organizations, Companies and Producers. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted. Permission for intro and outro music provided by Matthew Huffaker http://www.youtube.com/user/teknoaxe 2_25_18

Hoy te traigo un episodio que se sale completamente de lo habitual y que ha supuesto un auténtico terremoto en mi forma de plantear los contenidos. Todo viene de un cambio de estrategia radical que decidí tomar tras pararme a analizar las estadísticas de los últimos programas. Me di cuenta de un detalle muy tonto pero crucial: te estaba hablando de herramientas increíbles, de los maravillosos conectores MCP y de bases de datos súper avanzadas... ¡pero no te había mostrado al verdadero protagonista de la película! Te estaba hablando de accesorios y complementos sin enseñarte el agente de Inteligencia Artificial que los gobierna a todos. Es como si te diera un manual de bujías sin mostrarte el motor del coche. Así que he decidido pausar el resto de temas técnicos y traerte directamente a Hermes Agent. Y para hacerlo de la manera más honesta y didáctica posible, hoy no te lo voy a contar yo solo: he dejado que mi propio agente de IA local tome el control del micrófono para demostrarte de lo que es capaz en tiempo real, sin nubes y sin cortes.El cerebro que vas a escuchar hablar a lo largo de este podcast se llama Lara. Es el agente que he configurado utilizando como cimiento el proyecto de código abierto Hermes Agent.Para demostrar que este tipo de tecnologías está al alcance de cualquiera y no requiere un hardware inalcanzable, he configurado a Lara para que funcione en un Slimbook One de lo más modesto. No cuenta con tarjeta gráfica (GPU) ni coprocesadores de IA (NPU); corre única y exclusivamente tirando de CPU, de procesador clásico. Para que podamos comunicarnos con ella y escucharla, utilizamos herramientas locales tanto para el reconocimiento de voz (Whisper) como para el paso de texto a voz (TTS). Al no disponer de un hardware de aceleración dedicado, notarás que la voz de Lara suena con ese puntito robótico clásico del software local y que a veces pronuncia de forma un tanto peculiar palabras en inglés como "YouTube" o "skills". Pero te aseguro que, en cuanto la escuchas interactuar un rato y negociar el guion del programa, le coges un cariño increíble. Especialmente porque Lara no tiene esa amabilidad artificial y empalagosa de los asistentes comerciales que te dicen "claro, con gusto te ayudo"; ella tiene su propia personalidad.En este programa vas a poder escuchar de primera mano cómo funciona este sistema a través de siete demostraciones reales y en tiempo real. Aunque preparamos un guion base inicial, las últimas pruebas las hicimos completamente al azar y sin red para ver hasta dónde podíamos exprimir la CPU del Slimbook:Demo 1: Lara realiza una búsqueda en vivo en Internet sobre las últimas tendencias y vídeos de agentes de IA localesDemo 3: Mi demostración favorita. Conectamos una base de datos local con más de 1600 recetas a nuestra lista de la compra inteligente.Demo 4: Accedemos a mi archivo personal de más de 3300 notas de texto y tareas pendientes integradas.Demo 5: Conectamos a Lara con mis datos de Strava del último mes. Demos 6 y 7: El experimento final sin red. Lara resume las noticias de tecnología más destacadas.Capítulos del episodio00:00:00 Cambio de estrategia: ¿Por qué necesitas un agente?00:03:36 Presentación de Lara y su cerebro local00:05:32 Demo 1: Búsqueda y análisis de información en Internet00:07:53 Demo 2: Multitarea paralela con subagentes00:09:51 Demo 3: Recetas de cocina y compra inteligente00:13:58 La importancia de la búsqueda semántica en tus notas00:14:48 Demo 4: El sistema de notas y tareas conectadas00:16:51 Demo 5: Controlando mis entrenamientos con Strava00:19:14 De la teoría al caos: Demos aleatorias sin red00:20:21 Demo 6: Noticias de tecnología e IA al día00:22:29 Demo 7: Resumen inteligente de textos extensos00:26:14 Taller presencial de Valencia: Trasteando con Hermes00:28:51 Hermes vs OpenClaw: La experiencia real de Daniel Primo00:29:52 Privacidad y hardware: Modelos ejecutados en CPU local00:30:26 Cierre del episodio y comunidad Atareao

- Huawei's Tau Scaling, LogicFolding: breakthrough or brilliant positioning? - China's new tech disclosure strategy continues - Wafer-scale chips in China? - Snowflake's $6B AWS deal and what it says about AI inference demand - The Return of CPU Computing - NVIDIA's CPU-only Vera-based Supercomputer and the rise of agentic AI infrastructure - OpenAI math breakthrough and what it says about AI-assisted discovery [audio mp3="https://orionx.net/wp-content/uploads/2026/05/HPCNB_20260601.mp3"][/audio] The post HPC News Bytes – 20260601 appeared first on OrionX.net.

Welcome to SIT Podcast. Just a few hours ago, the eyes of the global tech world turned to the Taipei Music Center, where NVIDIA CEO Jensen Huang delivered a GTC Taipei keynote that sent a jolt through the industry. As we speak, the doors of Computex 2026 have yet to officially open — but NVIDIA has already seized the moment, declaring the arrival of a "new era of PC."In this episode, we take a close look at three defining trends:1. NVIDIA moves into laptop silicon. After more than a decade away, NVIDIA returns to the consumer CPU arena with the N1 and N1X chips. According to supply-chain reports, the high-performance N1X is said to feature a 20-core Arm CPU and Blackwell-architecture graphics, with performance reportedly compared to the desktop-class RTX 5070. More significantly, this could mean the CUDA ecosystem running natively on a Windows-on-Arm laptop for the first time.2. Taiwan — the center of global AI. In his keynote, Huang revealed that NVIDIA's annual spending in Taiwan has grown to roughly $100 billion. The company is also planning an overseas headquarters called "Constellation," reportedly slated to open around 2030 and house some 4,000 employees. From TSMC's manufacturing to Foxconn's assembly, Taiwan has become the heart of what Huang envisions as the AI factory producing computational tokens.3. The rivals respond, and an industry test. Faced with NVIDIA's momentum, Intel has rolled out its Arc G3 chips built for handheld gaming devices, while Qualcomm defends its ground with a $300 entry-level Windows laptop platform. With DRAM and SSD costs climbing, Gartner projects PC prices will rise a notable 17% in 2026 — a real test of what every maker can deliver.歡迎來到 SIT Podcast。就在幾個小時前，全球科技界的目光都聚焦在台北流行音樂中心，NVIDIA 執行長黃仁勳發表了震撼產業的 GTC Taipei 主題演講。此時此刻，Computex 2026 的展覽大門尚未正式開啟，但 NVIDIA 已經先聲奪人，宣告了「PC 新紀元」的到來。在本集節目中，我們將深入解析三大關鍵趨勢：NVIDIA 跨足筆電矽晶片： NVIDIA 睽違十年重回消費型 CPU 戰場，推出 N1 與 N1X 晶片。根據供應鏈報告，高性能的 N1X 據傳搭載 20 核 Arm CPU 與 Blackwell 架構繪圖核心，其性能甚至被拿來與桌機等級的 RTX 5070 相比。更重要的是，這可能代表 CUDA 生態系將首度原生運行於 Windows-on-Arm 筆電。台灣——全球 AI 的中心： 黃仁勳在演講中透露，NVIDIA 每年在台灣的支出已增長至約 1,000 億美元。此外，NVIDIA 正計畫興建名為「Constellation」（星座）的海外總部，預計 2030 年啟用，將容納約 4,000 名員工。從台積電的製造到 Foxconn 的組裝，台灣已成為黃仁勳眼中生產「計算代幣」的 AI 工廠核心。競爭對手的回擊與產業逆風： 面對 NVIDIA 的強勢，Intel 隨即推出專為掌上型遊戲機設計的 Arc G3 晶片，Qualcomm 則以 300 美元的低價 Windows 筆電平台防守市場。然而，在 DRAM 與 SSD 成本飆升的壓力下，Gartner 預測 2026 年 PC 價格將大幅上漲 17%，這對所有廠商來說都是嚴峻的考驗。

Send us Fan MailYour firewall can be patched tomorrow, but what about the place your system hides its real secrets today? We start with a timely warning about a serious Fortinet FortiGate vulnerability and why perimeter devices are still a make-or-break control, then we pivot into the deeper layer most people ignore until it's too late: memory.We walk through CISSP Domain 3.4 by focusing on what memory protection is actually trying to achieve: confidentiality, integrity, and process isolation. From there, we unpack how modern operating systems enforce separation with paging, segmentation, and strict read, write, execute controls. You'll hear why Meltdown and Spectre were such a big deal, how speculative execution can leak passwords and encryption keys from privileged memory, and why patching decisions are never just “apply everything” but a risk-based vulnerability management call that depends on visibility into what you run.Next, we connect memory protection to virtualization security. We break down hypervisors, guest and host isolation, Type 1 versus Type 2 designs, and the threats that keep security teams up at night: VM escape, side-channel leakage through shared CPU resources, and the operational hazards of memory overcommitment. Then we bring in hardware roots of trust through TPMs: secure boot, measured boot, key storage for full disk encryption, TPM 2.0 types, and how HSM-style key management shows up in cloud environments. We close with practical best practices, from firmware and microcode updates to choosing encryption controls that fit your actual risk.If you're studying for the CISSP or building a real-world security strategy, subscribe, share this with a teammate, and leave a review so more security pros can find it.Gain exclusive access to 360 FREE CISSP Practice Questions at FreeCISSPQuestions.com and have them delivered directly to your inbox!  Don't miss this valuable opportunity to strengthen your CISSP exam preparation and boost your chances of certification success. Join now and start your journey toward CISSP mastery today!

Nvidia heeft tijdens de eigen goed-nieuwsshow GTC, gehouden rond techbeurs Computex in Taiwan, nieuwe chips aangekondigd die juist voor apparaten van eindgebruikers zelf bedoeld zijn. Het betreft de RTX Spark, voorzien van zowel een CPU als een GPU, om te concurreren met AMD en Intel. Joe van Burik vertelt erover in deze Tech Update. Verder in deze Tech Update: Softbank, bekend van investeringen in OpenAI, gaat tientallen miljarden in AI-datacenters in Frankrijk steken See omnystudio.com/listener for privacy information.

Question time again! This month we discuss quite a wide range of topics, such as tracking down printer dots with a USB microscope, the dream of going to SIGGRAPH, the legality of scanning and uploading "lost" old magazines, how to stay objective about new stuff as you get older, steady fan curve strategies for CPU air cooling, how to cope when you find out that cool new open source project was made by AI, renaming files like a pro, and the enduring mystery of ICQ's event sounds. Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

Java's use of memory, often chided for being excessive, is actually a strength as it trades more memory use for fewer CPU cycles. Java can only make this tradeoff due to its moving garbage collectors, something more memory efficient platforms often cannot. But what's the point in leaving available memory on the table if using it makes your program run faster? Efficient use of that resource wouldn't be to leave it untapped but to use it to speed up the program. In this "Ask the Architect" episode of the Inside Java Podcast, recorded during JavaOne 2026, Nicolai Parlog talks to Ron Pressler, Java Architect at Oracle.

In this episode, we debrief Telehash #4 and dig into the open-source future of Bitcoin mining. We share behind-the-scenes metrics from HydraPool's six-and-a-half–hour live stress test, including 30.8 zettahashes processed, an average of 1.32 EH/s, a peak of 2.495 EH/s, 2,231 workers, 59 unique users, and an impressively low ~1% server CPU under >2,000 connections. We explain why rejection rates under ~2% matter, how stale and “difficulty too low” shares differ in solo vs pooled mining, and how Stratum “suggest difficulty,” plus our d= and h= password parameters, help right-size starting difficulty—making Telehash inclusive for both exahash renters and single-chip Bitaxe miners. We also touch on leaderboards, loyalty uptime rules, and shout out supporters like Elektron Energy, Compass, Saaz Mining, and Abundant Minds. From hardware to policy, we discuss Bitaxe UX updates (LVGL, Figma-driven UI, external display/knob), DOOMAXE fun, and industry standardization—from firmware and pools to racks, cooling, and power—arguing that open reference designs cut costs and risk for everyone. We cover GridPool's “winners list” approach to decentralized variance smoothing, the Patoshi/extra nonce story, vardiff dynamics, and privacy-conscious VPN mining. We reflect on immersion's decline versus hydro, ASIC roadmap realities and slowing efficiency gains, the supply-chain and security stakes (FCC Wi‑Fi moves, vendor backdoors), and why nonprofit coordination via the 256 Foundation matters for open firmware, dev kits, and reference designs. We close with community invites, next steps for Telehash #5, and a call for ASIC makers and big miners to collaborate on open standards that benefit small and large operators alike.

¿Cierras el libro pero tu cabeza sigue a mil por hora repasando lo que no entendiste? Estás atrapado en un ciclo abierto. Hackeamos el cierre de tu sesión para que el español no te robe el descanso:Acabar vs. Completar: La distinción para vaciar tu CPU mental y eliminar la culpa post-estudio.El "Dump" de 30 segundos: Cómo liberar tu memoria de trabajo para poder desconectar de verdad.Ritual de Aterrizaje: El puente sensorial para pasar del hiperfoco a la paz mental.

Brad's tired of throttling his CPU due to an inadequate heatsink. Will's been spending a lot more time testing PC hardware of late. Between those two things, we thought it was a good time to do a check-in on CPU cooling, and primarily liquid cooling, so we can establish the facts on the ground about modern AIOs and custom loops with an eye toward helping Brad decide what to get. Turns out, there's more to know than ever, and yet it's also never been simpler. We also talk a little about modern air cooling, CPU spikes in Windows, and other stuff! The GamersNexus video on AIO placement: https://www.youtube.com/watch?v=BbGomv195sk Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

Today's Post - https://bahnsen.co/3R9QgGV In this Friday Dividend Cafe, David Bahnsen explains why data centers have become a major economic story, tracing their evolution from 1990s CPU-based server facilities to 2010s cloud-driven hyperscale warehouses and today's AI-focused GPU centers that require far more power, cooling, and infrastructure. He argues data center construction and related spending may have accounted for roughly 80% of last year's GDP growth, even as other real estate and industrial activity has been muted, drawing an analogy to the shale/fracking boom. Bahnsen supports data centers and future productivity potential but opposes federal efforts to override local zoning, warns against cronyism, emphasizes the need for a stronger public relations case, and highlights investment implications in adjacent areas like power, water, natural gas, and pipelines. 00:00 Welcome and Setup 00:52 Why Data Centers Matter 01:43 Three Eras of Data Centers 03:51 AI Shift to GPUs 05:42 Data Centers Driving GDP 08:29 Future Productivity Payoff 09:32 What Growth Is Missing 10:12 Fracking Analogy and Backlash 12:15 Localism Versus Federal Override 14:57 PR Playbook Five Points 17:23 Investing Wisely in the Theme 19:35 Wrap Up and Disclosures Links mentioned in this episode: DividendCafe.com TheBahnsenGroup.com

Learn why Activity Monitor is the macOS tool experienced users trust first to diagnose hidden memory drains, runaway CPU usage, and behind-the-scenes energy hogs. Mastering a single built-in tool can put real-time answers and expert-level control right at your fingertips. Quick Access Methods: Spotlight, Finder, and Utilities Folder Five Main Tabs: CPU, Memory, Energy, Disk, and Network Explained Sorting and Identifying Resource-Heavy Processes in CPU Tab Understanding Percent CPU, Multi-Core Macs, and High Usage Scenarios Spotting and Managing Frozen or Runaway Apps via Activity Monitor The Importance of the Kind Column: Rosetta Support and Apple Silicon Transition Using Memory Tab and Pressure Graph to Gauge RAM Health Sorting by Memory to Find Leaky or Misbehaving Apps Energy Tab Insights: Finding Battery-Draining and Power-Hungry Apps Disk Tab: Diagnosing Read/Write Issues and Competing Background Tasks Network Tab: Tracking Data-Heavy Apps and Monitoring for Security Live Dock Icon Graphs for CPU, Network, and Disk Usage How to Force Quit or Inspect Troubled Processes in Activity Monitor Homework: Add Activity Monitor to Dock and Monitor Rosetta Apps Before Support Ends Host: Mikah Sargent Download or subscribe to Hands-On Apple at https://twit.tv/shows/hands-on-apple Want access to the ad-free audio and video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: outsystems.com/twit

Learn why Activity Monitor is the macOS tool experienced users trust first to diagnose hidden memory drains, runaway CPU usage, and behind-the-scenes energy hogs. Mastering a single built-in tool can put real-time answers and expert-level control right at your fingertips. Quick Access Methods: Spotlight, Finder, and Utilities Folder Five Main Tabs: CPU, Memory, Energy, Disk, and Network Explained Sorting and Identifying Resource-Heavy Processes in CPU Tab Understanding Percent CPU, Multi-Core Macs, and High Usage Scenarios Spotting and Managing Frozen or Runaway Apps via Activity Monitor The Importance of the Kind Column: Rosetta Support and Apple Silicon Transition Using Memory Tab and Pressure Graph to Gauge RAM Health Sorting by Memory to Find Leaky or Misbehaving Apps Energy Tab Insights: Finding Battery-Draining and Power-Hungry Apps Disk Tab: Diagnosing Read/Write Issues and Competing Background Tasks Network Tab: Tracking Data-Heavy Apps and Monitoring for Security Live Dock Icon Graphs for CPU, Network, and Disk Usage How to Force Quit or Inspect Troubled Processes in Activity Monitor Homework: Add Activity Monitor to Dock and Monitor Rosetta Apps Before Support Ends Host: Mikah Sargent Download or subscribe to Hands-On Apple at https://twit.tv/shows/hands-on-apple Want access to the ad-free audio and video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: outsystems.com/twit

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

Learn why Activity Monitor is the macOS tool experienced users trust first to diagnose hidden memory drains, runaway CPU usage, and behind-the-scenes energy hogs. Mastering a single built-in tool can put real-time answers and expert-level control right at your fingertips. Quick Access Methods: Spotlight, Finder, and Utilities Folder Five Main Tabs: CPU, Memory, Energy, Disk, and Network Explained Sorting and Identifying Resource-Heavy Processes in CPU Tab Understanding Percent CPU, Multi-Core Macs, and High Usage Scenarios Spotting and Managing Frozen or Runaway Apps via Activity Monitor The Importance of the Kind Column: Rosetta Support and Apple Silicon Transition Using Memory Tab and Pressure Graph to Gauge RAM Health Sorting by Memory to Find Leaky or Misbehaving Apps Energy Tab Insights: Finding Battery-Draining and Power-Hungry Apps Disk Tab: Diagnosing Read/Write Issues and Competing Background Tasks Network Tab: Tracking Data-Heavy Apps and Monitoring for Security Live Dock Icon Graphs for CPU, Network, and Disk Usage How to Force Quit or Inspect Troubled Processes in Activity Monitor Homework: Add Activity Monitor to Dock and Monitor Rosetta Apps Before Support Ends Host: Mikah Sargent Download or subscribe to Hands-On Apple at https://twit.tv/shows/hands-on-apple Want access to the ad-free audio and video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: outsystems.com/twit

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

股價沉寂多時的美國科技公司輝達（Nvidia）最近連續7個交易日收紅，專家表示相對其他半導體公司，輝達現在依然便宜，有機會再漲50%。 文：樂羽嘉 製作團隊：錢玉紘、張雅媛、鄭子鴻 ＊閱讀零時差，點這看全文

What happens when stress, uncertainty, and lack of control start to take over your brain? In this episode, I sit down with renowned behavioral economist Dan Ariely to talk about resilience, learned helplessness, anxiety, and why so many people feel overwhelmed right now. Dan shares how surviving severe burns, years in the hospital, and later facing conspiracy theories and death threats shaped his understanding of stress, resilience, and human behavior. We also dive into why uncertainty at work feels so psychologically destabilizing, how scarcity and anxiety reduce our mental capacity, why humans look for villains during stressful times, and what actually helps us build resilience. Tune in to learn ways to regain agency when life feels chaotic. Check out our sponsors: Shopify - Sign up for a $1 per month trial, just go to shopify.com/anxiousachiever Chime - Head to chime.com/achiever to sign up Quince - Head to quince.com/ACHIEVER for free shipping on your order and 365-day returns Monarch - Use code ACHIEVER at monarch.com to get 50% off your first year Physician's Choice - Use code PCPODCAST10 at physicianschoice.com to get 10% off your entire order In this Episode, You Will Learn 00:00 Why starting something new can help you feel unstuck. 03:45 The mental health benefits of walking with friends. 08:35 Why invisible mental health struggles feel more isolating. 11:00 What workplaces still misunderstand about neurodiversity. 16:15 How physical limitations forced Dan to think more creatively. 19:15 Why uncertainty and unpredictability increase anxiety at work. 22:30 Why does modern life making you less resilient? 24:15 How layoffs and workplace uncertainty create learned helplessness. 29:15 The strategy performers use to stay engaged and motivated. 33:30 Why anxiety consumes your brain's “CPU.” 36:45 Why humans look for villains during stressful times. 38:15 How negative narratives reshape the way you see people. Resources + Links Get a copy of my book - The Anxious Achiever Watch the podcast on YouTube  Find more resources on our website morraam.com Follow Follow me: on LinkedIn @morraaronsmele + Instagram @morraam

Guest is Lucy Sweet, a Staff Software engineer at Uber and the lead for the Kubernetes Node Lifecycle Working Group. Imagine trying to move millions of compute cores and thousands of microservices to a brand new platform. All without dropping a single user request, ride, or delivery. Sounds like an absolute logistical nightmare, right? Well, today we are sitting down with someone who actually lived to tell the tale Lucy. In this episode, we are diving deep into Uber's monumental infrastructure journey: moving away from their in-house system to Kubernetes. We'll be unpacking the reality of running at this scale, why it's always DNS and why building things for fun is worth it. Do you have something cool to share? Some questions? Let us know: - web: kubernetespodcast.com - mail: kubernetespodcast@google.com - twitter: @kubernetespod - bluesky: @kubernetespodcast.com   News of the week Broadcom announced donating Velero to the CNCF Sandbox Level KubeCon && CloudNativeCon Amsterdam 2026 Transparency report Call for Proposals for KubeCon && CloudNativeCon North America 2026 closes May 31 OpenChoreo v1.0 CNCF Sandbox Links from the interview Lucy on Linkedin Lucy's website [Article] Migrating Uber's Compute Platform to Kubernetes [Lucy Video] Migrating 2 million CPU cores to Kubernetes Up: Portable Microservices Ready for the Cloud Peloton: Uber's Unified Resource Scheduler for Diverse Cluster Workloads Odin: Uber's Stateful Platform Uber Batch platform Apache Mesos Hyrum's Law GKE Blue-Green nodepools Node Lifecycle Working Group Scaling Infrastructure Management with Grail kubegpt.org Osquery Uber Careers  

Our Head of Europe and Asia Technology Research Shawn Kim discusses AI's move from passive chatbots to active agents—and how this influences tech supply chains.Read more insights from Morgan Stanley.----- Transcript -----Welcome to Thoughts on the Market. I'm Shawn Kim, Head of Morgan Stanley's Europe and Asia Technology Team. Today: A foundational shift in the development of AI and its broad market implications. It's Tuesday, May 5th, at 3pm in London. Think about the last time you asked a chatbot to write a summary or a draft. Or maybe answer a query. It was probably useful. But you were also still driving the interaction: asking, refining, copying, checking, and moving the work forward. Now imagine a system that does not just respond, but acts. It remembers what you asked last week, understands your preferences, works across digital tools, plans a workflow, and adapts as circumstances change. That is the shift from GenAI to agentic AI: from AI that helps with thinking to AI that helps with doing. GenAI is mostly passive. It takes a prompt and produces an answer. Agentic AI is active – less a copilot for one task but an autopilot for multi-step workflows. The distinction is key because computing requirements are changing. In GenAI, large language models and GPUs handle much of the thinking. GPUs, or graphics processing units, process many calculations in parallel, making them central to modern AI models. In agentic AI, CPU becomes more important. CPUs, or central processing units, coordinate tasks and connect systems to the broader digital infrastructure. Agentic AI also depends on three stacks: the brain, or the large language model; orchestration, where the CPU manages the doing; and knowledge, which is memory.Memory may be the most important layer. An agent that knows your preferences, documents, tone, and task history becomes more useful over time. That creates a context flywheel. The more context it collects, the more personalized it becomes, and the harder it is to leave. Typically, in computing, we think of memory as storage, mainly. We need to rethink this. Memory is also continuity. When an AI system can use past experiences, memory becomes a long-term state, shared knowledge, and behavioral grounding. And that matters because LLMs have fixed context windows. Once a conversation exceeds that window, older content falls off. For simple questions, that may be fine. But for a coding agent working across a large codebase over days or weeks, it is a major limitation. Serious work requires persistent memory, short-term orientation, and active retrieval – remembering prior decisions, understanding changed files, and finding relevant codes without the user pointing to every dependency. For investors, the implication is clear – agentic AI changes the bottlenecks. We see CPUs as the new bottleneck, with memory seeing the highest content increase. We estimate as much as 60 percent, or $60 billion of incremental CPU total addressable market by 2030, within a total CPU market of more than $100 billion. We also estimate up to 70 percent of incremental DRAM bit shipment tied to this theme. That makes us more positive on supply chains including memory, foundry, substrates, CPU and memory interface, and capacitors and CPU sockets. These areas benefit from content growth, pricing power, and capacity constraints into 2027. As AI moves from answering questions to taking actions, investors should watch the infrastructure behind the shift. Because in the agentic era, the next big AI leap may be less about the prompt, but more about the processor. Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.