Podcasts about UI

Welcome to Dev Game Club, where this week we complete our series on Tom Clancy's Splinter Cell: Chaos Theory. We talk about some of the late levels before turning to our takeaways. Dev Game Club looks at classic video games and plays through them over several episodes, providing commentary. Sections played: Through Battery (B) and Seoul (T) Issues covered: whether Tim is North Korea, Steam Deck support, the intimate level design of Hokkaido, nightingale floors and ninja, ninja kids, level design that wraps around, changing up the levels, timed sections, cool ideas for the space, connectivity, different types of cameras, a frustrating metric challenge, directing the player, contextual movement and tagging, telegraphing metrics, negative design metrics, finding additional story, the set-up for Seoul, contrast against Battery, escalating the dynamic objectives, the "reversal," upping the ante, getting 100%, the whistle, finding a body as a negative stat, deducing player intent, unconscious witnesses, combining states, layering tools and immersive sims, wanting more guidance to the systems and verbs the player has access to, building tutorial stuff last, executing on the tone, dynamic changes to the plan, leaning into the tech appropriately, communicating enemy AI state clearly without UI, the limited reach of this genre.  Games, people, and influences mentioned or discussed: BioStats, CalamityNolan, OI Interactive, RealmSoft, Clockwork Ambrosia, Michael Patton, Nathan Hiemenz, Ian Clark, Lian Hearn, Across the Nightingale Floor, Teenage Mutant Ninja Turtles, Team Ninja, Vanquish, Platinum Games, Metal Gear Revengeance, Clover Studios, Fallout 3, Skyrim, Jedi Knight, LucasArts, Kevin Kauffman, Matt Tateishi, Jake Stevens, Knute Rockne, Prince of Persia: The Sands of Time, Mysteries of the Sith, White Men Can't Jump, Woody Harrelson, Star Wars: Rogue One, Hal Barwood, Hitman: World of Assassination, Nintendo, Majora's Mask, SW: Republic Commando, Mission: Impossible, Project: Octavia, DOOM (1993), Alien: Isolation, Kirk Hamilton, Aaron Ever, Mark Garcia.  Next time: Psychonauts! Twitch: timlongojr and twinsunscorp  Discord  DevGameClub@gmail.com

Send us Fan MailGet vidIQ Boost for an exclusive price! https://vidiq.com/podcastWant a 1 on 1 coach? https://vidiq.ink/theboost1on1Join our Discord! https://www.vidiq.com/discordWatch the video: https://youtu.be/_kUOrWhNynwWe break down two YouTube experiments that could change how viewers navigate the app and how creators earn early momentum. We also zoom out into what these UI tests say about control, recommendations and the mental game of posting on YouTube.• Subscriptions tab moving from bottom nav to a top tab on mobile• How UI changes can spike or crater feature usage• Why initial velocity still ties to subscriber behavior• The subscription feed becoming less chronological and more algorithmic• DMs and “invite to chat” showing up inside Subscriptions• YouTube's push toward all-in-one community features• Shorts testing a missing dislike button and a heart icon• Why dislike signals matter for personalization and scam detection• Better intros by watching retention graphs and viewer intent• Avoiding the YouTube Studio refresh spiral after uploadleave a comment below. Let us know what you think about the the the like button being changed and of course the subscription tab being moved on mobile.Make sure you hit that subscribe button, like button. If you're listening to an audio podcast, there will be a link in the show notes to take you over here where you can do the same.

Today we are talking about Test Driven Development, ebooks, and Drupal with guest Oliver Davies. We'll also cover Juicer Social Feed as our module of the week. For show notes visit: https://www.talkingDrupal.com/557 Topics What Is Test Driven Drupal Why Automated Tests Matter How TDD Works AI and Test Quality Balancing Test Coverage When to Write Tests Why Write the Book Why Write an Ebook From Email Course to Ebook Ebook vs Print Tradeoffs Who the Book Helps What You Will Learn Keeping Content Updated Publishing Tools Workflow Lessons and Drupal Changes Podcast and Future Books Mob Programming Explained Free Ebook and Wrap Up Resources Juicer io Drupal 11: The Upgrade Experience I've Been Waiting For codethatships Test-Driven Drupal Sculpin Guests Oliver Davies - oliverdavies.uk opdavies Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Scott Falconer - managing-ai.com scott-falconer MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Have you ever wanted to embed social feeds into your Drupal website? There's a module for that. Module name/project name: Juicer Social Feed Brief history How old: created in Mar 2026 by Denis Omerović (drupalchille) Versions available: 1.0.2, that works with Drupal 10.3 or 11 Maintainership Actively maintained (version released today!) No open issues Usage stats: 4 sites Module features and usage This module embeds an aggregated social media feed from Juicer.io directly into Drupal as a configurable block. It natively supports content from Instagram, LinkedIn, Facebook, X (Twitter), TikTok, Bluesky, YouTube, and more. Traditionally, displaying feeds from platforms like Facebook, X, or Instagram requires creating developer accounts, managing rotating OAuth tokens, and keeping up with constantly shifting API restrictions. Juicer handles all API authentication on its platform, shielding your website from sudden breaking changes by individual social networks. To use this module, you will need an active account on Juicer.io. They offer both free and paid tiers depending on how many sources you want to aggregate and how frequently you need the feed to sync. The module is created and maintained by the official Juicer.io team. That should ensure that the module is closely aligned with the product's features and any potential API changes over time. The embedded feed is made available as a Drupal block, to make it easy to control where it should appear on your site. When placing the Juicer block, the UI exposes several user-friendly settings: Feed Slug: Just paste your unique Juicer feed ID to establish the connection. Post Limit: Control exactly how many items populate initially. Source Filtering: If your Juicer account aggregates five networks, but you only want to show LinkedIn posts on a specific page, you can filter down to a single network right inside the block settings. SEO/Semantic Control: You can set titles/subtitles and choose the exact heading level hierarchy ( through ) to ensure your pages remain semantically correct and accessible. I did get a chance to test out the module and the service today, and I can tell you from experience, it's a huge improvement on having to create and pull in feeds directly. I did notice that the block didn't show up in the Drupal Canvas component library, but I was able to determine that two lines of code to declare the block as FullyValidatable were all that was needed. So I opened a Feature Request to add that, and it was merged in and a new release cut in less than an hour. So it's now Drupal Canvas compatible too! It's worth pointing out that the standard Juicer's embed script loads HTMX, which conflicts with the version of HTMX included in Drupal 11 core. As a result, the module fetches feed HTML directly from the Juicer API and includes a minimal HTMX shim to prevent errors. John, you nominated this module, why don't you start us off by telling us about how you got started using it?

An airhacks.fm conversation with Alvaro Hernandez (@ahachete) about: discussion about the quarkus Insights episode "#337 The Database Cloud" stackgres live demo, StackGres as a Quarkus and GraalVM native kubernetes operator for running Postgres, comparing CloudNativePG (CNPG) by EnterpriseDB to StackGres, Patroni for Postgres high availability, the split-brain risk of relying on Kubernetes and etcd alone, distributed consensus and leader lock election via etcd, why distributed systems and cryptography should not be self-implemented, async, synchronous and quorum (semi-synchronous) Postgres replication trade-offs, cascading and cross-region replication topologies, the false-positive problem and heuristic exceptions in two-phase commit, the ondb ("own your database") project for self-hosted Postgres, losing control with managed cloud services and untestable backups, vanilla unmodified Postgres on StackGres, the "Kubernetes without Kubernetes" (Kubeless) pattern, talking directly to ContainerD through the CRI API, runc and the Docker to ContainerD chain, a self-contained native binary that embeds ContainerD over Unix domain sockets, the slony node-local component named after the Postgres slonik elephant mascot, the Matriarch orchestrator component, reverse gRPC tunnels with Slonies phoning home across NAT and firewalls, a multi-tenant cloud control plane provided as a service, curl-pipe-shell node installation with a token, end-to-end encrypted Postgres protocol tunneling for JDBC from anywhere, psql compiled to wasm in the web console, Tailscale-inspired user experience, unifying nodes, Kubernetes clusters and cloud pools as resources, Slony Kubernetes controller, Java 25 source-mode scripting without dependencies, implementing your own MCP server for Postgres JDBC metadata, the Goose agentic UI donated by Block to the Linux Foundation, AI Rails BCE, Java, Web Components skills Alvaro Hernandez on twitter: @ahachete

“Tęsknię za Ballmerem na scenie.” Łukasz po keynote'cie Build 2026, na którym Satya wymuszał z widowni klaskanie - “nie było wow” - a po osobowościach pokroju Guthriego i Russinovicha został korporacyjny autopilot. Bo to pierwszy od lat Build, gdzie zamiast Azure'owych fajerwerków dostajemy Windows, Windows, Windows.

Apple is handing parents unprecedented control with new child safety and parental approval features. Rosemary Orchard and Mikah Sargent break down what's changed, how it works, and more iOS goodness during WWDC26 week! Apple's WWDC keynote reveals iOS 27 and platform-wide focus on AI, privacy, and performance Liquid Glass transparency and appearance now user-adjustable for accessibility Toolbars and UI restored for improved navigation and vision support Performance boosts: faster photos, AirDrop, and Spotlight search highlighted Intelligent networking promises smarter Wi-Fi and cellular switching Maps overhaul: more vivid detail with AI and satellite imagery New child safety features: granular app, contact, and website approvals Siri gets personal context, deeper app integration, and smart replies Advanced developer APIs and app intents previewed for on-device AI features Generative photo editing arrives: extend, clean up, and a new Reframe tool System-wide suggestions and information surfacing in Mail, Messages, and calls Quality-of-life updates: independent alarm/ringer/music volumes, swipable now playing Shortcuts Corner: Natural language shortcut creation and changes to automation workflows Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: shopify.com/ios

Apple is handing parents unprecedented control with new child safety and parental approval features. Rosemary Orchard and Mikah Sargent break down what's changed, how it works, and more iOS goodness during WWDC26 week! Apple's WWDC keynote reveals iOS 27 and platform-wide focus on AI, privacy, and performance Liquid Glass transparency and appearance now user-adjustable for accessibility Toolbars and UI restored for improved navigation and vision support Performance boosts: faster photos, AirDrop, and Spotlight search highlighted Intelligent networking promises smarter Wi-Fi and cellular switching Maps overhaul: more vivid detail with AI and satellite imagery New child safety features: granular app, contact, and website approvals Siri gets personal context, deeper app integration, and smart replies Advanced developer APIs and app intents previewed for on-device AI features Generative photo editing arrives: extend, clean up, and a new Reframe tool System-wide suggestions and information surfacing in Mail, Messages, and calls Quality-of-life updates: independent alarm/ringer/music volumes, swipable now playing Shortcuts Corner: Natural language shortcut creation and changes to automation workflows Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: shopify.com/ios

Apple is handing parents unprecedented control with new child safety and parental approval features. Rosemary Orchard and Mikah Sargent break down what's changed, how it works, and more iOS goodness during WWDC26 week! • Apple's WWDC keynote reveals iOS 27 and platform-wide focus on AI, privacy, and performance • Liquid Glass transparency and appearance now user-adjustable for accessibility • Toolbars and UI restored for improved navigation and vision support • Performance boosts: faster photos, AirDrop, and Spotlight search highlighted • Intelligent networking promises smarter Wi-Fi and cellular switching • Maps overhaul: more vivid detail with AI and satellite imagery • New child safety features: granular app, contact, and website approvals • Siri gets personal context, deeper app integration, and smart replies • Advanced developer APIs and app intents previewed for on-device AI features • Generative photo editing arrives: extend, clean up, and a new Reframe tool • System-wide suggestions and information surfacing in Mail, Messages, and calls • Quality-of-life updates: independent alarm/ringer/music volumes, swipable now playing • Shortcuts Corner: Natural language shortcut creation and changes to automation workflows Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: shopify.com/ios

Apple is handing parents unprecedented control with new child safety and parental approval features. Rosemary Orchard and Mikah Sargent break down what's changed, how it works, and more iOS goodness during WWDC26 week! Apple's WWDC keynote reveals iOS 27 and platform-wide focus on AI, privacy, and performance Liquid Glass transparency and appearance now user-adjustable for accessibility Toolbars and UI restored for improved navigation and vision support Performance boosts: faster photos, AirDrop, and Spotlight search highlighted Intelligent networking promises smarter Wi-Fi and cellular switching Maps overhaul: more vivid detail with AI and satellite imagery New child safety features: granular app, contact, and website approvals Siri gets personal context, deeper app integration, and smart replies Advanced developer APIs and app intents previewed for on-device AI features Generative photo editing arrives: extend, clean up, and a new Reframe tool System-wide suggestions and information surfacing in Mail, Messages, and calls Quality-of-life updates: independent alarm/ringer/music volumes, swipable now playing Shortcuts Corner: Natural language shortcut creation and changes to automation workflows Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: shopify.com/ios

Apple is handing parents unprecedented control with new child safety and parental approval features. Rosemary Orchard and Mikah Sargent break down what's changed, how it works, and more iOS goodness during WWDC26 week! • Apple's WWDC keynote reveals iOS 27 and platform-wide focus on AI, privacy, and performance • Liquid Glass transparency and appearance now user-adjustable for accessibility • Toolbars and UI restored for improved navigation and vision support • Performance boosts: faster photos, AirDrop, and Spotlight search highlighted • Intelligent networking promises smarter Wi-Fi and cellular switching • Maps overhaul: more vivid detail with AI and satellite imagery • New child safety features: granular app, contact, and website approvals • Siri gets personal context, deeper app integration, and smart replies • Advanced developer APIs and app intents previewed for on-device AI features • Generative photo editing arrives: extend, clean up, and a new Reframe tool • System-wide suggestions and information surfacing in Mail, Messages, and calls • Quality-of-life updates: independent alarm/ringer/music volumes, swipable now playing • Shortcuts Corner: Natural language shortcut creation and changes to automation workflows Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: shopify.com/ios

Apple is handing parents unprecedented control with new child safety and parental approval features. Rosemary Orchard and Mikah Sargent break down what's changed, how it works, and more iOS goodness during WWDC26 week! Apple's WWDC keynote reveals iOS 27 and platform-wide focus on AI, privacy, and performance Liquid Glass transparency and appearance now user-adjustable for accessibility Toolbars and UI restored for improved navigation and vision support Performance boosts: faster photos, AirDrop, and Spotlight search highlighted Intelligent networking promises smarter Wi-Fi and cellular switching Maps overhaul: more vivid detail with AI and satellite imagery New child safety features: granular app, contact, and website approvals Siri gets personal context, deeper app integration, and smart replies Advanced developer APIs and app intents previewed for on-device AI features Generative photo editing arrives: extend, clean up, and a new Reframe tool System-wide suggestions and information surfacing in Mail, Messages, and calls Quality-of-life updates: independent alarm/ringer/music volumes, swipable now playing Shortcuts Corner: Natural language shortcut creation and changes to automation workflows Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: shopify.com/ios

Apple is handing parents unprecedented control with new child safety and parental approval features. Rosemary Orchard and Mikah Sargent break down what's changed, how it works, and more iOS goodness during WWDC26 week! Apple's WWDC keynote reveals iOS 27 and platform-wide focus on AI, privacy, and performance Liquid Glass transparency and appearance now user-adjustable for accessibility Toolbars and UI restored for improved navigation and vision support Performance boosts: faster photos, AirDrop, and Spotlight search highlighted Intelligent networking promises smarter Wi-Fi and cellular switching Maps overhaul: more vivid detail with AI and satellite imagery New child safety features: granular app, contact, and website approvals Siri gets personal context, deeper app integration, and smart replies Advanced developer APIs and app intents previewed for on-device AI features Generative photo editing arrives: extend, clean up, and a new Reframe tool System-wide suggestions and information surfacing in Mail, Messages, and calls Quality-of-life updates: independent alarm/ringer/music volumes, swipable now playing Shortcuts Corner: Natural language shortcut creation and changes to automation workflows Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: shopify.com/ios

Royce Sin spent a decade at HSBC automating things nobody asked him to automate. He didn't ask for permission. He just did it, showed people the results, and let the time savings speak for itself. That instinct, to question why things are done a certain way and then actually do something about it, is what eventually led him into the AI space.In this episode, Peter and Dave sit down with Royce Sin to talk about what it actually takes for AI to stick inside an organization. Spoiler: it's not about the tools.We get into the tension between flexibility and reliability, why most people are being set up to fail with AI, and what it means to think like a manager when you're not one. Royce also shares his MIND framework, a practical way to think about AI adoption that he developed through hands-on work across enterprise and startup environments.There's also a good conversation about the trades, no-UI as an ideal, and why the most dangerous move in transformation is knocking down fences you don't fully understand.This week's takeaways:Think of AI as a new type of employee. Set it up for success the same way you'd set up your staff. Design roles and processes to match what it's actually good at.Not every rule is a hard rule. Before treating a constraint as a blocker, understand what's behind it. Some fences are load-bearing. Some aren't. Know the difference before you act.Don't just bring in AI. Know what outcome you're after. If you can't tell whether it's working, you don't have a tool problem, you have a clarity problem.Have a thought on any of this? Reach us at feedback@definitelymaybeagile.com

I've been seeing a recurring pattern with companies selling APIs, MCPs, data feeds, and other developer-focused AI products. While the technology is often sound if not impressive, sales momentum sometimes slows when prospects have to imagine how the product will create value in their own environment. My perspective on this is that the flexibility that makes these tools powerful can also make them harder to evaluate. Flexibility can adversely increase the Invisible Intelligence Gap, and I think certain types of AI-based solutions (LLM) may actually increase this because the boundaries of the product are often so much wider than ever before (if not invisible to the buyer). So, how to close this gap? Well, one way is to build a visual UI that showcases what's possible with your API/feed/data solution. You take the buyer out of the conceptual space and make things concrete. So today, that's what we dig into: when to consider adding a UI, how far you need to go with it, how you can use Copilot/AI agents to help customize these example implementations, and the benefits you might see.  Highlights / Skip to: The challenges of selling API-based analytics and AI products (0:56)  Why this topic matters right now (2:48) The Invisible Intelligence Gap that may be slowing your sales (3:34) Strategies for bridging the Invisible Intelligence Gap with a UI (user interface) layer (7:01) Client case study: the impact and results you may see adding a UI on top of your technical product (14:05) Signs that you should consider adding UI to your technical product (18:23) Leveraging humans' highly developed visual system to help potential customers see the full value of your product (26:24) Conclusion (27:32) Links Invisible Intelligence Gap Azeem Azhar's Exponential View (6/4/26 episode)  

Send us Fan MailThe Salesforce job market is finally showing a pulse, but it is not a comeback story. We sit down with Sasha Semyonova, a leading tech journalist at Salesforce Ben, to unpack what the latest hiring signals really mean for Salesforce careers, from admins and developers to architects and ISVs. If you have been watching job boards fill up with “unicorn” roles and deflating salaries, you are not imagining it, and we explain why it keeps happening.We talk candidly about the realities candidates face right now: market saturation, ghost jobs, layoffs, and the growing influence of AI on staffing decisions. Sasha shares what she is seeing across the Salesforce ecosystem, including the shift toward specialized specialists who can blend platform knowledge with business analysis, architecture thinking, and practical AI skills. We also dig into why blasting out AI-generated resumes is a losing strategy for most people and how being undeniably human can still cut through the noise when everyone else looks the same on paper.Then we look ahead. Headless Salesforce, a Slack-first workflow, Agentforce, and rapid UI changes are reshaping how users interact with the platform and what employers will expect next. For ISVs and partners, we cover the pressure to build agent-oriented solutions and the simple question that matters most: what do customers actually want from AI?Subscribe for more honest market breakdowns, share this with someone job hunting in Salesforce, and leave a review with your biggest question about where the ecosystem is headed next.

Recorded thirty minutes after the WWDC26 State of the Union keynote ended, The Trio delivers a hot-take reaction to everything Apple just announced. Steve makes his boldest claim yet: 2026 is the year the "Universal UI" era begins, anchored by a Siri demo that appeared to actually work in real time. Xcode quietly Sherlocked the Codex app, SwiftUI got reorderable containers and (finally) AsyncImage caching, and Aaron spotted some very suspicious folding phone tea leaves in the new Simulator replacement.## Chapters00:08 Introductions 01:31 Reviewing The Trio's "Universal UI" Concept 02:39 Comparison of "AI" Apps: Siri, Claude, Codex, ChatGPT 05:43 Multimodal Prompts & Private Cloud Compute 07:26 Foundation Model Device Requirements 09:50 Dynamic Profiles and Custom Model Configurations 13:41 Xcode 27 Sherlocked the Codex App 15:42 Xcode and Developer Tool Evolution 21:47 SwiftUI Updates: Reordering and AsyncImage Cache 26:14 A Grab Bag of Random Stuff 28:23 App Actions and Siri Integration 35:04 No Apple Claw? 39:09 Swift Compiler Unable to Type Check Error 40:37 Final Impressions 42:02 Folding Phone Tea Leaves 43:07 Snow Leopard Speed Improvements 43:53 Wrap Up & One More Thing... 45:50 Tag ## Show Notes- Steve declares 2026 the start of the "Universal UI era," with a live Siri demo that actually worked as his primary evidence.- Aaron clocked the demo as mostly staring at a loading spinner; Steve argues Apple had to prove the on-device inference wasn't faked this time.- The Foundation Models framework supports dynamic profiles: configurable system prompts, temperatures, and thinking budgets per scenario within a single app.- Xcode 27 ships an agentic coding UI seemingly inspired by the Codex app, prompting Kotaro to ask point-blank: "Are you saying they Sherlocked Codex?"- SwiftUI finally has a reorderable container, which The Trio immediately wants in Bento Fit after a previous attempt even an "AI" agent couldn't pull off.- AsyncImage gets a built-in cache after years of third-party workarounds; Steve suspects some intern with an unlimited Claude Code budget finally got it done.- App Actions now supports natural language invocation without requiring specific phrases or app name mentions, though exact limits remain fuzzy.- Aaron flags resizable iOS windows (previously iPad-only) and an arbitrary-aspect-ratio Simulator replacement as very suspicious folding phone tea leaves.- Kotaro closes on Snow Leopard-style speed wins across the board, including 80% faster AirDrop, because speed is still a feature worth shipping.## Links**One More Thing**Cleo Family: https://www.cleofamily.app/track**PhillyCocoa:** https://phillycocoa.orgIntro music: "When I Hit the Floor", © 2021 Lorne Behrman. Used with permission of the artist.

Fredrik snackar Kotlinconf 2026 och språket Kotlin i allmänhet med Johan Blomgren och Emil Kantis. Hur var konferensen? Hur fungerar utvecklingen av Kotlin, och vad är på gång i språket? Det blir tips på intressanta presentationer värda att se när de släpps på nätet, och en förklaring av varför Kotlinconfs officiella app inte känns helt hemma på Appletelefoner. Vi snuddar också - inte helt oväntat - vid språkmodeller. Vi pratar om AI, teknikutvecklingen, och presentationen av saker som oundvikliga kontra att bygga en bättre värld genom att helt enkelt prata mer med andra människor. Gärna öga mot öga också. Det är en mänsklig superkraft! Som avslutning bjuds på en snabb genomgång av anledningar att byta till Kotlin från Java. Ett stort tack till Cloudnet som sponsrar vår VPS! Har du kommentarer, frågor eller tips? Vi är @kodsnack, @thieta, @krig, och @bjoreman på Mastodon, har en sida på Facebook och epostas på info@kodsnack.se om du vill skriva längre. Vi läser allt som skickas. Gillar du Kodsnack får du hemskt gärna recensera oss i iTunes! Du kan också stödja podden genom att ge oss en kaffe (eller två!) på Ko-fi, eller handla något i vår butik. Länkar Johan Emil Java Kotlin Komma igång med Kotlin som Java-utvecklare Att övertyga andra om Kotlins storhet Helping decision makers say yes to Kotlin React Vue ATG Junit Kotest Kotlin-test Kotlinconf 2026 Keynoten för Kotlinconf 2026 Jetbrains utvecklar både IDE:er och Kotlin Javazone i Oslo Kotlin på Youtube - inklusive inspelninigar från Kotlinconf 2026 när de släpps KEEP - Kotlin evolution and enhancement process Local lifetimes i Kotlin Value semantics i Kotlin Rich errors i Kotlin Sum types Union types Javas projekt Leyden och projekt Valhalla Virtual threads i Java Kotlin multiplatform Compose multiplatform Kotlinconf-appen Stöd oss på Ko-fi! Erik Hellman Eriks presentation på Kotlinconf 2025 om IOT MQTT Matter Spec-driven development Jake Wharton - pratade om composebaserat terminal-UI-bibliotek Jesse Wilson Okhttp - Squarebyggt ramverk Lena Reinhard snackade om utvecklares roll i AI-världen Professional development - presentation från Google IO 2026 Clean code Kotlins LSP Junie - Jetbrains kodagent Lars Wikman Coursera-kurs om Kotlin för Javautvecklare Kotlin-övningar Builder pattern Bygga DSL:er med Kotlin WASM Uber snackar Kotlin Titlar Min Kotlinbana Kotlin på många olika sätt (Min upplevelse av) Sex år i Kotlin Bypassa hela stdout Jag är ju redan i utlandet Här slutade nullpointers En konstruktor som har alla parametrar

Evelyn was diagnosed with dyslexia as a child and spent much of her early education struggling to read and keep up in school. After moving to the United States in high school, she received Lindamood-Bell instruction that transformed her learning experience. She went on to attend her first-choice college, earn her degree, and build a successful career as a UI designer in the technology industry. In this episode, Evelyn reflects on the challenges she overcame and why she's passionate about helping others look beyond a diagnosis and recognize the strengths of individuals with dyslexia.  

Welcome to episode #277 of The COD Casuals, where we discuss all things Call of Duty, casual and professional!In this episode, we begin with some bigger and more important leaks regarding MW4. As last week's trailer was on the campaign, we now got a lot more information regarding multiplayer. Questions that were being asked in our last episode have now been answered, and we have some positive and negative feedback. We give our thoughts on what we know now, as well as give our first impressions on some leaked screenshots of the updated UI and custom class system. We then discuss the preordering for this game, and some of the pros to preordering MW4, and some of the reasons why it's best to wait. Finally, we close with our CDL update, as we have some roster changes that have been put into place, and now we get to see them in action after a weekend of qualifier matches. What do you think about some of the confirmed MW4 multiplayer news? Does it get you more or less excited for the game? Do you agree that this is a very important year for COD, and it will be treated that way, or will it fall short like it has the past few years? Will you be preordering the game even though we have no multiplayer gameplay yet, or do you preorder COD every year? Please let us know as we discuss this and much more! Hope you all enjoy the episode and we'll see you next week.Join the Discord!https://discord.gg/XjBWUj4KtVFollow us:Twitter: ⁠⁠@TheCODCasuals⁠⁠Instagram: ⁠⁠@TheCODCasuals⁠⁠TikTok: ⁠⁠@TheCODCasuals⁠⁠Contact us:Business Inquiries: TheCODCasuals@gmail.com

Six years after our “Score preparation and production notes” episode — Episode No. 2 — essentially launched the podcast, 163 episodes later, Philip Rothman and David MacDonald return to the article that inspired the conversation: David’s score preparation checklist. The principles — respect for performers, readable parts, enough time for page turns — are as true as ever. But almost every specific tool reference in the original has a fuller story now. The conversation moves section by section, serving as a reminder of the timeless principles and exploring all of the meaningful changes in the technology. Dorico’s live-reference cue system has become the standard no one else has matched — and the ease of it has quietly changed how generously cues get applied. The Dorico 6 Proofreading panel represents a new category of preparation tool, while the Sibelius plugin ecosystem has its own parallel answers. The condensing and decondensing workflows now available in both Dorico and Sibelius 2025.2 have transformed what was once among the most tedious jobs in parts preparation, and Sibelius 2025.7’s Auto-Respace toggle closes a gap that used to just be accepted. Two sections are entirely new to the checklist: digital delivery — where the iPad has become as common in rehearsal as a music stand — and a pointed look at the file-organization habits that make or break a delivery package. This one’s chock-full of tips, resources and advice — with David’s updated accompanying article to come soon. Products mentioned Notation software Dorico (Steinberg) Sibelius (Avid) MuseScore Studio (Muse Group) Finale (MakeMusic) (mentioned as discontinued) Fonts MusGlyphs (available at Notation Central) NYC Music Services / Notation Central PDF Batch Utilities Desktop publishing and document tools Affinity (Canva) (now free) Apple Pages Microsoft Word LibreOffice Other tools mentioned Claude Cowork (Anthropic) (mentioned for AI-assisted file organization) Name Mangler / Renamer (mentioned briefly for file naming) forScore (mentioned as a score-reading app) Previous Scoring Notes posts and podcast episodes Directly mentioned or closely related: Score preparation and production Notes (David’s original 2018 article) Score preparation and production checklist (Episode 2, 2020) Partying with parts, part 1 (podcast, December 2021) Partying with parts, part 2 (podcast, December 2021) Orchestra librarians want you to know about parts paper sizes (May 3, 2022) Orchestra librarians want you to know about instrument names (June 20, 2022) Behind “Behind Bars” with Elaine Gould (podcast, July 2023) Behind Bars: General Conventions edition published (June 2023) Dorico 6: Proof positive (review, April 2025 — Proofreading Panel) Dorico 6.0.22 extends proofreading capabilities (July 2025 — ignore feature) Sibelius 2025.7 brings note spacing control, UI updates (July 2025 — Auto-Respace) Sibelius 2025.2 introduces decondensing parts with staff filters (February 2025) Sibelius 2022.5 brings multi-section headers, other workflow boosts (May 2022) MusGlyphs: an advanced music text font (April 2021) PDF Batch Utilities get a major rebuild — and a brand new app (March 2026) Freshly pressed (podcast, April 2026 — PDF Batch Utilities in depth) Calculate the weight, basis weight, or grammage of paper (April 2025) Chronology of a perfect music printing job (January 2022) DJA’s Notes: Music preparation basics (Darcy James Argue, September 2023) Documenting the documenter: Lillie Harris (podcast, April 2021 — Dorico manual) David MacDonald’s updated Score Preparation and Production Notes article Other references Elaine Gould, Behind Bars: The Definitive Guide to Music Notation (Faber Music) — cues: p. 566; front matter: chapter 17, pp. 501–504 Elaine Gould, Behind Bars: General Conventions (Faber Music) — the first third of Behind Bars as a standalone paperback and e-book MOLA Guide (Major Orchestra Librarians’ Association) — free PDF download Sibelius plugins page (still active at sibelius.com) Darcy James Argue, Music Preparation Fundamentals for Jazz Composers & Arrangers — free download Darcy James Argue, Music Preparation for the Large Jazz Ensemble — free download (supplement to the above)

Priznana literarna revija Granta je pred meseci v Veliki Britaniji objavila nagrajeno zgodbo, ki je bila napisana z umetno inteligenco. V Združenih državah pa odmeva primer velike založbe, ki je tik pred izidom ustavila izdajo romana, in to po pritisku bralcev in novinarjev, da avtorica knjige ni napisala sama, ampak ob pomoči umetne inteligence. Zakaj strokovna javnost, ki podeljuje nagrade, ne loči med strojem in človekom? Kako se na pojav umetne inteligence v literaturi in založništvu odzivajo domače založbe in kako bralci sprejemamo spremenjena pravila igre?Aljoša Harlamov je bralec, pisec in urednik. Letos je izdal kriminalko Dohtar in povodni mož, ki jo je v celoti napisal sam. Zapiski: Odbit Discord Oglasite se lahko na odbita@rtvslo.si Poglavja: 00:02:41 Nagrajena kratka zgodba, ki jo je napisala umetna inteligenca 00:05:29 Razmere v založniški industriji in vpliv tehnologije 00:09:49 Kako UI vpliva na slog pisanja in moralna panika 00:15:19 Primer umaknjene knjige Shy Girl 00:22:16 Kako slovenska literarna stroka sprejema umetno inteligenco 00:25:34 Primer Super!založbe in normalizacija tehnologije 00:31:00 Raziskovanje z UI vs. človeška izkušnja (Primer Olge Tokarczuk)

Greg Ceccarelli is Chief Product Officer at Spec Story, an AI-first startup building tools to make AI coding easier and safer. Before Spec Story, Greg held product leadership roles at Pluralsight (CPO), GitHub, Dropbox, and Google, and earlier spent years as a consultant at Alixpartners and IBM. In this conversation, Greg and Tom cover: Moving fast vs. planning — Greg's "cut twice, measure once" philosophy, why most decisions are reversible, and what happened when he pushed back on a private equity firm's annual planning process AI and software development — How AI agents are compressing implementation time, changing the economics of software, and flipping the traditional "longest pole in the tent" from engineering to decision-making Spec Story and Stoa — How Spec Story started by preserving AI chat history for developers, and why Stoa is now focused on capturing collaborative meeting context so teams can move from decision to implementation faster SaaS pricing — Why seat-based pricing is past its expiration date, and how Stoa's $5/hour model is designed to remove friction, align with value delivered, and eliminate the token-opacity problem The future of SaaS — Headless software, API-first systems, and whether agents will make traditional UI obsolete Distribution and marketing — Why distribution has gotten harder, not easier, why authentic human content outperforms engineered content, and what questions every founder needs to keep asking about their customer Core competency — Greg's answer: asking questions, and the compounding value of learning velocity over specialization

Most growing companies are held together by spreadsheets that nobody fully understands — built by someone who left three jobs ago, maintained by someone who doesn't know why it exists, and quietly critical to daily operations. In this episode, Jeff Mains sits down with Garrett Fritz, co-founder of MetaCTO, a fractional CTO firm that helps mid-market companies transform outdated operational processes into custom, scalable software.Garrett breaks down why so many organizations are trapped in the "if it ain't broke, don't fix it" mindset, how AI has lowered the barrier to custom software without eliminating the need for expertise, and when it actually makes sense to build your own tool versus buying off-the-shelf SaaS. He also shares how internal tools can evolve into white-labeled revenue generators — and the most common mistake founders make when they try to take that leap too fast.Whether you're drowning in manual processes, questioning your SaaS spend, or wondering how to implement AI responsibly, this episode delivers a practical, no-hype roadmap.Key Takeaways4:37 — **The #1 operational inefficiency Garrett sees:** Hundreds or thousands of employees running mission-critical operations on a spreadsheet built a decade ago by someone who's since been promoted — and nobody knows why it has the formulas it has. 6:15 — **What "turning spreadsheets into apps" actually means:** MetaCTO embeds in the business, decodes the spreadsheets, understands the workflows, and builds working software that can replace the internal process — or be taken to market as a SaaS product. 7:54 — **Profitable from day one:** Because Garrett and his partner came with a thick Rolodex from 15–20 years in tech leadership, MetaCTO launched with clients already lined up — no burning cash to find product-market fit. 13:27 — **70% of AI POCs never see the light of day:** The excitement dies when teams realize how much effort is involved. MetaCTO's focus is getting those 90%-done prototypes all the way to the finish line. 18:34 — **Build custom vs. buy SaaS — the real decision framework:** After 2–4 weeks embedded in a business, MetaCTO looks at licensing costs, actual feature utilization (often just 2% of the SaaS product), man-hours wasted, and growth trajectory to determine the ROI break-even point. 28:25 — **Niches win:** SaaS isn't dead — it's narrowing. The companies gaining ground are building hyper-specific tools for specific industries (think: Procore, but only for commercial plumbers) where the UI, reports, and workflows are built around exactly how that niche operates. 31:33 — **The #1 mistake when productizing internal software:** Not talking to the second customer. Your problems aren't always everyone else's problems. Validate outside your organization before building for market, or you risk six months of rework when the deltas turn out to be core to the platform. 33:40 — **How to actually quantify the ROI of custom software:** Bake usage analytics into every product from day one. Track utilization, time on platform, transactions processed, and revenue generated — then compare to the man-hour cost baseline captured during discovery. 39:14 — **Responsible AI implementation starts with one rule: Resist "Accept All."** Don't grant admin tokens to AI agents for convenience. Suffer through permissions early so you don't face irreparable reputation or business damage when a bad actor exploits an over-permissioned agent. 41:22 — **The smartest first step for any leader feeling stuck:** Use AI tools like Replit to build a prototype with fake data. Don't try to connect it to real systems — just use it to force yourself through the problem-solving process. Come to the conversation with a working wireframe and you'll skip weeks of expensive discovery.Tweetable QuotesAt the heart of it is some Excel spreadsheet that some employee made 10 years ago — and it is critical to the operation." — Garrett Fritz"70% of AI proof of concept projects have never seen the light of day. It's pretty common to get excited about something and then realize, oh, this is a lot more effort than we thought." — Garrett Fritz"You can't just give a layman a chainsaw and expect to be a carpenter. A little bit of finesse and experience goes a long way." — Garrett Fritz"The niches win. The companies gaining ground are building hyper-specific tools for specific industries — where the UI, reports, and workflows are built around exactly how that niche operates." — Garrett Fritz"We never build it and run away. And as you can imagine, anyone who's created a piece of software has never said 'I'm done' either." — Garrett Fritz"Resist 'Accept All.' Give the AI admin access for convenience, and you're one bad actor away from irreparable damage to your business." — Garrett Fritz"AI is most valuable when it's applied to real business friction — not just trendy experiments or chatbots. Nobody needs another one of those." — Jeff MainsSaaS Leadership Lessons1. Familiarity is the enemy of efficiency. The "if it ain't broke, don't fix it" mentality keeps organizations locked in spreadsheet-driven operations for years — sometimes decades. The pain point has to get big enough to justify change, but by then the cost of switching is enormous. Don't wait for a crisis to modernize.2. The barrier to custom software has dropped — but expertise still matters. AI tools like Replit and Lovable have made it possible for non-developers to prototype software. But there's a massive gap between a 90%-done prototype and a production-ready, secure, maintainable application. Knowing what you're doing still matters.3. Don't buy features you'll never use. Most enterprise SaaS customers use 2% of the product's functionality — but pay for 100% of the license. When your team is only using 2% of the product and only 50% of the people who should be using it actually are, you're compounding inefficiency at every layer.4. Build for the second customer before you build for the market. If you think your internal tool has market potential, validate it with people outside your organization before investing further. Your problems are not automatically everyone else's problems. The cost of discovering core delta requirements after six months of development is enormous.5. Measure everything from day one. Custom software that doesn't have baked-in usage analytics is a black box. You can't demonstrate ROI, you can't justify ongoing investment, and you can't make intelligent roadmap decisions. Instrument every product with utilization metrics, transaction data, and performance monitoring from the start.6. AI governance isn't optional — it's the first conversation. The most dangerous thing you can do is grant your AI agents broad permissions during development and never revisit it. Treat AI like a junior employee: define its scope, limit its access, and require human approval for anything with downstream consequences. Someone always has to be the final buck.Guest Resourcesgarrett@metacto.comhttps://metacto.com/https://www.linkedin.com/in/grfritz/https://www.linkedin.com/in/grfritz/Episode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

A bi-weekly news show informing you on the latest in Bitcoin, privacy and open source tech hosted by Ungovernables, Max and Q. AOBFTF with ZachQ eurotripNew Foundation websiteNEWSU.S. Treasury seizes nearly 1B in Iran-linked crypto, Tether freezes 344M USDT on Tron https://bitcoinmagazine.com/news/u-s-treasury-the-united-states-iranThe Mined in America Act would put the Bitcoin network at riskhttps://www.therage.co/mined-in-america-act-bitcoin-at-risk/CVE in Core Lightning: Optech #407 disclosurehttps://bitcoinops.org/en/newsletters/2026/05/29/Introducing Cube: Burak unveils a trustless Bitcoin smart contract L2https://medium.com/cube-bitcoin/introducing-cube-8b3702e470a5Published: May 2026Anonymous plaintiff sues for title to $293 billion in dormant Bitcoinhttps://bitcoinmagazine.com/news/anonymous-plaintiff-seeks-legal-bitcoinPublished: 2026-05-28The U.S. Constitution inscribed on the Bitcoin blockchain via expanded OP_RETURN https://bitcoinmagazine.com/news/someone-inscribed-the-constitution-bitcoinPublished: 2026-05-29RELEASESBitcoin Protocol, Core, Knots, SecurityCore Lightning v26.06rc2 — 2026-05-22Release candidate 2 for CLN 26.06. Documentation and gRPC interface refinements on top of rc1's graceful command, sendamount RPC, and BOLT12 payer-proof support. Routing-node operators should test on a non-production node before adopting.Eclair 0.14.0 — 2026-05-21Significant Lightning release from ACINQ. Final versions of channel splicing, simple taproot channels, and zero-fee commitments all ship in this version. This is the Eclair side of the same protocol work showing up in CLN and LDK. If you run an Eclair routing node, this is the upgrade to track.Hardware Signers and Hardware-Wallet AppsColdcard MK5 launch — 2026-05-29New flagship hardware. Larger Gorilla Glass screen, redesigned buttons, improved NFC, dual secure element architecture retained. Already supported in Bitcoin Safe 2.0.0rc0 from earlier this fortnight.Frostsnap 0.3.0 — 2026-05-27Headline change: deterministic firmware build with cryptographic digest verification. So end users can independently verify the firmware binary matches the source. That is the right direction for any hardware signer carrying real money.Keystone 3 v2.4.4 — 2026-05-26Wallet connection removal, Zcash SLIP39 support added, device verification fixes.Trezor Suite v26.5.1 — 2026-05-27 (FTD re-surfacing)Adds ERC-681 QR code support in the send form. Show editorial: only relevant if you use Trezor for Ethereum-side workflows, not a Bitcoin-only change.Ledger Live Desktop 4.5.0 — 2026-05-21Bridge integration refactoring across desktop and mobile.Ledger Live Mobile 4.6.0 — 2026-05-28Async API updates and bridge resolution improvements.Software WalletsSparrow Wallet 2.5.0 — 2026-05-21Headline feature: Silent Payments receiving wallets, including support for airgapped hardware wallet signers. Adds frigate.2140.dev as a Silent Payments capable public Electrum server, auto-selected when required. Plus a BIP32 derivation fallback when retrieving signing nodes for high-index inputs. This is the biggest privacy upgrade of the fortnight in any consumer-facing Bitcoin wallet, and the airgapped-signer support means Coldcard and similar users get it without going hot.Sparrow Frigate 1.5.3 — 2026-05-30Adds a privacy-preserving hourly aggregate of historical scan stats, locally generated server.features response when the backend returns a method-not-found error, improvements to the hosts field in server.features.Bitcoin Seed Tool 2.3.0 — 2026-05-19 (borderline, in grace)Educational interface redesign with violet accent color and integrated learning features.Nunchuk Android 2.5.2 — 2026-05-27"Bug fixes and improvements," nothing detailed publicly.Liana Business v0.1 — 2026-05-20First alpha of Liana's business product line. Environment variable support for signet testing. New product tier from Wizard Sardine for business-focused multisig with timelocked recovery.Peach Bitcoin 0.69.0 (build 350) — 2026-05-19Encrypted backup of custom payout addresses, restoration guidance, camera permission fix, push notification translations.Lightning, L2, ScalingPhoenix 2.8.0 — 2026-05-22UI fixes on Android: scanning inverted QR codes, a button to use the entire available balance when paying Lightning.Phoenixd 0.8.0 — 2026-05-20Upgraded lightning-kmp dependency to 1.12.0.ZEUS 13.0.2 — 2026-05-21Stable release of the RC chain we previewed last fortnight. New default RGS server at rgs.zeusln.com with 15-minute graph updates instead of 3-hour. Improved clipboard, NFC, UI improvements.Arkade arkd v0.9.6 — 2026-05-26Package and component renaming, CI workflow improvements, golang version bump.Arkade TS SDK @arkade-os/sdk 0.4.32 — 2026-05-29Maintenance bump.Arkade TS SDK @arkade-os/boltz-swap 0.3.37 — 2026-05-29Maintenance bump on the Boltz-swap helper.ThunderHub v0.18.4 — 2026-05-29Native display formatting for trading distribution, better CLTV headroom in route building.Blink Mobile 2.4.49 — 2026-05-30Bug fix: removes ABI-prefixed versionCode overrides.LNbits v1.5.5-rc1 — 2026-05-24Release candidate.Mostro v0.17.4 — 2026-05-22Payout confirmation to winner, solver-directed dispute slash, concurrent taker bonds with first-to-lock wins, MOSTRO_NSEC_PRIVKEY environment variable, Yadio price tolerance fix.Bisq v1.10.1 — 2026-05-30Raises trade amount limits to 0.250 BTC after the v1.10.0 post-exploit reset. Adjusts risk-based reduction factors. Fixes a BSQ swap validation bug.Bisq v1.10.0 — 2026-05-17 (carries over from last fortnight as final tag on cutoff day)The post-incident hardening release we covered last fortnight: trade protocol validation, PGP supply-chain verification, 0.125 BTC initial cap, macOS Apple Silicon support.EcashCashu TS v4.5.1 — 2026-05-23Deprecates the current checkProofsStates method in favour of a v5-compatible one. Wallet builders should plan the migration.Fedimint SDK canary release — 2026-05-27React Native transport: flattened RPC payload, persistent callback. Rolling canary channel.Bitcoin Dev InfrastructureBDK FFI 3.0.0 — 2026-05-29Major version of the BDK language bindings. Anyone shipping a wallet on top of BDK should read the migration notes carefully.Liquid GDK 0.77.4 — 2026-05-27Rate-limiting error handling, Rust dependency updates, UTXO retrieval fixes, build improvements.Self-Hosting and Sovereignty InfraJoinMarket-NG 0.31.1 — 2026-05-30Privacy-critical fix: prevents a Sybil DoS where relayed !hp2 floods could starve a maker's own post-ioauth commitment broadcasts. Also installs whiptail in maker and taker container images so the jm-ng TUI works out of the box. JoinMarket-NG continues to ship hardening on a tight cadence.Tor Browser 15.0.14 — 2026-05-19 (borderline, in grace)Important Firefox security updates rolled in.Mullvad Browser 15.0.14 — 2026-05-19 (borderline, in grace)Firefox 140.11.0esr base, NoScript 13.6.19.1984.Nostr (Bitcoin-relevant)Amethyst 1.11.0 — 2026-05-20Restores Lightning Address and LNURL fields in Edit Profile. Useful: those fields were missing for a stretch and creators relying on zaps as a revenue stream were getting cut off in profile edits.EDUCATIONTFTC retrospective: Why Keonne Rodriguez is in prison for building Samourai Wallet — 2026-05-28Bitcoin Optech Newsletter #407 — 2026-05-29CLN vulnerability disclosure (already in news), transcripts from a May Bitcoin Core developer meeting covering SwiftSync, cluster mempool, Erlay redesign, package relay. Eclair 0.14.0 and CLN 26.06rc2 release context.Bitcoin Optech Newsletter #406 — 2026-05-22BIP322 advances to Complete status with human-readable prefixes and PSBT support. TCP hole punching for Bitcoin nodes behind NATs (we flagged this Delving Bitcoin thread last fortnight). Services section highlights Ibis Wallet (BDK-based with coin control and Tor), LDK Server, Mempool.space taproot visualization.Bitcoin Optech #406 recap podcast — 2026-05-26Discussion of BIP322 updates, TCP hole punching, Ibis Wallet, LDK Server, Mempool.space v3.3.0, peer-observer infrastructure.Bitcoin Optech #405 recap podcast — 2026-05-19Bitcoin Core CVE-2024-52911 discussion and the UTXO-set P2P sharing draft BIP with Fabian Jahr.Rainey's book on financial censorshipMentioned by Gladstein on 2026-05-21 as quoting his work on the war on cash and the blocksize war. Plug in education / further reading.TO DONATE TO ROMAN'S DEFENSE FUND: https://freeromanstorm.com/donateHELP GET SAMOURAI A PARDONSIGN THE PETITION ----> https://www.change.org/p/stand-up-for-freedom-pardon-the-innocent-coders-jailed-for-building-privacy-tools DONATE TO THE FAMILIES ----> https://www.givesendgo.com/billandkeonneSUPPORT ON SOCIAL MEDIA ---> https://billandkeonne.org/VALUE…

Comment on the show?..send me a text!The latest iRacing Development Update has arrived and there is plenty to discuss!In this episode of The Lone Road iRacing Podcast, host Guy Robertson takes a deep dive into the biggest announcements from the May 2026 Development Update, including the arrival of Dirt AI, the brand-new Formula Vee, the BMW M2 Racing car, UI improvements and what these changes could mean for the future of iRacing.Then we tackle one of the biggest debates in sim racing:Which continent has the best race tracks?Europe, North America, Oceania, Asia, South America or Africa?From Spa and the Nürburgring to Bathurst, Suzuka and Road America, we explore what truly makes a great racing circuit and ask listeners to vote for their favourite continent.Finally, in this week's iRacing Agony Uncle, we answer a listener question about buying content. How much content do you really need? Should you buy every new release? Or focus on mastering the cars and tracks you already own?

Godforge Beta feedback continues with a deeper look at what could make the game stand out from other hero collectors. In this episode, Quantum and MTG Jedi join Brad and Stephen to discuss cinematic visuals, performance, combat UI, customization, narrative, economy pain points, placeholder art, Beta 2 expectations, and how Fateless is using community feedback to shape the game. Fateless is a dynamic game studio founded by passionate content creators Simon Lockerby (Hellhades), Dan Francis (Phixion), and Hisham Saleh (Sham). Our mission is to create community-driven, immersive RPG Hero Collector games that emphasize player agency, storytelling, and strategic gameplay. Join us as we share our journey from concept to launch and beyond.Support the show

If you aren't a tech nerd like us, you probably didn't even know Computex and Microsoft Build were happening. And that's alright. Everyone besides Apple is trying to get their announcements out there before WWDC kicks off next week. Watch on YouTube! - Notnerd.com and Notpicks.com INTRO (00:00) WWDC next Monday at 10! (03:30) MAIN TOPIC: NVIDIA, Microsoft, and More drop their news before Apple (04:45) Computex 2026: All the news and announcements What we learned at Microsoft Build: Autopilots, MAI-Thinking-1, and Nvidia RTX Spark Dell stock skyrockets 32% for its best day ever as AI server revenue soars Microsoft is killing Office 2019 for Mac and iPhone, and you can't do much about it DAVE'S PRO-TIP OF THE WEEK: Rule of Thirds! (19:55) JUST THE HEADLINES: (27:20) Perfect randomness realized for the first time A fundamental principle of aeronautical engineering has been overturned Meta AI support bot helped hackers hijack Instagram accounts Roku updates its UI for the first time in a decade Something made Earth's molten core reverse direction in 2010 YouTube to automatically detect, label AI-generated videos Google requests permission to release 32 million mosquitoes in California and Florida LISTENER MAIL: From Hey Grandma - The Year 2038 problem (31:05) WITHIN REACH! Dave 8-6, Round 14, Nate goes first (35:25) TAKES:  Facebook Plus, Instagram Plus subscriptions launch for $3.99/month (42:15) Nintendo's new Pictonico iOS game turns your photos into minigames (46:45) Not news: iOS 28 will reportedly be 'far more significant' than iOS 27 (48:20) BONUS ODD TAKE: Magnified Sand (50:15) PICKS OF THE WEEK:  Dave:  PUGG Wall clock, stainless steel, 12 ½" (54:25) Nate: Upgraded 67mm Phone Lens Filter Adapter Mount for iPhone 17 16 15 14 Pro Pro Max Plus Air, Double-Sided Magnetic Phone Lens Filter Ring with 1/4-20" & Cold Shoe Pull-Out to fit 17 Pro Max (No Filters (58:20)

We've informally heard that Satya is a listener to LS for a couple years now, but it was still absolutely surreal to meet him and do a live pod at Build, together with our friends at No Priors, the leading VC AI Podcast that we also greatly admire!We covered the MAI model technical takeaways on yesterday's AINews, so I will focus our recap of Satya's main messages around three elements:* Satya's adaptation of the Bill Gates Line for positioning Microsoft as the Frontier Intelligence Platform — customers must gain much more value from the Microsoft ecosystem than Microsoft itself, by building on multi-model harnesses like OpenClaw and Scout, drawing on the full enterprise context exposed by context layers like Work IQ (heavily dogfooded by his C-suite), and building up private evals and traces as a new form of Token IP* AI ROI: On one hand, enterprises are having difficult conversations around Tokenmaxxing and Layoffs, and on the other hand, there are serious re-evaluations of the End of SaaS since the Build vs Buy equation has changed so much. Our previous SemiAnalysis guest had… interesting comments on Microsoft's position on this as the ur-SaaS titan, and Satya had great answers* Making the Impossible Possible: Kevin Scott's inspiring framing around what the most ambitious version of applying AI and technology at large to business and social problems, like education and social impact.Enjoy!Full VideoTranscriptVoiceover: Welcome swyx, Sarah Guo, Elad Gil,, and Chairman and Chief Executive Officer of Microsoft, Satya NadellaSarah Guo: Welcome to a crossover episode of No Priors and Lane Space with Satya Nadella. Um, congratulations on an amazing build. No, thank you so much, and it's great to be with both of you. I listen to both of you or b- both the podcasts all the time. It's great to be on it.Thank you so much. [00:01:00] So you're just talking about, um, these amazing, uh, announcements from across the Microsoft estate all morning for, I think, three hours. What is the, uh, what's the most important reflection or takeaway you have?AI as an Ecosystem PlatformSarah Guo: I, I'd say there are, uh, perhaps the, the biggest one for me is let's sort of conceptualize this more as an ecosystem play as opposed to a single model or even a single platform, right?Satya Nadella: I mean, you know, whatever I... At least for me, having grown up at Microsoft, having seen, whatever, four major platform shifts, uh, I sort of fall into that, um, uh, camp where a platform is defined by fundamentally its ability to create more value about the platform versus what's captured in the platform. And so if you, you view what's happening right now, I think this morning's keynote was how can any company, whether it's an AI native company or a traditional enterprise company, participate as a first-class participant where they can point to AI they created, [00:02:00] right?It's not that they don't use other people's AI. Of course they will. But to me, what's the path? What's the recipe? How do I do it? What does a stack look like? What does the tooling look like? What is valuable? How do you do that? That's it. That's sort of our job to do. Yeah. Ecosystem strategy is, uh, very complicated, right?Sarah Guo: Because you end up building certain components, partnering for certain components, supporting them. You just announced this big suite of models. Like, tell us a little bit about the, uh, training strategy for Microsoft now. Yeah.MAI Models & Training StrategySarah Guo: So, so the thing that we wanted to do with the MAI models was to build, and as Mustafa talked about, first of all, a great lineage, right?Satya Nadella: Starting with pre-training, uh, with very good data quality, uh, doing all the ablations, making sure because in, in some sense it's becoming even harder to build a clean lineage model just because there's so much stuff out there, uh, that you truly need to ablate out to be able to have a fantastic [00:03:00] pre-trained model.In fact, that's one of the challenges of a lot of the open weight models is they look great on one benchmark or two, but they're not great on practice. So that's why, in fact, even in the RFDEs are, they, they are pretty gone really excited about these MAI models because how the heck can a small five B model hill climb?Uh, and it goes back a little bit to what I think is ultimately the key thing to do, which is try to pursue finding that cognitive core. Uh, so to me, starting with a clean lineage- Then creating that ability for companies to be able to use this, right? Not just as a generalist, but to create their own specialist by building this hill climbing scaffold around it, right?So it's not just the model, but you have a hill climb scaffold around it, then you will start building your RLE. You will start collecting the traces. Most importantly, you'll have private evals because we know all the evals out there are good, interesting, [00:04:00] but they're not really that critical- They're work, yeahSwyx: at this point because they all can be maxed. And so the point is each company will have its own private eval. And so that end-to-end platform story around our models is sort of, uh, what I think is interesting. And then the one other thing, Sarah, since you brought that up, is I do feel there's a new frontier.Satya Nadella: Like people talk about the frontier and are you operating at the frontier. Um, interestingly enough, if you add a little temporality to it, you can use, let's say, in, in, in fact, the, the Lando Lakes demo we showed was pretty cool. We used, whatever, GPT-55, right? Then you collected a bunch of traces, and then you took a 5B reasoning model and achieved higher.Sarah Guo: Uh, so that is another aspect of what it means to appear... uh, you know, operate at the frontier Yeah. I, I think, uh, I first of all have to congratulate you on basically building a frontier neo lab inside of Microsoft in two years. Um, I'm wondering, you know, you have all this AI strategy that you're rolling out.Lessons from Two Years of AI DevelopmentSwyx: I'm wondering, what do you know now that you wish you would tell yourself two years ago where- or two or [00:05:00] three years ago? Three years for the Jensen partnership, two years for, uh, MEI. Yeah, I mean, I think the, the thing when, that I reflect quite a bit, right, which is sort of obviously I got into all this when I got excited by the, the scaling laws paper and, you know, when, you know, even the OpenAI partnership came about when those folks said, “Hey, we're gonna really throw a lot of computer transformers.”Satya Nadella: Uh, and they've helped. I- the thing that I always look back and say, “Wow, these things, uh, do have capability that they're climbing up.” W- I mean, this, you know, this crude way of saying it is intelligence is log of compute kind of works. Now what I think we underestimated perhaps is the real-world complexity of deploying these so that they actually deliver the value in the real world, right?So the outcomes as measured by any benchmark is interestingly important, but the true eval is when people out there are able to do unique things that they only can value, and it's very [00:06:00] measurable, right? That I wish we had sort of even, like, had more in our consciousness, right? Which is as an industry.Sarah Guo: Because right now I think when people say, “Wow, I don't want a token max,” it's an artifact of us not having thought ourselves as an industry that we are using tokens to create value every step of the way. So I think that's kind of what I wish we had gotten there, but I'm glad we are here.Real-World Value & Use CasesSarah Guo: What are some of the use cases that you've seen that have created the most value for your customers?Because I know that people talk a lot about code, and I think it's pretty clear that that's something that's having very large scale impact. Are there other areas that you find in common that your customers are really benefiting from? Yeah. I think, yeah, to your point, obviously coding is now got... But it's interesting, by the way, Elijah, to even talk about the coding, right?Satya Nadella: Which is coding has worked so well that we now have to rebuild the IDE, right? I mean, it's kind of nuts to see what we sh- launched is like, oh my God, I have these hundred agent sessions. I... The cognitive load it transfers back to me as a human is so [00:07:00] excessive that now I need a new UI. Uh, oh, by the way, I, like the, the chat as the only artifact was also impossible, so that's why we need a canvas.So it's kind of interesting for all the things about where is software needed or where is UI needed, uh, you kind of need that even for code, right? In a fully agentic world. But that said, one of the things that we are starting to see, we started seeing with co-work, but even some of the work we, we showed with auto com- uh, um, autopilot Right on what you see with claws is a good one because if you sort of think about a lot of human capital is doing the glue work, right?If you now can augment that with tokens/agents that are long-running, durable, right, then your ability to scale even what is still judgment and glue work gets amplified like coding does. Uh, so you can... Like, I'm positive that six months from now we'll all be saying, “Oh, wow,” like, all through ni- the night there was a bunch of stuff that [00:08:00] all these autopilots that I have working on my behalf with my delegated authority, so to speak, right?I can... Sort of given even my identity, did a bunch of work, then of course I'll need my new ADE to say, “Well, what did you do?” Like, I might... “Did I do this work?” And so on. So I think that that's where compressing of workflows, uh, completing of tasks, uh, that's where I think a lot of the value gets created. I think you raised a really interesting point, which is there's the actual agent that's doing the code, and then there's a harness around it, and that's the environment, that's the context, that's everything you're setting up as a developer around actually a coding agent.The Harness Concept for Enterprise AISarah Guo: What is the harness for the enterprise? Is there an equivalent concept for broader productivity work, or how do you think about that concept sort of generalized? That's right. So, so in some sense you kind of want the harness to define the models, the, the data, uh, and the tools, and so that you have a loop across those three.Satya Nadella: And so what we are trying to, first of all, make sure is each of our products that we build, right, whether it's GitHub Copilot or the security copi- the, the [00:09:00] stuff we showed with MDASH or even the discovery for science, it doesn't matter, all of them are multi-model harnesses, um, with tools access so that you can do this progressive, uh, disclosure of tools even so that they're token efficient.Uh, and then you're feeding it with very rich context because that's sort of the other hard lesson we have learned in the last two years is, oh my God, the amount of work you need to do to prep the context layer, uh, such that your plan can execute in the most efficient way is where the magic is. So we have, in our case, we have the GitHub harness, which essentially we're using across all our products.It's available in Foundry, and we are open, like you can use your Llama harness, whatever. Or you can use the, um, uh, you know, any open harness or any harness of yours and train with your tools and multiple models and your context. And so that's the pitch. Because right now a lot of dialogue is, um, “Hey, if I train the harness plus tools and the model together, you get [00:10:00] evals.”Elad Gil: And what we are proving out is... And the best example of that is what we did with MDASH, right? Because when it launched, uh, it found bugs or vulnerabilities that were not found by Mythos Uh, and so there is existence proof, I would claim, that you can have a multimodal harness, uh, that can in fact be more, uh, performant in the real world So a premise behind the, uh, training at the independent frontier labs is really, you know, we're gonna have these models, and we'll have an API business, and we'll support enterprises and startups.Sarah Guo: ButPlatform Strategy & Developer EcosystemSarah Guo: a first-party product, be it productivity or code or search, drives the majority of revenue. That's a different value equation than you're describing, I think, with the Microsoft ecosystem. Uh, if, if that's the case, tell me if it's the case, uh, ‘cause obviously you have first-party products and you have enablement products.Satya Nadella: Um, what is the role of the develop- Like what is gonna be hard and the set of skills and the value capture the developer has in that world? Yeah. So I think that there's always [00:11:00] gonna be the case that someone who is super successful in- as a platform builder can also have first-party products. It was true with Windows.It is true, uh, with, uh, the, the SaaS side and the cloud side as well with us and others and so on. But the thing that is, is it should not be a limiter to other people achieving that same success, right? That I think is the core difference, which is the, the network effects this time around, around intelligence are such because they learn from data, and not really lots of data.It's just a few samples that you have to see to understand what's novel about something. So that's why the game becomes how to protect. So that's why I would say every company, having private evals may be the biggest IP, right? Think about it, like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces may be one of the biggest [00:12:00] drivers, uh, of IP.Like, so in other words, another te- acid test is you have an eval that's private. You're using, uh, a g- a Model A. Can you switch it to Model B and e- you know, climb up? If you can, then you're in control. If you can't, you're not in control, and that's where even the harness decision becomes super important, right?swyx So therefore, having an open harness, letting all models come in, having your evals, your context, your tools help you hill climb, I think is the skills that an AI native startup needs, a SaaS company needs, or every enterprise needs. Yeah, I think in, in a very real way you are ... Microsoft historically is an operating systems company and th- then become a cloud company.Maybe like the third act is that you're a harness or evals company. Whatever w- ... whatever the, the sort of conglomerate of concepts that you wanna put together. Um, and, and I think like enabling every company to have like frontier intelligence or what- what- Yeah ... I forget the, the [00:13:00] exact term that you used, um, is the, is the mission, right?Satya Nadella: That's it. Like that is, that is the platform promise, that you build with us, you will get your intelligence, uh, for your data. That's it. That ... To, to me, that is the ... Like if there was one tagline, uh, for this entire developer conference is- Can everybody operate at the frontier with their frontier intelligence, right?To me, that is so important because otherwise it, I, I don't know how you achieve stable equilibrium, right? Which is how do I then go and say, “Well, my company is gonna have a terminal value because I now know how to continuously compound-” Yeah ... on top of what's a platform that gets better,” right? So when, like Windows obviously came out, Adobe built, Autodesk built, uh, or even like take what Jensen said.We built DX and he built, you know, CUDA on top of it. Um, right? I mean, I always say to Jensen, “God, I got the short end of that,” right? “I wish, uh, we had recognized it.” But nevertheless, but that, that idea that you can build a platform layer [00:14:00] that someone else can then extend out, um, and build their own intelligence layer in this case, I think is everything, right?Without it, why have a developer conference? I can just come and have you all sort of just worship at the altar of one model. Yeah. But that's not a developer conference. Uh,IP, Evals & Company Valueswyx: backstage we, we had a discussion about what is IP or what is the, the value in a company. It used to be the length of, uh, human experience at a company, and now it's this other thing which is the evals, the, uh, experience in sort of applying agents to the company. Can you... I just want you to like flesh that out a bit more ‘cause- Yeah ... it was very insightful.Satya Nadella: It's a great way to frame it, right? Because yeah, at the end of the day, every company is gonna have both the human capital that is still gonna be super valuable, uh, because humans, uh, and their ability to find the gaps that exist at all times is going to be the way we all will create value, right?I mean, so I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition even as token capital goes up, right? So let's say a cor- any corporation [00:15:00] has lots of tokens and lot of human capital. The question is how do you compound the two? So if you have a... Like if you take in Teams I have a bunch of agents doing work and a bunch of humans doing work, and the traces between those, that is really important context of how that enterprise is creating value.Then that goes back to train not a generalist model, but to train the company veteran agent, uh, right? That is super valuable again, right? Which is when a company goes says, “It should in fact go onto the balance sheet,” is how I think about it, right? That's so... In fact, there may be... Like human capital was never possible to go put on a balance sheet, uh, because you didn't know how to capture the tacit knowledge.swyx: Whereas now I think you can with the agents that have learned through the h- through, through time, through all the traces. Uh, so that's what at least we think will happen. I, I think the SEC is gonna have to have accounting standards- ... for token, uh, expertise Uh, y- y- you're talking about the equilibrium [00:16:00] state, um, and a stable equilibrium where companies have this compounding value and can see terminal value for themselves.Future of SaaS & Business ModelsSarah Guo: Another challenge to, you know, the considered equilibrium of, okay, there are applications and workflows that are sort of common to a vertical or a horizontal. Um, and this was, like, the generation of SaaS companies and, you know, Microsoft has lots of SaaS properties as well. And then there are things that are very specific to every enterprise that they're differentiated against.Elad Gil: Um, I'm sure you have heard much and participate in much of the debate about the end of software because all these workflows are, are cheap to generate now. Um, do you think the equilibrium looks different between what agents get built- Yeah ... in enterprises versus in their vendors in the future? Yeah. So I think what's happening there is, see, we, we had a particular way we captured, um, I would say workflow in apps, right?Satya Nadella: Because we built a, a data model, right? We schematized some part of some business process. Mm-hmm. We then built a bunch of business logic. Yep. And then we put a bunch of UI [00:17:00] on top of it, right? So that's kind of what every SaaS company- And a little configuration. For, like, 20, 20 years that was the plan.Right, that- Yeah ... and that was it. So interestingly enough, now you kind of get to re-litigate that vertical stacking, right? So I still think, for example, that data model that you built underneath every SaaS application is super good, right? Like, why reinvent it? Like, I, I, my general ledger better be a general ledger.I don't need new schema creation. No. Uh, in fact, that entity relationship, uh, is actually pretty good, robust thing that I want to feed. And you want it to be stable. That's right. Yeah. Then same thing with business logic, right? If, if you look at, uh... We have this product called Power BI, right? It is like dashboards galore people created.The beauty underneath that dashboard is a very rich semantic model, right? Someone took the pain to create a dashboard and do all the measures, and you want that. That's business logic, right? I want that to be available to me. So I think the [00:18:00] challenge of the SaaS business model is we packaged one way. We now have to learn how to unbundle these things and rebundle in new ways and discover new business models, right?I mean, if you look at it, d- what's happening today with Microsoft 365 is a great example, right? We have this thing called Work IQ. In fact, like, what we are realizing is, oh my God, like, you know, if you look at... In fact, there's a pa- historical parallel too, right? We sold first Exchange and SharePoint and, uh, you know, before Teams, we had a thing called Lync Server and what have you, and we thought, “Oh, that's all gonna move to the cloud.”But little did we realize that, um, the number of people who will use servers in the cloud is 10X, 100X, right? Because people were not buying servers, they were just buying a subscription. Mm-hmm. The same thing is now happening with M365 because with Work IQ, we have exposed what is perhaps the most important database in a company that never got used as a database because it was only captive to our apps.Mm-hmm. Right? It, it was all email operated on it, Teams operated [00:19:00] on it, Word, Excel, PowerPoint, SharePoint. But now, like this is one of the coo- coolest things I get to do with Work IQ. I go to a GitHub repo and I say, “Hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make?”I mean, think about that, right? It literally can go look at all those transcripts, come back with a plan to change a code base, right? Previously, you could never have thought of using M365 for something like that. So the value creation opportunity now in the agent world is in fact 10X more, but it does require us to have...Sarah Guo: For example, there's going to be usage around M365, right? Which is going to be perhaps more than even the e- end users and we have to even re-architect. Like, in fact, like what I use to serve an inbox or a mailbox cannot be used to serve an agent. Uh, and so that's sort of what we are doing.Pricing Models: Per-User, Consumption & OutcomesSarah Guo: I don't believe in, like, permanent business models for any of these domains, but in the [00:20:00] near term, do you have a prediction between, uh, you know, outcomes-based pricing, token-based pricing?Elad Gil: Enterprise bundles Yeah. The way I- I think about this is always we've had... Like, let's even take the per-user pricing. Mm-hmm. The per-user pricing is really an artifact of someone creating a budget needing certainty, right? Because it's the most important thing. Like, somebody wants a budget- Mm-hmm ... they need a per user.Satya Nadella: And, and per user is just a set of entitlements to usage, right? That's kind of what it is. And so the way is, if the first bundling will be take some usage, bundle it into per user stacks and, you know, then sell subscriptions. So subscriptions I think are gonna be there, per user is gonna be there. Then the next big thing will be consumption.So people will say, “I want consumption.” And it's also possible that people will say, “I don't even want to pay for any of the subscriptions or the consumption's outcome.” Mm. But remember, most people love outcomes until they have an outcome, because once you have an outcome, it's like giving away royalty, [00:21:00] right?Mm. I mean, like I, I've talked to customers who love, you know, outcome-based pricing, and I say, “I'm all in,” until they, “Oh my God,” like, “what are you talking about? You're sharing in my outcome? No, no, no. I want you to go back to per-user pricing, and I want you to consumption price,” right? So I think that debate will go on.Uh, but and all, all, all of these business models have a particular time and a place versus one to rule them all. And if anything, if you're a SaaS vendor or you're a platform vendor, having that flexibility... And quite frankly, we face this with GitHub, right? We just recently announced a per-user pricing on GitHub because little, you know, we- GitHub Copilot was constructed at a per-user level before we understood even, uh, the intensity of usage of agents, right?It was an interactive way for a developer to use code complete, maybe tasks. It was not like, oh, I launched 10,000, you know, agents that are going on all day, right? So that is what the adjustment is about. So now that we really want, there will [00:22:00] always be a per user, but there will have to be a consumption meter.Durability of SaaS & Build vs BuySarah Guo: How do you think about the durability of SaaS more generally? One thing I've observed is in a lot of enterprises internally, there will be teams that almost have agent euphoria. They're so excited about the explosion of things they can build that they're trying to rebuild a lot of applications or going to their SaaS vendors and saying, “We're not gonna work with you anymore,” or, “We're considering an internal project.”And it seems like in six to nine months, maybe some of those people will come back and say, “Actually, we, we can't rebuild everything.” How do you think about what's durable in this world and what isn't? Yeah, it's a... It... I think we have to go through one full budget cycle on this to really see the, um- Uh, the sort of the emergence of the equilibrium, because at the end of the day, there's marginal cost to even generating the app, right?Elad Gil: In, in fact, there can be even a, a simple way to say it, like if you should always acquire something if the marginal cost of building and maintaining, uh, something on your own is higher. Uh, right? That should be like it's a quantifiable- Yeah. Right? A quantifiable thing. And [00:23:00] the maintenance part is important, right?Even, like you got to remember like, hey, you know, all the security stuff that now AI will find, you better fix them too fast. Uh, of course, there's a coding agent to help you with, but then that burns tokens, right? So whose responsibility is it? It's kind of like a, a cycle that you've got to think through.And I think we have gone through the excitement that I can generate a lot of software. I think the next thing would be what software do I really want to generate? Mm-hmm. What software do I want to use from others? How do I compose these two into some agentic workflow that I have agency over, right?Sarah Guo: Because I think there'll be very little tolerance for anybody who's inflexible, uh, at the vendor level. Uh, but at the same time, I think that anyone who has got that flexibility shows up, delivers the value, will be back at again, right? We're selling software, uh, but with just different business models, in fact Uh, speaking about building software, um, one of my favorite moments from, I think, a previous build maybe one or two years ago was they had a b- they, they...Swyx: There was a section of you building your [00:24:00] own software. I'm curious if you're building anything now. Yeah. So I, I think the... You know, first of all, let's face it, right? Building software has made it possible for even the incompetence of a CEO of a company- ... like ours, uh, you can build, so thank God. But that said, I, I, I, I do feel that, you know, something like, um, GitHub Copilot to me, and especially the new Sessions app or the new app, has just made it so much more possible for you to have agency over artifacts that you felt you couldn't touch before, right?Satya Nadella: So to, for me as a CEO, even to go to a code base, uh, to be able to learn about it, like I remember joining Microsoft long back, you know, first and then you say, man, everybody had to go in and look at, you know, whatever, Cutler's, Malik, or what have you to learn how to do good C, uh, C++ code. Um, so now that ability to be more full stack up and down is so good, but that doesn't mean every one of us should be doing the same thing.The question is: [00:25:00] how do you then have the ability to inspect things, learn things, see things, um, I think is just so much more. And so to me, what I'm building a lot of is these long-running Foundry agents. Uh, right? So there's autopilots. So the easiest thing is, to me, I think I just built one, uh, even last week, where the idea was, hey, can I have an agent that is continuously monitoring essentially my own chief of staff autopilot, right?We're gonna have that obviously in, uh, Scout. That's what, uh, uh, we showed. But it is so easy and trivial to build. I took Work IQ. I said, “Take Work IQ, go, uh, and build a Foundry long-running agent.” Uh, store all the memory in, um, uh, using Ray Fin, right? Basically at my backend as a service. And lo and behold, it built it, and not only built it, I could say publish to Teams, and it published the damn thing to Teams.Sarah Guo: So the ability, uh, to have a, you know, some end-to-end project like this complete is just pretty [00:26:00] miraculous. How do you think, uh,Future Engineering RolesSarah Guo: that impacts the different types of engineering roles that exist in the future? Because right now I think there's, you know, a dozen different types of engineers that you can be, from QA, front end, et cetera.You know, there's a big swath. I've heard some people argue that in four or five years we'll basically end up with four engineering roles. It'll be people who are managing agents, it'll be four deployed engineers or FDEs, it'll be security engineers, and then people working on large scale infrastructure for a small number of services, and then everything else just collapses into the agentic world.Satya Nadella: Yeah, I- Do you think that's a correct view of the world? Yeah, I mean, I think, I think we'll have to experiment our way through it. But what you said is what... There are some very at scale things. At LinkedIn, they did structurally change- Mm-hmm ... uh, and it, you know, basically built up a new discipline called full stack builder, right?So they went and said, “Hey, let's bring, uh, people from design and product management, front end engineering, all put them together.” Uh, but also have an edge, right? It's not like the design person still doesn't have the design edge, or the front end [00:27:00] person doesn't have the front end edge, but you can give yourself bigger scope in roles so that you're not confined to one role.Um, and then r- equally, infrastructure has become very critical, right? So in other words, like, I mean, RLEs, I mean, one thing we've realized is even for the Excel team, for example. Mm-hmm. Building the RLE in which a reward can be learned is actually one of the hardest sort of infrastructure problems.Mm-hmm. Uh, and so you kind of need even new talent, right? Distributed systems people even in what was considered an end user app team, uh, because it's a different skill set. So yes, infrastructure, science is the other one, obviously. Um, so I think we'll see how these evolve, right? Where's the s- real... I mean, always the world will have a bunch of specialists.Okay. Um, you know, I think the generalist role is going to be the most exciting, right? Because the leverage of a generalist- Mm-hmm ... um, is where we are going to see the maximum returns, right? When, when you said, “Hey, are you coding?” I'm now a gen- Like, what... I've basically translated [00:28:00] knowledge work Right?Which I did, where I created a Word document or a spreadsheet, or even, uh... And now I can build an app, right? It's in the same sentence. Uh, right? That idea that, “Oh, wow, my generalist skills have gotten higher leverage,” I think is what we're gonna see across the board. Music to the ears of CEOs and VCs that are, like, a little dangerous and a lot of- Golden age for idea peopleSarah Guo: idea people. Yeah. Uh- With a lot of agency. I- if you take that idea of personal agency and you just zoom it out to the organizational context, um, uh, my partner Mike Renall, who, uh, actually started his career at Microsoft, just wrote an essay where one of the big takeaways is i- it's an age where you can be much more ambitious, and you need to be, given the pace of the environment and how quickly, actually, users and companies are open to adopting new technologies.Satya Nadella: Um, how do you think about... I, I feel silly asking this of somebody running a, you know, trillion-dollar-plus company already, butAmbition & Making the Impossible PossibleSatya Nadella: how do you think about how Microsoft can be more ambitious now? It's a great question. Um, I [00:29:00] think, um- I think the, the thing in these type of transitions is to have a conceptual model of how work can change to go after outcomes that you could hardly imagine previously, right?In fact, Kevin Scott has this nice line, right, which is, um, when you can make the impossible... Like, when you're making hard things easier, that's sort of one point of leverage. But true ambition is about making the impossible possible. So now the thing that is missing a little bit in all of our organizations is what is that new conceptual model of what can we build?What was impossible and what can we build? And I'll give you one example of this, right, which is I take great inspiration from sort of the people who were managing the Azure net- network. And they came to the... This was from even last year. You know, we were scaling. You saw that I, I [00:30:00] talked about sort of how we built in the last 15 months more Azure capacity than we built in the first 15 years.I mean, it's crazy. Wild. Yeah. Right? It's pretty wild. And it's the same team. So they saw that and they said, “Bob, this just ain't gonna work if we don't reconceptualize our work.” So they built... Essentially they said, “Our job is not to do Azure networking. Our job is to build the agentic system does, that, that does Azure networking,” right?These are the folks managing the 500-plus fiber operators managing the VAN, right, all over. And fiber operations ultimately is a physical operation. Things get cut, things get, uh, you know, have to be repaired. You know, we have fancy words called DevOps and so on. Basically, emails are coming in and you gotta go respond to them, take care of it.So they built this agentic system. They even have a character for it. It's called Miles, and it sort of does all this stuff, right? They started sort of screaming for more tokens and so on. And so they were saying, “Look, uh, we don't need a headcount. We need tokens in order to be able to [00:31:00] manage, uh, our operation.”That reconceptualization- Mm-hmm ... of what their work is, right? They, they basically took their work and made it meta, right? That meta work is now their new work. Mm-hmm. Right? In the ‘80s, if somebody had come to us and said, “4 billion people are gonna get up in the morning and start typing,” my model would've been, we need 4 billion typists?But we're not doing typing, we're doing knowledge work. So that, to me, I think is it, right, which is whether it's Microsoft or whether it's any organization, is to give ourselves permission to do new types of metacognition, meta work, using these new tools to change the outputs that matter, uh, and then really make the impossible possible.Sarah Guo: So completing that dot or the, the connective tissue across those, I think, is where a lot of the enterprise value will get created.Data Center Build-Out & Community ImpactSarah Guo: Should we talk about data centers? Yeah, please ask. Oh, okay. Well, uh, uh, w- we-- this leads nicely into the data center build-up. I always think, I- I just-- I'm just impressed at the sheer scale of the [00:32:00] build-out from Microsoft, but also everyone else, that this is redefining what it means to be a hyperscaler.And I just feel like that, that, that is at unprecedented scale on finances, uh, on the way you run the company, but also the communities that are, that are impacted. Um, yeah, just talk a bit more about what you're seeing on the ground, like when you visit your- Yeah, I think there are two aspects of it.Satya Nadella: Obviously, the, the build-out is, uh, extraordinary. Um, you know, nothing like this has happened, and it's great to be, uh, one of the participants in it. Uh, but you brought up the other part, right? I think at this point it's clear that unless we as an industry, uh, are very principled about ensuring that the benefits of all the stuff we're talking about are felt in real ways, uh, at the community level, right?Because this is not just a, a campaign, um, right? It has to be real, where people are saying, “Look, this is not ch- changing the prices on energy for me.” In fact, if anything, it's bringing down prices because long term there's going to be a better [00:33:00] grid, there is going to be more energy. Water consumption is, in fact, not sort of, uh...In fact, water is being replenished, right? You gotta really, you know, educate folks on truly what's happening, the cl- uh, the closed loop systems we are building. We have to invest in the training, the jobs, the tax base. In fact, the least talked about stuff is the amount of jobs that get created during construction, after construction.What's the tax base that's there in the community? And, and all this has to be real. Um, and, and if that is the case, then we will have permission. If it is not, we won't have permission. It's as simple as that, right? Which is, uh, we, we... I think we have to take it as an industry pretty seriously. Uh, I think it's good for communities to be skeptical, ask the hard questions, for us to do the hard work, earn that.Um, but at the end of the day, if there's-- if we can really be the produ-- Wait. I've always felt like in human history, if you use a lot of energy but also create a lot of value for society- The story has been fantastic. If you don't [00:34:00] do that, it's not been that great. And this time around, I'm a firm believer that ultimately if you do have a token economy that drives productivity, that drives economic growth, that drives broad spread, um, you know, participation, better health outcomes, um, then I think we'll be in a great place.Sarah Guo: Uh, and that's at least what we all have to be focused on. Yeah. It, it makes me think actually that with all these initiatives that you're doing, might be e- easier to see ROI in the communities first before in enterprise. Yeah. I, I mean, I think both sides. Yeah. In fact, it comes back together. It has to be the people in the communities are going to be employed, are going to be participants, uh, in the real economy, right?Satya Nadella: That's I think the question is. Like, if we- if the broad economy is doing well and the communities are doing well, the dots get connected. It's sort of the market forces are such that we will connect the dots. And that I think is it. Like, you ought to be able to see the evidence. You can't be about o- any one company, uh, but it has to be broad economic growth and broad [00:35:00] ec- you know, community permission.Elad Gil: Yeah. I guess I wanna talk aboutSocietal Impact & Optimism About AIElad Gil: what you're most optimistic about currently or what have you most updated your personal models on regarding societal impact of AI? So you're saying what's the, the, the- What have you updated most on in terms of societal impact of AI? Yeah. I think the, um, the p- the most, um- Critical thing is the first question we even started with, which is we need to tell the story and make it real that everybody has a real shot to participate as a first-class participant in this new economy.Satya Nadella: Right? That's kind of, I think we- in the next 12 months, 18 months, we need a way for people to say, “Oh, wow, I get it.” Right? There's going to be tremendous capability, tremendous amount of infrastructure, but I can see what is going to happen, whether it's the benefits like health outcomes or my ability to create a startup or my ability to run my [00:36:00] local sort of, uh, store more efficiently.It's just happening, and I see that, uh, benefit myself, right? That to me, you know, earning that permission in a path-dependent way, we can't wait. See, the one thing, Eli, that I've now learned is I think the world is gonna be very skeptical of tech and tech companies that say, “Trust us, we've got it. The g- future is gonna be glorious.”Sarah Guo: Uh, you kind of have to deliver tangible benefits. Um, and quite frankly, politicians winning elections, uh, because they have advocated for that. That will be at least my adjustment because without it, um, thinking that somehow... Because it's too important this time around. It's too much of the economy for it not to be the case So one very simple framework I have for, you know, what are, what is gonna be the broad benefit of AI, um, beyond the communities just working in technology, are, are sort of wealth creation- Yepit's [00:37:00] gonna happen in a ton of different companies, startups and large companies. Then you have healthcare. Uh, you, you had amazing demos today. There are companies like Open Evidence. I think that is happening. Um,Education & Future of LearningSarah Guo: education seems like another one that's an- Yep ... obvious good where we haven't seen as much impact as I'd expect.Swyx: Do you have a hypothesis on why that might be, or if it'll come? Yeah, I mean, I think this is where, again, how we think about education, how... You know, recently I met with, uh, the founders of Alpha School and learnt a lot about what they were going and going about, and it's fascinating to listen, uh, to how to even rethink- MmSatya Nadella: uh, what does education really look like. Because I think it's actually very important. Mm. Uh, and I'm not saying anything traditionally being done is less important, right? I was even looking at the, uh... It's fascinating to see. I, I, I forget the which Stanford class it was, uh, the, the Asian guidelines for CS something.Mm. Uh, because you still need people to learn. Uh, like it was an interesting AI class that they were making sure people were learning how to apply softmax appropriately versus saying, “Hey, fix my training run.” Mm-hmm. Uh, so I think learning concepts is important. It's going to [00:38:00] be, uh, critical. But the way we create the incentives, what are the credentials, how we value those credentials, what is the employment opportunity for those credentials?So I think that there's a complete change that has to happen, uh, given the way to get to information, way to educate yourself, way to continuously keep yourself updated has changed so much. So I think interestingly enough, maybe the next big startup and success story could be someone who builds a new university, um, or a new, um, pedagogy even of how to get someone to go through a curriculum and find economic opportunity, uh, that's highly valuable.Well, that has felt, uh, perhaps impossible for a long time, but it's a great note to end on and something that might be possible. It's still possible. Yeah. Thank you, Satya. Thank you so much. Thank you. Yeah. I appreciate it. Thank you all. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Fredrik snackar med Martin Nordgren om hans nya ljudanalysapp Spectralscan. Appen är ett sidoprojekt byggt på ett par veckor (påskhelgen hjälpte!) på webbteknik och förpackad för både Android och Ios. Vi diskuterar inspiration och lärdomar från spel, var språkmodeller varit till nytta, matte på oväntade ställen, och mycket mer. Dessutom ett sidospår om gränssnitt i köket, och funderingar kring bättre appar för att lära sig nya språk. Gränssnitt som är lite kreativa, och det kanske är okej att saker inte ser standard ut och kräver lite inlärning om de är effektiva och gör en lite glad när man använder dem? Ett stort tack till Cloudnet som sponsrar vår VPS! Har du kommentarer, frågor eller tips? Vi är @kodsnack, @thieta, @krig, och @bjoreman på Mastodon, har en sida på Facebook och epostas på info@kodsnack.se om du vill skriva längre. Vi läser allt som skickas. Gillar du Kodsnack får du hemskt gärna recensera oss i iTunes! Du kan också stödja podden genom att ge oss en kaffe (eller två!) på Ko-fi, eller handla något i vår butik. Länkar Martin Alla avsnitt med Martin Avsnitt 293 Dataspaning (Spotifylänk, dataspaning.se finns inte längre

Ett fullspäckat liveevent om vad som faktiskt avgör vem som vinner inom e-handel de närmaste två åren. Kvällen rör sig från de fundamentala affärsmekanismerna som separerar tillväxtbolag från de som tappar mark, via det pågående skiftet från SEO till AI-driven synlighet, till konkreta exempel på hur AI-agenter redan idag automatiserar processer som tidigare tog veckor. Avslutningsvis sätter sig en erfaren panel och svarar rakt på frågan alla ställer sig, men få agerar på: vad gör du nu?05:10 - Fem mönster skiljer vinnare från förlorare - inga är AI-native06:12 - Affärsmodellens fysik bestämmer om du har med- eller motvind08:56 - Content driver trafik, konvertering och varumärke – allt17:09 - 50% har redan bestämt sig innan de når din webbplats33:17 - Arkitektur och kontext före UI - börja där det gör ont35:00 - Demo-catfish, DIY-illusion och CFO-allergi: de fem AI-bromsarna82:14 - Vi överskattar AI på 12 månader, underskattar det på 10 år99:50 - Mät adoption, inte experiment - det är kulturen som avgör107:29 - AI är inte en strategi - det är en hävstång som förstärker även svagheternaHär hittar du talarna & panelen:https://www.linkedin.com/in/johancarlmark/ https://www.linkedin.com/in/andreas-keifer-6334a664/ https://www.linkedin.com/in/efrew/ https://www.linkedin.com/in/anders-alknes/ https://www.linkedin.com/in/jimmy-bergstedt-64543082/ https://www.linkedin.com/in/lisa-karlsson-bruzelius-7403b119/ https://www.linkedin.com/in/lukas-rogvall-35a09196/ https://www.linkedin.com/in/freddysobin/ Sponsrat av: https://www.eidra.com/ https://junipeer.io/sv https://curamando.com/ https://etals.com/ Följ Björn på LinkedIn:https://www.linkedin.com/in/bjornspenger/ Följ Framtidens E-handel på LinkedIn:https://www.linkedin.com/company/framtidens-e-handel/ Besök vår hemsida, YouTube & Instagram:https://www.framtidensehandel.se/ https://www.instagram.com/framtidens.ehandel/ https://www.youtube.com/channel/UCEYywBFgOr34TN8NtXeL5HQPoddproducent och klippare Michaela Dorch & Videoproducent Daniel Boder:https://www.linkedin.com/in/michaela-dorch/ https://www.linkedin.com/in/daniel-boder-1aa91917b/ Tusen tack för att du lyssnar!Support till showen http://supporter.acast.com/framtidens-e-handel. Hosted on Acast. See acast.com/privacy for more information.

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Sure, you didn't miss Anthropic's BIG Opus 4.8 drop.

Chris, Andrew, and David kick things off with a detour through Dungeon Crawler Carl, Rails World tickets, and conference travel before diving into developer tooling, package manager security, and the latest Ruby ecosystem updates. The conversation covers everything from Hotwire-style UI patterns and pnpm/Corepack setup to Jeff Dickey's new package manager, the RubyGems malicious package attack, Ruby 4.0.4, Shopify's Rubydex, Claude/Codex chatter, and the increasingly strange future of AI agents and delivery robots. Hit download now to hear more! LinksJudoscale- Remote Ruby listener giftDungeon Crawler Carl Dungeon Crawler Carl RPG + UnstoppableMaciej Mensfeld XAubeJeff Dickey Xen.devThe Hacker News- RubyGems Suspends New Signups After Hundreds of Malicious Packages Are UploadedOne engine, many tools—Introducing Rubydex (Rails at Scale)Ruby 4.0.4 ReleasedFrontend Masters Workshop with Chris Oliver: Getting Started with RailsHoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.JudoscaleMake your deployments bulletproof with autoscaling that just works.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Chris Oliver X/TwitterAndrew Mason X/TwitterJason Charnes X/Twitter

Staci Miller, founder of Gen UX Consulting, shares her winding path from fashion design and psychology to human factors engineering in MedTech. Staci explains what human factors is—through stories from World War II aviation and modern healthcare—and why the FDA now mandates usability work to reduce catastrophic use errors. She breaks down formative versus summative/validation studies, the role of risk documentation (URRA/UFMEA), and why founders should think about usability as early as they think about risk. Staci also opens up about the challenge of starting a second business after losing her first in 2008, how she built Gen UX from $0, and the leadership lessons behind year-over-year growth.   Guest links: https://www.genuxconsulting.com/ | https://www.linkedin.com/company/gen-ux-consulting/  Charity supported: Feeding America Interested in being a guest on the show or have feedback to share? Email us at theleadingdifference@velentium.com.  PRODUCTION CREDITS Host & Editor: Lindsey Dinneen Producer: Velentium Medical   EPISODE TRANSCRIPT Episode 081 - Staci Miller [00:00:00] Lindsey Dinneen: Hi, I'm Lindsey and I'm talking with MedTech industry leaders on how they change lives for a better world. [00:00:09] Diane Bouis: The inventions and technologies are fascinating and so are the people who work with them. [00:00:15] Frank Jaskulke: There was a period of time where I realized, fundamentally, my job was to go hang out with really smart people that are saving lives and then do work that would help them save more lives. [00:00:28] Diane Bouis: I got into the business to save lives and it is incredibly motivating to work with people who are in that same business, saving or improving lives. [00:00:38] Duane Mancini: What better industry than where I get to wake up every day and just save people's lives. [00:00:42] Lindsey Dinneen: These are extraordinary people doing extraordinary work, and this is The Leading Difference. Hello, and welcome back to another episode of The Leading Difference podcast. I'm your host, Lindsey, and today I'm delighted to welcome as my guest, Staci Miller. Staci is the founder at Gen UX Consulting. Her expertise is in applying user-focused research to develop innovative solutions, and it's essential to the growth of any technology organization. As a detail-oriented and tenacious executive in human factors engineering and UX design, she has a proven record of elevating the end user experience and achieving targeted client outcomes. She has created innovative medtech and big tech solutions through a comprehensive user-centered development process, leveraging artificial intelligence and industry agnostic design tools to optimize products and services. In her current role with Gen UX, she's a key leader facilitating strategic company growth plans and service offerings while managing the capacity and workflow of the UX HF design team. Well, Staci, welcome to the show. I'm so excited to talk with you today. [00:01:49] Staci Miller: Me too. I've been looking forward to it all week, so I'm very excited to be here. And I don't know what the day has in store. I, I know that there was like a, a, a kit that you sent out and I didn't read it on purpose, so everything's gonna be organic. [00:02:03] Lindsey Dinneen: Perfect. Those are my favorite conversations anyway, so I'll take it and run. Some people I know really love to have the questions ahead of time, and others are just like, "Yeah, I don't want to know. I'm just gonna go off the cuff. Here we go." So, brilliant. All right, well, let's start, if you don't mind, by sharing a little bit about yourself, your background, and what led you to medtech. [00:02:24] Staci Miller: That is, those are my favorite questions. So, I have a background in fashion design, psychology. I spent most of my classes in cognitive psych, but it wasn't like a difference of degree, it was just psychology. And then I have a master's degree in human factors and ergonomics. So I went the psychology route and the design route. That's kind of my background. So when I graduated my master's degree, through my master's program, I was able to intern for both years and one was in tech, big tech. I interviewed and landed a, great one year long internship at Samsung, which was actually supposed to be just three months, and I stayed there for a full year. So they kept me through my whole, my whole semester, which is something they don't normally do, which was really fun. I mostly just said, "Hey, can I stay here for the year?" And they're like, "Great, no problem. Sure. We'll figure it out like that seems like a good option. We like you, you like us. Cool. We'll do that." And my second internship was in medical device at a company called Interface and Analysis. My, that was actually my internship. My second one was at Samsung, so I got to really look in like I, I guess you got the curtain. If you think about Wonderland and Oz and the curtain and being able to pull back the curtain between both industries, what did I like better? I ended up liking medical better, mostly because the research was more structured and not necessarily conversations about, "Yeah, so how do you feel about that? Did you like it?" Like to me, that's not really. What I would consider the best opportunity to gain data. Data to me, like there has to be like a clear objective as to what you're doing, the whys behind it, and what do you wanna learn. And I found that in, when I worked with engineers in medtech, they definitely had things that they wanted to learn, whereas in tech, they just had so much money. They were like, "Yeah, let's just see what people think about this." And I'm. Okay. And then when I would be really structured and I was working with people who didn't have backgrounds in research, had very strong, very good backgrounds in design, like legitimately awesome, they were leading the research and they were missing the boat. So the narratives started to be focused on the N of one. This one person said this really interesting thing, so let's base our whole design off of what they said. And I'm like, "Dude, wait a second. Wait a second. All of them said this thing about the design though, and like we have four or five data points about when you ask this question." They're like, "Yeah, but that's not interesting." And I was like, "Okay, keep my mouth shut. I got it. Move on." Like from that moment forward, I, it wasn't like "Staci, don't talk, it was more like this is how we design based on the narratives that we've learned how to, how to research on." And so it wasn't as I would say-- it wasn't considering the actual 360 view of the user. It was considering the really cool thing that happened this one time that was like totally an outlier. And it happened consistently when I was working in big tech. So I was like, uh, medtech, probably more my speed. And then my first job was at Abbott. [00:05:39] Lindsey Dinneen: Nice. [00:05:40] Staci Miller: And I ended up there. Yeah, [00:05:41] Lindsey Dinneen: Okay, great. Well. [00:05:42] Staci Miller: Cool. [00:05:43] Lindsey Dinneen: Lots of questions based on this incredible background. I want to go back a little bit. So fashion design, was this something that you grew up thinking, "Oh, this is what I wanna do and be okay?" Right. All right, so... [00:05:57] Staci Miller: it's all I ever wanted and I did that. So... [00:06:00] Lindsey Dinneen: Yeah. [00:06:02] Staci Miller: That's a, that's a great question. I think that my interest in fashion peaked around when I was 12 years old and during the time, Cindy Crawford and Naomi Campbell, and I was so fascinated by how beautiful these women were. And, and fashion was a thing in the nineties. There was like a lot of Dolce and Gabana around, and I loved it. And I couldn't wait to get my new print of Vogue every, every season. I loved Harper's Bizarre, and I would just pull pictures out of these models and what they were wearing. And then I would start you know, freehanding stuff and things like that. And I think a lot of people do that when they're really interested in clothing and things like that. And if you really think about it, fashion is art that people wear. So I was very attracted to that part of it. And it's all I wanted to do. So after high school, I went to FIDM and studied fashion design. And right outta FIDM, I started my first company in fashion design, and I was a clothing manufacturer, and we had 500 open doors in the United States and in Canada, and I was hoping to expand, but unfortunately 2008 hit and they hit it hard and fast and I lost most of my managing capital in the year that I think was my tipping point. So it was the, the year that I finally got a lot of traction and had a lot of repeat business and a lot of new business as well. And a lot of those new businesses just refused orders. Just from the east coast to the west, and it was just tons of money out that wasn't gonna come in. So there was really no way to, make that work after that, like I lost literally all the money I had in my business in like the span of, I would say three, four weeks. It was just mortifyingly scary. But I was young and people who are young are resilient and they move on and they find a new dream. And it took me a minute, like I didn't really know what the french toast I was gonna do. And I was like, well, I was still planning on staying in fashion and long, short, I was offered a job to do and run production for a one, a different company. So make sure that their goods were produced on time. Deal with the, the timing of all the orders, making sure the product line. So it was basically operations for manufacturing. And I was super excited about the job and I moved back to my parents' house at the time because things were just that tight financially for me. My parents were like, "Yeah, just, you know, come back, we'll figure it out." And I remember saying to my mom and dad, I'm like, "If this job falls through, do you mind if I just go back to school and stay here?" And they both started to laugh at me like, "Your job is fine, but if the sure why, why not?" And they, they thought it was crazy. And then I ended up back in school. So, they were like, "Whoa, that was really insane," 'cause that was in the end of 2008, starting 2009. And so the company rescinded their offer and they were really like, so sad about it, but they went to a market to sell their clothes and they got zero orders that year or something like close to that. So it was just, it was just a really intense time in the fashion industry and I was looking for jobs and I wasn't getting anywhere. So I only had an AA, and at the time that really didn't matter, but I went back to school and I'm like, "If I'm going back to school this late in age, I'm getting a master's degree." I had no idea what I was gonna get a master's degree in. I was like. I like clothes and design. We'll figure it out from there like that. And I was like, "Well, maybe I'll be..." this is crazy. But I was thinking about being a lawyer, like a property law lawyer. So, because when you are a designer in clothing, people can just knock you off. And you've seen that happen like pretty much everywhere. And people can just take advantage of your intellectual property and never pay you for it if they change enough of it. And so I was like, "You know, this would be something I'd probably be good at." So I went back to school thinking I was gonna go into that type of law. I took psychology courses and I took philosophy courses. And philosophy courses really do lean you, get you thinking very specifically about law. That's what philosophy was basically geared towards anyways. And you take these psychology courses and they're about people and how people process information, how people behave based on their behavior and things like that. So I thought the combination would be really good. Well, I ended up not liking, I did like philosophy, but philosophy's "let's think about thinking about it." And psychology is-- which is great. It's great, but psychology is like more applicable when you're interacting with others. And I found it super fascinating. And then I got really into like cognitive psychology and I'm like, "What the french toast am I gonna do with this? I can't do anything with cognitive psychology. Like I need to make money. I'm a grownup. This isn't ah, I'm gonna study underwater basket weaving and come out and go work in communications at Fox." Like I had to have an actual plan. So in my college at the time, there were these classes and they were like introductory to what you can do with your degrees. And that's literally where I found human factors. And there weren't very many schools that did it, but I was taking most of my classes at that point in cognitive psychology, which is how people process information, not their feeling based stuff. Like I didn't wanna have conversations with people about their feelings. Get that off of me. Like that's not, that's not my jam. I'm like, "Sorry, you're sad, but I'm not sad and I don't wanna be sad, so I'm gonna keep, keep going." And I'm like, "How am I gonna work this into my, you know, I love design, I wanna keep that in my background, and how am I gonna, what am I gonna do?" And so the study of human factors really is the intersection of design and research, and how people interact with said products based on the design. And you get to research that. And I'm like, "Sold. Good. I'm, I can do this. This is like this, I didn't even know this thing existed." This is crazy good. And I never looked back. [00:11:49] Lindsey Dinneen: Yeah. [00:11:50] Staci Miller: I got into a master's program the next year. I, and because I was in that specific program in San Jose State, that's why it was so easy for me to work for Samsung because it was in my backyard. And that's why it was easy for me to work for Interface Analysis because Tony was the owner of that company. Tony, he was my professor. So he just was like hiring people and I, I answered his response and I was like, "Hey, I, I'm looking for something." Do you like, he didn't say it was his company. He said, "I have a friend looking" and I'm, you know, like when I know I need to make some money, I'm gonna try to hustle up and make some money. So I'm like, "Hey, I'm open to that." He's like, "Why don't you come by my office and we'll talk?" And I was like, "That's weird." He said It was for some other, I'm like, "Sure, no problem." So I go to his office and he offered me an internship right then and there 'cause it was for me. "I just wanted to see who would respond," 'cause you are the only person that responded. I'm like, "Guess you're gonna hire me then." [00:12:37] Lindsey Dinneen: Amazing. All right. That's great. Thank you so much for that background. And it is so interesting how sometimes our paths are very, very windy to get to where we end up being and we Yeah, exactly. What, what ends up being a really good fit. But, so can you explain a little bit more about human factors, especially, maybe to help folks who have maybe some misconceptions or don't fully understand what it is just in general, but then also relate it specifically to medtech and why it's so important within the medtech industry? [00:13:11] Staci Miller: I can give you a story that probably would do both. So human factors was, was actually founded pretty recently in our timeline of psychology and understanding people. In World War II, there were a whole bunch of fighter pilots ejecting themselves from planes that caused, even in World War II, millions of dollars to produce and nobody could figure out what the problem was. They checked the planes. The planes were operating correctly. They did psychology, like psychological backgrounds on the people who are fighter pilots. I mean, they have to, to get into the military and to fly those planes, you have to be pretty good under pressure. They interviewed them, they were fine. They didn't have any breakdown of stress, and it wasn't happening on a small scale. This was happening on quite a large scale. So they, again, they went, they're like, "Okay, okay." Well, the military went back and " Well, it has to be the plane." So they looked through the plane, wasn't the plane, talk to the people, wasn't the people. So then the psychologist started to ask questions. They're like, "Well, if you're saying that it's not the person's emotional state and you're saying it's not the plane, well then what happened? Something had to happen. Something changed. What changed?" It turned out that the engineers had moved the throttle button with the ejection button in the planes. [00:14:31] Lindsey Dinneen: Oh. [00:14:31] Staci Miller: So the pilots were originally trained to hit the throttle button on the certain side that the throttle button was in the cockpit. So instead of hitting the throttle, because that was their original training, they hit the ejection button. So they ejected themselves out of the planes, which is why human factors was born. Those little changes that people don't understand about human beings. So when we learn something for the first time, because like even if you think about being a kid or being a baby, or learning a really tough lesson, right? You remember that lesson. And so what happens is that's your default setting. "This is the lesson I've learned. This is how I react." Now for that lesson, it doesn't matter if it's like an emotional exchange or if it's a physical one. So because they were taught where the, the pilots were taught specifically where the throttle was in the first place when they were under attack and they were in a high cognitive loaded space, they went back to their original training. [00:15:30] Lindsey Dinneen: Mm-hmm. [00:15:32] Staci Miller: And then the engineers were like, "Well, we told them. We told them." So, so, because they didn't wanna take the blame, right? Nobody wanted to take the blame ruining millions of dollars of planes. So this same type of thing happens in the medical industry. I mean, you can see it pretty easily, right? So you're trained on System X. There's an update, a 510K release to it. The system works differently. Errors are made, people are hurt. [00:15:57] Lindsey Dinneen: Mm-hmm. [00:15:58] Staci Miller: That's how it translates to medical. So aviation was a really big part of human factors and it still is to this day. Like NASA used to hire quite a few of my classmates. And I know that Boeing and a lot of those other, even BMW hire people that do what I do for a living and test the responses during drive time. And if you think about it, if you look at a Tesla versus a BMW, those are very different driving experiences. Like I had to relearn how to drive a Tesla, right? And like it has a one pedal situation. So now when I get into regular cars, I'm like, "Wait, what? What am I doing? What? What kind of car is this? Like how do I drive this thing again?" I know that sounds silly, but it, it's true 'cause you kind of just get used to the thing that you have. And that's exactly why human factors is prevalent in medical device or in aviation or in, you know, like any kind of like navigation systems. The reason the FDA mandated it is because a lot of products were coming to market and there was a very large influx of critical catastrophic errors in hospitals. People were suffering consequences of bad interfaces or lack of instructions on products. I know that there were a lot of intravenous medications given that weren't supposed to be IV medications in like in certain-- yes, you're supposed to inject it, but not. Intravenously and those charged caused people to perish. So that's when the FDA stepped in and said, "Okay, we were asking you as a favor to do these usability studies, but now officially they're part of your risk requirements and they're part of your requirements to get to market." And I think that happened about the time I graduated grad school, around that time. So about 15, 16 years ago. [00:17:50] Lindsey Dinneen: Okay. Yeah. Well that's a fascinating story, and I'm sorry that that is the impetus for the results that we have today, but also how incredible that that is something that's being prioritized and mandated now. And I'm wondering too, when a startup company is developing their technology, how soon should they be thinking about human factors, usability, UX/UI. [00:18:17] Staci Miller: As fast as they're thinking about risk. if you're already thinking about risk at phase zero, that's when you should be thinking about usability and UI and interactions based on user processes, because that's when this kind of conversation really needs to start with regulatory, with your team, with the engineers. So even if you don't have a human factors engineer on staff, like you can find a company that can give you like some fractional support, just, you know, to talk to and to understand what their, what, what their responsibilities are, and what their requirements are to get to market. I have found that a lot of founders don't think that it's a requirement. And I, and I'm really not sure why, but that's been happening a lot lately. [00:18:59] Lindsey Dinneen: Yeah. So because it's a requirement, because you should be thinking about it from the get go, what are some things that you've seen work really well in terms of, putting together this kind of this testing and whatnot versus things that might seem like they could work. Like perhaps somebody feels that they could maybe do some of this testing themselves. You know, just, just things that maybe people who aren't really familiar with all the regulations would perhaps do, and that could cause problems down the road. [00:19:32] Staci Miller: So there's a, these are all really great questions and let's, let's unpack the idea of research, right? So some people think that research is finding out if somebody is happy about a product and would use it, like product market fit, right? Some people do marketing for that, and I can, that's the type of research that is not technically human factors, but it is something that Gen UX can do, right? So it's just research. I, I call it like insert white meat or insert protein. We can do the research, right? So when it comes down to it, there's, I would say that research is split into two buckets, which is UX/UI, which is very popular and people understand that, which is a formative in the FDA guidance and then validation slash summative. So the validation studies are very clean cut. So I'll explain those first. And they are to validate that the user can use the system in its environments safely. So the alpha for that is the user is successful at using this product and the uses, uses and use environments correctly and safely. And this is all based on your risk documentation from your URRA or your UFMEA. Some people use ADFMEA, which is based on design, and I suggest that they don't use that because that focuses more on the system than it does on the user. And the FDA has really cracked down on that. So if you are a founder and you think you can get just one system, ADFMEA, you are probably already starting off on the wrong foot. Make sure you have your own usability. Because human factors work really focuses on two things in the medical industry. One, it focuses on helping develop the device while breaking down risks. So if you have mitigations and your system's designed a certain way to avoid a risk, that's very important, and that's really also usability testing. And I can explain this in two ways. I've worked at Meta, I've worked at Samsung, I've worked at a lot of different big tech companies, and I've worked at a lot of medtech companies. So I think that people think that human factors is different than user research, and they're right. Human factors is much harder than user research. And you really actually need a background in research methods and an understanding of how the application of research works. Formatives can be used for two reasons. One, to support the need of the product in use and to check how people are actually using the system in real life. So sometimes people are really good at thinking-- so engineers are amazing at building systems, right? I can't do what they can do. I'm not gonna pretend like I can. What I can do is help them build it for their end user, because a lot of the times engineers think very differently than the average human being. They're much more educated. Schooling for engineering is extremely difficult. A lot of it's mathematical computations, understanding actual physical properties of things in their environments and how that they work, right? So those are the things that engineers think about all day long. That's fine. I think about the user all day long. So you can create a system that an engineer thinks that is fine, but then the user is " I don't really know how to use this. What are you talking about?" Right? And so that's what user research informatives avoid. They avoid, they break down risk and they are able to help form the product. So those, those user research studies, like before, let's say phase zero to phase four in a market cycle, if phase five is market release, are for those things. And then as you get later in the cycle, you wanna do more rigid research, that's really breaking down the risk and really focusing on the user interactions within the system and med device. And making sure that they're assessing the risk based on your user, but they're very specific to the user interactions that are critical tasks and higher. Or things that lead up to the critical test and come away. So like you have to be able to do the steps before, do the thing that's really hard to do, that could hurt somebody and then make sure coming away from them you don't cause any harm either. That's the best way to look at these types of tests. And we do the exact same thing in validation for systems. So, in software you test to see if the software can do the thing that it's supposed to do. When you check that box, the software does the thing and it did it, and we're good to go. You do the same thing with mechanical engineering. The system has this, this range of motion here and this range of motion here, and it doesn't deviate from plus X to plus Y and therefore the system does what it's supposed to say. So you're verifying and validating that the system does what the system is planned to do. It's really no different in users, it's just that you're dealing with human beings and it's not, it doesn't work the same way, right? Because like people are variables no matter what. And that was really long worded. So there's like tons of different research to do, but if you don't do your summative and you don't do your risk documentation, you're not getting to, you're not gonna get to market approval. Just, there's no way. [00:24:34] Lindsey Dinneen: Yeah. Thank you. Yeah, that is incredibly helpful insight. And you know, so I wanna go back to, you had this company before, right? So you had already built a business and it was thriving, and then unfortunately life intervened a little bit. When you went to start Gen UX, did you have moments... [00:24:57] Staci Miller: Of PTSD? [00:24:58] Lindsey Dinneen: Of, yeah. [00:25:01] Staci Miller: Yes. [00:25:01] Lindsey Dinneen: Yeah. [00:25:02] Staci Miller: Yeah. I had major PTSD. Like I, so the concept of Gen UX was a play on words like, so I'm a Gen Xer, no biggie, but like I think that every Gen Xers, millennials, I feel like both of our generations very much identify with our generation. And I thought it would be kind of a fun play on words to identify to people that are also Gen Xers that, yeah, we do UX work and we're Gen UX, as a Generation X, like it was very important, right? So I kind of came up with that idea, thought it was cute. But at the time I was working for Meta, and Meta had been doing quite a bit of layoffs at the time. Nothing wrong with that, that happens with every company. But I have survived in Medtronic and Abbott and all these other companies. I had survived so many rounds of layoffs. I'm like, "One day my number is gonna be, it's just, it's just gonna happen." So, we started at Meta internally, really like they, they were very open and honest with people. They're like, "This is when this is gonna happen. We are gonna lay off more people. This is when this round is gonna happen. We're gonna lay off more people, and then this is the final round and this is when we're gonna lay off these people." So each of our groups of things like, so it was like engineers, lawyers, researchers. Like we, we had timelines that we knew if, if it was gonna happen, this is when it was gonna happen, this would be the day. [00:26:17] Lindsey Dinneen: Yeah. [00:26:17] Staci Miller: So I started to really think about what that meant, and I'm like, "Okay, well I'm not gonna start looking for jobs right away because I want my severance package." I definitely wanted that 'cause I, and then I wanted a break if I could have it. So I was like, okay. I, in between working at I was working at EDA as a contractor and that was super fun. Like I had my own time kind of, and I enjoyed the work and I got put on other projects whenever they needed me. And it was like, but I was constantly on a project, so I'm like, "I, maybe I'll go into doing IC work by myself" and I'm like, "No, I can't make enough. If I'm gonna do this, I'm gonna build something." And then I'm like, well, I started to talk to my friends every single one of my friends, including Interface Analysis' owner, Tony Andre was like, "Start your own business, Staci. Start your own consulting firm, just do it. Don't even look back. Just do it. People will end up coming to you because you know how to do this." He's like, he's it's, "You know, the first years they are what they are and everybody knows what that looks like. It's, it's rough. You have, it's like a mental game. You're like, I am gonna do this. And you just have to be consistent and can continue down your path. And more and more people will show up." And that's been true every year. But that's how GenX was started. And yes, there was this whole trepidation about, "Am I gonna make it? Am I gonna make it through this?" And I was like, "You know what, Stac, you're starting in a recession in your, in your industry. If you can get it done, if you can get two years in and be successful, you're fine." I'm in year three. [00:27:50] Lindsey Dinneen: Yeah! [00:27:51] Staci Miller: Yeah, I mean, year three, woohoo. And we're increasing 50% year over year in year three, and I started it with $0. So, and I'm not, I'm not saying like a hundred to 50, like $50 to a hundred, we're, we're talking a couple hundred thousand dollars here, a couple hundred thousand there. But it's modest and I do expect that growth, and I do expect that to continue. And the other thing I think about is becoming very malleable in, in your spaces, like what's working for you and what doesn't work for you. But I feel like that's kind of off topic from what you asked. But yeah, I had PTSD gave myself at least two years and I'm like, "I can do anything for two years. If it doesn't work out, you know, like I have everything that I have and I can go back into corporate if I need to." And I really, I really was tripping, like just to be nineties about it, I was tripping. Like I was really like, "You know, I don't know." And my husband was like. He was my biggest cheerleader. He was like, "You've gotta do this. He's you're gonna, you're gonna be able to do this. You have something that I don't have. You're really great at networking people like you." I'm like, "Do they really like what?" And he's, " No, people like being around you. You make friends easy and people really do enjoy being around you and they like know that you're smart and you're gonna be able to do this." So, that's how this all started. And yes, I was really freaked out when I first started, but every day when I had bad days, I'm like, "Everything always works itself out." [00:29:14] Lindsey Dinneen: Yeah. [00:29:14] Staci Miller: "Have you ever not been in a situation where everything works itself out?" "No. No." So I'm like, "Well, if I, if it doesn't, I'll get a new dream, but I don't-- once you hit this, this year, like year three and you know you're still growing, you don't have to get a new dream, you just keep going and you're like, this dream is happening. I'm gonna keep it going." [00:29:34] Lindsey Dinneen: Yeah. What was it like building a team? Did you start off as a one-woman show, or did you have support at the beginning? How did that work? [00:29:43] Staci Miller: So at first, actually my designer's father was working with me and he called me out of the blue and he's " Hey. I have this client, she doesn't have any human factors person working with her, but I know that she needs it and do you wanna talk to her? I know you're not working at Meta," because I put on my, oh. LinkedIn profile Open to Work. So he called me like within two days, like seriously, like people started to call me and that was when I was already like, "I'm gonna do my own thing. I'm just gonna do my own thing." So the universe just brought me a gift, right? And I met this first client and I started to work with her, and at first everything was super cool. The first year it was great, and I really liked working with her, but she also needed a couple of other things. She needed an IFU and she needed design quality assurance. I'm like, "Check, check. I can get both those things done." So I called my friend Maria, "Hey, do you wanna work with me? She's " Hey. Yeah, totally." Because we had already worked together and we knew each other pretty well. So it wasn't like it was difficult to make that connection. And, and she knows my personality. I know her personality, and I know we both work extremely hard and we have that in common. So I wasn't, never, would I be worried about Maria. And then I found I wasn't, I didn't even have a designer yet on staff. And I found someone who used to do instructions for use for a different company I worked for. I called him like, "Hey, can you do this?" He's " Yeah, yeah." So I got all that done for this other client. I'm like, "I can do this. I can do this. I can, I can find people." I know so many intelligent people who love what they do and have a fire for it every day. And then the evolution started to happen. And then I asked someone to work with me to do sales, and then they said, "Yes." And then we started to pitch people that I was friends with and knew, and sometimes they said yes, and sometimes they said no. I think the first year, I think I pitched over like $4 million in business and I got 20,000. No, I got, I got 80,000, something like that. Something, something small and I'm like, "Why am I pitching so much? This is like taking so much time outta my day," that I found someone to work with me. His name was Adam and I still actually work with Adam and he, but he's a big picture guy and he started to work with me a little bit and help me like navigate through some things. Even to this day, we talk and he's not fully, fully, fully on onboarded, but if, if some. Of the clients that he lands do come on board, he will be back on board and he will be working with me again. And then I had a salesperson this last year and I realized just I needed more of a hunter-gatherer. So like we're just going in a different direction, right? So I had that, and then last year my goal was to bring my designer Maddie on full-time. And I was able to do that too. So everything that I've kind of just said, "I'm gonna do this this year, I've been able to do this year." And I'm not taking this lightly. Like I have a board of directors, which are people who are, have different perspectives on finance because that's my weakest link, I would say. A professor at UCLA, his name's Sean Pat, also a good friend of mine. He's on my board. And my brother-in-law and my nephew, who is new in his life and on his journey, is on my board as well, and I kind of wanted him on my board so he can see what it looks like to be an entrepreneur and see what growth looks like year over year because he is already working for companies. He's, he's like 25, I think, and he's already being groomed to be in upper management. He's got upper management written all over him as like the, as like people would say in like cute little circles. And then my my brother-in-law, he is one of the CFOs at Mayo Clinic, so these are people who have some in medical, some in finance, some in finance, in medical, just helping me like grow. I throw things past them and they help, you know, make decisions for the year. And they tell me like, they give me feedback and, and work through things that I'm doing and what they think is right, what they don't think is right. And sometimes I listen, sometimes I don't. You know, like... [00:33:28] Lindsey Dinneen: Well, yeah. [00:33:29] Staci Miller: Just really depends like where I'm at and what I wanna do and where we wanna grow. [00:33:34] Lindsey Dinneen: Yeah. Excellent. Okay. So I'm curious, especially within medtech specifically, are there moments that really stand out to you as just affirming, "Oh my goodness, I am in the right place at the right time." [00:33:49] Staci Miller: Things keep happening, so, every time I speak, like I, I spoke at Project Medtech, people bombarded me. They're like, "We wanna work with you. We wanna work with you. We should talk, we should talk." Anytime I go to a symposium I walk away with two or three leads. People coming up to me, "Oh, do you do this thing? We should really talk. We should really talk." So, just being in the situation like that kind of tells me that I'm in the right direction. And the other thing is we're growing year over year. If you take a 10,000 foot view of where I was year one versus year three now, very, very different. Extremely different. And like I said, I do have, I do have other consultants that work with me. I don't want you to think it's just like a two person shop. It's not, there's other consultants that work with me but they're as needed. They're not full employees, which I think is really helpful in a situation like this. If you're a founder starting up from scratch and you're not, you don't have, I'm not trying to get angel investors. I'm not trying to get people to push money into my company. I am building it literally from zero to whatever it is that I make. And so that, that's a, what I would call like a slow burn of, you have to build your foundation, you have to manage to the capital that you do have, and then you, then you go to the next level and you do the same thing and then you do the same thing. And there's a lot of consistency with the business now, and I see a lot of people targeting me for that consistency. And as, as we are growing, like people are engaging with us on a different level, which is exciting to see. That's always exciting. [00:35:20] Lindsey Dinneen: Yes. [00:35:20] Staci Miller: That's kind of how I know. Yeah. [00:35:23] Lindsey Dinneen: I love that. Awesome. Okay, so pivoting the conversation a little bit just for fun. [00:35:28] Staci Miller: Cool. [00:35:30] Lindsey Dinneen: Imagine that you were to be offered a million dollars to teach a masterclass on anything you want. Could be within your industry, but it doesn't have to be. What would you choose to teach? [00:35:40] Staci Miller: That's a great question. I love, I think it's very important when you do what you do for a living to have something that isn't that for yourself. So I, there's very specific ways as to how I unwind at the end of the day. One of those things is cooking. I would totally do a masterclass in being a home chef. Like I'm, I'm not even a chef like that. I've never gone to culinary school, but I absolutely, I make my own breads. I make chutney sometimes when, when I want some. I would do a masterclass on-- I'm not Gordon Ramsey. I'm not Thomas Keller. Here's what it looks like to be a home cook. And here's the, the five things that you actually need. And this is what you should learn how to make first. Like I remember the first time I was trying to make pasta or something, I boiled the water to death. There was no water left in the pond. Like I didn't even know what I was doing. I, maybe I walked away from it, I don't know, but I destroyed the pot. My mom's " What were you doing?" I was like, "Making pasta." And she's " What, what, what happened? You ruined the pot." I'm like, "I'm not, I just did it wrong." So I would probably do a masterclass in how to just take that first step learning how to make your own food, right? And talk about food 'cause I like food. There you go. That's what I would do. [00:36:52] Lindsey Dinneen: Love it. I love food and I love talking about it. So, that sounds like a great class. [00:36:58] Staci Miller: I would do, I would totally do it. [00:36:59] Lindsey Dinneen: Okay, and then how do you wish to be remembered after you leave this world? [00:37:07] Staci Miller: This might be dating me, but Roy Orbison who wrote the song, "Pretty Woman" that was also in the movie, "Pretty Woman" wrote that he "just wanted to be remembered." And I thought that was really interesting. And I think that everybody knows that song knows that it's the guy like, I don't know if you know like the artist, but I think even to this day, that song, generationally, people know that song. I don't know how I wanna be remembered, but this is how I wanna impact the world. So it's kind of like that, but kind of not. I believe that knowledge transfer is the most powerful thing that we have amongst generations. And I want the next generation to be better than me, which is probably, in my opinion, I'm kind of kind of strict about this, probably a tall order, 'cause I'm like very picky. But, I have mentored and, and taught people my craft, and I want them to be better than me so they can mentor people and be better at this craft. So if I leave one mark on this world, it's that I have taught somebody what I know how to do and I expect them to do it better than me. And I don't mentor just anybody. So if I'm mentoring you is, and I'm putting all this energy into you, you better, you better bring it. And the people that I have worked with and have mentored are doing extremely well in their careers, and that's, that's kind of a thing that I like about, like what we do and how I do it. So I don't know if I would be specifically remembered for that, but I do know that it would move our industry forward and that makes me happy. [00:38:39] Lindsey Dinneen: I love that. That's a beautiful legacy. All right, and then final question. What is one I know, what is one thing that makes you smile every time you see or think about it? [00:38:52] Staci Miller: When I see what I'm building or, or how I'm building it in the future and I really go deep within my, my consciousness about this is what I'm gonna do next. This is how I'm gonna do it. This is what makes me feel really alive. I get so excited. I get like goosebumps. I start smiling. I, I'm a big-- I don't know if you do this, Lindsey, but I do this-- I kind of dance around a little bit. Like I dance when I'm making food, I dance and most people dunno that about me. But I, but my closest friends I remember I was working with this one guy and he looks at me, he's " Do you ever stop dancing?" I'm like, "Nope. Nope, Nope. Gotta dance." So all that stuff like starts to happen. And I just get really excited about the things that I'm trying to build, what I'm trying to master in my own world, what I'm trying to create. And that's what gives me like so much excitement. And then a number two would be my cats, because they're ridiculous and I love them and they give me so much love and they make me smile all the time too. [00:39:52] Lindsey Dinneen: Oh yes, those are great answers. I love that so much. It is exciting to see. Dreams come true. I can totally understand that answer of getting the, the excitement, the tingles, and then yeah, I, yeah, I, I obviously relate to dancing around all the time, and especially like celebratory dances. They're, my celebratory dances are the goofiest, most ridiculous things you've ever seen, but I'm happy! So. [00:40:20] Staci Miller: As long as you're happy, that's all that really matters, right? Like that vibe that you're putting out there and the happiness and the giddiness, like the things that I'm building in my mind, like they haven't happened yet, but I'm dancing like they have, you know, because I hope that they do. Like there you go. And I think that's important. I love it. [00:40:35] Lindsey Dinneen: True embodiment of the vision. I love it. Well, well, Staci, this has been a great conversation. Thank you so much for your insights and your stories, and we are so honored to be making a donation on your behalf today to Feeding America, which works to end hunger in the United States by partnering with food banks, food pantries, and local food programs to bring food to people facing hunger, and also they advocate for policies that create long term solutions to hunger. So thank you so much for choosing that charity to support. And gosh, I just wish you the most continued success as you work to change lives for a better world. [00:41:15] Staci Miller: Thank you, thank you. It was so much fun being with you today. I appreciate this and it was so much fun to talk about. And yeah, I can't wait to see you in the next couple weeks too. So we'll see each other soon. [00:41:26] Lindsey Dinneen: Yay! Sounds good. Well, thanks again and have the best rest of your day. [00:41:32] Dan Purvis: The Leading Difference is brought to you by Velentium Medical. Velentium Medical is a full service CDMO, serving medtech clients worldwide to securely design, manufacture, and test class two and class three medical devices. Velentium Medical's four units include research and development-- pairing electronic and mechanical design, embedded firmware, mobile app development, and cloud systems with the human factor studies and systems engineering necessary to streamline medical device regulatory approval; contract manufacturing-- building medical products at the prototype, clinical, and commercial levels in the US, as well as in low cost regions in 1345 certified and FDA registered Class VII clean rooms; cybersecurity-- generating the 12 cybersecurity design artifacts required for FDA submission; and automated test systems, assuring that every device produced is exactly the same as the device that was approved. Visit VelentiumMedical.com to explore how we can work together to change lives for a better world.

Valve hiked Steam Deck prices by up to $300 as RAMageddon hits consumer electronics. Bloomberg detailed Apple's Siri overhaul ahead of WWDC, Meta rolls out subscription plans for Instagram, Facebook, and WhatsApp, and Oura unveils a 40% smaller Ring 5. Valve hikes the Steam Deck OLED's prices due to "rising memory and storage costs": from $549 to $789 for the 512GB model and from $649 to $949 for the 1TB model (The Verge) Illustrations based on sources detail Apple's Siri overhaul, including a new UI, a chatbot-style app, and other major iOS 27 changes, ahead of WWDC on June 8 (Bloomberg) Meta rolls out Plus plans for Instagram, Facebook, and WhatsApp globally, and tests $7.99/month and $19.99/month Meta AI plans, and a $49.99/month creator plan (TechCrunch) Reactor, which says its AI platform can generate video in real-time with near-zero latency, emerges from stealth with a $59M Series A led by Lightspeed (Variety) Oura unveils the Oura Ring 5, with a 40% smaller form factor, improved sensing, and repositioned LEDs, on sale from June 4 for $399, up from the Ring 4's $349 (Bloomberg) Learn more about your ad choices. Visit megaphone.fm/adchoices

Benjamin and Chance discuss the illustrations Bloomberg published depicting iOS 27 and the new Siri interface. Also, we talk about rumors of a revamped AirPods settings UI, phone snatching detection in iOS 26.6 code, and Digitimes says the Apple Watch Ultra 4 will feature a significant redesign. Also, has AirDrop got worse? And in Happy Hour Plus, Apple showcases the iPhone 17 Pro by producing and broadcasting the first full live sports game using ‘just' iPhones. Subscribe at 9to5mac.com/join. Sponsored by Bartender: Bartender Pro is a new option for users who want to take things up a notch. Visit macbartender.com/happyhour to check it out. Sponsored by Copilot Money: Get two months free with code 9TO5MAC at copilot.money/9to5mac. Sponsored by Shopify: See less carts go abandoned and more sales. Sign up for a $1 per month trial at shopify.com/happyhour. Hosts Chance Miller @ChanceHMiller on Twitter @ChanceHMiller on Instagram @ChanceHMiller on Threads Benjamin Mayo @bzamayo on Twitter @bzamayo@mastodon.social @bzamayo on Threads Subscribe, Rate, and Review Apple Podcasts Overcast Spotify 9to5Mac Happy Hour Plus Subscribe to 9to5Mac Happy Hour Plus! Support Benjamin and Chance directly with Happy Hour Plus! 9to5Mac Happy Hour Plus includes:  Ad-free versions of every episode  Pre- and post-show content Bonus episodes Join for $5 per month or $50 a year at 9to5mac.com/join.  Feedback Submit #Ask9to5Mac questions on Twitter, Mastodon, or Threads Email us feedback and questions to happyhour@9to5mac.com Links iOS 27 leak reveals new Siri design, Camera app, more Report: iOS 27 to revamp the AirPods settings UI Report: watchOS 27 to improve heart-rate tracking; AI health coach may not debut at launch Apple Intelligence image models to boast 'major' visual upgrades in iOS 27: report Apple Watch Ultra 4 getting two major new upgrades, per report Apple Watch could soon gain new high blood pressure feature iOS 26.6 adds new alert when you try blocking too many contacts Apple working on iPhone anti-snatching feature that locks the device automatically New Oura Ring 5 unveiled with dramatically smaller design, hypertension detection, more Apple TV to air first major live sporting event shot entirely on iPhone 17 Pro How Apple Shot an Entire MLS Game Using Only iPhone | PetaPixel Eddy Cue named 2026 Cannes Lions Entertainment Person of the Year

Mikayla Maki, software engineer at Zed, digs into what makes this Rust-built code editor tick... from GPUI, their GPU-accelerated UI framework with a Tailwind-inspired API, to CRDTs powering real-time live collaboration without merge conflicts. She talks about the Zed 1.0 release, their approach to AI, how the team builds popular features directly into core instead of relying on extensions, and why Rust might be the best language for agentic coding. Plus: native app comeback, GPUI on mobile, and where the framework is heading. Links LinkedIn: https://www.linkedin.com/in/mikayla-maki Bluesky: https://bsky.app/profile/rad.gendervibes.online GitHub: https://github.com/mikayla-maki Resources Zed 1.0 announcement: https://zed.dev/blog/zed-1-0 DeltaDB / Sequoia Series B post: https://zed.dev/blog/sequoia-backs-zed ACP overview: https://zed.dev/acp GPUI engineering post: https://zed.dev/blog/leveraging-rust-and-the-gpu-to-render-user-interfaces-at-120fps Builder.io "Is Zed ready for AI power users in 2026?": https://www.builder.io/blog/zed-ai-2026 Mikayla's RustConf 2025 talk: https://www.youtube.com/watch?v=rpEU9DNbXA4 filtra.io interview with Mikayla: https://filtra.io/rust/interviews/zed-aug-25 We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com, or tweet at us at PodRocketPod. Check out our newsletter! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form, and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. Chapters

Scott and Wes sit down with Alex Sexton and Amadeus De Marzi from Pierre Computer to dig into the gnarly performance challenges behind building blazing-fast code review tools, covering virtualization, progressive rendering, and why GitHub's UI feels so sluggish. They also chat about how major AI coding tools like Claude, Codex, and Cursor are adopting Pierre's diffs library, plus the role of web components, benchmarking, and what it takes to build “VS Code 2.0.” Show Notes 00:00 Welcome to Syntax! 04:00 The Need for Better Infrastructure 05:53 Understanding Diffs and Trees diffs.com Trees by the Pierre Computer Co 08:16 Performance Challenges in Code Review 10:49 Virtualization Techniques for Smooth Scrolling 15:04 In-Page Find and Virtualization Limitations 17:00 Browser Limitations and Content Visibility 19:29 Progressive Rendering and Syntax Highlighting 23:05 Tools and Techniques for Performance Testing 33:35 Optimizing Performance with AI 36:31 Mastering Auto Research for Efficiency 42:00 Exploring Web Components and State Management 44:05 Innovations in Rendering and Virtualization 49:12 Business Insights and Future Directions 53:58 Sick Picks Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

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.

The corporate attack surface is expanding as autonomous AI agents and developer tools dissolve traditional security boundaries. The software supply chain is now a strategic vulnerability, allowing compromised “trusted tools” to bypass legacy defenses and move directly into internal environments.Recent incidents demonstrate the scale of the risk. GitHub confirmed unauthorized access to roughly 3,800 repositories after a malicious VS Code extension compromised a developer device. Google Cloud infrastructure also exposed a critical “time-to-vulnerability” gap: deleted API keys remained active for an average of 16 minutes, and in some cases up to 23 minutes, despite appearing revoked in the UI. These delays create exploitable windows for autonomous systems to access AI services or sensitive data before responders can intervene.The Cloud Security Alliance warns of an emerging “agentic threat” driven by excessive privileges, weak configurations, prompt injection, poor accountability, and flaws in machine-to-machine interaction. The challenge is no longer simply malicious code, but malicious intent expressed through natural language.Meanwhile, the labor market reflects a “low hire, low fire” reality rather than mass AI unemployment. Layoffs remain historically normal, but hiring and career mobility have slowed as firms adopt leaner operating models and assess automation's long-term impact. Entry-level opportunities are narrowing as companies demand higher productivity from fewer employees using generative tools.Industry leaders remain divided. Steve Wozniak argues AI cannot replace human creativity, while figures such as Sam Altman and Elon Musk warn disruption may eventually require interventions like Universal Basic Income. Many firms are also using “AI transformation” narratives to justify restructuring and post-pandemic cost corrections.Creative industries are shifting from resisting AI to monetizing it. The AI-generated film Hell Grind reportedly required a $500,000 budget, with most costs tied to compute power. Maintaining visual consistency demanded prompts averaging 3,000 words, revealing that AI production remains management-intensive rather than effortless. Spotify and Universal Music Group are also developing licensing frameworks where artists retain control over AI-generated remixes while platforms monetize premium AI creative tools.Technology companies now face growing friction between rapid AI deployment and user trust. Google's “disregard” search glitch showed how AI systems can misinterpret user queries as commands, undermining reliability. Apple's roadmap, including context-aware Siri capabilities and private cloud compute, highlights the industry's push toward personalized assistants.Ultimately, AI adoption depends on trust. Consumers will embrace assistants only if companies prove the infrastructure behind them is reliable, accountable, and secure enough to protect personal data.

V človeški naravi je, da se spominjamo in tudi pozabljamo. Naši spomini so selektivni in subjektivni, v času hitre digitalizacije pa dobivajo nove razsežnosti in možnosti. Raziskujemo, kako na naš spomin vplivajo sodobne tehnologije in kakšno vlogo pri tem igra umetna inteligenca, pa tudi, kako je s pravico do pozabe in etičnimi vprašanji, ki jih prinaša digitalna doba. Spomin nas zanima tudi s kulturološkega, filozofskega in političnega vidika. Kakšne so – in kakšne bodo – magdalenice naše umetnointeligenčne prihodnosti v iskanju izgubljenega časa? Sogovorniki: Kate Eichkorn, raziskovalka digitalnega spomina Natalija Majsova, kulturologinja Grega Repovš, psiholog in nevroznanstvenik Aljoša Kravanja, filozof Wendy Hui Kyong Chun, raziskovalka digitalnih tehnologij Filip Filković, režiser in poustvarjalec spominov z umetno inteligenco V rubriki Xpertiza se predstavlja mlada kemičarka Nika Atelšek Hozjan. Zapiski: Možgani na dlani: Kako možgani načrtujejo prihodnost?  Projekt Spomenar Poglavja: 00:02:42 Generiranje spominov z umetno inteligenco 00:05:27 Kate Eickhorn o digitalizaciji spominov 00:10:32 Filozof Aljoša Kravanja o spominskih vmesnikih 00:17:21 Spomin in možgani, dr. Grega Repovš 00:21:59 Kulturni in politični spomin, Natalija Majsova 00:29:03 UI magdalenice, Wendy Chun 00:35:56 Xpertiza: Nika Atelšek Hozjan

Wes and Scott talk about the foundational decisions that make AI-assisted coding actually work—database schemas, validation, routing, CSS structure, and more. They explore why consistency matters more than specific tools, and how a little upfront planning can keep agents from turning your codebase into chaos. Show Notes 00:00 Welcome to Syntax! 03:19 Planning your database schema before AI touches it 06:08 Picking a validation strategy that won't drift 07:18 Mapping your routing structure and auth flow 08:48 Brought to you by Sentry.io 10:52 Locking in your CSS methodology and UI framework 13:31 Choosing how your client and server communicate 15:03 Creating a folder structure agents can follow 16:16 Don't be afraid to switch up your AI setup later Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Brent's been hacking smart speakers, Wes has a surprise, and Chris gives up on OpenClaw.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:ConnecTen Internet — Get $35 off your order total with Jupiter35

This week on More or Less, the crew unpacks IPO speculation around OpenAI and SpaceX, debates whether AI's economics ultimately favor recurring API spend or owning infrastructure outright, questions if Google's distribution advantage is enough to win the AI race despite muddled product execution, and wrestles with whether today's AI valuations are driven by real fundamentals or pure mimetic momentum, alongside broader debates on broken AI user experience, data center concentration risk, agentic search killing SEO, and whether skilled trades like plumbing may ultimately prove more durable than many white-collar jobs in the AI era.Chapters:01:35 — Brit's fish disaster story + the fish microbiome economy04:50 — Dell World, AI PCs, and the tokenomics debate (API spend vs. owning infrastructure)11:00 — Google I/O recap: agentic search, generative UI, Android glasses, and whether Google is actually back16:30 — Google's UX problem: why AI still feels broken for normal users20:00 — Anthropic vs. Google: focused monolith vs. sprawling empire22:10 — OpenAI IPO speculation + Anthropic's mega-round: what do you have to believe at $1T valuations?29:30 — The “hate invest” thesis: public sentiment, retail risk, and crypto déjà vu34:20 — Portfolio debate: SpaceX vs. Anthropic vs. OpenAI35:00 — Sam builds an AI token pricing dashboard live + how companies actually burn $30K/month on tokens37:00 — What's next in AI research? Memory, world models, and where infra plays went42:30 — White House AI model oversight rumors + OpenAI's Elon legal update50:10 — AI side projects, kids learning to code, and why plumbers may win the AI eraWe're also on ↓X: https://twitter.com/moreorlesspodInstagram: https://instagram.com/moreorlessSpotify: https://podcasters.spotify.com/pod/show/moreorlesspodConnect with us here:1) Sam Lessin: https://x.com/lessin2) Dave Morin: https://x.com/davemorin3) Jessica Lessin: https://x.com/Jessicalessin4) Brit Morin: https://x.com/brit

David Daiches: Inside INSHUR — From Manhattan Uber Rides to Insuring Autonomous Fleets In this episode of Scouting for Growth, Sabine VanderLinden speaks with David Daiches, co-founder and COO of Insure, about building insurance solutions for the on-demand economy. The conversation traces Insure's origins to a simple yet powerful insight: traditional insurance models were not designed for gig workers like Uber drivers, who operate entirely on their smartphones and cannot afford downtime. David explains how Insure addressed this gap by creating flexible, usage-based insurance embedded directly into platform ecosystems. They explore the importance of “fluency over features,” emphasizing that successful insurtechs solve real operational problems rather than just showcasing technology. A central theme is that claims, not policies, define the true value of insurance, leading Insure to bring claims in-house to improve customer experience and data insights. The discussion also looks ahead to emerging challenges, including electric vehicles and autonomous mobility, where insurance must evolve to cover complex ecosystems of software, hardware, and data. Finally, David shares candid lessons on scaling, partnerships, and the growing role of AI, highlighting the need for adaptability, continuous learning, and strong teams in building resilient insurtech businesses.   KEY TAKEAWAYS What stands out most is the importance of starting with the problem, not the technology. David and his team didn't build Insure by showcasing features; they immersed themselves in the daily realities of gig workers and platform operators. That mindset shaped everything, from product design to partnerships. It reinforces my belief that fluency in a partner's business model is far more valuable than any standalone innovation. Another key insight is how insurance must adapt to changing customer behaviors. The on-demand economy is no longer a niche; it supports millions of people. Traditional annual policies simply do not fit this model. By aligning insurance coverage with actual usage, Insure has shown how to close protection gaps while improving affordability and access. What resonated deeply with me is the idea that claims are the product. Customers only truly experience insurance when something goes wrong. Investing in claims operations, empathy, and responsiveness is therefore not optional; it is the core value proposition. I was also struck by the operational lessons. Scaling too quickly, hiring without enough rigor, and taking partnerships for granted are common pitfalls. Building a strong, empowered team and maintaining close alignment with partners is essential for long-term success. Finally, the future of mobility and insurance will require entirely new thinking. Autonomous vehicles, AI, and data-driven ecosystems are reshaping risk. The winners will be those who can navigate this complexity while staying grounded in customer needs.   BEST MOMENTS “Claims is the product. Everything else just gets us to that point.” – David Daiches “We didn't just sell insurance, we solved problems in the platform's business model.” – David Daiches “People are not interested in a fancy UI when something goes wrong. They want a product that is there at the moment they need it the most.” – Sabine VanderLinden “Make yourself easy to do business with.” – David Daiches “The best insurtech founders aren't selling insurance, they are removing friction from someone else's business model.” – Sabine VanderLinden “If you're not spending time learning AI now, you risk being left behind.” – David Daiches   ABOUT THE GUEST David Daiches is the co-founder and Chief Operating Officer of Inshur, a digital-first managing general agent focused on the on-demand economy. With a background in technology and retail, he entered the insurance industry over 15 years ago and identified significant opportunities for digital transformation.  At Inshur, David has led the development of embedded, usage-based insurance solutions for platforms such as Uber, Amazon, and DoorDash. David is particularly focused on innovation in mobility insurance, including the future of autonomous vehicles and AI-driven claims and underwriting.   ABOUT THE HOST Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet. If this episode sparked your thinking, follow Sabine VanderLinden on LinkedIn, Twitter, and Instagram for more insights. And if you're interested in sponsoring the podcast, reach out to the team at hello@alchemycrew.ventures

JPEG Store, long the biggest NFT marketplace in the Cardano ecosystem, is shutting down, which means NFT holders and project teams need to decide where their listings go next. In this episode, Peter walks through a practical migration from JPEG Store to WayUp and shows what the process looks like using a real wallet and live listings.The tutorial covers why this migration matters, how WayUp pulls in existing JPEG Store listings, what the wallet transaction is doing behind the scenes, and how to verify the contract movement on Cardanoscan. Peter also shares an important clarification on timing: even after the JPEG Store interface goes down, smart contract level migration may still be possible, but it is better to test before the UI disappears in case edge cases need manual relisting.Key Takeaways:- JPEG Store is shutting down, so NFT holders should review and migrate active listings before the interface goes offline.- WayUp offers a migration feature that can pull in existing JPEG Store listings and move them into a new marketplace contract.- The migration requires a wallet signature, so users should review the transaction addresses and metadata before confirming.- Cardanoscan can be used to verify that assets moved from the JPEG Store ask contract into the WayUp contract.- Supporting active marketplaces helps keep NFT trading infrastructure alive during a weak market cycle.- The JPEG Store website going offline does not necessarily mean NFTs are lost, because the assets remain in smart contracts.- There may still be contract mismatch edge cases, so testing the migration before the UI shutdown is the safest approach.Links & References:- Cardano Apps Directory: Wallets, DEXes, NFTs & More | Cardano: https://link.learncardano.io/JIJdb3- Add your Application | Cardano: https://link.learncardano.io/W52NlV- Cardano (ADA) Blockchain Ecosystem and Project Explorer: https://link.learncardano.io/k9zcxs- Adastack Ecosystem Explorer: https://link.learncardano.io/gdYxSdWebsite: 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.

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

Nathan Wrigley interviews Leo Losoviz, focusing on translating WordPress sites using AI-powered tools, including his Gato AI Translations for Polylang plugin. They cover the evolution from manual, costly translations to efficient, high-quality AI solutions, the legal and business cases for multilingual websites, and UI advancements relating to collaborative editing with WordPress 7.0. Leo stresses the importance of preparing content before translating and discusses SEO benefits and plugin integration. The conversation highlights translation as both a growth opportunity and a necessity to remain competitive as AI lowers the barriers.

The Great Talent Redistribution: Where is Talent Actually Going in 2026 and beyond?  Is the start-up compensation model broken? How about big Big Tech? How about non-tech small & medium businesses? What is happening to talent, going forward? This and many other topics in this episode of Tech Deciphered. Navigation: Intro The Broken Contract? The Great Unbundling The Three (?) Destinations Alternative Cap Tables, Alternative Compensation Models Investor Landscape Fragmentation Operator Playbook and Predictions Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Nuno Goncalves Pedro Introduction Welcome to episode 77 of Tech Deciphered. This episode will focus on the great talent redistribution. Where’s talent actually going in 2026 and beyond? The Silicon Valley deal of the last 30 years, very low salary, stock options, you will either sell for a ton of money or IPO, and everyone gets rich, is seemingly broken. Or is it really? The dominant narrative says the tech middle class is dying. We disagree. There is obviously a lot of stuff going on whereby big tech is partially barbelling. There’s a superstar concentration on the top. There’s a bit of a seemingly allowing of the belly. We’ll come back to that. We don’t quite believe that is totally true. There’s a collapse at entry level. The belly is migrating into three, potentially even more, very different destinations: AI native startups, human-verified premium businesses, and the read the industrialized middle of the S&P 500 and SMB world. Each has its own cap table, each will have its own compensation model, and each will have its own investor profile. In some ways, this is the third episode in our Reset trilogy. We started with episode 75 on the SaaS-apocalypse. We talked about the great private capital reset in episode 76, and now we talk about talent redistributions. Bertrand, exciting times, not always positive times.   Bertrand Schmitt Yeah, it’s exciting times because it’s a time of change. Of course, we have the doomsayers. If you listen to Dario Amodei of Anthropic, every white-collar job on Earth is going to disappear. I think I strongly disagree, and I suppose you too as well, we strongly disagree. It’s going to be more of a redistribution. If you look at the history of technology, this is what always happened. We forget how many jobs have disappeared over the past 150 years. We move from a time of 150 years ago. People were mostly in agriculture. Then you had a lot of weird jobs that disappeared from people transporting water to people bringing ice from the pools to people doing the job of computers. People forget that computer was a title given to human beings. We’re doing calculations. Then, of course, secretory jobs in the ’80s, ’90s, where suddenly anyone can type using a word processor, the rise of Excel, that sort of stuff. Many things have changed. Some jobs have indeed disappeared. Some jobs have totally transformed. Where you do these jobs have changed. I think we are at a similar stage where, thanks to AI, and I would say for now, or at least the rise of AI coding, there is a dramatic change happening. I don’t think it means that people will be without a job. It just means, from my perspective, that jobs are changing. You are not just doing a lowly coding level task that actually indeed could be replaced, but you are going to have more of builder type of mindset, a product manager type of mindset going forward. We also expect that the distribution of jobs, depending on the type of business, will be quite different.   Nuno Goncalves Pedro The Broken Contract? Maybe let’s reset a little bit to the broken contract, or if it’s really a broken contract. There’s been this image in technology and tech that basically you get paid very little to work in tech. You get a bunch of stock options. The earlier you are in the company, the higher the level of stock option grants you get. Then you make a ton of money at some point because the company will either sell or IPO, and that’s heard of it. Obviously, there’s a lot of movements happening right now that are changing how these dynamics work. The first part is obviously AI, and in some ways, AI is shrinking companies. It’s not unheard of that companies with as little as four or five people reach 50 million in ARR. There’s companies with one person that have gotten bought for hundreds of millions of dollars or billion of dollars. Obviously, things are moving very, very fast, and therefore, there isn’t a large employee cap table. How would you share the upside? Would you actually give a couple of percentage points to an early employee rather than your 0.2-0.5% kind of thing for early employees? The second part is a little bit the other side of the table, which is the IPO market is seemingly in a drought. There’s not much happening in IPOs. Maybe 2026, at some point, there will be an unlock, but right now, it’s seemingly difficult to get your upside. Even if you’re an employee, you have to wait a long time. The median time of IPO has climbed over 10, 11 years, the longest in over a decade. Basically, not only you have to wait a long time as if there is an IPO drought, like we might be going through right now, when do I actually get my cash back? Unless the company gets bought, maybe there are secondary transactions along the way, maybe there’s something else. But obviously there’s a little bit of a reduction and lowering of the upside seemingly for this contract and for this place. The easy conclusion that I think many are taking is, because of all of this and all the layoffs that are happening, even in big tech, that serve the tech middle class is dying, that basically AI screwing the workers, et cetera, there’s also a lot of discussion that even it might be affecting the entry-level jobs as well. Everyone coming out of undergrad right now can’t get a job, et cetera. There’s this doomsday scenario that you’re alluding to that everything is changing. We have a slightly different perspective. We think there’s a realignment of market. In layoffs, there was a lot of layoffs that were warranted. Big tech, in particular, had actually hoarded a lot of engineering capacity over the last decade or so. There’s a little bit of a realignment that needed to happen in any case. When everyone’s saying, “Well, AI is compressing everything,” well, it’s compressing right now, but we don’t think actually it’s going to compress over time. You’ll still need engineering and science talent to come on board for you to be able to scale up. It’s not like AI is going to take care of everything and teams are going to be five people for companies that are worth a trillion dollars. That’s not happening. Today’s thesis, I think a little bit of this doomsday scenario needs to be seen with a more nuanced lens. I think that’s how we’re framing today’s episode, that there’s a bit of a nuance, there are some extremes happening. We’re going to talk about those extremes, but ultimately, it’s not quite as simple as saying that the tech middle class is disappearing in early jobs are going to be a thing of the past.   Bertrand Schmitt At the same time, what you started with is true. I mean, that 50 million ARR company, just five people. At a bigger scale, that’s exactly the matrix for Anthropic. They have reached a stage where they are at a range of 12 million ARR per staff per employee. It’s metrics that are definitely never seen before. I don’t think any company raised to this level. Best in class, best run companies, one, two million per employees. I mean, that was your target if you can make it. We are definitely in a different game. But I think what matters at the end of the day, and that’s what we’re arguing, is that you have to see the big pictures. Yes, some positions might disappear inside some companies, but some other positions will be created in other companies. Usually, what people do is keep talking about the jobs who disappear and not looking at the bigger picture of jobs that are being created as well. What is true, and I think you alluded to that, is that the big tech the past 10, 15 years had some strategy of hoarding talent in a war where having the best talented people will make the difference in numbers, will make the difference between winning or losing. The Google of the world, the Microsoft of the world, the Amazon of the world, they were hoarding talent. They would try to make sure that they might not have such needs in talented number of people. But if they have the talent, it means their competitors didn’t have the talent. It means that the startup trying to reach scale couldn’t pay the giant salaries that the Google of the world were paying. There was definitely some hoarding. But it went so far in the 2020, 2021, that I think since then there has been a coming back to normal. There is also now in 2026, the recognition that it’s not true anymore. Yes, talent can be very valuable, but there is now a bigger and bigger gap between the extremely talented versus the rest that are merely talented because of AI. AI is able to replace at scale your software engineers, your software managers. I would say it’s quite new. I don’t think it was true a year ago. We’re really talking about a recent dramatic change in what can be achieved thanks to AI. We can see most of the big AI companies are moving to coding. It was started by Anthropic as a trend, OpenAI has followed through. Obviously, the Cursor of the world existed before, but they were not as successful. All the Chinese open-source models are moving very fast to coding optimization the past few weeks. It’s quite an incredible change. I think there is that dramatic change, recognition that coding can be done differently. As a result, we are going to see change in the distribution of jobs. I think it will start from the top because we see the news of the big Google, Microsoft, Amazon, and others who used to hold talented software developers to a change in realization that no, we actually need to invest in AI. We need to invest in compute because compute is going to do the job of most of these people. Therefore, we can’t pay for both at the same time, even us with all our money, we cannot. Wall Street is not going to let us do that. They start by removing a lot of position. I think we see that accelerating, quite frankly. We have only seen the beginning, but in the next 2 years, we see a dramatic shift. But I think my position, I guess yours, and you know as well, is that there will be a lot more opportunities created as well, probably by also entities.   Nuno Goncalves Pedro The Great Unbundling Yeah, there will be more opportunities created. The hoarding is just taken also a little bit of a different view. To your point, there’s hoarding of resources, compute, et cetera. But there’s also hoarding of top talent. We are seeing people getting paid, packages all in that could run up to 100 million, in some cases even over 100 million over several years. This is unheard of. I mean, an officer of Meta would make, I don’t know, maybe 20, 25 million a year. It’s like now there are people that are on the top end of AI researchers that are getting paid around that amount just to join some of these companies. There’s a little bit of a different hoarding. It’s very selective hoarding of certain talent. We’ve seen some acqui-hires. We’ve talked about it in previous episodes that are just literally about getting one or two people specifically to come on board. Alexander Wang, again, going to Meta to lead their intelligence labs there. I feel, I don’t know what you feel, but I feel this is a transition moment where there is overpaying for certain talent on the top of the market. At some point, this will stabilize. You can’t keep paying people 100 million over 4 years or something like that across the board. To your point, a lot of this is actually going to scale up quickly also on the AI side. There’s a little bit of a different hoarding happening on the top end, not just the resources, but also of people, which seems to give further this notion of barbell, that there’s two extremes, the haves and have-nots, the super-duper talented people that get paid a ton of money, tens of millions of dollars a year at the very least. Then the emptying of the middle where there’s a ton of tech layoffs going on in some ways, the belly, as they would call it, is being expelled. The middle market, the managers are being fired because there’s nothing to manage. There’s a lot of positions going away. In some cases, you might keep some of the more junior talent, but with a little bit of experience. But even the talent coming out of colleges is not getting hired either. It’s a little bit of a weird thing where there’s hoarding at the top, there’s an emptying of the belly, the middle, and then the early, early, early is also not getting recruited. It’s like what gives? How is this going to look in the future? I agree fully with you, Bertrand, that there’s a migration of this talent, not only to other companies, but also to other jobs. There will be new jobs that will emerge out of this. The DevOps, dev tools market didn’t exist until maybe 20 years ago at scale, and it got created. In some ways, we’re seeing there will be new markets, there will be new roles and new jobs that will be created around engineering teams going forward. We can’t anticipate all of them. But basically, the emptying of the belly is true as it’s happening right now. The low hiring on the early and the top end, getting tons of money. We think this is a transition to something else. There’s the hoarding of engineering in general is coming to an end at momentum. Now it’s time to rightsize teams, to get the right at the table, et cetera, and start figuring out what works and what doesn’t work. We’ve already had some horror stories coming out even from Amazon where they were breaking systems with their use of AI tools, and I’m sure it’s happening across the board. I’m on a board of a company and been tremendously affected by Meta and its algorithms, where basically because of advertising, there have been people served with ads for this specific company where the ad doesn’t match the company, so basic stuff like that. It’s been actually very, very difficult because in some ways, the company goes back to Meta. It’s like, “Hey, dudes, you guys are serving ads that are not even our ads with our copyright and stuff. How does this work?” They’re like, “Oh, it’s AI.” It’s like, “Well, it’s AI but can you give me my money back?” They’re like, “No, we won’t give you money back.” This creates huge issues for companies, for example, that are very dependent on advertising, which obviously there’s a lot of industries that are. They’re actually in production systems at scale. Meta is, I think now, the largest digital advertising in the world. I think they outgrew Google in one of the last quarters. Basically, this has a tremendous effect that systems that are in production at scale are getting inputs and changes driven by AI tooling, and somehow nobody can say what the hell is happening. Again, there will be a reckoning, there will be a redistribution, there will be a rightsizing of teams and an adequacy of teams going forward. I personally think this is a transition period.   Bertrand Schmitt I think we are moving from hoarding or software engineering to hoarding the top of the top scientists in AI and hoarding of GPUs, GPUs/data center. For me, it was quite interesting to see the deal of Cursor with xAI, where basically they couldn’t get access to computing resources to run their model. But xAI had, I forgot the exact numbers, but close to half a million GPUs that no one, I mean, “no one was using” because their services are not so successful yet in terms of AI chatbot and the like. Basically, suddenly they are like, “You know what? We control access to resource.” But the new resource is, again, a mix of extremely talented AI engineering or AI scientists versus GPUs/data center. There is this race of controlling boss and everything else is going to be collateral damage. Some examples, I think, are quite interesting. You talk about some example of Amazon, even some production issues. I remember reading a quick post-mortem of one of the issues, and the conclusion was it was AI, definitely part of the issue. But the other part of the issue was AI used by junior engineers. For me, it’s interesting. It shows that actually junior plus AI is actually a danger zone. That’s why many companies are going to be way more careful. “Why do we need the junior people if they are just playing with fire?” I think we go back to that situation of barbell, as you call it. The top talents are extremely valuable because they know how a production system works. They are here to develop better AI systems. But the junior guys playing with fires, yeah, maybe it’s cute in startups, but in a big time production environment, a different story.   Nuno Goncalves Pedro There will be a barbell with top-end talent super-mega paid and then mid-level talent that is individual contributors still doing a lot of great work, et cetera. Along the way, a lot of emptying of entry, a lot of emptying of the middle. Where does the talent go? The Three (?) Destinations I think we could say there’s three destinations for this talent. Maybe there’s four, maybe there’s more. Three that we can immediately identify. One is the AI native startup piece, where we have smaller teams that potentially get to a lot of revenue or top line over time, and where the Series Seed is the primary round, where we’re seeing Series Seed being raised of tens of millions of dollars, actually even hundreds of millions of dollars in Series Seed. In some ways, the stars there can get incredible compensations in terms of stock. They will stay for private and selling in secondaries later down the road because there’s so much capital at the table. Actually, in some ways, salaries are very high as well in some of these companies. It’s not like you’re trading off anything. You can get paid a lot of money. If your company at Series Seed for 10 or 15 employees has raised 50-$100 million, you can pay great salaries. In some ways, this is the extreme destination. The AI native startups that can make it is the extreme destination. Now, there aren’t a ton of AI native startups that can raise 50-100 million to 400 million in Series Seed, just to be clear. There’s a handful of hot deals in that space, but that’s one clear destination for top-end talent going through that. In that market, I think that’s one of the destinations. The second one is more what we would call the human-verified premium. It’s more of a play of companies that has still the need of human in the loop, either in terms of development, also in terms of activity, either because go-to markets are very intensive, and so therefore you need to have sales forces, partnership teams, et cetera. Or on the engineering side, it needs to have a lot of customization, integration. Companies are not just going to the, “Oh, you can come in and just apply your AI tooling and somehow magically the systems all work.” there needs to be quite a lot of and work and high touch work in getting stuff done. A significant part of that market, I’m not sure, is super VC investible. Maybe it’s a hybrid of private equity in VC, more PE style in many cases. It’s a PE-hold, sell to someone else market. As we’ve discussed in a previous episode on the SaaS-apocalypse, that hasn’t quite worked out for PEs. Question marks on how that human-verified premium market is going to evolve. But obviously, there’s a lot of work still to be done there, even on the engineering and science side. That’s the second potential destination. Then the third more aggressive destination is the reindustrialized middle companies that have a lot of specificity in going after small and medium businesses, local or regional affectations like ERPs or CRMs for specific markets, et cetera. Those are the three natural destinations. I would add the fourth, which is big tech. I mean, big tech doesn’t magically disappear, and I don’t think it fits neatly into any of these three markets. In some ways, big tech is now looking at the extreme for top talent a little bit like the AI native startup because they can pay. They can pay the 100 million every four years, et cetera. I do think it will typify taxonomically into a fourth type emerging, where, as we discussed, you’ll have top-end individual contributor talent. You’ll have the absolute top-end of the market because they can get paid. Then you’ll start having the emergence of earlier talent that is highly capable, et cetera. That will go back to a bit of a normal distribution in terms of talent on big tech. For me, those are the four destinations that I would put at the table.   Bertrand Schmitt For me, big tech moving to big tech, I’m not sure if it’s really a destination. I mean, yes, in some ways it’s a reshuffle between the big tech companies. They are definitely all fighting in some ways for some of the same people. I can see that dramatic shift where big tech has to remove a lot of positions in order to replace by AI. Again, I think at this stage, it’s mostly driven by AI coding. We are still at the beginning because this is brand-new phenomenon that AI coding is so successful at its task. I don’t think it was true even 6 months ago. Some companies, take Anthropic, take OpenAI, are definitely there or close to be there in terms of no more writing of a single line of code by a human, zero. This is, again, 6, 12 months ago. Not true. But now it’s true in a few top companies. Take OpenClaw as well, most successful GitHub project of all time, not a single line written by its author. It would have been impossible. We’re talking about hundreds of thousands of line of code in a few months. It’s impossible to achieve that manually. If you look at the other big tech companies, the Google of the world, the Meta of the world, the Microsoft of the world, they are absolutely not there yet. They are going to be there because they have no choice. It’s you either go fast there or you die. You are not going to be able to survive competitors that are shipping 10, 50, 100 times faster than you are shipping. It’s a life and death situation. All the big tech companies are going to move, and mark my word, in the next 2 years from 10, 20% of AI-written code to 100%. During that transition, the next 2 years max, if you don’t do it in 2 years, you are going to die. Your stock price is going to crash. Then, of course, you will have to make changes. You will have to invest more in GPUs. You will have to invest less in your standard typical software engineer employees. Like you, I’m very optimistic that there are new buckets. AI-native startups definitely will be there. It will be transformational. Human-verified premium, very interesting category. In a way, it will be businesses that are inevitably less scalable through AI, and there is definitely a spot from there. I think the biggest would be the reindustrialized middle SMBs. Most of S&P 500 type of business are going to dramatically offer new software opportunities, new opportunity story to talented software employees because they will need to implement AI in everything they do. They will do it. They will need people who have software engineering knowledge in order to implement these systems. For them, what’s changing dramatically really is that thanks to much cheaper cost as thanks to AI coding, a lot of software projects that they couldn’t afford to do, that they couldn’t imagine doing by themselves, they are able to do it. They will invest in a lot more software capabilities than ever before. That will be a big game changer. And software, very tuned to their business model. There might be less buying of your traditional off-the-shelf SAF software and a lot more investment in a highly custom software by their own team, assisted with AI. I think that would be the part that is most transformed by all of this in a positive way.   Nuno Goncalves Pedro Alternative Cap Tables, Alternative Compensation Models This will lead to a very fundamental shift, right back to the broken contract. What does the new contract look like? It looks like alternative cap tables depending on which bucket are you transitioning into. If you’re going into your AI-native bucket, and you’re a top-end talent, you’re like, “Dude, I’m worth 100 million over 4 years, so just compensate me accordingly with a mix of options in the company plus my salary.” If you’re top 1%, you can probably get away with salaries that you’d get anyway at mid-level from 300K, 400K and above, and you can get actually a lot of options already in the company. A lot of this is happening right now. There’s a premium for AI, we know that. There’s a premium for AI at the top end of AI researching, in particular on companies that are doing hardcore research on staff AI engineers, so companies that require actual AI engineering. There is a premium that is significant. It could be as high as 18% over non-AI peers, and it widens actually with seniority, shockingly enough. This is more of an average than anything else. Now, for me, and it’s for debate, but the perspective is this extreme comp will need to compress at some point. There will still be the haves and have-nots paid much better than the have-nots, so to speak, but there will be a compression. The variance can’t be the variance we’re seeing today for absolute top-end talent. That said, there will be variants. We know that big tech for over a decade, decade and a half, for example, in the Bay Area, has been paying a lot of money for director and above levels that used to be the VPs, so a million, a million and a half a year, all in compensations. It’s not unheard of that this will actually increase after this stage. That said, I do think that the compensation extreme that we’re in will get diluted down the middle. It will actually come down at some point. It’s part of where we are today. As we know, it is still a bubble.   Bertrand Schmitt Yeah, it’s an interesting point. I think it’s possible. At the same time, that compression coming 2, 3, 5 years. At the same time, we have examples where there is no such compression. Take the top sports players in the world, golfing, basketball, NBA players. There has not really been any compression at all. For me, it’s interesting. If you look at the big tech companies, each being one of this top NBA team, why would such compression happen? As long as they are competing against each other and generating plenty of cash, I think there will be some fair question. We will see. I don’t have a strong opinion, but for me, it’s not a total given.   Nuno Goncalves Pedro For me, the shocking thing is the faster AI becomes better, the more that compression will happen, because at some point, it’s like, why do you need the top talent as well? I don’t know. It feels like you’re trying to evolve a system that’s there to replace you. It’s like, “Okay, I’m getting paid 100 million over the next 4 years”, and then you develop something that’s so good that replaces you. Thank you. That’s cool.   Bertrand Schmitt That’s a total possibility, yes, because we are in that very unusual market where the game is to only replace yourself and people like yourself. At some point, it is a possibility, I guess this one. Right now, we’re talking about replacing your “average software talent”. In 2 years, could we absolutely replace the absolute best top experts in the world? Probably. I think it’s just that at some point we’ll be reaching the stage where we strictly have no control anymore on our AI systems because no human is able to challenge and understand what’s produced. It’s not just a question of scale anymore. We’re talking about a gap in IQ, basically.   Nuno Goncalves Pedro Exactly. It will happen at some point in history. We don’t know exactly when. For the second bucket, the human-verified premium bucket, it’s difficult to see how an HVAC company or an HVAC roll-up of scale or a regional health care platform or high touch go-to-market, B2B, SaaS play, et cetera, for a vertical will compete. At the same end, they have to compete and they will compete. There will be more and more jobs, we believe, for engineering talent in these companies. They’ll have to be more and more AI-enabled themselves. The cash salaries will have to be competitive within the local markets, not necessarily with Silicon Valley. There will be potentially profit sharing and revenue sharing and actual dividends played at the table. The model there on the cap table needs to change a little bit, needs to be probably propped up more on salary and on some way of doing profit sharing or actually having dividends paid to employees and figuring out employee to equity in a more aggressive manner. This is the market that probably was already very attacked, so to speak, or let’s say, occupied by private equity firms. There are still obviously part of that model that would work well. There needs to be a fundamental shift, certainly on the quantum of salary compensation, dividend compensation, profit sharing, and all of that. Then last but not the least, obviously, we had the bucket around basically the reindustrialization of the middle, so everything else, which will take most of the belly that we were talking about. This is probably a poor analogy, the belly fat. It’s not belly fat, it’s people that were doing their jobs that now are getting disrupted. In some ways, that bucket will absorb a lot of that belly, will absorb a lot of talent. The small and medium businesses that Bertrand was saying will need to crucially become more AI, software-enabled by themselves, even with some core stuff and underpinnings that actually might not even require AI in terms of infrastructure platforms. There, you need to get properly paid. Again, how many people do you need in your engineering team if you’re a small business? Probably not a lot. It’s maybe you need one or two people and that’s it. They’ll need to be very nicely paid because they’re running the stuff in the rails. This is probably a market that over time, as AI gets more and more competent, will also be disrupted, but let’s not talk about the disruption to the disruption because otherwise, we’ll stay here the whole day, but certainly a market that has a lot of potential to shift and to absorb a lot of the moments that we’re seeing in terms of layoffs happening in the US in particular.   Bertrand Schmitt This category was a category that historically could not compete with Silicon Valley salaries, could not attract the most talented engineers. It’s not a category that didn’t want to bring these people on board. It’s a category that just couldn’t afford to bring this talent on board, typically. I think it would be a dramatic shift for them when suddenly there are opportunities to hire these people. There is an opportunity to hire them at maybe more reasonable prices from this company’s perspective. You talk about small companies, the great thing is that there are millions of small companies at some point. I think things could be truly transformational. Of course, some of these engineers, software engineers, might decide to become entrepreneurs on their own. Solo entrepreneurs, small businesses, build their own, easier to build their own product to market so to serve other companies. I think there will be quite dramatic changes because not all companies will be disrupted by AI as much, but not every company will benefit from improving processes, improving software through AI. At least early on, you will need this human touch to make it work inside a business. Interestingly enough, I was hearing that some companies like IBM were hiring more younger people to do the work of going to the client, understand their needs, propose implementation plans. That forward deployed engineer, those positions, I think there will be more and more available.   Nuno Goncalves Pedro Investor Landscape Fragmentation What happens to investor into the landscape? We already had an episode, the previous one, Episode 76, where we talked quite a lot about the big capital reset on the private equity and private reset, including venture capital. Just maybe to summarize, how does it align with the buckets that we’ve just been discussing? I think the AI-native bucket clearly is going to be the key bucket. There, we’re going to see two movements. One movement, which is the mega funds, as we discussed in the last episode, are no longer just VC funds. They’re really mostly multi-asset private equity funds, maybe even private equity hedge funds in some cases. Those funds will be all over the high-growth AI-native companies and will be pouring money into companies that are scaling really, really quickly. The early stage, so to speak, VCs, the actual VCs that will stay in the market will be the guys probably identifying the next big wave of AI-native companies. We’ve discussed that as well in the last episode, some research that we did at Chamaeleon that I shared in episode 76. We’ll see that as emerging. What happens to the second bucket, the bucket around human premium, human in the loop? Likely we’ll have more and more private equity capital going into it and the large-scale VC guys, the Thrives of the world, they’ve just announced Thrive Holdings, and others going after those markets as well. It’s trying to converge into the private equity market, which aligns with the point we made in the previous episode that the VC mega funds are no longer VC, that they are private equity, multi-asset class. They’re going after a bunch of things. There’s a conversion happening from VC into private equity. It was going to happen anyway because the private equity guys were coming into VC as well and the hedge funds were coming to VC as well. There’s a convergence in the middle of very, very large funds and large assets under management happening to go after some of these opportunities, certainly in Bucket B. Then this Bucket C, so to speak, the bucket of reindustrialization, as Bertrand was saying, very well, likely will be self-funded for a significant period of time. Will self-fund with their own cash flow. Doesn’t need to have a ton of capital intensity. Maybe you need one or two engineers to do stuff, but that’s it. You don’t need tons of capital. You didn’t need in the past, you won’t need it today. Not sure there’s going to be a fundamental shift to that market.   Bertrand Schmitt Yes, I certainly, overall, agree with you. That last pocket, probably little change to the capital and capital structure. Again, I see that as the biggest opportunity for a lot of people who might be less needed by big tech and also top tech companies. What is sure for the first category, the high native startups? I would say more overall in the VC ecosystem, there is no space left for SaaS anymore. I think SaaS, as we used to know it, is dead in some ways in the sense that new pure SaaS software startup are definitely out. Existing ones that are critical to run your infrastructure, the Salesforce of the world, I think they’re in a decent spot. Actually, interestingly, they changed their pricing model to now sell to AI agents, not just per seat. There is a change in pricing there. But this day and age of funding a pure SaaS software startup through VC money, no way. VC money going to AI-native startups, AI-focused startups, to biotech, to deep tech, to defense tech, yes. SaaS as a fundable category early on, I think it’s over.   Nuno Goncalves Pedro I’m a bit more nuanced as we shared in The SaaS Apocalypse episode. We can call it whatever we call. It’s applied AI is the new SaaS thing. Horizontal applied AI is the new horizontal SaaS or vertical applied AI is the new vertical SaaS. I agree in common with your point that very specific point solutions around SaaS will be disrupted by nature with all the easy stuff you can do today with AI. It will take a while. This is not something that’s going to happen this year. It’s going to happen over the next years. Maybe interesting to also talk about the exit markets. I think the IPO market, as we’ve also discussed in the past, there is, in my view, going to be a reopening of the IPO market, I think this year, probably later in the year, third or fourth quarter. The median time to IPO actually is going to be really weird because there’s going to be potentially some companies in the current landscape, bubble or no bubble, that are going to IPO, the OpenAIs of the world, Anthropics of the world, et cetera. There will be more and more aggression, I think, on M&A. Big tech has already shown it, that they want to buy into markets. Large non-tech companies have also started doing acquisitions in space. To prop up their IT teams, their engineering teams with this world that we’ve also discussed in previous episodes that I’m going to own my own engineering stack for now. As we see, that normally doesn’t withstand the test of time. At some point it will get unbundled and served by someone else. Then finally, the secondary market is very hot right now. Obviously, there’s heavy discounting on some areas, high premiums on others. The exit market, strangely enough, is going to be propped up, in my opinion, over the next year to 2 years, dramatically. Then we’ll see if there’s a big reckoning around the bubble that we are clearly in or not, if it’s a soft landing or hard landing. Definitely, there’s going to be a lot of exit paths over the next year to 2 years.   Bertrand Schmitt Concerning the “bubble”, I have two perspectives on this. One is it’s a bubble in the sense that money is going to a lot of players and some players are going to blow it up. There will be a concentration of players at the end, like it usually happens. If you look at, for instance, long time ago, the railway revolution, there was that intense influx of capital. At the end of the day, there was a dramatic change in transportation in the US and a complete railway system put in place. Yes, some investors lost money, some companies went bankrupt, but the transformation was fully real. There were a lot of top leaders at the end of this revolution. The change after that only happened, we guess, post-World War II, with the construction of the highway system and the rise of airlines and plane transportation overall. Here I feel it’s similar in the sense that, yes, there is a lot of money going in. Some players are going to blow it. They will misuse the money in different ways, but that’s part of dynamic allocation of capital. Of course, you make mistakes. That’s what happens. At the same time, I feel it’s a similar level in the sense of this is a dramatic change in the US infrastructure. This buildup of AI data centers filled with GPUs, integrated at scale with some of the best software in the world and running it, supported by a dramatic shift in energy infrastructure. This is for me similar to the Railroad Revolution. Some players might not own the data center they build because they didn’t manage well their debt, they didn’t manage to run proper software. You know what? They will get acquired by somebody else. I think we are at this level of fundamental transformation. The fact that in a matter of maybe 2 years, the move from 0% of code written by AI to 100 % written by AI is an insane dramatic shift. Just to be clear, when you move from manually coded to AI coded, we’re talking about a 100X difference in terms of speed at similar, if not better level of quality. The shift is dramatic, and on top of it, you don’t pay salaries anymore to achieve that. You pay CapEx, and with GPUs and OpEx with electricity. It’s a very big shift, positive shift in business model. New unions, no management over it, AI working 24/7. Personally, I think for me, bubble has a bad connotation in the sense of it was all for a waste. I don’t think it’s all for a waste. I think we are witnessing a dramatic revolution of our lifetimes, quite frankly, bigger than SaaS, bigger than mobile. From my perspective, it’s exciting times.   Nuno Goncalves Pedro Operator Playbook and Predictions Let’s move to if you are this person, what would you do in the future? Let’s start with two extremes and go from there. One is you’re non-tech, so you’re not an engineer, et cetera. You’re trying to figure out, how do I scale my activity? Maybe physical labor is where I want to go. It’s not, “Go west” anymore. Definitely not necessarily go west. You should go to, I guess, the states that have no sales tax with very cheap energy because that’s where the data centers are being built if you want to be in that market. Obviously, there’s a lot of stuff that needs to be done: HVAC, electricity work, et cetera. Don’t go west. Go low sales taxes, low cost of energy. That’s likely where the data centers are being built. You probably can just follow. There’s, I’m sure, some way for you to follow where the data centers are being built, but that’s next, I think on that extreme of the table. The other extreme of the table, let’s say you are super ambitious, maybe you’re no longer an engineer, but you’re a product manager in your prompt engineering. You could do prompt engineering all day long. You’re 28, 29-year-old superstar. What do you go and do? Likely either you start your own thing, start your own company because you’re so good at prompt engineering, you probably can do a lot of the code yourself, particularly if you have an engineering background, or you go and join very early an AI-native startup that you think has the chance of going through the roof, and you take a pretty good salary early on, a ton of upside on the company because guess what? Companies like that need product managers. They need people to figure out UX, UI. It’s not going to be, at least for now, yet AI figuring that out for you. Those are two extremes, just to give two of the extremes, like engineering, product management persona, and physical labor at the other extreme, non-tech, et cetera.   Bertrand Schmitt In some ways, every software engineering job is going to become the equivalent of a software engineering manager or a product manager, because suddenly you don’t have to do the coding anymore. You’re managing AI that is coding for you. Either you start to have some manager hat, but we saw the humans, so it’s a very different type of manager, obviously, or you are going to be really an empowered product manager. You’re skipping the middleman. You’re skipping the traditional engineering organization because your engineering organization is AI running and doing the work for you. I still believe that it requires some serious skills. I don’t believe in the vibe coder type of value proposition. I don’t believe in the prompt engineer becoming suddenly super incredible, able to manage that. I still think it requires some serious chops to do the best from all of this and to do it in a safe and sane way. It’s very easy to have poor taste, make mistakes. I don’t know you, but keep reading these stories on the heads of companies who lost everything because of the AI agents. That deleted stuff in production, and they had no backups or the backups weren’t deleted as well. Crazy situation. You cannot run companies like this if you let your agents running wild. You could argue it’s the early days. I would argue it that that issues would be there for a while. You need to have some engineering discipline at core in the company running the business to make sure things don’t go sideways because it would be easy for things to go sideways.   Nuno Goncalves Pedro I totally agree. If you’re thinking, Oh, should my kid go into science and engineering and computer science, et cetera? Absolutely, still, because of everything that Bertrand just said. You need to understand actually what code does and what technology does and what all of that does. That’s still a skill of the future. It’s not a skill of the past. In some ways, it’s still a skill of the future very much. Maybe let’s try two more extremes. Around the same level, the person that decided to do an AI native company bootstrapped initially, having difficulty raising a mega round, but could probably get away with raising a 2-3 million seed round, et cetera. Is that still viable? The answer is yes. There’s tremendous capital efficiency right now happening in the market still, 10 plus higher than if you were doing a SaaS company, and you were a founder in 2019 or something like that. That capital efficiency is going to reverberate. You can run a tighter team, smaller team. Actually, you don’t need that many salaries. If you’re a decent engineer as a founder or if you understand enough as a product manager to just generate that code, you can do a lot of stuff yourself, can bring in maybe one or two technical elements to the team early on as you would have done if you were bootstrapped anyway. There’s obviously a path for that. The other extreme is you’re in big tech, you’re level five, individual contributor, making a ton of money, or you were a manager, and you’re now out of a job, where do you go? You can go to a big company that is non-tech, S&P 500 company that’s non-tech, something like that. You join the company, you’ll probably get paid pretty well, maybe not as high as you were paid in big tech. There’s some stock at the table, but guess what? You’ll have probably more work-life balance than you ever did. That’s the trade-off. You’ll have a better job. On the upside, you can transform the company. You can help and be part of transforming a company from non-AI to AI-first or AI-enabled in the future, whatever BS that will look like in terms of the argumentation to the board. You can actually create tremendous productivity enhancements in a big non-tech company if you come with that background. Again, you’ll have certainly a better work-life balance, so not a bad deal, to be honest.   Bertrand Schmitt Also, to be clear, I talk a lot about AI coding because it’s truly transformational. You could argue that it’s going to be self-improving. We are in the situation of a self-improving AI that keeps improving itself thanks to automated coding. It’s a dramatic, virtuous loop. Obviously, AI is also going to improve everything else. It’s going to improve your marketing, it’s going to improve your search process, it’s going to improve your DNA. Improvements will be everywhere. It’s just that right now we are at a point in the quote-unquote revolution where there is one clear piece of the puzzle that is moving faster than the rest.   Nuno Goncalves Pedro Bertrand, the senior executives at non-tech don’t know anything about that. It could be just a great prompt engineer. That’s the only job you do. “I’m the chief marketing officer. I have someone below me that’s doing the whole work.” Nobody knows. Nobody’s the wiser, I guess. I’m being facetious, but not fully.   Bertrand Schmitt Yeah. There would be a transition period where what you described happen. I want to say, going back to AI coding, I think that the part of AI that as of today has reached a stage of limited AGI. We have reached, from my perspective, a limited type of AGI for coding. If you take coding as a discipline today, I think we reach AGI. If you go beyond coding, that’s true. If we are talking about coding, leveraging the latest LLMs: OPUS 4.7, ChatGPT 5.5, combined with Claude Code, Codex, and OpenCode for harness, I think we’ve reached AGI in the context of coding. I’m not sure everyone fully realize that and the consequence of that. I think the rest is going to come as well. We are going to see that category by category, usually categories that are more scientific in nature, where you can replicate, where you can test easily, where you can create clear success. Metrics will be the “easiest” to follow in that direction of self-improvement. I just want to highlight that this part is truly transformational, the root cause of everything we’re talking about today. At the same time, it’s coming beyond coding.   Nuno Goncalves Pedro I think it is true. There are a couple of markets where that might not hold true, which is maybe the final path. If you’re thinking of starting your own business in plumbing and in HVAC maintenance and installation, this is a pretty good time for the reasons we already said before. There’s a lot of buildup of data centers and all that stuff, but also for other reasons, because it’s an activity that won’t be disrupted by AI yet. You need them embodied AI. You need physicality to AI to do stuff like actually fixing pipes.   Bertrand Schmitt Until Optimus replace you.   Nuno Goncalves Pedro Yeah, but if we’re 3, 4 years out in terms of a lot of these optimizations that we’re talking about at the software layer, we’re 10 years plus out on embodied AI, right?   Bertrand Schmitt Oh, yeah, it’s 10 years.   Nuno Goncalves Pedro We’ll probably be optimistic as we speak. That’s a nice business. I’m thinking of starting to go into that market. If you guys are interested in listening to this, just reach out to me. What’s the angle? I think there’s a lot of stuff you can do in the buildup of some of these businesses, plumbing, HVAC, all sorts of maintenance. There are markets that are just totally messed up. Handyman market in the US is totally messed up. There’s a bunch of companies out there that try to go after it with marketplaces and stuff. I honestly just start something from scratch, a small business, and go from there.   Bertrand Schmitt Yes. They’re an interesting middle. Think about accounting firms, consulting firms. I think they are not as easy to replace, but at the same time, there is no way on what they do is not going to be dramatically changed with AI. I don’t know if it’s 50, 80, 90% of the job, but this is changing quite dramatically, would be my expectation in the coming few years. Conclusion Thanks for listening episode 77 of Tech Deciphered about that great talent redistribution. As you heard it from us, we believe there is a dramatic change in play, enabled by AI coding, and that ultimately a lot of the big tech companies are changing their employee distribution, way more focused on the top talents and bringing more GPUs. As a result, we will see a change in their staffing. Some of this change will benefit AI-focused startups, but probably more likely will benefit the bigger SMBs, the S&P 500 companies of the world that will finally be able to bring inside and afford some of the talent that were in some ways trapped by the top 5, 10, 20 software companies of the world. Thank you, Nuno.   Nuno Goncalves Pedro Thank you, Bertrand

Benjamin and Chance discuss changes to the Apple education store, the cool new Apple Developer icon, rumors about some design changes for iOS 27 and macOS 27, and whether we can think of anything compelling AirPods with cameras could be used for.  And in Happy Hour Plus, we discuss the state of iOS keyboard autocorrect and dictation accuracy.. Subscribe at 9to5mac.com/join. Sponsored by Copilot Money: Get two months free with code 9TO5MAC at copilot.money/9to5mac. Sponsored by Framer: The only free design tool that brings your ideas to the web. Visit framer.com/happyhour for 30% off a Framer Pro annual plan. Sponsored by Quince: Refresh your wardrobe with Quince. Visit quince.com/happyhour for free shipping on your order and 365-day returns. Hosts Chance Miller @ChanceHMiller on Twitter @ChanceHMiller on Instagram @ChanceHMiller on Threads Benjamin Mayo @bzamayo on Twitter @bzamayo@mastodon.social @bzamayo on Threads Subscribe, Rate, and Review Apple Podcasts Overcast Spotify 9to5Mac Happy Hour Plus Subscribe to 9to5Mac Happy Hour Plus! Support Benjamin and Chance directly with Happy Hour Plus! 9to5Mac Happy Hour Plus includes:  Ad-free versions of every episode  Pre- and post-show content Bonus episodes Join for $5 per month or $50 a year at 9to5mac.com/join.  Feedback Submit #Ask9to5Mac questions on Twitter, Mastodon, or Threads Email us feedback and questions to happyhour@9to5mac.com Links Apple Developer app gains Liquid Glass design and WWDC 2026 iMessage stickers Apple now requires verification for Education Store, adds Apple Watch with discounts iOS 26.5 adds end-to-end encryption for RCS messaging, rolling out now iOS 26.5 now available: Here are all the new iPhone features Apple hits milestone in development of AirPods with cameras: report Report: macOS 27 to feature UI tweaks to address some Tahoe design complaints Apple Plans Customizable iPhone Camera App, Siri Overhaul: iOS 27 - Bloomberg iOS 27's upgraded Camera app will be ‘fully customizable,' per report iOS 27 to make key design changes to ‘streamline' Liquid Glass: report iOS 27's ‘completely rebuilt' Siri will include a new system-wide search gesture: report Gemini Intelligence brings gen UI, Gboard 'Rambler' to Android Gemini Intelligence brings proactive AI to Android