Podcasts about UI

In this episode of The GaryVee Audio Experience, I sit down with Naveen, founder of Glance, at Cannes 2026 to talk about agentic commerce — the first time in 30 years the user interface of how people buy things is going to change. We discuss why agents will quietly handle the categories you don't care about so you have more time for the ones you love. I also explain why brands that aren't structuring their websites and content to be read by agents will have no opportunity of being bought.You'll learn about:• What Agentic Commerce Really Is• Why the UI of Shopping Is Changing• How Brands Get Found by Agents• The Late-90s Google Moment Again• Why You Should Participate Early

Is the web dead, or just evolving? Wes Bos breaks down his JS Nation Amsterdam talk on agentic interfaces, why chat won't replace everything, how Web MCP lets agents interact with your existing sites, and what “Clicks and Clankers” really means for the future of UI. Show Notes 00:00 Intro 00:33 Welcome to Syntax! 00:46 Wes's Talk: Agentic Interfaces at JS Nation 01:37 Is the Web Dead? Chat vs. Traditional UI 03:13 No UI, Voice UI, and the Smart Home Vision 04:00 What Is Web MCP and How It Works 05:10 Clicks and Clankers: When to Click vs. Prompt 06:57 The Future of Shopping and the Open Web Problem 08:46 Delegating the Boring Stuff: Groceries and Expense Categorization 11:55 MCP Apps and the Happy Path Problem 12:55 Brought to you by Sentry.io 13:23 Generative UI: Can the LLM Make a Better UI Than You? 14:54 Smart Home Dashboards and the Jarvis Dream 17:24 Is the Web Dead? Final Thoughts 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

For episode 747 of the BlockHash Podcast, host Brandon Zemp is joined by Bret Kenwell, a US Investment and Options Analyst at eToro. eToro is a retail brokerage platform offering access to crypto and traditional asset classes to 40 million users globally. They allow users to trade diverse financial assets like stocks, cryptocurrencies, and ETFs.

Everyone is racing to the same place chasing a limited set of buyers—how will your “AI for BI” product stand out? I've been seeing teams heavily invest in copilots, agents, semantic layers, governance frameworks, and increasingly sophisticated models, yet many still hear the same feedback from sales prospects: “We may just build this ourselves?" Or they don't hear it, but suspect the customer is doing just that.  Whether they actually can DIY the solution is the wrong question. The bigger question is *why they believe they can.* Your product may have a genuine competitive advantage, but your real challenge is that this advantage isn't obvious to buyers. The moat exists, but it is invisible. What makes this relevant is that many capabilities once considered differentiators are rapidly becoming normalized. AI copilots, agentic analytics, governed data, semantic layers, and broad integrations now appear across nearly every platform in the category. As AI accelerates development, sophisticated engineering alone becomes harder to defend as a lasting advantage. So what actually creates a durable moat if the engineering and product seems easy to copy? I explore four areas: proprietary data, trusted relationships, and products that accumulate institutional knowledge remain difficult to replicate. And finally, user experience itself as a strategy. As users increasingly access your intelligence through AI agents rather than dashboards, their experience may become the moat that competitors can't copy. Highlights / Skip to: AI for BI and analytics products is facing a race to commoditization (2:09) Common moats that everyone is using right now and why they fail (3:28) Proprietary data as a moat (9:29) Being embedded in your community as a moat (11:14) Compounding institutional knowledge as a moat (15:22) UX design asa moat even when there is little/no UI to see (18:36) Find the baseline for customer experience to build into later strategies (25:11) Actionable questions to ask your team to move forward on finding your competitive differentiation as a B2B analytics product (28:02)   Links CED: A UX Framework for Designing Analytics Tools That Drive Decision Making

Your AI code review tools read the diff. They stare at your code. But they never actually run it. So the bugs that only show up at runtime, the broken user flows, the bad query plan, the duplicate submission, sail right past review and land in front of your customers. In this episode, Joe Colantonio sits down with Evan Marshall, founder of Ito and a fifteen year engineer who spent five years in applied cryptography securing hundreds of millions of dollars for millions of people. Evan is taking that ship fast without breaking things discipline and pointing it straight at testing. Ito is an agentic QA platform that builds and runs your actual app on every pull request, navigates it like a real user, exercises the frontend and backend as one system, and brings back real runtime evidence: video replays, logs, the exact lines responsible, and steps to reproduce, posted right in your PR. You will learn: Why static code review misses the bugs that cause real production incidents How Ito spins up ephemeral environments and tests across UI, API, and database Why QA is not disappearing, it is leveling up into a manager and quality strategist role How to keep your test layer separate from your code generation so your signal stays honest The skills testers and engineers need as AI writes more of the code If you are shipping AI generated code at high velocity and your QA cannot keep up, this one is for you. Try Ito on your own code. Your first ten pull requests are reviewed free, no credit card required. Check it out at https://testgld.link/itoai now. And as Joe always says, seeing is believing.

Il mondo della tecnologia è diviso tra chi crea e chi vende, e Alex Raccuglia — in una tipica mattinata milanese tra traffico e flussi di coscienza — torna a riflettere su una verità scomoda: il marketing conta quanto (o più) del codice stesso. In questo episodio di **Techno Pillz**, esploriamo la frontiera del **Vibe Coding**: l'arte di lasciarsi guidare dall'Intelligenza Artificiale per costruire applicazioni di "media grandezza" a velocità supersonica. Alex ci porta dietro le quinte del suo workflow: dalla creazione di una libreria di controlli UI personalizzati per macOS, fino all'integrazione di feature basate su LLM capaci di riscrivere il contesto di una trascrizione. Ma la vera sfida rimane la comunicazione: come trasformare ore di sviluppo in una narrazione efficace senza impazzire? La risposta potrebbe essere un nuovo approccio "IA-centrico" alla documentazione. Mettetevi comodi, si parte!

ohn Toon, Eriona Bajrakurtaj, and Leigh Stallard cover FYI's first AI features, two separate Xero conversations, BrightPay Oscar, Sodium, Record OS and the Maple Review.   FYI has added its first AI features, built around the existing automation layer rather than added on top as a chatbot. Firms that have properly embedded the product will benefit most. It runs on AWS Bedrock, which doesn't retain data or train models, which the hosts consider important for client confidentiality.   Xero comes up twice. First, incremental bank rec improvements: view, add and delete files and change account codes in the reconcile screen, search by payment reference, and upload multiple files through the accounting app. Then a more uncomfortable story: Xero sent an email to all users saying "your Xero numbers are now in Claude," which alarmed a lot of people. The hosts work through what the integration actually means, who owns client data when it flows through a third-party LLM, and what the GDPR implications are. John explains the difference between read-only MCP connections and write access, using the example of a US marketing company whose entire database was deleted by Claude Code overnight. Eriona raises what happens when Xero moves from sharing insights to taking actions - she has already seen Claude ask to take control of her computer mid-session.   BrightPay's Oscar gets a revisit after Accounting Web covered early adopter feedback. Mark Francis of Francis Bookkeeping Solutions reported that onboarding which previously took one to two weeks now takes five to ten minutes. Eriona is cautious about how this translates for small-client practices where the business owner, not an HR team, is handling the process. Leigh then covers Sodium adding billing and walks through the commercial logic: a slice of payment processing interchange could nearly double their average revenue per customer. John uses it to open a debate on why practice management has never been solved - and all three agree it probably never will be.   Record OS has launched publicly after raising £2 million in pre-seed funding. The model pairs AI data capture with a qualified tax professional reviewing the return before submission, priced at £125 for a standard self-assessment filing. Eriona's concern is whether the economics hold when cases get complex. John is more optimistic, arguing it represents a shift from human capital cost to product cost in compliance work. Leigh adds the sharpest point: Record OS is one government policy change away from not having a business model, and the same risk applies to any practice built mainly on compliance.   Also covered: FreeAgent's new landlord statement upload feature ahead of MTD; Plaid opening its MCP server to AI agents for bank feed diagnostics, with Eriona and John debating how comfortable they are with AI that close to financial infrastructure; Brief's latest update, including a UI overhaul, AI client profiles, two-way client scoring and automated group check-ins; and the Maple Review, a government report on barriers to entrepreneurship in the UK. All three back its recommendations on financial and business education in schools, and Xero gets a namecheck for supporting the report.   00:00 Intro and Disruptor Awards 01:54 Episode preview 02:52 Check-ins 06:35 FYI: First AI features 09:52 Xero: Bank rec improvements 11:45 Xero meets Claude: Data, privacy and agentic risk 15:09 BrightPay Oscar: AI employee onboarding 18:58 Sodium: Practice management and billing 24:30 FreeAgent: Landlord statement upload 26:19 Plaid: AI agents and bank feed diagnostics 28:23 Brief: Client relationships, scoring and check-ins 31:48 Record OS: Self-assessment productised 38:13 The Maple Review 46:12 Outro

Fraud doesn't usually announce itself with a flashing warning sign. It shows up as a chargeback, a fake account that looks “normal,” or an account takeover that slips through the exact same checkout flow your best customers use. Greg Myers sits down with Tamas Kadar, Co-Founder and CEO of SEON, to unpack how modern fraud actually works and how digital businesses can protect revenue without burying users under friction.Tamas shares the origin story that started with a real loss: a crypto checkout experiment that got hit by fraud almost immediately. That experience turned into years of studying how fraudsters operate and, eventually, into SEON's mission: help businesses prevent fraud, verify identities, and stay compliant in real time using the minimum data points companies already collect, like an email address or phone number, plus hard-to-fake device and digital footprint signals. We dig into when step-up verification makes sense, how to reduce false positives, and why trust and safety teams deserve to be seen as revenue drivers, not cost centers.The conversation goes deep on AI in fraud prevention beyond the buzzwords. Tamas explains where classic machine learning helps, where it breaks, and how LLMs can speed up investigations by summarizing cases, surfacing patterns earlier, and reducing the “five tabs per investigation” problem. We also explore the shift toward headless software, where analysts can ask questions in natural language and get answers from the system of record without clicking through a UI, while still keeping decisions explainable with human-readable rules.We close with what's next: synthetic identities, deepfakes, account takeover, stablecoins and changing payment rails, plus the rise of agentic commerce where good agents and bad bots can blend into the same traffic.

La prima implementazione di Ark è online e nel giro di poche ore, grazie agli LLM, vengono trovati e corretti due bug importanti. È la nuova rapidità di sviluppo e controllo del codice a cui ci dobbiamo ormai abituare. L'AI sta per cambiare il mondo.Inoltre: gogna mediatica per Michael Saylor, arriva la Cypherpunk library, cos'è la Bitcoin Design Guide, Bitcoin funziona come un organismo biologico e il difficulty adjustment lo domostra, e la Cina banna i finfluencer.It's Showtime!

This episode goes behind the scenes of Godforge's audio design with Norbert, diving into combat sounds, UI feedback, music, voice acting, cutscenes, and the hidden details that make the game feel more immersive. The discussion also covers beta audio feedback, missed debuff cues, realm music, Foley-style sound creation, and future improvements coming to the game's soundscape. 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

This episode starts with a recap of the biggest

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

Portals and intranets continue to play a critical role in Knowledge Management—whether you're serving internal teams or delivering value directly to clients. In a GenAI-driven world, having a centralized, well-governed home for playbooks, standards, and trusted knowledge is more important than ever.   In this podcast, we spoke with a KM leader who shared insights and lessons learned from building and launching a mature KM platform. The speaker shared practical insights on organizing knowledge, designing sustainable processes, and making thoughtful UI and experience decisions. They also looked back at lessons learned along the way and explored what ongoing governance and maintenance really look like once the platform is live. Moderator: @Brandie Knox - Principal & Creative Director, Knox Design Strategy Speaker: @Caitlin Gibson - Counsel, Debevoise & Plimpton Recorded on 06-17-2026. 

Engineering teams are building ten times — even a hundred times — more than they could two years ago. That's a win, but one not without challenges. Because the cost of building the right thing has climbed exponentially. In this episode of the ServiceNow Insights podcast, host Bobby Brill sits down with three leaders who are living this tension from three distinct angles: the content and design leader who first spotted the productivity math problem, the design VP pushing for discernment over speed, and the research lead keeping the human at the center. ━━━━━━━━━━━━━━━━━━━━━━━━IN THIS EPISODE ━━━━━━━━━━━━━━━━━━━━━━━━ DAVID HOARE — Group VP, Digital Content & Design, ServiceNow ANAND THARANATHAN — Group VP, Product Research & Insights, ServiceNow DANTLEY DAVIS — SVP of Design, ServiceNow ━━━━━━━━━━━━━━━━━━━━━━━━ CHAPTERS━━━━━━━━━━━━━━━━━━━━━━━━ 0:00 Introduction & Guest Intros 1:13 David: The AI Philosophy — ChatGPT as genuine inflection point 3:02 David: Economic viability — why AI unlocks what was never possible before 3:12 Anand: Three-person startups scaling to $100M+ 3:45 Dantley: From 3D Studio Max to Jarvis — AI as human superpower 6:29 Anand: The customer north star hasn't changed 7:10 David: Engineering's survival problem — the 100x production gap 8:32 David: Andrew Ng's PM-to-engineer ratio + the cost of building wrong 9:40 Dantley: Nine concepts in an hour — design velocity and discernment 12:04 Dantley: The hip-hop tastemaker — slowing down as part of the process 14:20 David: Content governance — the fox guarding the hen house 16:21 Anand: Trust and the human-AI system 17:20 Dantley: AI surprise — UI tech stacks, feature completeness & hidden tech debt 20:21 18-Month Close — Anand, Dantley & David ━━━━━━━━━━━━━━━━━━━━━━━━ KEY TAKEAWAYS ━━━━━━━━━━━━━━━━━━━━━━━━ • Engineering is the first function to see massive AI productivity gains — but that creates a gap every other function has to survive • The cost of building has dropped. The cost of building the wrong thing has climbed exponentially • Discernment is the bottleneck — not speed. Nine concepts in an hour still needs a tastemaker • AI quality is only as good as the content signals it receives — governance is not optional • The customer north star hasn't changed. AI just changes how fast you can move toward it • Customer value is the only metric that matters. Everything else is the path to it ━━━━━━━━━━━━━━━━━━━━━━━━ ABOUT THIS PODCAST ━━━━━━━━━━━━━━━━━━━━━━━━ Subscribe for new episodes on AI, product, engineering, and the future of work. #ServiceNow #AI #ArtificialIntelligence #ProductDesign #SoftwareEngineering #ContentGovernance #DesignLeadership #AIStrategy #ProductManagement #EngineeringLeadership #TechLeadership #FutureOfWork #ServiceNowInsights #MachineLearning #Innovation #DesignThinking #TechPodcast #AIProductivity #DigitalTransformation #CustomerValueSee omnystudio.com/listener for privacy information.

Engineering teams are building ten times — even a hundred times — more than they could two years ago. That's a win, but one not without challenges. Because the cost of building the right thing has climbed exponentially. In this episode of the ServiceNow Insights podcast, host Bobby Brill sits down with three leaders who are living this tension from three distinct angles: the content and design leader who first spotted the productivity math problem, the design VP pushing for discernment over speed, and the research lead keeping the human at the center. ━━━━━━━━━━━━━━━━━━━━━━━━IN THIS EPISODE ━━━━━━━━━━━━━━━━━━━━━━━━ DAVID HOARE — Group VP, Digital Content & Design, ServiceNow ANAND THARANATHAN — Group VP, Product Research & Insights, ServiceNow DANTLEY DAVIS — SVP of Design, ServiceNow ━━━━━━━━━━━━━━━━━━━━━━━━ CHAPTERS━━━━━━━━━━━━━━━━━━━━━━━━ 0:00 Introduction & Guest Intros 1:13 David: The AI Philosophy — ChatGPT as genuine inflection point 3:02 David: Economic viability — why AI unlocks what was never possible before 3:12 Anand: Three-person startups scaling to $100M+ 3:45 Dantley: From 3D Studio Max to Jarvis — AI as human superpower 6:29 Anand: The customer north star hasn't changed 7:10 David: Engineering's survival problem — the 100x production gap 8:32 David: Andrew Ng's PM-to-engineer ratio + the cost of building wrong 9:40 Dantley: Nine concepts in an hour — design velocity and discernment 12:04 Dantley: The hip-hop tastemaker — slowing down as part of the process 14:20 David: Content governance — the fox guarding the hen house 16:21 Anand: Trust and the human-AI system 17:20 Dantley: AI surprise — UI tech stacks, feature completeness & hidden tech debt 20:21 18-Month Close — Anand, Dantley & David ━━━━━━━━━━━━━━━━━━━━━━━━ KEY TAKEAWAYS ━━━━━━━━━━━━━━━━━━━━━━━━ • Engineering is the first function to see massive AI productivity gains — but that creates a gap every other function has to survive • The cost of building has dropped. The cost of building the wrong thing has climbed exponentially • Discernment is the bottleneck — not speed. Nine concepts in an hour still needs a tastemaker • AI quality is only as good as the content signals it receives — governance is not optional • The customer north star hasn't changed. AI just changes how fast you can move toward it • Customer value is the only metric that matters. Everything else is the path to it ━━━━━━━━━━━━━━━━━━━━━━━━ ABOUT THIS PODCAST ━━━━━━━━━━━━━━━━━━━━━━━━ Subscribe for new episodes on AI, product, engineering, and the future of work. #ServiceNow #AI #ArtificialIntelligence #ProductDesign #SoftwareEngineering #ContentGovernance #DesignLeadership #AIStrategy #ProductManagement #EngineeringLeadership #TechLeadership #FutureOfWork #ServiceNowInsights #MachineLearning #Innovation #DesignThinking #TechPodcast #AIProductivity #DigitalTransformation #CustomerValueSee omnystudio.com/listener for privacy information.

Tariso takes us through his 3 part blob on upgrading 9.0 to 9.1, a significant project with a UI tool to help you through the planning process. He has upgraded and then takes us through whats easy and what needs advanced planning.

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?

The Fork In Your Ear Ep#215 Eternia of Games! - Podcast Show Notes Summary 6-13-26 Gaming News – Oops All Games Edition! Tim and Nate kick off with an "oops all games" episode packed wall-to-wall with fresh showcases. They open with the Xbox Games Showcase (June 7). Tim watched with Jessica while making breakfast; Nate and Cara ran it live in the Discord. Key highlights: Gears of War: E-Day (Oct 6, Xbox Series X/S, PC, Game Pass day one, Unreal Engine 5 built from the ground up by The Coalition). The prequel Emergence Day story stars Marcus and Dom. The Coalition's deep Unreal expertise and "from-scratch" approach have everyone hyped for performance and visuals. Tim and Nate debate third-person cover shooters (Nate's not a fan of the mechanic; Tim loves the destroyed world, underground critters, and chainsaw guns). Fable – New cinematic trailer confirms Haley Atwell (Agent Carter) as the long-teased villain Isabel. Jack of Blades gets a surprise tease, hinting at multiple villains. A 30-minute full gameplay video dropped days later showing a talking pig quest, romance options, UI, and systems in action. Delayed to February 23, 2027 (Xbox, PC, Game Pass, and still coming to PS5) to polish and avoid GTA 6. Tim is very excited; Nate is cautiously optimistic but holding judgment on open-world RPG scope. Halo: Campaign Evolved (July 28, Xbox, PC, PS5, Game Pass) – UE5 remake of Halo 1 with core Blam engine (physics/game logic pulled from Halo: Reach) for that authentic "spongy" feel. New Sergeant Johnson voice actor, prequel missions (Assault on the Control Room & Silent Cartographer) locked behind the main campaign, vehicle jacking, new weapons (energy sword, fuel rod, brute spiker), sprint, clambering, 40+ skulls, and a "Remix Mode" for wild enemy swaps/randomization. 4-player co-op (local split-screen + online). Previews are extremely positive; limited collector's editions sold out fast. Tim is all-in on the respectful-yet-improved remake treatment and wants to share it with new players. Other Xbox news: Persona 6 trailer reactions (mixed/underwhelmed), new Spyro game from Toys for Bob (Tom Kenny returning as Spyro, Dragonflight mechanics), Clockwork Revolution (InXile steampunk RPG, Xbox ecosystem exclusive vibes), S.T.A.L.K.E.R.-era title getting Unreal Engine help from The Coalition + Grounded-style cross-world system, new dark Metro game from the Ukrainian team (still developing in wartime conditions), Resonance (Plague Tale legacy?), Minecraft Dungeons 2, new Senua Hellblade-universe action game (Nate loved the first, mixed on the second; Tim was never a fan but this one looks more classic Ninja Theory), Crazy Taxi World Tour (2027, pure arcade nostalgia), and Castlevania: Belmont's Curse (Oct 15 – Tim is already hooked into his veins; Switch 2 version delayed, which upsets him).

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

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

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…

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

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

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

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

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