Podcasts about Blender

Jetpack Pals opens with its recorded theme and a live browser demonstration of a multiplayer game set in a version of downtown Los Angeles. Players fly around with jetpacks, meet friends, and play smaller games inside the world.The game has been in development for about three weeks in the recording and is still unfinished. AI coding tools, phone-based prompts, coworkers, and a city map are part of the build process.Coffee ordering leads to a detour through app loyalty points, store habits, and the software behind big food chains. A simple purchase can connect to a surprisingly large technical system.An AI shopping assistant raises questions about what an agent can do in a browser and how personal data shapes its suggestions. Marketplace recommendations and advertising become harder to separate when software shops on someone's behalf.Payment tools for agents move the discussion from suggestions to transactions. A funded card or account could let an agent buy things for its user, raising questions about controls and trust.Turning a Zoom recording into clips and scheduled posts requires more than one good prompt. The workflow conversation covers documentation, repeated mistakes, prompt design, and how each run can preserve what was learned.Meta glasses become a test of both hardware appeal and product presentation. The conversation examines the images, the landing page, and how the device might fit into daily use.Yahoo search leads to a discussion about AI features, publisher links, and whether an older web brand can become more useful again. The segment also touches on other AI software announcements.Downtown Los Angeles appears both as a real city and as a place represented in Jetpack Pals. Hollywood, buildings, and local landmarks shape the conversation around the game map.Software costs and model limits affect how much can be built in a week. The game still needs graphics, phone performance, and more work before it can be treated as finished.The iPhone's top display area inspires ideas for small apps and reminders that stay visible during normal phone use. The concept turns a familiar interface detail into a place for lightweight software.Blender is used for game models and Unity for the game itself in the workflow described in the recording. Remote control from a phone lets desktop tools keep working while the developer is away from the computer.Internet slang and creator videos take the conversation away from software products. The detour moves through online forums, short-form videos, and what makes a creator worth sharing.The spoken sign-off is followed by more conversation about transcripts and production systems. That aftertalk remains in the no-cut episode rather than being trimmed at the first goodbye.A short Jetpack Pals promo bumper closes the recording after the aftertalk. It also jokes about Meta glasses and the metaverse.Chapters00:00 Jetpack Pals theme and game demo03:05 Three weeks of AI-assisted game building10:08 Coffee and app detour14:20 AI shopping assistant18:18 Paying for AI agents21:13 Building the Zoom-to-clips workflow25:24 Meta glasses and landing page28:16 Yahoo search and AI software30:25 Los Angeles and the game map33:57 Software headlines and Jetpack Pals progress38:19 iPhone interface ideas40:09 Blender and Unity workflow43:31 Football and online slang45:48 Spoken sign-off and workflow aftertalk47:26 Jetpack Pals promo bumperLinkshttp://instagram.com/rickerandbonhttp://tiktok.com/@rickerandbonhttps://youtube.com/@rickerandbonhttps://open.spotify.com/show/0n1m0eR2sYZU2EFhSwlqX1https://podcasts.apple.com/us/podcast/ricker-and-bon/id1367523204

Luke, Chris, and Peter break down the horror phenomenon of the year. Kane Parsons started posting Backrooms videos on YouTube at 16, teaching himself Blender from his bedroom. Four years later he directed A24's highest-grossing film ever, starring two Oscar nominees, on a 30,000 square foot practical set so large the cast and crew actually got lost in it. $402 million worldwide on an $8 million budget. The youngest director to open number one domestically and globally. We break down how a creepypasta became the biggest horror movie of 2026.Live at 8:15pm CT. New setup, first episode.

The Art of Becoming Excellent in All That You Do with Casey JenningsWhat does it really mean to pursue excellence... not just as an athlete or coach, but as a husband, father, leader, and human being? In this episode, I sit down with Casey Jennings, 11x Pro Tour Champion, AVP Champion coach, and co-founder of p1440. Fresh off coaching Taylor Crabb and Andy Benesh to the 2026 Manhattan Beach Open championship, we explore belief, manifestation, adversity, energy, humility, spirituality, and the pursuit of excellence.A conversation about letting go of outcomes, learning through failure, and becoming the person you're capable of becoming.In this episode we discuss:The Art of BecomingThe Pursuit of ExcellenceCoaching & Healthy DetachmentBelief, Manifestation & EnergyAdversity & FailureFOPO & External ValidationEgo, Humility & SpiritualityGratitude & Sending LoveFamily & ExcellenceFrom Champion to CoachTaylor Crabb & Andy Benesh at Manhattan Beach 2026This conversation is for:Athletes pursuing excellence without losing themselves in the processCoaches, leaders, and mentors who want to bring out the best in othersAnyone navigating a transition, setback, or new chapter in lifeEntrepreneurs and creators building something bigger than themselvesAnyone interested in mindset, manifestation, spirituality, personal growth, and becoming their best self Casey Jennings'Links:• J5 Beach Volleyball: https://www.j5beachvolleyball.com/• p1440: https://p1440.com/• Instagram: @caseytjenningsRelated Episodes: • Kerri Walsh Jennings - 3x Olympic Gold Medalist on the Law of Attraction & How to Manifest Greatness: https://youtu.be/84G4F3-5OrQ

AI news this week: Claude Opus 5.5 is INSANE and we love it. Faster, cheaper, better. So much for the AI slowdown. Oh and OpenAI's new thing is good too. On today's AI For Humans, Kevin Pereira and Gavin Purcell go hands on with Claude Opus 5.5, which might be the best model we've ever used (for now). Anthropic says it hits Fable-level performance on most things at about 40% less cost than Opus 5, the writing is clearer, and yes, the five hour usage limits went up and you can now bank your resets. Then we get into what people are already making with it: a guitar store sim, a music-based side-scroller, a Mario Kart benchmark, an underwater exploration game in 3MB, claymation built in Blender, a pop punk single made entirely in code and a lot of very juicy animations. Also: OpenAI answered with GPT-6 Sol and GPT-6 Luna, two new models that are faster and a lot cheaper than what came before. We compare them with Opus 5.5 (robot hands drawing, pelicans on bicycles in Blender), wonder what OpenAI has lined up for DevDay, and talk about Bel, the reported secret model that's beating the math. Oh, and there's a new Grok. Grok 4.7. It's out. (crickets) Plus: Gavin used Meshy 7.1 to turn a few photos of himself into a rigged, animated 3D model and then built Gavin At Large with his Opus 5.5 agent, a game where a giant Gavin wrecks a lil town. In Actually Useful AI, GPT-6 Astra makes simple motion graphics for a real book trailer. And in AI See What You Did There: Dario vs Sam in a very weird gun fight showdown and what happens when you point Claude at the Moving Image Archive. AI SLOWDOWN? NEVER HEARD OF HER. // Chapters //   00:00 Claude Opus 5.5 & GPT-6 Sol: So Much For The AI Slowdown 03:35 Claude Opus 5.5: Fable-Level Performance For Less (And Bigger Usage Limits) 11:45 Opus 5.5 Is Crazy Good At Making Games 18:02 Opus 5.5 Creative Use Cases: Claymation, Animation, & Education Tools 23:28 GPT-6 Sol & Luna: OpenAI's Faster, Cheaper Models 27:47 What's Next? OpenAI's Rumored Bel Model & Grok 4.7 33:22 Giant Gavin: Turning Myself Into A 3D Model With Meshy 7.1 39:03 Actually Useful AI: GPT-6 Astra Makes Motion Graphics 41:35 AI See What You Did There: Dario vs Sam & The Moving Image Archive   // Show Links // Introducing Claude Opus 5.5 https://www.anthropic.com/claude-opus-5-5 Opus 5.5 guitar store sim https://x.com/bijanbowen/status/2102532400353829356 https://x.com/bijanbowen/status/2102647714857222312 Opus 5.5 music-based side-scroller https://x.com/riku720720/status/2102547249834385584 Opus 5.5 on the Mario Kart bench https://x.com/sawyerhood/status/2102550264914038834 Underwater exploration game in 3MB https://claude.ai/artifact/ReCBQGZ4EirKSiEmT8XXfs Card pack opening (made with a very simple prompt) https://claude.ai/artifact/FiowCxgcuqAvjL9ANRYjQU Claude juice animation https://claude.ai/share/33a38af0-97cd-4d4b-a9af-ae065de70436 Using Blender to make claymation animation https://x.com/JacobMolBio/status/2102569129026916389 Full blown animations https://x.com/other__reality/status/2102514581684052169 A pop punk single made entirely in code https://x.com/aj_dev_smith/status/2102575577563570450 Interactive lens lab https://x.com/RyanSael/status/2102591147927654847 Introducing GPT-6 Sol and Luna https://openai.com/index/introducing-gpt-6-sol-and-luna/ Robot hand drawing comparison https://x.com/dimentary/status/2102539444834402314 Pelican on a bicycle in Blender test https://x.com/atomic_chat_hq/status/2102492834485895265 Grok 4.7 https://x.com/SpaceXAI/status/2102069815225586149 Meshy 7.1 multi-image to 3D on fal https://fal.ai/models/meshy/v7.1/multi-image-to-3d Gavin At Large (the Giant Gavin game) https://gavin-at-large.vercel.app Kim's New Book: Write a Novel in 100 Days https://kimpurcell.com/ Dario vs Sam weird gun fight showdown https://x.com/Xanderwow_A/status/2101800791795274106 Moving Image Archive https://x.com/covacut/status/2100990018391265297 Gavin's Claude makes a video from the archive (Fable 5.1) https://x.com/gavinpurcell/status/2101818839512281116 And the updated version with Opus 5.5 https://x.com/gavinpurcell/status/2102632595792199944   // Join the AI For Humans community // Join the AI For Humans Discord https://discord.gg/muD2TYgC8f Support AI For Humans on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans newsletter https://aiforhumans.beehiiv.com/ Follow AI For Humans on X: @AIForHumansShow https://x.com/AIForHumansShow Follow AI For Humans on TikTok: @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Speaking and booking https://www.aiforhumans.show/    

Claude Opus 5.5 just dropped, so we put it against GPT-6 Astra on our own benchmark. It modelled, textured, rigged and animated an octopus in Blender from scratch in about two hours, then tried to rebuild the same Vancouver building Astra nailed two weeks ago. Then the big one: we spent three days turning a Zillow listing into a walkable 3D house in Unreal Engine with a floor-plan dashboard hooked up to IKEA, so you can drag real furniture into a home that does not exist yet. And Ali, founder of SalesCloser AI, on AI sales agents that close deals in 70 languages.Sales Closer AI: https://salescloser.ai/Jack Hamr: https://jackhamr.ai/Try invideo Agent 2 (50% off selected annual plans):https://invideo.io/i/badxstudio

La IA está obligándonos a replantear cómo construimos software desde cero. En esta tertulia, Jordi y Bernat hablan de por qué el futuro puede tener muchas menos interfaces, menús y configuraciones, y muchas más aplicaciones capaces de entender objetivos, tomar decisiones y actuar por nosotros.Comentan los últimos lanzamientos de Anthropic y OpenAI, el salto en velocidad y coste de los nuevos modelos especializados y qué ocurre cuando tareas como clasificar emails, priorizar información o tomar pequeñas decisiones pasan a costar prácticamente cero.También ponen a prueba la promesa de los agentes personales: sistemas capaces de navegar, comprar, hacer check-in o ejecutar tareas de forma autónoma. El potencial es enorme, pero aparecen nuevos problemas alrededor de la confianza, el sentido común, la privacidad y cuánto estamos dispuestos a delegar.Y detrás de toda esta carrera aparece una pregunta todavía más grande: si los grandes laboratorios están invirtiendo miles de millones en construir los mejores modelos, ¿quién acabará capturando realmente el valor? ¿OpenAI y Anthropic, las compañías con la distribución y la relación con el usuario, o quienes venden la infraestructura que hace posible todo esto?Con Qonto, centralizas todas las finanzas en un solo lugar: cuenta de empresa remunerada, tarjetas para ti y para tu equipo, gestión de gastos y facturación integradas. Crear tu cuenta, aprobar un gasto, emitir una factura… todo rápido, en una misma solución.Obtén la claridad y el control financiero que necesitas.Abre tu cuenta hoy y empieza gratis.https://qonto.com/es#InteligenciaArtificial

Friday night finds Adam confessing that he's become a full-blown “vibe codaholic,” burning through AI tokens, dreaming up Blender creations, and plotting a digital afterlife while Daniel plays subscription-plan counselor. From there, the boys put the “camp” in countertop appliance with an Instant Chill warning label that buries a potentially alarming ingredient beneath a chemical drag parade, then usher in autumn with 3D-printed pumpkins, nesting-doll demands, and a giveaway Adam may or may not have scheduled correctly.The Contact segment brings Scottie the Little Aussie Batler to ask the essential question: who exactly will want to chat with Dead AI Adam? That launches a gloriously unfiltered debate about memory, mortality, vacation websites, and certain personal photos that probably should not enter the family archive. Daniel also mourns a 335-day Connections streak murdered by the very app meant to celebrate it, before unveiling a lavishly reimagined News Game complete with musical cues, five questions, bonus-round chaos, and a score only gay mathematics could produce.Adam then discovers networking, workshops his LinkedIn reinvention, and learns that “on sabbatical” and “at the intersection of” may be doing more work than intended. Birthdays closes the festivities with clues leading to James Marsden, Jason Sudeikis, and a Jonas who is definitely not Donny Osmond, followed by a brief closing-music identity crisis. The boys are taking next week off while Adam and Marc head to Berlin, but not before one final plea: call in for a pumpkin. We hope you enjoy the tokens, trivia, résumé shade, and seasonal gourds.Email: Contact@MixMinusPodcast.comVoice/SMS: 707-613-3284

Über Führung, Blender & Mut: Dr. Eberhard Sasse In der heutigen Folge spricht Michél mit Dr. Eberhard Sasse, dem Gründer der Dr. Sasse Gruppe, über seinen Weg von einer kleinen Gebäudereinigung zu einem Facility-Management-Giganten mit fast 10.000 Mitarbeitenden und 400 Millionen Euro Umsatz. Sie diskutieren offen und unterhaltsam über die Notwendigkeit, Blender in Führungspositionen schnell zu entlassen, und warum Deutschland für die Zukunft Mut, KI-Investitionen und eine gesteuerte Einwanderung braucht. Zudem gibt Eberhard spannende Einblicke in die familiäre Unternehmensnachfolge durch seine beiden Töchter und erklärt, warum Denken oft schwerer ist, als im Steinbruch Steine zu klopfen. Es ist eine inspirierende Reise durch 50 Jahre echtes Unternehmertum, die zeigt, warum die deutsche Wirtschaft längst nicht am Ende ist. ____________________________________________________   01:15 – Begrüßung und Vorstellung von Dr. Eberhard Sasse  03:02 – Warum Eberhard Sasse nicht in den Kindergarten wollte  08:07 – Die Aufbruchstimmung der 70er Jahre in München  15:08 – Der allererste Auftrag in einer Genossenschaftsbank  21:15 – Vom Promovierten zum Gebäudereinigungsmeister  33:47 – Die Heranführung der Töchter an das Familienunternehmen  41:16 – Die Gefahr von Blendern in Führungspositionen  51:15 – Zuwanderung als Schlüssel gegen den Fachkräftemangel  54:39 – Millioneninvestitionen in KI und Digitalisierung  57:48 – Ein positiver Ausblick auf den Standort Deutschland ____________________________________________________   Unsere Partner: https://www.jameshardie.eu/de-de James Hardie ist einer der weltweit führenden Hersteller innovativer Faserzement-Fassadenbekleidungen und hochwertiger Trockenbaulösungen. Schaut doch mal auf James Hardies Homepage vorbei! https://www.cosuno.com/de Cosuno ist die KI-basierte Plattform, die Ausschreibung und Vergabe im Bauwesen digitalisiert und Bauunternehmen extrem viel Zeit und Kosten spart. Spare Zeit bei deinem Projekt mit Cosuno! https://wmm-ag.de/startseite WMM Modulbau ist euer Experte für serielles Bauen, der bezahlbaren Wohnraum durch patentierte Ziegel-Massivmodule in Rekordzeit auf die Baustelle bringt. Kontaktiere WMM jetzt für dein Projekt! ____________________________________________________

Kane Parsons was 19, had never worked inside a professional visual effects pipeline, and had taught himself the shots he needed in Blender in his bedroom. Edward Douglas, VFX supervisor on Backrooms (also Long Legs, Keeper, and multiple Oz Perkins films), built the production pipeline to fit him: direct access to vendor files, animation, and cameras, so Kane could work the same way he always had. The film delivered 600 VFX shots inside an 830 shot runtime, on an indie budget and timeline. Chris and Daniel connect this to the same shift they discussed with Peter Timberlake on Obsession: movies built on constraint outperforming movies built on scale. Douglas walks through the CG grounding rule that shaped the film (the practical hat that never gets replaced), the VHS pass every found footage frame ran through, and why horror keeps producing the year's most interesting visual effects work while franchise budgets keep climbing. Guest links Edward Douglas on IMDB > Edward Douglas on LinkedIn > Tools and companies mentioned Blender, A24, Roxel Creative, Zoic, Red Komodo, After Effects, Electronic Arts, BioWare, Long Legs, Pig, The Monkey, Keeper, Oz Perkins films Special thanks to our premier sponsor: CenterGrid Virtual Studio >

US stratospheric craft flies 9,300 miles to Japan in world-first sky data network Aamir Khollam Interesting Engineering via MSNhttps://www.msn.com/en-us/news/other/us-stratospheric-craft-flies-9300-miles-to-japan-in-world-first-sky-data-network-test/ar-AA2bwVb1Scientists build tiny robots without motors that can fly using sound waves alone Olivia Maule LiveScience.comhttps://www.livescience.com/technology/robotics/scientists-build-tiny-robots-without-motors-that-can-fly-using-sound-waves-aloneThe 'wonder material' graphene can be made using a kitchen blender, a mobile phone and a newspaper Conor Boland Phys.orghttps://phys.org/news/2026-08-material-graphene-kitchen-blender-mobile.htmlThese Hearing Aids Will Tune in to Your Brain Shruthi Raghavendra IEEE Spectrumhttps://spectrum.ieee.org/hearing-aids-biosignalsMaking trains and airplanes recyclable - Empa and Elantas develop a sandwich material for aircraft and train interiors combining lightweight performance, fire protection and end-of-life component recovery Ann Ettlin Empa.chhttps://www.empa.ch/web/s604/brandschutz-fuer-flugzeug-und-zug-dank-recycelbarem-faserverbundwerkstoffResearchers develop electronic skin for prosthetics to sense temperature and pressure Tina Hilding Washington State Universityhttps://news.wsu.edu/press-release/2026/08/20/researchers-develop-electronic-skin-for-prosthetics-to-sense-temperature-and-pressure/Unprecedented: Scientists Have Built Miniature Brains That Experience the Passage of Time Ivan Farkas ScienceAlert.comhttps://www.sciencealert.com/unprecedented-scientists-have-built-miniature-brains-that-experience-the-passage-of-timeHeartbeats power a battery-free pacemaker designed to last a lifetime Gaby Clark MedicalXpress.comhttps://medicalxpress.com/news/2026-08-heartbeats-power-battery-free-pacemaker.html

Bentornati su Snap!In pochi minuti chatGPT Astra consegna un modello 3D del progetto. In qualche minuto in più ne fa uno parametrico, esportabile in IFC. E allora viene da chiedersi a cosa serva ancora il BIM come lo stiamo usando.In questa puntata parto dal rapporto CNI — l'85% dei professionisti si dichiara interessato al BIM, il 43% non ha lavorato a un progetto BIM negli ultimi cinque anni, solo il 7% ha competenze avanzate — e provo a capire dove si è rotto qualcosa. Ti racconto anche di una riunione in cui il modello non è stato aperto nemmeno una volta: il BIM da strumento è diventato regola, e la differenza non è da poco.Nella seconda parte vediamo cosa può fare ChatGPT Astra collegato a Blender via MCP: caricata la documentazione di progetto, lo si lascia lavorare da solo, facendogli cercare online i dati mancanti. Il risultato non ha convinto  per la mesh generata — geometria fedele ma impossibile da modificare — ma si prende la rivincita  per la versione web, con interfaccia interattiva, parametri modificabili in tempo reale ed export IFC. La cosa che mi resta addosso è un'altra: l'AI ha guadagnato una specie di comprensione spaziale, e può usare il tuo computer e i tuoi software.E poi c'è l'evento Apple del 9 settembre, che mi sono perso in diretta tra un acquazzone e la linea internet morta. iPhone Duo, apertura variabile sulle lenti, processore a 2 nanometri: ti spiego perché a un architetto interessano davvero.—>

This week, Tracy and Damien explore TMC's MechWarrior 5: Clans – Clan Mech Dioramas Director's Cut, a striking collection of battle scenes that sits somewhere between machinima, virtual photography and digital sculpture.Tracy introduces TMC, the German filmmaking collective whose work evolved from in-game footage and fan-made Hired Steel films into sophisticated Unreal Engine virtual production. That journey ultimately brought the team to the attention of MechWarrior developer Piranha Games, which commissioned TMC to create official cinematics for MechWarrior 5: Clans.Unlike the narrative action of Hired Steel, the Director's Cut suspends its battles in time. Its camera glides through intricately composed three-dimensional tableaux, revealing ruptured armour, weapons fire, smoke, sparks, water and airborne debris that would ordinarily pass almost unnoticed during gameplay. Tracy considers how this moving “digital eye” transforms spectacular combat into something contemplative, combining the monumental scale of war machines with the intimacy of painted tabletop miniatures.Damien compares the experience to walking through an art gallery, where each frozen moment exposes the extraordinary detail underlying a fast-paced game. Their discussion concludes by imagining how these dioramas might become fully explorable VR environments—allowing viewers to move freely through the scenes rather than remaining bound to the filmmaker's camera.Key time stamps -01:00 – Damien welcomes Tracy and explains Phil and Ricky's mysterious absence01:30 – Introducing TMC's MechWarrior 5: Clans – Clan Mech Dioramas Director's Cut02:10 – TMC's history and Completely Machinima's earlier coverage of Hired Steel03:25 – From MechWarrior players to 3D artists and filmmakers04:25 – TMC's evolution from gameplay capture to Blender and Unreal Engine production05:35 – How TMC's fan filmmaking led to official work with Piranha Games06:32 – The crucial difference between Hired Steel and the non-narrative dioramas07:20 – Machinima as virtual photography and digital sculpture08:35 – Arrested time, frozen combat and the camera as a “digital eye”10:25 – The tension between colossal machines and miniature tabletop aesthetics11:49 – Colour, composition and the immersive quality of the diorama collection12:31 – Bullet time, slow motion and contemplative spectacle13:25 – Lighting, smoke, sparks, muzzle flashes and layered particle effects14:54 – Damien's response: walking through a virtual art gallery16:10 – Revealing details that players rarely notice during fast-paced combat18:15 – How the film maintains variety across its numerous battle scenes19:03 – The dioramas' original purpose as proposed loading screens19:17 – Wanting to take control of the camera and explore each composition20:01 – Imagining the work as an immersive VR experience21:07 – The limitations of video—and why wanting more is a mark of its success22:03 – Could the Unreal Engine scenes become fully explorable environments?22:19 – From photography to three-dimensional sculpture22:57 – Final verdict and closing thoughtsCredits -Co-hosts: Damien Valentine, Tracy HarwoodProducer: Damien ValentineEditor: Phil RiceMusic: Phil Rice & Suno AI

Corey and Grant put GPT-6 Astra through six identical one-shot build tests to see what it can create with almost no follow-up instruction. The episode covers an interactive black hole lab, a Blender scene, a physics game, a photo-to-sci-fi-world reconstruction, a mechanical sound diagnostic prototype, and the latest version of Cat Doom. The biggest through-line is how much capability now comes out of a single prompt, while the remaining weaknesses show up in taste, UI restraint, game balancing, and reliability. The episode ends by looking at how far Cat Doom has progressed across models over the past year.Sponsored by Dell Technologies and NVIDIA. Learn more at https://www.techrepublic.com/hubs/the-enterprise-guide-to-scalable-ai/Subscribe to The Neuron newsletter: https://theneuron.ai

Sie liefern konstant ab, halten den Laden am Laufen und stellen die Sache immer über Ihr Ego? Schön und gut, aber im C-Level ist das leider nur die halbe Miete. Wenn Sie sich dauerhaft unter den Scheffel stellen, ziehen die lauten „Pfauen“ und geübten Blender links und rechts an Ihnen vorbei. Und das Schlimmste: Am Ende sitzen genau diese Selbstdarsteller als Ihre Vorgesetzten am längeren Hebel. - Warum Könner Sichtbarkeit brauchen: Weshalb erstklassige Ergebnisse ohne gezielte Selbstinszenierung Ihre Zukunft an der Spitze gefährden. - Die Hidden-Champion-Strategie: Wie Sie mit Substanz netzwerken und punktgenau die Entscheider erreichen, die wirklich über Ihre Karriere bestimmen. - Die 7-Schritte-Anleitung: Vom kristallklaren Markenprofil über die Positionierung auf LinkedIn bis hin zur gezielten Gremienarbeit. Aus dieser Folge werden Sie mitnehmen, wie Sie auf Basis meiner eigenen Top-Management-Erfahrung und über 30-jährigen Tätigkeit als Führungskräftecoach, Schritt für Schritt Ihre Personenmarke aufbauen, sodass Mitarbeiter, Kollegen, Vorgesetzte und Entscheider Ihren wahren Wert erkennen und wirklich zu schätzen wissen. (00:00) Personal Branding Überblick (01:33) Warum Sichtbarkeit zählt (02:58) Was Personal Branding ist (04:27) Ziele und Nutzen (07:19) Warum es unverzichtbar ist (10:07) Risiken ohne Branding (11:46) Branding für stille Könner (14:42) Sieben Schritte Start (15:03) Schritt 1 Klarheit (16:05) Schritt 2 Markenprofil (17:33) Schritt 3 Netzwerken (19:56) Schritt 4 LinkedIn Profil (21:24) Schritt 5 Reichweite (22:53) Schritt 6 Gremienarbeit (23:47) Schritt 7 Konsequenz (24:19) Kein Quick Fix (26:32) Einladung und Abschluss ___ **Links zur Folge:** Website: https://www.galileo-institut.de/personal-branding/ Link zur NL-Anmeldung: https://www.galileo-institut.de/newsletter/ ___ Sie sind neu an der Unternehmensspitze oder kämpfen bereits mit scheinbar unlösbaren Herausforderungen? Vielleicht klemmt es gerade in der Transformation? Vielleicht läuft sogar alles gut und Sie sind dennoch unzufrieden? In meinem kostenfreien Onlinecoaching zeige ich Ihnen Lösungen für diese Herausforderungen.

Wes and CJ dig into OpenAI's GPT‑6 Astra; the massive jump in computer use, the wild Blender MCP 3D demos, and whether all the AGI talk actually holds up. Plus Hugging Face's robot duck, Vitest 5 and the arrest behind the shai‑hulud supply chain attacks Show Notes 00:00 Welcome to Syntax! 00:52 30 Years of JScript: How Microsoft Cloned JavaScript 03:28 Ryan Dahl's Fight to Free the JavaScript Trademark 05:48 The Rise of Micro Ducks and DIY Robotics 13:58 VeriSign Is Killing Third Level .name Domains 18:31 How Many Domains Do You Own? Registrar Pricing and Transfers 21:00 GPT-6 Astra Is Here: Is This Actually AGI? Exploding 3D Rendering Formula 1 Rendering Astra in Three.JS 23:40 Computer Use: Letting AI Draw Our Thumbnail in MS Paint Astra Using MS Paint 33:10 Can AI Edit Video? The DaVinci Resolve MCP Hype 40:18 Blender, 3D Workflows and the Death of Educational Content 47:45 Audacity 4: A Long Overdue Facelift 52:31 Ripping CDs and Owning Your Music Library Again 55:35 Vitest 5: Faster Tests, Benchmarks and Trace View 59:45 Brought to you by Sentry.io 01:00:24 Prompt Injecting Claude Opus 5 With a Malicious Zip File 01:05:46 The Shai-Hulud Hackers Got Arrested: How They Slipped Up 01:09:15 The VS Code Documentary Is Out (and Wes Is In It) 01:11:14 216 Million Spy TVs: LG Is Watching and Listening Disconnect Your LG TV Now 01:17:05 Roku, Walmart and What Your Data Profile Really Knows 01:19:12 Google AI Mode Shows Products 21% More Expensive 01:22:31 Agentic Shopping and the Sameness of AI Taste 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

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So, what can OpenAI's Astra and GPT Image 2.5 actually do?Drew Brucker and Rory Flynn put OpenAI's Astra and GPT Image 2.5 models through a golf course build, a vintage typewriter animation, and a round of image edits. Rory's golf course experiment burns 70% of his weekly allowance. Drew spends three hours getting a typewriter to type. Their gift for turning a quick test into a side project remains undefeated. The pro-AI half of the show. The other half: a viral AI resignation that looks planned to the minute, Anthropic's 154-page report on how people misused Claude, and GPT Image 2.5 quietly fixing the reasons everyone kept running back to Nano Banana.This episode covers OpenAI Astra with computer use, two ChatGPT chats collaborating inside one project, Three.js and Blender renders, a fantasy football analytics hub, and Astra running Midjourney with mood boards and style references. GPT Image 2.5 multi-edit comments, sketch input, custom aspect ratios, and template creator. Anthropic's threat report, Moonshot and Kimi distillation, and the viral AI researcher resignation. ---⏱️ Fast Hour00:00 What's on Fast Hours this week?06:35 Why do traumatic moments burn into memory07:42 What was different before social media13:48 This week: Astra, GPT Image 2.5, a viral resignation15:04 Did an AI researcher really resign over ASI18:40 How to tell what is true in AI news20:42 Why quitting your AI job can't stop superintelligence27:01 What do the cited reports allege about Kimi28:33 What AI misuse examples do the hosts discuss?34:07 How did Astra build a golf course prototype? 35:35 How did research improve Astra's first attempt? 36:33 What happened when two AI chats collaborated?39:29 Why did Rory stop the drainage research?40:02 How did Drew approach his Blender experiment?40:52 How did Astra recreate Drew's typewriter? 43:49 How long does AI-assisted 3D work take?44:43 How did Drew build a ChatGPT dashboard? 48:32 What happened when Astra explored Midjourney? 53:56 How did mood boards shape Astra's prompts?58:49 What improved in GPT Image 2.5?01:00:24 How do GPT Image 2.5 edit comments work?01:01:47 How do you request multiple image variations? 01:03:30 How well did GPT Image 2.5 resize ads? 01:07:11 How reusable templates in GPT Image 2.5 can help brands 01:08:37 What's coming next for Fast Hours?

Apocalypse par l'IA : IA : faut-il croire les semeurs de peur ? • Apple lance l'iPhone Duo pliant • OpenAI bat un record en maths • Meta dévoile son agent Muse • Revolut teste le paiement par IA • Mistral lève 3 milliards • Faux départ pour la brosse à dents Dyson • Shadow ressuscite les vieux PC par le cloud • Arlequin AI invente les neurones topologiques⭐️ Découvrez Frogans, l'innovation française qui réinvente le Web

Addy and Joey explore GPT-6 Astra's ability to generate complex, accurate 3D cityscapes in Blender from simple prompt goals. They also review DaVinci Resolve 21.1's new AI agent integration and discuss why an Indonesian TikTok meme character might forc...

AI news this week: GPT-6 Astra is here and it does everything. We spent a week inside it (Blender, GarageBand, a full live game) to find out what it's actually great at. On today's AI For Humans, Kevin Pereira and Gavin Purcell go hands-on with OpenAI's Astra, aka GPT-6, and it's real good (and real expensive). The headline for us is computer use, and specifically Blender: Astra builds 3D scenes from a sentence, and the internet is already making Backrooms worlds, a hyper-real Palace of Fine Arts, and the Billie Jean dance. Gavin ran his own experiments, including a Parappa The Rapper style animation and a head-to-head with GPT 5.6. Plus: Astra identifying sounds from spectrograms, driving MS Paint and Canva, editing a full project, playing Zork as a walk-around game, remaking Paperboy in 3D, editing Super Nintendo games in real time, making music in Logic, and a League of Legends "cease and desist edition" that raises the big question of how copyright even keeps up with this. Also: the OpenAI Navier-Stokes math drama. NYU's Tristan Buckmaster and Anthropic's Levent Alpöge used AI to make a real advance on a 200-year-old fluid problem, and then, according to Buckmaster, OpenAI raced in with a 100-page proof of the same route and things got very ugly over credit. When your AI collaborator's lab can see what you're close to, who owns the idea? Plus: Gavin built King of the Prompts, a live competitive prompting game made with Astra and MiniMax H3 Max, and he'll tell you what it is, why he made it, and what it costs to run. And an AI See What You Did There with Skyrim: Senior Living, AI Denzel Washington delivering maybe the best AI video ever made, and a fruit fly brain that learned the YMCA (and lost the ability to love). WE'RE DEEP IN THE ASTRA JUICE. SEND HELP (AND TOKENS). // Chapters //   00:00 GPT-6 Astra Is Real Good (And Real Expensive) 03:35 What Astra Is Actually Good At: Computer Use 06:15 Astra In Blender: Backrooms, Palace Of Fine Arts & Billie Jean 10:57 Astra Remakes Paperboy & Will People Play AI Games? 16:20 Astra Makes Music In Logic (And Simulates A DJ Stage) 19:40 Kevin's Existential Crisis Of App Release 22:50 The OpenAI Navier-Stokes Drama: Who Owns An AI-Assisted Proof? 27:59 Gavin Built King Of The Prompts (Astra + MiniMax H3 Max) 35:15 AI See What You Did There: Skyrim Senior Living, AI Denzel & FLY-M-C-A   // Show Links // OpenAI introduces GPT-6 Astra https://openai.com/index/gpt-6-astra/ Duncan Trussell's Backrooms in Blender (the whole thread is great) https://x.com/duncantrussell/status/2096003511104508411 Hyper-realistic Palace of Fine Arts in Blender https://x.com/sharifshameem/status/2095653641164329143 Recreating the Billie Jean dance in Blender https://x.com/flavioAd/status/2097067419248296389 Gavin's Parappa The Rapper style animation https://x.com/gavinpurcell/status/2096454789328752682 Gavin: Astra builds a scene from scratch vs GPT 5.6 https://x.com/gavinpurcell/status/2096234912441626724 Astra identifies sounds from spectrograms https://x.com/maxxrubin_/status/2096892510241268094 Astra in MS Paint (and Canva) https://x.com/The_Alex/status/2096776453283360803 Zork as a walk-around game https://x.com/emollick/status/2096047660662722620 Paperboy 3D remake https://x.com/builtbysketch/status/2096515959469072630 League of Legends: Cease and Desist Edition https://x.com/konstiwohlwend/status/2097164513527357608 Matt Shumer's AI agent Unreal project https://x.com/mattshumer_/status/2096034353344139519 SuperAstra: edit Super Nintendo games in real time https://x.com/scottastevenson/status/2096972908828426747 https://github.com/ScottStevenson/SuperAstra Astra connected to Logic to make music https://x.com/MarioSaputra/status/2096908100871836123 DJ stage simulations https://x.com/cerspense/status/2096960824535572498 The OpenAI Navier-Stokes credit drama (Buckmaster's account) https://x.com/cguth_7/status/2097206622602854882 King of the Prompts (Gavin's new game) https://kingoftheprompts.com/ Skyrim: Senior Living https://x.com/GORMTHEOLD25/status/2096994048758137013 AI Denzel Washington on AI slop https://x.com/Nekodificador/status/2096682353011630237 FLY-M-C-A: a fruit fly brain learns the YMCA https://x.com/dhruvbhatia0/status/2097047404239581265   // Join the AI For Humans community // Join the AI For Humans Discord https://discord.gg/muD2TYgC8f Support AI For Humans on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans newsletter https://aiforhumans.beehiiv.com/ Follow AI For Humans on X: @AIForHumansShow https://x.com/AIForHumansShow  

We spent 48 hours training GPT-6 Astra to make motion graphics in After Effects, and then had it recreate Higgsfield and Iman Gadzi style animations from scratch, with pre-training and post-training versions side by side so you can see what it took. Then we took a real Zillow listing and had Astra read the floor plan and rebuild the house in Blender and Unreal Engine. We show three community projects, a robot that paints, a Blender-built web game, and an interactive bookshelf site, before the debate we could not avoid: the singularity, and what the next two years do to every digital job. Plus first tests of GPT Image 2.5.

Was tun gegen Narzissten, Betrüger und Blender? Neues Buch: Unbreakables

毎日届くニュースレターの購読はこちら https://wbnlive.substack.com/このライブの全編はこちらからhttps://www.youtube.com/watch?v=4iu12eRXCiw「WBN Live」(2026年9月7日)から、金城の深掘りパートを切り出しました。GPT-6 Astraは9月3日の限定公開から前倒しで、米4日夕方までにChatGPTのPlus・Pro・Business・Enterprise全員へ追加料金なしで行き渡りました。9月7日にはNVIDIAのジェンスン・フアンCEOが「10万基超のGrace Blackwell NVLink72で訓練した。AGIは到来した。次は40万基」と投稿し316万表示。その前日、OpenAIのチーフサイエンティストJakub Pachocki氏はエッセイ「An Alien Mind」で「現時点でどの研究所もアライメントと監視を解決できていない」と自主的な減速を訴えています。この動画は、売る側が「AGI到来」と言い切り作った側が減速を訴えた同じ週を、Astraを実際に触った感想から整理しています。▼ 目次00:00 GPT-6 Astra全員展開。きょうの本題00:37 Fableの直後にAstra。入れ替わりが早すぎる01:53 開発者はFableとAstraを同時に走らせている03:51 AI疲れ。差分がないのに両方使わなきゃ損04:32 事実整理: 有料プラン全員に追加料金なし04:46 ジェンスン・フアン「AGIは到来した。次は40万基」05:59 AstraのLow設定はSolのHighより上07:13 自動化AIリサーチインターンの目標に到達08:38 社内は6ヶ月前倒し。9月29日のDevDay09:41 社員1人あたり1日平均7万円分のトークン消費10:56 スケッチからBlenderで3,295オブジェクト13:10 総合力ではまだFable 5.1が上16:01 チーフサイエンティストの「An Alien Mind」17:34 ビジネス側はAGI到来、研究側は減速を訴える19:51 独り言を読んで守る手法が効かなくなる3つの理由※ Astraが頭の中でループして考えるという技術面の話は報道と観測に基づくもので、OpenAIはAstraをAGIとも公式には認定していません。ARC-AGI-3の99.9%は独自接続での数字で、標準の測り方では62.7%です。▼ 出演金城(Kinjo) X: https://x.com/illshinおいなり(Cohost / Startup Now MC・JobTales Inc. CEO) X: https://x.com/oinariiisan▼ 主要ソース(一次情報)OpenAI 公式X: https://x.com/OpenAI/status/2095595757072191802Jensen Huang(X): https://x.com/JensenHuang/status/2096700264569090384Greg Brockman(X): https://x.com/gdb/status/2096721633876771094ARC Prize: https://arcprize.org/blog/astraML_Bear Times 9/7: https://www.ml-bear-times.com/20260907_00_morningWBNは平日毎日13:00から、AI・テック・スタートアップのニュースをライブでお届けしています。#AI #AIニュース #OpenAI #GPT6 #NVIDIA #AGI #WBN

GPT-6 Astra is out, and it is the first frontier model to put 3D front and centre. We got access two hours before the show, so we photographed a building across the street on an iPhone, gave Astra one short prompt, and watched it take over the computer and rebuild the whole block in Blender, ornaments, street signs and all, with zero iterations. Then it edited the screen recording too. The second half is the conversation we had to have: if Astra can do anything you can do on a computer faster, what happens to specialists, and do we actually have more time now or less?Sources:1. GPT-6 Astra release- https://x.com/OpenAI/status/20955957415281257802. Astra 3D examples (Manhattan in Unreal, Blender scenes)- https://x.com/mattshumer_/status/2095609734845927525- https://x.com/mattshumer_/status/2095596175705399482- https://x.com/sharifshameem/status/2095653641164329143Try invideo Agent 2:https://invideo.io/

AI video had a speed problem. The best paid models take a minute or two per clip, and running one free on our own machine took us 20 minutes. Now there is a version that makes a 15 second clip in about five seconds, and we tested it against real Seedance shots using their exact prompts. Then ZAI released a model that autonomously built a full 3D kitchen inside Blender over 16 hours, though we have questions. And Hugging Face put out a $399 open source robot you can train yourself.Sources:1. Minimax H3 Max on Fal - 15 second clips in about 5 seconds- https://fal.ai/models/minimax/h3-max/image-to-video2. ZAI's GLM 5.3 Flash builds 3D scenes in Blender- https://x.com/louszbd/status/20930475485505251653. Hugging Face releases Microduck - $399 open source robot- https://x.com/ClementDelangue/status/2092931447644442635

In this episode, Ray Cochrane digs into Anthropic’s Model Hardware Standard. It is a shared driver that lets an AI agent run real lab equipment, from pipetting robots to the lasers inside a quantum computer. He also covers OpenAI’s builder’s guide to GPT-5.6, Google’s new Expert Intelligence book feature, Apple’s M5 Ultra Mac Studio, and a judge’s order forcing Google to stop hiding rival app stores. Finally, he weighs in on Apple’s proposed 15 percent link-out fee, Meta’s Australia numbers, the White House deputizing private hackers, and why rivers obey a 1957 math rule. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens with a quick personal update. He is hunting for tickets to Michigan for his dad’s anniversary, and he has been learning Blender and Godot on the side, mostly modeling and blocking out levels. Consequently, he asks listeners for advice on starting a big game project, and he plans to record his progress, maybe as a time lapse. Then it is straight into the featured story. Anthropic’s Model Hardware Standard: A Driver for the Physical World The featured story comes from Anthropic, which opened a research preview of the Model Hardware Standard, or MHS. Cochrane frames it as the other side of the question NVIDIA’s world models raised two weeks ago: when do AI agents start touching actual machines? A typical lab runs a microscope, a liquid handler, a robotic arm, and a plate reader, each from a different vendor with its own control software. One Janelia researcher in the post launches seven programs in three languages just to start an experiment. Anthropic says wiring a setup like that takes weeks or months of specialist work. MHS is a driver, the same kind of translation layer a printer uses, except every device gets described with a tiny set of commands like read and write. Devices announce themselves on the network. A plain-English reference file then records what each machine measures, what can be adjusted, and which safety limits get enforced no matter what the agent asks. Agents then reach the hardware through the Model Context Protocol, the command line, or plain code. Cochrane sees the same move the industry keeps making, from coding harnesses to RSS and JSON: agree on a standard and let everyone build against it. In fact, he calls MHS the hardware version of MCP. The partner results carry the segment. QuEra builds quantum computers from individual atoms held by lasers that must hold their frequency to about one part in a trillion. A four-person team spent months on a relock script that worked 58 percent of the time. However, four copies of Claude iterating overnight through MHS produced a decision-tree script that recovers the laser in about six seconds, and it passed 99.3 percent of 700 blind trials. Carnegie Mellon wrote MHS drivers for four instruments across three incompatible computers in about eight hours, then ran dose-response experiments three times faster and blocked all six deliberately induced faults. Genentech, meanwhile, showed the limits. Claude used the same pump speed for water, a foamy protein solution, and a human had to explain that the bubbles were a physics problem. That gap in physical intuition is what sticks with Cochrane. He doubts it will change soon, and he suspects the fix will arrive as sub-agents or sub-models that judge a request against an expected outcome. He also connects MHS to a video of racing robots that never learned to stop at the finish line. What happens, he wonders, once they can read a distance sensor through a shared standard? Still, he calls the announcement a fantastic read and points listeners to the full article. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. GPT-5.6 Does the Same Work for a Fraction of the Cost OpenAI’s builder’s guide to GPT-5.6 leads the headlines. Cochrane recaps the three tiers from episode 1870, Sol, Terra, and Luna, plus the separate dial for reasoning effort. On BrowseComp, a benchmark for digging up obscure facts on the web, the old GPT-5.5 flagship scored about 84 percent on a run that cost 33 dollars three months ago. Luna now matches that score for a dollar thirty-three, and OpenAI has since cut Luna’s price another 80 percent. Browser Use reports Luna finishing 78 percent of its hardest browser tasks for about 14 dollars, against 80 percent for roughly 235 dollars from the best available model. The guide’s other big addition is a multi-agent beta flag. It lets the model handling a request spawn parallel helper agents that report back to a root agent inside a single API call. However, Cochrane is unimpressed by the timing. He has been running that pattern in Claude Code for months, so he sees OpenAI copying a workflow other companies already ship rather than inventing its own. Along the way, he plugs Claude Code’s remote-control sessions, which let him send prompts from his phone to a terminal session at home. Google Lets Gemini Read the Books You Actually Bought Google launched Expert Intelligence, a name Cochrane calls quite the reach. The feature lets you drop a book you bought on Google Play Books into Gemini Notebook, formerly NotebookLM, and ask questions answered only from that book, with citations. Cochrane sees real power here for students, since he once used NotebookLM to organize scattered course PDFs. Additionally, publishers get a cut, which he calls a far better deal than the wholesale scraping of books that trained earlier models. Nevertheless, he asks who loses out, because a paid publisher does not automatically mean a paid author. He floats the same idea for artists, even a penny per use, then admits that may be too idealistic. Apple’s M5 Ultra Mac Studio Is Built to Run Big Models at Home Back in episode 1861, when Apple killed the Mac Pro, an M5 Ultra Mac Studio was expected later this year. Now it is here. The M5 Ultra brings up to a 36-core CPU, an 80-core GPU, and 512GB of unified memory moving 1.2 terabytes per second. Apple claims up to 4.3 times the AI performance of the M3 Ultra. Thunderbolt 5 can also cluster four machines into one memory pool for up to three times faster inference. The M5 Max model starts at $2,499 and the Ultra at $5,499, with shipping on September 22 and the 512GB configuration arriving in late October. Cochrane finds the clustering pitch ridiculous at that price, but he invites anyone who spends the money to report back. Apple Opens a Manufacturing School in Houston Apple also opened a 20,000-square-foot Advanced Manufacturing Center in Houston. It offers free classes for small and midsize manufacturers, from circuit board design to hands-on time on a scaled-down production line, with college students joining later. Cochrane calls it a solid step in the bring-manufacturing-home movement. The bigger story is the campus itself, which builds Apple’s AI servers and will add the first US-assembled Mac mini line later this year. That ties back to the Mac mini shortage that followed the OpenClaw rush, when Tim Cook warned of months-long waits. Cult of Mac was still reporting four-month waits in late July. However, Cook blamed chip supply rather than assembly, so Cochrane is not counting on relief just yet. Amazon EC2 Turns Twenty Amazon EC2 turned twenty this week, which Cochrane admits makes him feel old. The 2006 beta offered one server size in one region for ten cents an hour. Each came with a 1.7 gigahertz Xeon and under two gigabytes of memory, and accounts were capped at twenty servers. Today AWS offers more than 1,200 instance types across 39 regions. Consequently, Cochrane credits the company with turning that tiny product into the backbone of cloud and AI computing. Intel Gamer Days: Two Free Games, With Fine Print Intel Gamer Days runs through September 13. Buy a qualifying Core Ultra Series 2 or 14th Gen desktop chip, a Core Ultra Series 3 laptop, or an Arc graphics card. In return you get Star Wars: Galactic Racer plus the Tomb Raider: Legacy of Atlantis remake. GamesRadar values the pair at about 120 dollars. However, neither game is out yet, and codes must be redeemed by October 31 even though the Tomb Raider remake ships in February. Cochrane calls that awful, but he still tells qualifying buyers to claim the deal early. Note that 13th Gen chips do not qualify. Judge Orders Google to Stop Hiding Rival App Stores A jury found Google’s Android app monopoly illegal in late 2023, and Judge James Donato ordered rival stores into the Play Store in 2024. On August 13, Epic’s lawyer demonstrated that searching Play for “store for apps” returned Walmart instead of any app store. Donato called that “not acceptable” and ordered three fixes within a week. Searches must surface third-party stores, listings need a plain install button, and the “are you looking for” interstitial has to go. Cochrane welcomes the monopoly being chipped away, but he notes that a controlling entity still sits atop every app store. In his view, community hubs like app stores and social media need a public infrastructure layer. He suspects governments skip that investment because companies already run the services, while selling your data. Apple Wants 15 Percent of Purchases Outside Its Store The other half of the Epic saga is Apple’s proposed link-out commission. After the 2021 anti-steering injunction, Apple charged 27 percent on purchases made through external links. A judge held it in contempt last year, and the Ninth Circuit then allowed a fee limited to the cost of running the system. Judge Yvonne Gonzalez Rogers refused to wait for the Supreme Court, writing that “further delay is unwarranted.” Apple filed 15 percent for standard apps, 10 percent for subscription renewals and partner programs, and 5 percent for small businesses. It also conceded the rate would be “essentially zero” under the appeals court’s cost yardstick. Since Apple has charged nothing on link-outs since the contempt ruling, Cochrane sees this as a raise. He calls a cut on purchases made on a developer’s own website disturbing. He also recalls reading about the size of Uber’s payments to Apple, and he questions whether that kind of percentage is sustainable for companies without funding. Meta Says It Has Cut Off 750,000 Australian Kids Meta reported locking out more than 750,000 Facebook and Instagram accounts in Australia by the end of June under the country’s under-16 social media law. Over 500,000 of those were removed before the law even took effect. Detection relies mostly on AI scanning posts and bios for tells like birthday messages, plus user reports and blocks on re-registration. However, the post gives no count of mistaken removals or appeals, and the regulator’s early data shows under-16 usage falling only from about 86 to 81 percent. Meta wants a single age signal at the operating system or app store level, and Cochrane agrees completely. He connects it to the MHS idea from the top of the show: platforms need a standard flag to reference instead of guessing. The White House Deputizes Private Hackers Earlier this month the White House signed a National Security Presidential Memorandum that lets vetted private security firms run surveillance and disruption operations against overseas criminal groups. The Justice Department and Homeland Security hold the contracts and oversee the work. Firms need a proven track record, vetted staff, and a bond of at least $1 million, and must submit operating procedures within 60 days. Cochrane finds the measure aggressive in a good way and hopes it deters attacks on innocents. Still, he takes Kevin Beaumont’s warning seriously that the private security industry profits from ransomware existing. He compares it to the old Head and Shoulders myth: why solve the problem that drives your revenue? A Weather Satellite Watched the Eclipse Shadow Cross Europe Cochrane skips the readout on this one and simply sends listeners to ESA’s site. The MTG-I1 weather satellite captured the Moon’s shadow sweeping across Europe during the August 12 eclipse. Watching a shadow cross an entire continent, he says, was a first for him. Additionally, it leaves him excited about the research happening beyond the planet. Rivers, Deltas, and the Number 0.6 Quanta Magazine explains Hack’s law, which John Hack discovered in 1957 while measuring streams in Virginia and Maryland. A stream’s length tracks its drainage area raised to the power of 0.6, regardless of the rock underneath, and satellite data later confirmed it worldwide. Computer models in the 1990s showed why. Channels that capture extra runoff cut deeper and steal from their neighbors until the network settles into the arrangement that wastes the least energy. Now a University of Texas Rio Grande Valley team has found the same 0.6 exponent in river deltas, which spread water out rather than gathering it. Nobody knows why yet, and Cochrane calls it a really cool read. Sugar Helped Grow the Human Brain, Too A new paper in Science, co-authored by Jennie Brand-Miller at the University of Sydney, adds a third ingredient to the story of early human brain growth. Alongside meat and cooking, natural sugars from ripe fruit and honey may have fueled it too. The brain is about two percent of body weight but burns twenty percent of resting energy. It runs on glucose, which meat and marrow barely supply and raw starch cannot release without fire. The team modeled ancestral diets from a chimp-like baseline through Homo erectus and concluded that the earliest hominins may have drawn over 65 percent of their energy from natural sugars. Cochrane stresses that it is a model, not fossils, and notes that paleoanthropologist Marina Lozano thinks the authors place widespread cooking too early. Still, he loves this kind of deep research. Retracing the steps to our own intelligence, he suggests, could hint at what it takes for intelligent life to develop at all. A Brain Rhythm That Tells Doctors Where to Aim Finally, Science Daily covered a University of Cologne study on deep brain stimulation. That is the implanted-electrode treatment that eases Parkinson’s tremors for some patients but not others. Andreas Horn’s team recorded from 50 patients using both the implanted electrodes and an external magnetic scanner. They identified a circuit between the electrode’s target and the frontal cortex that oscillates at 20 to 35 cycles per second. Stronger coupling there predicted bigger improvement after surgery, though the study, published in Brain, shows correlation rather than cause. First author Bahne Bahners hopes the finding helps tune DBS more precisely, especially for patients who have not responded well. Cochrane half-jokingly asks whether MHS might one day drive those electrodes, and he calls brain disorders the hardest thing in the body to treat. Cochrane wraps with housekeeping: become a GNC Insider at geeknewscentral.com/insider, email geeknews@gmail.com with questions or comments, subscribe to the newsletter, and grab a modern podcast app at podcastapps.com. He thanks GoDaddy for over twenty years of keeping the show on the air, promises to catch everyone next Monday, and wishes listeners a great night. The post Eyes, Hands, and a Sense of Timing #1874 appeared first on Geek News Central.

UniCredit-Chef Andrea Orcel hat sich gegen den Willen von zwei Bundesregierungen und des Managements die Kontrolle über die Commerzbank verschafft. Wie ihm das gelang und warum ihn niemand mit klassischer Abwehr stoppen konnte, darüber spricht in diesem Podcast Henning Hinze, Redakteur des manager magazins, mit mm-Autorin Katharina Slodczyk. Weiterführende Links: Angriff der UniCredit: So lief der Commerzbank-Coup des Andrea Orcel Bund soll Aktionär bleiben: Commerzbank-Chefaufseher fordert Reform des Übernahmerechts Kampf um Commerzbank: Geheime Gespräche zwischen Bettina Orlopp und Andrea Orcel Problematische Persönlichkeiten: Narzissten, Blender, Überforderte – viele CEOs scheitern an sich selbst Zum manager-magazin-Abo Geldanlage Wenn es an den Börsen drunter und drüber geht, schlägt die Stunde der Spezialisten. Erfahren Sie hier alles Wissenswerte über Aktien, Rohstoffe und Alternative Investments. Hier geht es zur Anmeldung! Host: Henning Hinze (Redakteur manager magazin)Gast: Katharina Slodczyk (Redakteurin manager magazin)Schnitt, Mixing/Mastering: Felix KleinProduktion: Nele Geiger, Sven Bergmann+++ Alle Infos zu unseren Werbepartnern finden Sie hier. Die manager-Gruppe ist nicht für den Inhalt dieser Seite verantwortlich. +++ Alle Podcasts der manager Gruppe finden Sie hier. Mehr Hintergründe zum Thema erhalten Sie bei manager+. Jetzt drei Monate für nur € 10,- mtl. lesen und 50% sparen manager-magazin.de/abonnieren Informationen zu unserer Datenschutzerklärung.

Hey there Voices of the Bench community, this is Trish Jones with Ivoclar. If you've been curious about fast-firing zirconia to improve efficiency but aren't convinced it can deliver predictable, high-quality results, I'd encourage you to connect with us. Our new IPS Emax Zirconia offers multiple fast-fire protocols designed to help save you valuable production time while maintaining consistent results. Time is money in every lab. Don't wait. Reach out to your local Ivoclar rep today and discover how IPS Emax Zirconia can help streamline your workflow. As full-arch dentistry continues to grow, so do the demands on today's dental laboratories. That's why Knight Dental recently launched SimplyARCH Studio, a dedicated production environment built exclusively for full-arch restorations. To support this specialized workflow, Knight invested in an XTCERA milling system and carefully evaluated multiple CAM software solutions before choosing hyperDENT. The decision came down to exceptional milling quality, minimal hand finishing, and impressive production efficiency. But what truly set hyperDENT apart was the implementation process. From the very beginning, the team provided more than software training—they shared the knowledge and experience needed to build an optimized workflow for complex full-arch cases. With proven expertise in advanced milling strategies and laboratory production, hyperDENT helped ensure SimplyARCH Studio was designed for long-term success from day one.Matt Everatt joins Elvis and Barb for a fascinating conversation that takes a deep dive into his journey through the dental laboratory industry and the story behind his book, The Invisible Profession. From discovering dental technology at 16 and specializing in maxillofacial prosthetics and orthodontics to working in hospitals, developing early sleep-apnea appliances, and nearly leaving the profession altogether, Matt's career has taken some seriously interesting turns. Eventually, he helped co-found S4S, building a successful business around sleep-apnea appliances, occlusal splints, orthodontics, and more before eventually stepping away from ownership. From polishing hearing-aid earpieces to becoming the first female clinical dental technician to qualify in the UK, Caroline Kirkpatrick has taken a pretty incredible—and definitely unconventional—path through dental technology. Caroline joins Barb and Elvis to talk about how a broken denture repair sign outside a dental laboratory sparked her interest in the industry, her early years learning crown and bridge, orthodontics, and working in a dental hospital, and how her curiosity pushed her to learn every side of the profession. Eventually, that curiosity led Caroline to become one of the first four technicians accepted into the UK's pioneering clinical dental technology program in 2010. But Caroline's story doesn't stop at the bench. She built her own laboratory, taught dental technology, treated patients clinically, raised a family, and continued pushing herself into digital dentistry. She shares how she experimented with early digital workflows, 3D printing, digital dentures, and eventually exocad—and why she believes the best digital workflow isn't necessarily the fanciest one, but the one that actually works in the real world. Caroline also talks about working with her sister and daughter in the family lab, the importance of exposing young technicians to different laboratories and workflows, and why sharing the mistakes you've made can sometimes be one of the best ways to help someone else learn. From Scotland to the world of digital dentures, this is a great conversation about curiosity, adaptability, education, and never being afraid to learn something new. You know what's expensive in a dental lab? Most people would say the mill, but the real cost is downtime. When production stops, everything backs up—cases get delayed, technicians get frustrated, and customers start asking questions. That's why reliable equipment matters. Roland DGA's DGSHAPE DWX milling solutions are built for dependable production, helping labs stay on schedule and keep work moving day after day. Less downtime means fewer surprises and more time doing what your lab does best. And when your equipment is reliable, your customers notice the difference. Reliability isn't just a feature—it's the foundation of a successful lab. Visit rolanddental.com to learn more about Roland's DGSHAPE DWX milling solutions.Special Guest: Caroline Kirkpatrick.

¡Ni la destilación de interconexión mas pura entre la humanidad y sus artificios podra detenernos!¡Porque es lunes y SpreadShotNews Podcast ya esta aquí! En este episodio: Nico retoma el Big Walk, y entre él y Maxi continúan hablando sobre Ace Combat 7: Skies Unknown. Además Maxi continua el Beast of Reincarnation. En el Rapid-Fire, tenemos noticias sobre el retiro de Warren Spector, la ultima adicion a VS Studio es el ex-director de Tekken 8, el plan de juegos live service de Sony tiene cada vez menos exponentes, Riot le dice “hasta aca llegamos” a 2XKO, Marvel Tokon: Fighting Souls es pirateado y corre mejor que la version oficial y buena parte del equipo de Invincible VS es despedido. En la Main Quest, repasamos nuestras predicciones para el Opening Night Live de la Gamescom 2026 y leemos sus predicciones. Para finalizar, en el Special Move, Maxi recomienda el video ensayo de Jacob Geller deconstruyendo Ace Combat y el video de Harada's Bar donde charla con Seiji Aoki de Virtua Fighter . Nico por su parte recomienda el canal de youtube de Pira Academy , donde se anotó para hacer un curso de Blender. Por último, recuerden que nos pueden escribir preguntas directamente a través de google forms en el siguiente link: spreadshotnews.com/preguntas

On this episode of The Bourbon Life Podcast presented by Repeal Oak Fired Steakhouse, Mark and Matt are joined by John Wadell, Head Taster, Blender, and Single Barrel Curator at Kentucky Peerless Distilling Co., for a long-overdue return to the show. Recording from the Kentucky Peerless Suite at Hotel Distil in Louisville, John catches them up on how his role has evolved and reflects on his journey from the early days of Peerless to helping guide the whiskey the distillery is producing today. John talks about the growth of his palate over the past decade, the collaborative tasting and blending process at Peerless, and how the team decides which barrels become batches, single barrels, or special releases. He also shares the fun behind naming Peerless single barrels, discusses the success of the High Rye Bourbon, and explains why the distillery's sweet mash process, non-chill filtration, and focus on mouthfeel remain such important parts of the Peerless identity. The conversation also turns to a major milestone for the distillery: the release of Henry Kraver's 10-Year Bourbon. John shares what it was like tasting through roughly 200 barrels to select the final group for the release and why working with whiskey he remembers filling and rolling years ago made the experience especially meaningful. He also looks ahead to the 10-Year Rye planned for next year, the possibility of older age-stated releases and single barrels, and some intriguing older Double Oak barrels still aging in the Peerless inventory. Along the way, Mark, Matt, and John taste and review "The Phoenix," a 108.5-proof Kentucky Peerless Double Barrel Rye Single Barrel selected for Repeal Steakhouse and Hotel Distil. They talk through its oak, rye spice, citrus, toffee, chocolate, coffee, and malty notes while John explains Peerless' unique approach to Double Oak whiskey—letting the palate, rather than a fixed timetable, determine when each barrel is ready. It's a fun, wide-ranging conversation about whiskey, patience, blending, the evolution of Kentucky Peerless, and what comes next for one of Louisville's most distinctive family-owned distilleries. Pour yourself a great glass, sit back, and join us for another great episode of The Bourbon Life Podcast. This episode of The Bourbon Life Podcast is presented by Repeal Oak Fired Steakhouse and sponsored by Pappy & Co., District 7 Social, The Kitchen Table at the James B. Beam Distilling Co., and Hotel Distil.

Ben and Rob noclip into ‘The Backrooms', Kane Parsons' 2026 A24 horror debut that transforms the viral internet creepypasta and his Kane Pixels YouTube series into a feature length nightmare. Starring Chiwetel Ejiofor, Renate Reinsve and Mark Duplass, ‘The Backrooms' follows a struggling furniture store owner who discovers an impossible doorway beneath his shop, and the therapist who follows him into an endless maze of liminal spaces.How did a teenager teaching himself Blender during lockdown go from YouTube animator to directing one of A24's biggest movies? How did a single creepy photograph become one of the internet's biggest horror myths, and how much of the Backrooms mythology did Kane Parsons actually invent? What does The NeverEnding Story have to do with any of this?! And is the real monster hiding in those yellow corridors actually regret? CONSUUUME to find out all this and much, much more!PLUS we have a Patreon with EXCLUSIVE content just for you starting at less than £2 a month! Click the link below!Find us on your socials of choice at www.linktr.ee/everymovieeverpodcastGET 20% OFF CINEWORLD UNLIMITED MEMBERSHIP WITH DISCOUNT CODE: ' TEL013 ' You're Welcome!

Best Of 2GG: Jay's Attachment to His Blender by Two Girls and a Guy

Cadi Zhang joins Reid Hoffman and Parth Patil to explore how generative AI is changing game development, world models, and robotics. Drawing on her work across Unity games, VR teleoperation, AI accounting, and robotics product operations, Cadi explains what virtual worlds can teach embodied intelligence—and why physical robots still lack the tactile, real-world data that can't be scraped from the internet. She shares how she uses GPT, Blender, PixelLab, and Aseprite to prototype games faster, including a duck-themed imposter game and PokéTax, her Pokémon-inspired tax-filing game. AI can rapidly generate code, gameplay mechanics, and 3D assets, she says, but it still can't judge whether a game feels fun, maintain a consistent art style, or model the precise force needed to fold a sheet of paper. Reid, Parth, and Cadi discuss simulation-to-reality gaps, the limits of current world models, robot safety, humanoid versus task-specific form factors, and why human taste remains the decisive creative skill.

✨ On this dinner party episode of Zillennials Podcast, Kaylee and Lian make chana saag (an Indian curry with chickpeas and spinach). They talk about why they chose the recipe and what they thought of it. They share possible modifications and additions and their overall cooking experience. The conversation closes with summer cooking updates about what Kaylee and Lian have been cooking and eating.00:00 Dinner Party Introduction02:19 Why Chana Saag04:31 Servings and Leftovers07:28 Spices and Ingredients10:16 Blender and Cleanup15:02 Dinner Party Fails19:27 Family Potluck Dynamics21:50 Summer Cooking Routines28:23 Conclusion and Next Book Club Announcement

In this episode of More Plants, we're diving into chilled soups for summer — inspired by the classic cold soups of Spain. You'll hear about the history and origins of gazpacho, salmorejo, and ajo blanco, including how Spanish chilled soups evolved from older peasant recipes and regional influences. We also explore other chilled soups from around the world, from cold borscht to tarator and vichyssoise. You'll learn: How Spanish chilled soups developed historically. What makes a great chilled soup base. Easy blender soup ideas for warm weather. How to build flavor, texture, and balance. Garnishes and toppings that make cold soups shine. Why fruit is an ingredient often included in chilled soups in Spain and why you'll love it Whether you want something light for lunch, a no-cook dinner, or a new way to enjoy summer produce, this episode will give you plenty of plant-based meal inspiration. For all the links mentioned in today's episode, click here or visit brownble.com/blog

Joshua & Jason have a fantastic sit down with Sammy Karachi, Head Blender of Found North Spirits. You can tell the boyz all knew one another for years and years. Lots of fun, laughs, and learning about blending Add to this, Jess and Joshua discuss details of the new ROW # 13 Release of Single Cask Nation whiskies! ...as usual, have a seat, have a pour, and listen in. Unless you're driving. If you're driving, be smart and stay sober but be sure to listen into the conversation! Special thanks to: - Weigh Down for allowing us to use their song "Wooden Monsters" as our theme song - RØDE for making *really* great microphones - Focusrite for making awesome USB receivers - Joshua Hatton for producing and editing

Send us Fan MailOne great bourbon story can change your whole drinking life, and Ed Blake's starts with a bottle he avoided for years. After a bad early run-in with bourbon, he returns to it later through a Pappy Van Winkle 15 and suddenly the rabbit hole opens. From there, we follow the real path from enthusiast to trusted palate: learning how to taste, learning how to talk about flavour, and learning how to help other people find what they actually like. Ed (Rising Tide Spirits) shares how barrel picks and community feedback sharpened his selection skills, including the practical reality of tasting barrels in winter, when whiskey can show harsher edges before it warms up. We also get into what it's like building brands as a non-distiller producer, how Old Stubborn came to life through a strong distillery relationship, and why he's committed to small production and high quality instead of chasing shelf space. Then we go deep on rye whiskey, especially MGP rye. We unpack the dill pickle note that turns some drinkers off, how fermentation timing can influence it, and why longer aging often brings more caramel sweetness, richer oak, and a smoother balance. If you're curious about Old Swagger, Old Stubborn, premium bourbon packaging, or what separates a hype bottle from a genuinely great pour, you'll leave with a clearer buying compass and a better vocabulary for your own palate. Subscribe for more whiskey conversations, share this with a rye or bourbon friend, and leave us a review with the best bottle you've had this year. What tasting note instantly makes you want another sip?We sit down with Ed Blake of Rising Tide Spirits and trace the real steps from bourbon fan to respected barrel picker and brand builder. We dig into how he sources and blends Old Stubborn and Old Swagger, why his packaging is part of the experience, and what he's learning from rye whiskey as the market shifts.  • Ed's early bourbon misfire and the moment Pappy brings him back  • Building a whiskey community and learning through shared pours  • The liquor store “help a shopper” moment that turns into a job  • How private barrel picks create trust and demand  • Sourcing as an NDP and why winter barrel samples can mislead  • Designing premium packaging that matches premium whiskey  • Staying small by choice while releases sell out fast  • Rye whiskey education, MGP dill notes, and how age softens them  • Barrel aging plans in Kentucky and a teased higher-aged release  Remember, we're the Scotchy Bourbon Boys, www.scotchyburbonboys.com for all things scotchy bourbon boys. Remember, you can get t-shirts, Glenn Karen's. Contact me direct for those. Whether you listen to us or you watch us, make sure you leave us good feedback.   If You Have Gohsts voice over Whiskey ThiefSupport the showhttps://www.scotchybourbonboys.comThe Scotchy bourbon Boys are #3 in Feedspots Top 60 whiskey podcasts in the world    https://podcast.feedspot.com/whiskey_podcasts/

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

In this episode of For The Dads with Former NFL Linebacker Will Compton, hosts Will and Sherm bring back the beloved segment MOTHERF*****, discuss Sherm’s latest Dad Loss involving his doggo’s and get some marriage advice from one of our interns — all while keeping the episode fun, fresh and of course, under an hour. The episode kicks off with Will telling the story of finding some of Scottzilla’s poop all over the carpet before getting into some hilarious topics such as: Dad Hacks for Baby Photos ScarScar’s Mermaid Themed Birthday A Call In From a PTFish SICKO Other highlights include: Two amazing write ins A PT6ICKO Bear Attack

video: https://youtu.be/CVkZvrh7qpw Ubuntu is shipping a kernel that can make certain AMD workloads up to 42 times slower. Linus Torvalds is drawing clearer boundaries around AI-generated code in the kernel. Secure Boot just passed a major certificate expiration deadline. COSMIC Desktop 1.3 brings a polished new Frosted Glass design. And one developer decided to add another place and that's a Sega Genesis from 1994. All of this and more on This Week in Linux. Now let's jump right into Your Source for Linux GNews! Download as MP3 Support the Show Become a Patron = tuxdigital.com/membership Store = tuxdigital.com/store Chapters: 00:00 Intro 00:44 Ubuntu publishes Update that introduces regression for AMD users 03:52 Linus Torvalds talks on AI Code in Linux 07:12 Secure Boot Linux Certificates Expired? 09:27 COSMIC 1.3 with Frosted Glass 11:33 AppManager for AppImages 14:51 FreeBSD Removes All GPL Code 17:16 Rapid Fire Lightning Round 17:40 stillOS 10.2 Released 18:32 Blender 5.2 LTS Released 19:00 Clonezilla Live 3.3.3 Released 19:31 Linux on Sega Genesis and 32X Addon 20:26 Outro Links: Ubuntu publishes Update that introduces regression for AMD users https://discourse.ubuntu.com/t/amdgpu-performance-regression-in-kernel-7-0-0-28-28/85237 Linus Torvalds talks on AI Code in Linux https://lore.kernel.org/linux-media/CAHk-=wi4zC+Ze8e+p3tMv8TtG_80KzsZ1syL9anBtmEh5Z40vg@mail.gmail.com/ https://www.phoronix.com/news/Linux-Is-Not-Anti-AI https://www.gamingonlinux.com/2026/07/linux-creator-linus-torvalds-puts-foot-down-on-anti-ai-comments/ https://itsfoss.com/news/linus-torvalds-on-ai/ Secure Boot Linux Certificates Expired? https://lwn.net/Articles/1079808/ https://almalinux.org/blog/2026-07-14-secure-boot-2023-certificates/ https://linuxiac.com/debian-13-6-released-with-120-security-fixes-and-124-stability-updates/ COSMIC 1.3 with Frosted Glass https://9to5linux.com/cosmic-1-3-desktop-environment-released-with-frosted-glass-effect https://www.phoronix.com/news/COSMIC-Epoch-1.3 AppManager for AppImages https://github.com/kem-a/AppManager https://www.omgubuntu.co.uk/2026/07/appmanager-appimage-installer-linux https://github.com/VHSgunzo/uruntime FreeBSD Removes All GPL Code https://www.phoronix.com/news/FreeBSD-16-Goes-GPL-Free https://fossforce.com/2026/07/freebsd-16-cleans-house-no-gpl-left-in-the-base-system/ https://www.phoronix.com/news/FreeBSD-Intern-AMD-ROCm https://www.phoronix.com/news/FreeBSD-Laptops-June-2026 https://www.phoronix.com/news/FreeBSD-Desktop-Install-NVIDIA stillOS 10.2 Released https://stillhq.io/stillos-gets-gnome-49-a-better-first-boot-and-major-swai-improvements/ Blender 5.2 LTS Released https://www.blender.org/download/releases/5-2/ Clonezilla Live 3.3.3 Released https://clonezilla.org/downloads/stable/release-notes.php Linux on Sega Genesis and 32X Addon https://cakehonolulu.github.io/linux-on-32x/ https://github.com/LinuxMD/linuxmd https://www.tomshardware.com/software/linux/developer-successfully-ports-linux-to-1994-sega-32x-genesis-and-megadrive-expansion-runs-open-source-os-on-paltry-23mhz-processors-and-256kb-of-ram Support the show https://tuxdigital.com/membership https://store.tuxdigital.com/

Joey tests OpenAI Codex + Blender MCP to reconstruct a real building in 3D from phone photos — and the results have real implications for production workflows. Plus: what to expect at SIGGRAPH and a free AI Workflows Summit in LA.Also: Nuke's SmartRoto...

Welcome back to this week's Friday Review where I can't wait to share with you the best of the week!     I'm looking forward to reviewing:     Reimagining Health Summit Tickets on Sale The Beast Blender (product review) Your Environment Becomes Your Biology (tip of the week) Chicken & Chlorine (research) One Minute Radiation-Free MRI (research)     For all the details tune into this week's Cabral Concept 3815 – Enjoy the show and let me know what you thought!   - - - For Everything Mentioned In Today's Show: StephenCabral.com/3815 - - - Get a FREE Copy of Dr. Cabral's Book: The Rain Barrel Effect - - - Join the Community & Get Your Questions Answered: CabralSupportGroup.com - - - Dr. Cabral's Most Popular At-Home Lab Tests: > Complete Minerals & Metals Test (Test for mineral imbalances & heavy metal toxicity) - - - > Complete Candida, Metabolic & Vitamins Test (Test for 75 biomarkers including yeast & bacterial gut overgrowth, as well as vitamin levels) - - - > Complete Stress, Mood & Metabolism Test (Discover your complete thyroid, adrenal, hormone, vitamin D & insulin levels) - - - > Complete Food Sensitivity Test (Find out your hidden food sensitivities) - - - > Complete Omega-3 & Inflammation Test (Discover your levels of inflammation related to your omega-6 to omega-3 levels) - - - Get Your Question Answered On An Upcoming HouseCall: StephenCabral.com/askcabral - - - Would You Take 30 Seconds To Rate & Review The Cabral Concept? The best way to help me spread our mission of true natural health is to pass on the good word, and I read and appreciate every review!  

AI news: Moonshot AI's Kimi K3 is a big AI model and Moonshot's early benchmarks put it surprisingly close to GPT-5.6 Sol and Claude Fable 5. And… Kevin's Opus 5 SCOOP!! Also: OpenAI's reported screenless AI speaker, a Seedance 2.5 preview, the Suno hack, robot fights and AI-built games in Unreal Engine and Blender. On today's AI For Humans, Kevin Pereira and Gavin Purcell unpack Kimi K3's benchmarks, pricing, Flappy Bird and Minecraft tests, and giant-model economics. Then, Kevin DRIPS Opus 5 alpha and says it's VERY good and blows the doors off of Fable but it's… slow.  Plus Demis Hassabis's AI-governance proposal, AI 2040's Plan A, OpenAI's reported screenless speaker, Codex Keyboard, a Seedance 2.5 preview, the alleged sources exposed by the Suno hack, spectacular robot violence, polite office-robot dabbing, and what happens when GPT-5.6 Sol meets Unreal Engine, Blender and two hosts with free time. THE AI FRONTIER IS MOVING AGAIN—AND CHINA IS RIGHT THERE WITH IT. // Show Links // AI FOR HUMANS Survey https://aiforhumans.beehiiv.com/forms/b7c77287-2cfd-4b64-a278-eb1a2ccb5744 Official Moonshot AI Kimi K3 launch video https://x.com/Kimi_Moonshot/status/2077521842080817296 Official Kimi K3 launch and benchmark thread https://x.com/Kimi_Moonshot/status/2077830229968683203 Official Kimi K3 technical launch article https://kimi.com/blog/kimi-k3 Kimi K3 head-to-head with GPT-5.6 Sol https://x.com/chetaslua/status/2077701096924229744 Kimi K3 Flappy Bird test https://x.com/jun_song/status/2077396996865003739 Demis Hassabis on a new framework for AI governance https://x.com/demishassabis/status/2076957440109625718 AI 2040: Plan A https://ai-2040.com/ Bloomberg's report on OpenAI's first device https://www.bloomberg.com/news/articles/2026-07-14/openai-s-first-device-will-be-moveable-screenless-speaker-built-as-ai-companion OpenAI Developers' Codex Keyboard post https://x.com/OpenAIDevs/status/2077425991790870644 BytePlus Seedance 2.5 World Cup preview https://x.com/BytePlusGlobal/status/2077321849806234080 Variety's report on the Suno hack and training data https://variety.com/2026/music/news/suno-hack-youtube-music-deezer-genius-data-trained-ai-music-1236811772/ Ultimate Robot Knockout Legend (UKRL) Fight https://x.com/ErenChenAI/status/2077750358302921029 Soft floating robot demo https://x.com/clankrmedia/status/2076593164744376707 Two NEO robots talk to each other—and then one dabs https://x.com/BerntBornich/status/2077749438630805648 GPT-5.6 Sol plus Unreal Engine experiment https://x.com/NomadsVagabonds/status/2077577815684202960 Gavin's first GPT-5.6 Sol plus Blender attempt https://x.com/gavinpurcell/status/2076736788320927925 Kevin's Find The Cursor Game: CURSED https://us-lax-8710957c.colyseus.cloud/ Gavin's Fig + Moss Watch autonomous studio https://x.com/gavinpurcell/status/2077155825274229122 Fig's stand-up set https://x.com/gavinpurcell/status/2076382092842475948   // Join the AI For Humans community // Join the AI For Humans Discord https://discord.gg/muD2TYgC8f Support AI For Humans on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans newsletter https://aiforhumans.beehiiv.com/ Follow AI For Humans on X: @AIForHumansShow https://x.com/AIForHumansShow Follow AI For Humans on TikTok: @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Speaking and booking https://www.aiforhumans.show/  

In Episode 73, Salma Aboukarr joins the show. A creative director and founder, she explains how she moved from painstaking CGI workflows in Blender and 3Ds Max to AI-native campaigns for brands including Coca-Cola, Panasonic, and Google Labs. She breaks down her viral IKEA exploding-room video that helped brands see the commercial potential of generative AI video, the detailed JSON prompting method behind it, and the modern AI creative stack she uses across Claude, Midjourney, Nano Banana, Seedance, FAL.ai, Z-Image Turbo, Qwen, style LoRAs, and custom AI agents.The conversation goes deep on AI advertising, product fidelity, photorealistic skin, color correction, video upscaling, automated client workflows, and why high-end AI work still depends on original concepts, trained taste, and obsessive finishing. They debate whether AI can truly be original, why technical teams struggle to manufacture taste, how creators survive a feed flooded with AI content, and why the next creative moat may come from the experiences, references, and strange little details nobody else can copy.---⏱️ Fast Hour00:00 Meet Salma Aboukarr02:17 From CGI agency to AI-first studio04:44 Product fidelity before AI got good07:34 The duct-taped road to photorealism11:10 Art direction beyond basic prompting14:28 Salma's current AI creative stack16:08 How she stress-tests every new model20:20 Why color correction still matters22:25 The IKEA video that changed everything27:13 Going viral and handling AI backlash30:38 Originality as the next creative moat33:05 Can AI actually be original?38:52 Inside an AI-native creative agency39:55 Z-Image Turbo and aesthetic base models43:06 From client brief to automated pipeline47:54 Style LoRAs for brand consistency49:11 Claude, MCP, FAL, and leaving ComfyUI51:07 The model that cut a day to 15 minutes55:13 Why AI content stopped feeling special01:00:57 The value trapped in AI archives01:02:06 Create for yourself or the audience?01:04:31 Can engineers manufacture taste?01:08:38 Finding inspiration outside the feed01:11:57 Jackie Chan and thumbnail fuel01:15:29 Final lessons from a creative trailblazer#AICreative #AIAdvertising #GenerativeAI #AIVideo #CreativeDirection #Midjourney #ClaudeAI #NanoBanana #SeedanceAI #AIWorkflow #AIAgency #AIContentCreation #BrandMarketing #CreativeTechnology #ProductPhotography #AIBranding #FutureOfAdvertising #FastHours

An Austin-based multimedia artist with 17 full length albums as well as being the author of two books and being a prolific visual storyteller that expresses himself through photography, music, drawing, painting and writing. 

Claude Sonnet 5 just launched and Fable 5 is returning. But OpenAI's GPT-5.6 Sol is still locked down and the government's grip on the most powerful AI models throws it all into chaos.. We dig into what Sonnet 5 can actually do, why GPT-5.6 Sol is still gated, and the breaking news that Fable 5 is expected back, plus a huge creative MCP festival with the Blender to Seedance workflow. This week on AI For Humans, Gavin Purcell and Kevin Pereira open on a strange new reality: the best AI models keep launching and then getting locked up, but this week the gate started to crack. Anthropic just shipped Claude Sonnet 5, its most agentic Sonnet yet, landing near Opus 4.8 performance at a much lower price, Meanwhile OpenAI announced GPT-5.6 Sol, Terra, and Luna, but at the US government's request the flagship is only available to a small list of vetted partners.  Then the story moved while we were recording: the government cleared Anthropic to restore Mythos 5 to critical-infrastructure organizations, and Fable 5 is now reported to be on track to return for general use (timing and terms still unconfirmed). We recorded two quick in-episode updates to keep pace with the news as it broke.  AND Meta's Brain2Qwerty mind-reading research, NanoBanana 2 Lite, and a full creative MCP festival built around the Blender to Seedance video workflow, ComfyUI's MCP integration, and Gavin's own microdrama experiment. THE AI FRONTIER IS HERE. WAIT, NOW IT'S NOT. OH, WAIT. IT IS! // Show Links // Anthropic introduces Claude Sonnet 5, its most agentic Sonnet yet (official) https://www.anthropic.com/news/claude-sonnet-5 Anthropic's launch post for Sonnet 5 https://x.com/claudeai/status/2072017450611142835 BREAKING: Anthropic's official post on restoring Mythos 5 and working to bring Fable 5 back https://www.anthropic.com/news/redeploying-fable-5 OpenAI previews GPT-5.6 Sol, Terra, and Luna in a limited government-approved preview (official) https://openai.com/index/previewing-gpt-5-6-sol/ Sam Altman on why the GPT-5.6 rollout is government-restricted https://x.com/sama/status/2070607488274358364 Meta's Brain2Qwerty research on decoding typed text from brain activity https://facebookresearch.github.io/brain2qwerty/ NanoBanana 2 Lite is out https://x.com/NanoBanana/status/2071988792970330186 Logan Kilpatrick on the fast-and-cheap tradeoff https://x.com/OfficialLoganK/status/2071988351083921690 The Blender to Seedance workflow (reid hannaford, who helped popularize it) https://x.com/reidhannaford/status/2070145120658137385 More Blender x Seedance examples https://x.com/koldo2k/status/2071307945002815967 Even more Blender x Seedance examples https://x.com/Flagiuss/status/2071335816190902624 A further Blender x Seedance example https://x.com/reidhannaford/status/2071595581508563168 ComfyUI announces full MCP integration https://x.com/ComfyUI/status/2071625866912944151 X launches an MCP, though the API is very expensive to use https://x.com/XDevelopers/status/2071752389183647758 Gavin's microdrama experiment https://x.com/gavinpurcell/status/2070937492858208540   Join our Discord https://discord.gg/muD2TYgC8f Support us on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans Newsletter https://aiforhumans.beehiiv.com/ Follow us on X @AIForHumansShow https://x.com/AIForHumansShow Find us on TikTok @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Book us for speaking or consultation https://www.aiforhumans.show/  

Welcome to Anime Watch Club, a bi-weekly group discussion and review where the hosts of the wdysa podcast nominate and vote on shows either we havent seen, or shows that will hopefully lead to a great discussion. On todays episode we are reviewing Milky Subway!Socials/Discord - https://linktr.ee/whatdoyousayanime0:00 - Intro0:49 - The milkiest questions4:15 - Background and synopsis5:23 - Impressions13:27 - Favorites among the cast29:39 - Syncing the sounds of the world and action to the music32:54 - The use of Blender for its 3DCG animation35:21 - Strength of the writing43:27 - Welcome to MyGO talk!47:18 - Advantages and limitations of only having a single production member56:59 - How the Netflix compilation changes the short YouTube structure1:00:05 - Final thoughts & scores1:07:05 - What we're watching next

Anthropic just accused Alibaba of the largest known corporate espionage campaign against it, alleging 25,000 fake accounts and 28.8 million queries aimed at stealing Claude. We get into the AI cold war heating up between the US and China, why Apple and Microsoft just raised prices, OpenAI's first chip Jalapeno, the wild new Seed Audio 1.0 model, Claude landing in Slack, and a Blender plus Seedance video workflow that gives you real control. This week on AI For Humans, Gavin Purcell and Kevin Pereira open on a genuine spy-novel turn: Anthropic has accused Chinese tech giant Alibaba of running an industrial-scale distillation campaign to siphon Claude's capabilities, laid out in a letter to US senators. It is an accusation, not a proven finding, and Alibaba has not responded, but it puts the US-China AI race front and center. From there we get into why the new models everyone expected this week didn't actually arrive, the AI memory crunch driving Apple and Microsoft price hikes, and OpenAI designing its first chip, Jalapeno, with Broadcom. On the fun side, Seed Audio 1.0 generates full songs and layered soundscapes, Claude shows up inside Slack via Claude TAG, TheWrap experiments with AI microdramas, and we break down a Blender pre-viz plus Seedance 2.0 workflow that makes AI video remarkably controllable. WE ARE NOT SPY. WE NEED FABLE 5 BACK. WE PLEAD.  // Show Links // Anthropic accuses Alibaba of brazenly and illicitly extracting Claude's capabilities (CNBC) https://www.cnbc.com/2026/06/24/anthropic-alibaba-distillation-campaign.html The post that put the espionage story on our radar (unconfirmed single-source thread) https://x.com/S0N_IA/status/2069893802802745673 Apple raises MacBook and iPad prices as the AI memory crunch bites (CNBC) https://www.cnbc.com/2026/06/25/apple-macbook-ipad-price-hike-memory.html OpenAI unveils its first chip, Jalapeno, built with Broadcom (official) https://openai.com/index/openai-broadcom-jalapeno-inference-chip/ No new flagship this week, but OpenAI did ship a GPT-5.5 Instant update https://x.com/OpenAI/status/2069843083701915755 Seed Audio 1.0 generates full songs and layered audio scenes (via fal) https://x.com/fal/status/2070138257891791237 Claude TAG brings Claude into Slack for everyone https://x.com/ashwingop/status/2069814177624121469 Andrej Karpathy on the new Slack workflow https://x.com/karpathy/status/2069822834160124091 Blender pre-viz into Seedance 2.0 for incredible video control (shared by venturetwins) https://x.com/venturetwins/status/2069809200788799582 Original creator of the Blender to Seedance workflow https://x.com/craftcapitallab The full AI Warper workflow breakdown https://x.com/AIWarper/status/2069847773034488262   Join our Discord https://discord.gg/muD2TYgC8f Support us on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans Newsletter https://aiforhumans.beehiiv.com/ Follow us on X @AIForHumansShow https://x.com/AIForHumansShow Find us on TikTok @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Book us for speaking or consultation https://www.aiforhumans.show/