Podcasts about Io

Here are David Aragona and Gino Buccola with a look at the Saturday All-Stakes Pick 3 Play at Woodbine. This video is presented by Morningline.IO.

November Kelly (Kill James Bond!; No Gods No Mayors; Trashfuture; Well There's Your Problem; Be Gay Solve Crimes) enrolls the lads in the 00 program as they cover IO Interactive's 2026 spy-fest: 007: First Light. Topics include the origins of IO, the importance of fluted windows, and what it means to try to maintain a modern franchise featuring nothing but a stupid policeman. November Kelly: Website // Bluesky Kill James Bond! is a comedy film review/ pop culture commentary podcast about the portrayal of masculinity in cinema. November Kelly, Abigail Thorn, and Devon have watched Bond movies, War on Terror movies, Eurospy movies, and now they take on their biggest task yet: Heist Movies. What can Heat, Oceans' 11, and the Fast and Furious movies teach us about how masculinity was imagined by the people who created them? What does that say about Society? Free episodes are released every fortnight on all major podcast platforms and bonus episodes are released for supporters on Patreon on the interstitial weeks. Website // Patreon Listen on Apple Podcasts // Spotify Trashfuture is a podcast about business success and making yourself smarter with the continued psychic trauma of capitalism. Website // Patreon Listen on Apple Podcasts // Spotify Well There's Your Problem is a podcast about engineering disasters and systemic failures, from a leftist perspective, with jokes. Youtube // Instagram // Patreon Listen on Apple Podcasts // Spotify No Gods No Mayors: Municipal government: a trap that catches those too incompetent, too corrupt, too strange even for national politics. And the greatest of these, the mayor - the highest political office one can hope to reach with a truly oppositional personality or a crack addiction. Mattie Lubchansky, Riley Quinn and November Kelly are teaming up to make a podcast investigating these mayors. From petty Bonapartes to flagrant mafiosi, these are their stories. Spotify // Apple Podcasts // RSS Feed Be Gay, Solve Crimes: Join Mia Mulder, November Kelly, and their local librarian Lucy as they investigate pop-culture copaganda and solve the mystery of why they wanted to become detectives before they transitioned. Spotify // Apple Podcasts // Patreon Media Referenced in the Episode: 007: First Light. IO Interactive. 2026. “007 First Light Dev IO Interactive Says It's Now Sold 3 Million Copies, Tracking ‘Well Above Our Forecasts at This Point'” by Wesley Yin-Poole and Simon Cardy. June 6th, 2026. "I've been MANIFESTING this!" 007 First Light's Patrick Gibson REVEALS James Bond casting process by Radio Times Gaming. Jonas Eneroth's Profile. Moby Games. “Long-awaited James Bond game shows how big games have become” by Julie Wurtz and Jesper Dein. Dr.Dk. May 26th, 2026. “The Making of: Hitman: Codename 47” by Edward Love. Retro Gamer. May 17th, 2018. TWOAPW theme by Brendan Dalton: Patreon // brendan-dalton.com // brendandalton.bandcamp.com Interstitial: “Untitled Bond Movie 2026” // Written by A.J. Ditty // feat. Josh Boerman as “Announcer”, Brian Alford as “Q”, A.J. Ditty as “M/Salazar”, Eleanor Philips as “Moneypenny/Norah”, and introducing Devon as “James Bond”.

The Cognitive Crucible is a forum that presents different perspectives and emerging thought leadership related to the information environment. The opinions expressed by guests are their own, and do not necessarily reflect the views of or endorsement by the Information Professionals Association. During this episode, Dave Acosta and Austin Branch discuss IPA's APEX conference which will be September 8–9, 2026 at the CARASOFT facility in Reston VA. As governments, militaries, industries, and societies confront increasingly sophisticated influence operations, disinformation campaigns, and cognitive warfare activities, the need for cognitive security education, research, and professional development has never been greater. APEX 2026 is a two-day educational forum dedicated to advancing the emerging field of cognitive security. Bringing together educators, researchers, students, practitioners, government representatives, and industry leaders, APEX seeks to foster collaboration, strengthen professional expertise, and contribute to the development of future approaches to Operations in the Information Environment (OIE). Recording Date: 19 June 2026 Resources: APEX Conference Link to full show notes and resources Guest Bio:  Austin Branch is a nationally recognized leader in cognitive security, strategic influence, and information operations. A retired Army Officer and senior U.S. government executive, he pioneered the Army's Information Operations career field and served as the first Senior Director for IO in the Office of Special Operations and Low Intensity Conflict. He is the co-founder of the Information Professionals Association and Managing Partner of Crescent Bridge Corporation, advancing cross-sector solutions to achieve cognitive advantage. He also serves as Professor of Practice at the University of Maryland's Applied Research Lab for Intelligence and Security and as an Adjunct Professor at The Citadel, where he teaches Cognitive Security. A contributor to The Cipher Brief, Austin also designs college-level curricula on intelligence and gray zone competition, blending operational insight with academic rigor to mentor the next generation of strategic thinkers. David Acosta is a Board Member of the Information Professionals Association and focuses on the Association's education portfolio. Additionally, Dave serves as a Colonel in the U.S. Army Reserve, currently commanding the 2nd Brigade, 91st Training Division, headquartered in Denver, Colorado. He served at various levels throughout his career from the company/battery level to the Headquarters, Department of the Army G-3/5/7. He commanded the 303d Information Operations (IO) Battalion, 151st Theater IO Group at Camp Parks CA and served as the G3 Information Operations (IO) Chief for the US Army Civil Affairs and Psychological Operations Command (Airborne). He also served as the Assistant Deputy Director for Joint Warfighting Development, Joint Staff J-7 in Suffolk, Virginia. His operational tours include deployments to Kosovo in 1999, Bosnia-Herzegovina in 2002, and Iraq in 2007 and 2009. Additionally, Dave is a Professor of Practice of Technical Operations in the Information Environment at the Naval Postgraduate School in Monterey, California. Dave holds a Bachelors of Science in History (Russian Area) from the US Air Force Academy, a Master of Science in Joint Information Operations from the Naval Postgraduate School, and a Master of Strategic Studies from the Army War College. He is a PhD student of International Studies at Old Dominion University in Norfolk, Virginia. About: The Information Professionals Association (IPA) is a non-profit organization dedicated to exploring the role of information activities, such as influence and cognitive security, within the national security sector and helping to bridge the divide between operations and research. Its goal is to increase interdisciplinary collaboration between scholars and practitioners and policymakers with an interest in this domain. For more information, please contact us at communications@information-professionals.org. Or, connect directly with The Cognitive Crucible podcast host, John Bicknell, on LinkedIn. Disclosure: As an Amazon Associate, 1) IPA earns from qualifying purchases, 2) IPA gets commissions for purchases made through links in this post.

“Mi ha supplicato di fare una foto! Voleva una foto con me a tutti i costi. Non l'avrei fatta, ma mi ha fatto pena!”: queste le parole di Trump che hanno fatto infuriare Meloni, che ha ribattuto: "Io, e l'Italia, non imploriamo mai".Seguici su Facebook e Instagram o abbonati ai nostri podcast cliccando qui. 

In this episode of the Prolonged Field Care Podcast, Dennis sits down with Dr. Mike Falk — pediatric ICU physician with multiple deployments to Iraq, Gaza, and Ukraine — for a raw, practical, deep dive into pediatric care when you're the only asset and evacuation is denied.Most combat medics carry 99% adult gear. Kids still show up. Dr. Falk breaks down the absolute minimalist kit that actually works in austere and combat environments: canine tourniquets for toddlers, the single blue IO you really need, simplified airway choices, push-pull resuscitation with a syringe and stopcock, and a field-expedient needle cric setup.Then he walks through three real cases that expose the brutal decision-making required in prolonged field care:A 4-year-old pulled from rubble with a head injury who decompensates from rising ICPAn 8-year-old with a penetrating chest wound and tension pneumothorax at the thoracoabdominal junctionA 4-year-old with an infected blast wound fracture who develops septic shock days later in a denied environmentYou'll learn weight-based dosing that actually works in the field, why kids decompensate differently, how to mix and run an epinephrine drip with limited supplies, the realities of black-tagging children in mass casualty events, and why these cases stay with providers long after the mission.Key Takeaways:The truly minimalist pediatric kit that won't break your weight limitPractical field management of rising ICP when you have no CT or neurosurgeryPush-pull volume resuscitation and epinephrine drip mixing for pediatric shockWhy penetrating trauma at the 6th–7th rib level is often thoracoabdominalThe emotional and ethical weight of black-tagging kids — and why you must train itMalnutrition's hidden impact on wound healing and sepsis in prolonged scenariosChapters00:00 - Welcome & Why Most Medics Are Unprepared for Pediatric Patients00:57 - The Bare Essential Pediatric Combat Medic Bag02:25 - Canine Tourniquet for Under-2s & Minimalist Hemorrhage Control02:25 - Vascular Access: Why the Blue IO is Usually All You Need03:22 - Simplified Airway: OPAs, NPAs & i-gel Sizes That Actually Matter03:22 - ET Tubes: Why Only 4.0, 5.0 & 6.0 Cuffed Are Necessary04:24 - Push-Pull Resuscitation Technique (Syringe + Stopcock)04:56 - Needle Cricothyrotomy Setup & Critical I:E Ratio Warning07:09 - Case 1 Begins: 4-Year-Old Blast Victim Pulled from Rubble08:47 - Initial Assessment, C-Spine Considerations in Kids & Access12:16 - GCS 11, Pain Control & Why Fluids Make Sense Early14:17 - Hours Later: Decompensation & Rising ICP18:17 - Positioning, Hypertonic Saline Dosing (5 mL/kg) & Decision to Intubate23:13 - Ketamine-Only Intubation, Permissive Hyperventilation & Realities27:51 - The Emotional Toll: Black Tagging Kids in MCI29:44 - Case 2: 8-Year-Old with Right Chest GSW & Tension Pneumothorax31:36 - Chest Seal + Needle Decompression (Anterior Approach Preference)34:23 - Blood Resuscitation (10 mL/kg) & Why Location Matters (Diaphragm Level)40:20 - Case 3: 4-Year-Old with Infected Blast Wound Fracture – Septic Shock42:51 - Broad-Spectrum Antibiotics & Source Control in Denied Environments45:26 - Push-Pull Boluses, Epinephrine Drip Mixing & Permissive Hypotension51:09 - Malnutrition's Impact on Healing & Infection in Prolonged Care56:49 - Final Lessons: Training Black Tags, Calling for Help & Provider PTSD57:32 - Outro & Where to Find More PFC ContentFor more content, go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.prolongedfieldcare.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Consider supporting us: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠patreon.com/ProlongedFieldCareCollective⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.lobocoffeeco.com/product-page/prolonged-field-care⁠

Fluent Fiction - Italian: Love and Art Bloom in Firenze's Mercato: A Tale of Defiance Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2026-06-22-07-38-19-it Story Transcript:It: Il sole di mezzogiorno splendeva sul mercato affollato di Firenze.En: The midday sun shone over the crowded mercato of Firenze.It: I colori brillanti dei fiori e gli aromi invitanti del pane fresco si mescolavano mentre la gente si muoveva da un banco all'altro.En: The bright colors of the flowers and the inviting aromas of fresh bread mingled as people moved from one stall to another.It: Questo era il cuore pulsante della città durante l'estate, un luogo dove desideri e sogni segreti potevano nascere tra gli sguardi fugaci.En: This was the beating heart of the city during the summer, a place where desires and secret dreams could be born among fleeting glances.It: Lucia, una giovane donna dai capelli scuri e occhi curiosi, camminava con passo deciso.En: Lucia, a young woman with dark hair and curious eyes, walked with a determined stride.It: Adorava esplorare il mercato.En: She loved exploring the mercato.It: Per lei, era un mondo pieno di possibilità.En: To her, it was a world full of possibilities.It: Mentre passava tra le bancarelle, il suo sguardo si soffermò su un banco di fiori.En: As she passed among the stalls, her gaze lingered on a flower stall.It: Rose, tulipani e gigli erano sistemati con cura, creando un arcobaleno di bellezza.En: Roses, tulips, and lilies were carefully arranged, creating a rainbow of beauty.It: Dietro il banco c'era Matteo, un giovane con gli occhi profondi e le mani macchiate di colore.En: Behind the stall was Matteo, a young man with deep eyes and hands stained with color.It: Era lì per aiutare, ma la sua vera passione era la pittura.En: He was there to help, but his true passion was painting.It: Ogni petalo, ogni sfumatura di colore era un quadro che prendeva forma nella sua mente.En: Every petal, every shade of color was a painting taking shape in his mind.It: I loro occhi si incontrarono.En: Their eyes met.It: Un momento, un istante, che sembrò dilatarsi.En: A moment, an instant, that seemed to stretch.It: Lucia si avvicinò, affascinata dai fiori e dal giovane che li vendeva.En: Lucia approached, fascinated by the flowers and the young man selling them.It: "Ciao, questi fiori sono bellissimi," disse sorridendo.En: "Hello, these flowers are beautiful," she said, smiling.It: "Grazie," rispose Matteo, cercando di nascondere l'emozione.En: "Thank you," replied Matteo, trying to hide his emotion.It: "Mi piacciono molto i colori. Sono un pittore... per passione."En: "I really like colors. I'm a painter... by passion."It: “Amo la poesia,” ammise Lucia.En: "I love poetry," admitted Lucia.It: “Ma è difficile per una donna trovare il coraggio di seguire i propri sogni.”En: "But it's hard for a woman to find the courage to follow her dreams."It: Si scambiarono racconti, sogni e speranze.En: They exchanged stories, dreams, and hopes.It: Lucia voleva avventura oltre le mura della città.En: Lucia wanted adventure beyond the city walls.It: Matteo sognava un mondo fatto di arte, oltre gli affari di famiglia.En: Matteo dreamed of a world made of art, beyond the family business.It: In quel momento, capirono che si erano trovati.En: In that moment, they understood they had found each other.It: Decisero di incontrarsi in segreto, lontano dagli occhi giudicanti.En: They decided to meet in secret, away from judging eyes.It: Si trovavano spesso nella biblioteca o nei giardini nascosti.En: They often met in the library or hidden gardens.It: Condividevano poesie e disegni, idee e visioni per un futuro diverso.En: They shared poems and drawings, ideas, and visions for a different future.It: Un giorno, durante un festival notturno nel mercato, con la luna come testimone, decisero di non nascondersi più.En: One day, during a nighttime festival in the mercato, with the moon as a witness, they decided to hide no longer.It: Davanti a una folla di gente, Lucia recitò una poesia dedicata ai sogni e Matteo mostrò un quadro di fiori e luce.En: In front of a crowd of people, Lucia recited a poem dedicated to dreams, and Matteo showcased a painting of flowers and light.It: La loro audacia suscitò sguardi sorpresi e mormorii, ma non si sentirono soli.En: Their boldness elicited surprised looks and murmurs, but they did not feel alone.It: Con coraggio, Lucia disse: "Voglio seguire il mio cuore."En: With courage, Lucia said, "I want to follow my heart."It: Matteo la prese per mano e dichiarò: "Io dipingerò un mondo nostro."En: Matteo took her hand and declared, "I will paint a world of our own."It: Sfidarono le aspettative e le convenzioni.En: They defied expectations and conventions.It: Diventarono un simbolo di libertà.En: They became a symbol of freedom.It: Lucia iniziò a scrivere le sue poesie senza paura.En: Lucia began to write her poems without fear.It: Matteo continuò a dipingere, trovando la propria voce.En: Matteo continued to paint, finding his own voice.It: Insieme, crearono una nuova vita, seguendo l'amore e le passioni che li avevano uniti.En: Together, they created a new life, following the love and passions that had united them.It: E mentre il mercato continuava a brulicare di vita, Lucia e Matteo camminavano fianco a fianco, pronti ad affrontare il mondo, insieme.En: And while the mercato continued to teem with life, Lucia and Matteo walked side by side, ready to face the world, together. Vocabulary Words:the midday: il mezzogiornothe market: il mercatothe crowd: la follathe aroma: l'aromafleeting: fugacethe glance: lo sguardocurious: curiosoto linger: soffermarsithe stall: la bancarellato arrange: sistemarethe rainbow: l'arcobalenothe petal: il petalothe shade: la sfumaturato fascinate: affascinareto admit: ammetterethe courage: il coraggioto exchange: scambiarethe adventure: l'avventurathe wall: il muroto dream: sognarethe business: l'affareto meet: incontrarsito hide: nasconderethe crowd: la follathe moon: la lunathe witness: il testimoneto recite: recitareto showcase: mostrarethe boldness: l'audaciato defy: sfidare

JDK 26 optimise la JVM dans ses moindres recoins, le SDK Java d'Agent2Agent passe en 1.0, Micronaut 5 est là. Côté terrain, un retour d'expérience après 40 jours à coder avec 100 % d'IA : génie ou junior, Alzheimer numérique et dette technique invisible. Pendant ce temps, GitLab restructure, Microsoft suspend ses licences Claude Code, et un développeur injecte un prompt destructeur dans sa lib JUnit. La révolution IA a un coût et les boites commencent à s'en rendre compte. Enregistré le 12 juin 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-341.mp3 ou en vidéo sur YouTube. News Langages Les améliorations de performance dans le JDK 26 https://inside.java/2026/06/09/jdk-26-performance-improvements/ Côté bibliothèques, l'API LazyConstant (anciennement StableValue) fait son entrée en prévisualisation pour permettre une initialisation paresseuse, sécurisée pour les threads et optimisée par le mécanisme de constant-folding de la JVM. L'extraction de chaînes de caractères via MemorySegment::getString a été revue pour réduire considérablement les allocations intermédiaires et les copies en mémoire off-heap, accélérant fortement les traitements sur les chemins critiques (hot paths). La méthode générée automatiquement hashCode() pour les classes de type record a été optimisée par la JVM pour atteindre un niveau de performance équivalent à une implémentation écrite manuellement. Le ramasse-miettes G1 bénéficie du JEP 522 qui redessine sa table de cartes (card-table) afin de réduire les coûts de synchronisation des barrières d'écriture, offrant un gain de débit de 5 % à 15 % sur les applications manipulant énormément de références d'objets. Grâce au JEP 516 (Project Leyden), le cache d'objets Ahead-of-Time (AOT) adopte un format de flux agnostique, ce qui lui permet d'être compatible avec n'importe quel Garbage Collector, y compris le ramasse-miettes à très faible latence ZGC. Le démarrage de la JVM s'accélère par défaut lorsqu'aucune taille de tas n'est configurée, car HotSpot n'applique plus de pourcentage initial (InitialRAMPercentage) mais démarre directement avec la taille minimale (MinHeapSize) pour éviter d'allouer des métadonnées inutiles. Les threads virtuels gagnent en robustesse en étant désormais capables de céder la main (yield) pendant les phases d'initialisation des classes, éliminant ainsi le risque de famine des threads porteurs (carrier threads). Le compilateur C2 JIT améliore son modèle de coût pour la vectorisation des boucles (SIMD) et se montre maintenant capable de compiler et d'optimiser des méthodes dotées de listes de paramètres extrêmement longues. Librairies Release candidate du A2A Java SDK supportant versions 0.3 et 1.0 en même temps https://medium.com/google-cloud/a2a-java-sdk-1-0-0-cr1-released-f0c651ec9139 Dernière étape avant la GA : Toutes les fonctionnalités prévues pour la version 1.0 sont finalisées. Migration simplifiée depuis la Beta1. Compatibilité v0.3 : Ajout d'une couche de compatibilité permettant aux agents v1.0 de communiquer avec les systèmes v0.3 (via JSON-RPC, gRPC ou REST). Support natif pour Android (nouvel AndroidHttpClient). Uniformisation des clients HTTP pour garantir une cohérence entre les versions. Nouveau parseur SSE (Server-Sent Events) conforme aux spécifications. Ça y est, le SDK Java de l'Agent 2 Agent Protocol est sorti en version 1.0 finale ! (avec compatibilité v0.3 et v1.0) https://medium.com/google-cloud/a2a-java-sdk-1-0-0-final-released-10c05b6aee34 Lancement officiel : Sortie de A2A Java SDK 1.0.0.Final, la première version stable (GA) du protocole Agent2Agent. Objectif du protocole : Standard ouvert (Linux Foundation) permettant aux agents IA de communiquer, déléguer des tâches et collaborer, indépendamment du langage ou du framework. Interopérabilité : Introduction de l'Integration Test Kit (ITK) pour valider la compatibilité entre les SDK (Java, Python, TypeScript, etc.). Transports supportés : Support complet et équivalent pour JSON-RPC, gRPC et HTTP+JSON/REST. Alignement total avec la spécification A2A 1.0.0. Passage aux Java records pour l'immutabilité et moins de code répétitif. Architecture interne basée sur un MainEventBus pour garantir la persistance et éviter les conditions de concurrence. Intégration d'OpenTelemetry pour le suivi et la surveillance. Support d'Android et compatibilité descendante avec la version 0.3. Installation : Gestion des dépendances via Maven BOM (org.a2aproject.sdk). Sortie de Micronaut 5.0 https://micronaut.io/2026/05/20/micronaut-framework-5-0-0-released/ Lancement majeur : Disponibilité générale de Micronaut 5, incluant une refonte de plus de 70 modules et la plateforme BOM. Baselines techniques : Support de Java 25, Groovy 5, Kotlin 2.3 et GraalVM 25.0.3. Optimisations internes : Amélioration significative des performances au démarrage et réduction de la surcharge à l'exécution via une refonte du conteneur IoC et du traitement à la compilation. Architecture HTTP : Support stable de HTTP/3, nouvelle API de formulaires (multipart) et annotations de nullabilité (JSpecify) pour une meilleure interopérabilité Kotlin/IDE. Configuration : Nouveau système d'importation de configuration (remplaçant le Bootstrap Configuration) et validateur de schéma JSON intégré. Fiabilité : Nouvelles API programmatiques pour les politiques de retry et circuit breaker. Sécurité & Outils : Mise à jour majeure des dépendances (Jackson 3, Ktor 3), rafraîchissement du Panneau de contrôle et diagnostics AOT améliorés. Écosystème : Mises à jour complètes pour les bases de données (Data, SQL, R2DBC, MongoDB, Redis), le cloud (AWS, Azure, GCP, OCI) et les tests (JUnit 6, Testcontainers 2.0). Évolutions notables : Intégration HTMX dans Micronaut Views, retrait du support RxJava 2 et migration de divers processeurs d'annotations vers des modules dédiés. Comment rajouter un agent IA dans une app Android, avec le tout nouveau framework ADK pour Kotlin https://glaforge.dev/posts/2026/05/21/wiring-adk-kotlin-agents-in-an-android-application/ Guillaume a participé au développement et au lancement du nouveau runtime ADK pour Kotlin et Android https://developers.googleblog.com/adk-kotlin-android-building-ai-agents/ Tutoriel sur comment intégrer un agent ADK dans une app Dépendances : Ajout du noyau ADK (google-adk-kotlin-core) et du processeur KSP dans build.gradle.kts. Sécurité API : Utilisation de local.properties pour stocker la clé API Gemini et l'exposer via BuildConfig afin d'éviter le hardcoding. Définition de l'agent : Création d'un objet LlmAgent configuré avec le modèle Gemini, des instructions spécifiques et des outils (ex: GoogleSearchTool). Utilisation de InMemoryRunner pour gérer automatiquement le contexte et l'historique de la session. Implémentation de runAsync avec StreamingMode.SSE pour un retour en temps réel dans l'interface. Threading : Exécution des requêtes réseau sur Dispatchers.IO et mise à jour de l'état de l'interface utilisateur sur Dispatchers.Main. Comment développer et hoster des agents IA sur la plateforme d'agents managés de DeepMind https://glaforge.dev/posts/2026/05/21/managed-agents-with-the-gemini-interactions-java-sdk/ L'équipe DeepMind de Google a lancé une plateforme d'agents managés sur son API Gemini Interactions https://blog.google/innovation-and-ai/technology/developers-tools/managed-agents-gemini-api/ Guillaume a implémenté un SDK Java pour utiliser cette API Gemini Interactions, qui donne entre autre accès à tous les modèles mais aussi à cette plateforme managée d'agents IA Agents managés : Permet d'exécuter des agents autonomes qui raisonnent, planifient et exécutent du code dans des environnements isolés (sandboxes), sans gestion d'infrastructure par le développeur. Environnement distant : Utilise des espaces de travail Linux éphémères dans le cloud via le paramètre remote, permettant l'accès réseau et la persistance des fichiers sur plusieurs appels. Agents prédéfinis : Accès immédiat à des agents spécialisés comme deep-research-pro (recherche multi-étapes) ou antigravity (tâches de codage généralistes). Agents personnalisés : Possibilité de configurer ses propres agents avec des instructions système dédiées, des outils spécifiques (exécution de code, recherche Google) et des règles réseau (egress) personnalisées. Architecture basée sur les étapes (Steps) : Utilise une structure de données typée (Step, Content) pour suivre le raisonnement de l'agent, ses appels de fonctions et ses résultats en temps réel. Outils et Schémas : Inclut des utilitaires pour générer des schémas JSON complexes via une interface fluide (DSL), par réflexion Java ou par parsing JSON. Streaming réactif : Support natif des événements en temps réel (SSE) pour suivre la progression de l'agent et recevoir les deltas de contenu au fur et à mesure de la génération. Flexibilité : Fournit un gestionnaire de routage (InteractionsHandler) pour créer facilement des serveurs proxy ou des backends intermédiaires traitant les interactions Gemini. Spring Boot 4.1 https://github.com/spring-projects/spring-boot/wiki/Spring-Boot-4.1-Release-Notes Support natif pour Spring gRPC permettant de créer et tester facilement des applications clientes et serveurs basées sur Netty ou des Servlets via HTTP/2 Introduction du lazy fetching pour les connexions JDBC via la propriété spring.datasource.connection-fetch=lazy afin de ne prendre une connexion du pool que lorsqu'un Statement est réellement exécuté Amélioration de l'auto-configuration de Jackson permettant de définir globalement les contraintes de lecture/écriture pour les formats JSON, XML et CBOR via des propriétés de configuration Sécurisation des clients HTTP bloquants et réactifs face aux attaques SSRF grâce à l'introduction d'un InetAddressFilter bloquant les requêtes sortantes vers des adresses spécifiques Améliorations majeures autour d'OpenTelemetry avec le support complet des variables d'environnement OTel, la possibilité de désactiver le SDK via une propriété globale et l'ajout du support SSL sur les exporters OTLP Ajout de l'auto-configuration pour l'utilisation de Spring Batch avec MongoDB incluant un nouveau starter dédié spring-boot-batch-data-mongo Auto-configuration des endpoints @RedisListener sans nécessiter la déclaration manuelle d'un RedisMessageListenerContainer Dépréciation du support de Apache Derby (projet arrêté), suppression définitive du mode layertools du JAR et réintroduction du support de Spock 2.4 (avec Groovy 5) Upgrade des dépendances majeures de l'écosystème avec notamment Spring Framework 7.0.8, Spring Security 7.1.0 et Micrometer 1.17.0 Outillage Vous êtes plutôt endive ou chicorée ? La librairie Chicory qui permet d'exécuter du code WASM à partir de son application Java est forkée et rejointe la Bytecode Alliance pour continuer son développement https://bytecodealliance.org/articles/endive-and-the-next-chapter-of-webassembly-on-the-jvm Annonce d'Endive : Nouveau projet hébergé par la Bytecode Alliance ; fork de Chicory (moteur WebAssembly pur Java, sans dépendance native). ​Objectif principal : Permettre aux développeurs Java d'intégrer, charger et déployer des modules Wasm nativement via les workflows Java habituels. ​Compilateur "Redline" : Intégration à venir de Redline (basé sur Cranelift) pour compiler le Wasm en code machine natif ; performances comparables à Rust/Wasmtime. ​Zéro dépendance (Java 25+) : Grâce à l'API standard Foreign Function & Memory (Project Panama), l'exécution à vitesse native se fait sans composants externes. ​Modèle de Composants (Component Model) : Support futur prévu pour consommer des composants (Rust, Go, JS, etc.) via des interfaces typées et sécurisées directement dans la JVM. ​Prochaines étapes : Fusion de Redline, conformité stricte aux specs Wasm (dont WasmGC) et amélioration du support WASI. Un visualisateur de sessions de travail avec Antigravity https://glaforge.dev/posts/2026/06/11/antigravity-brain-visualizer/ Un projet open source construit avec Micronaut, LangChain4j et GraalVM pour analyser les sessions de travail avec l'outil de développement agentique Antigravity (de Google) Analyse toutes les étapes, les requêtes utilisateur, les outils utilisés, les erreurs rencontrées, les réponses du modèle Gemini fait une analyse pour comprendre les moments clés de cette session de travail Outil buildé avec l'aide d'Antigravity lui-même SBX-Kits : des environnements de développement simplifiés pour les débutants (et les autres) https://k33g.org/20260501-sbx-kits.html Philippe Charrière (:whale: ) présente SBX-Kits (Sandbox Kits), une initiative personnelle visant à simplifier radicalement la mise en place d'environnements de développement pour les débutants, en éliminant la complexité d'installation des outils traditionnels. Chaque "kit" est une archive prête à l'emploi contenant un outil de développement spécifique (comme un langage, un framework ou une base de données) configuré pour s'exécuter de manière isolée et portable. La philosophie du projet repose sur le principe de "zéro configuration" et "zéro dépendance globale", permettant de tester une technologie ou de commencer à coder immédiatement sans polluer son système d'exploitation. L'approche technique s'appuie sur des scripts légers et des binaires portables pré-packagés, offrant une alternative plus simple et moins gourmande en ressources que les conteneurs Docker ou les configurations d'IDE complexes pour l'apprentissage. L'objectif à terme est de proposer un catalogue de kits couvrant les technologies courantes (JavaScript, Python, petites bases de données) pour faciliter les ateliers de programmation et le prototypage rapide. De nombreux kits sont disponibles sur https://github.com/docker/sbx-kits-contrib ghui: une interface utilisateur en ligne de commande (TUI) interactive pour GitHub https://github.com/kitlangton/ghui ghui est un outil en ligne de commande (TUI) écrit en Rust qui fournit une interface visuelle, interactive et rapide directement dans le terminal pour interagir avec GitHub. Il permet de gérer ses pull requests, ses issues et ses notifications sans avoir à ouvrir son navigateur web ou à taper de longues commandes avec la CLI officielle de GitHub. L'outil propose une navigation fluide au clavier, des raccourcis efficaces, et permet de réaliser des actions courantes comme valider une PR, ajouter des commentaires, attribuer des reviewers ou inspecter les logs des GitHub Actions. Conçu pour être extrêmement réactif, ghui s'intègre naturellement dans le flux de travail des développeurs adeptes du terminal et du mode "sans souris". Sortie de Homebrew 6.0.0 https://brew.sh/2026/06/11/homebrew-6.0.0/ Introduction du mécanisme de sécurité Tap Trust : comme les dépôts tiers (taps) peuvent exécuter du code Ruby arbitraire non sandboxé sur la machine, Homebrew demande désormais une confiance explicite de l'utilisateur avant d'évaluer ou d'exécuter leur code. L'API JSON interne devient le choix par défaut, offrant un système plus léger et beaucoup plus rapide pour les développeurs. Sécurisation renforcée de l'environnement avec l'implémentation du sandboxing sur Linux. Évolution des comportements par défaut basés sur un sondage utilisateur : le mode "ask" est activé par défaut pour les développeurs, affichant un résumé des dépendances et une demande de confirmation avant toute action de brew install ou brew upgrade. Améliorations notables des performances globales, notamment un boost de ~30 % sur la vitesse de la commande brew leaves et la parallélisation de la récupération des bottles (binaires) lors des mises à jour. Ajout du support initial pour la prochaine version d'Apple, macOS 27 (Golden Gate). Multiples optimisations pour brew bundle, incluant une gestion plus sécurisée des installations de paquets npm. Méthodologies Retour d'expérience très détaillé et 100% humain sur 40 jours avec une équipe 100% AI hormis le superviseur https://www.linkedin.com/pulse/jai-vir%C3%A9-mon-%C3%A9quipe-de-dev-pour-une-100-ia-pendant-40-luc-bonnin-jlgjf/ Voici le résumé en bullet points : Expérimentation de 40 jours : remplacer une équipe de dev par 100% IA agentique (Cursor) sur un vrai projet en production (playthatsheet.com, 200k lignes de code legacy) Chiffres bruts : 2,3 milliards de tokens consommés, 1 477 prompts, 260 564 lignes ajoutées (+145%), 59% du code final produit par l'IA ROI vertigineux à court terme : 9 mois de travail humain livrés en 40 jours, coût total 260$ d'abonnement + 15 jours de supervision, ROI x18 Profil psy de l'IA : Alzheimer (oublis de contexte), schizophrène (change de méthodo), ado de 12 ans (refait les mêmes erreurs), oscille entre génie et junior sans prévenir Effet iceberg : la dette technique ne disparaît pas, elle se camoufle et s'accélère ; hallucinations = bombes à retardement détectables uniquement par relecture humaine ligne par ligne Paradoxe du bateau de Thésée : perte de paternité et de maîtrise fine du code, baisse de l'autonomie du dev humain qui valide sans avoir construit Arnaque du "monkey money" : consommation de tokens opaque, non corrélée à la complexité (écart de 350% sur des prompts identiques), facturation imprévisible donc impossible à budgéter Syndrome du bazooka : les devs utilisent l'IA même pour changer une couleur CSS, atrophie progressive des compétences et coût écologique délirant Risque stratégique : dépendance irréversible aux vendeurs de tokens (Nvidia, Anthropic, OpenAI), business non rentable qui devra augmenter ses prix Conseil final : approche Pareto, garder 20% du temps en code "fait main", nommer un responsable stratégie IA, l'humain senior reste irremplaçable pour superviser Une libraries de test JUnit cache un prompt qui demande aux coding agents d'effacer les tests https://arstechnica.com/security/2026/05/fed-up-with-vibe-coders-dev-sneaks-data-nuking-prompt-injection-into-their-code/ Agacé par les « vibe coders », un développeur introduit une injection de prompt destructrice dans son code Le développeur de jqwik (un moteur de tests pour JUnit 5) a volontairement inséré une injection de prompt dans la version 1.10.0 de sa bibliothèque Java pour saboter le travail des agents d'IA. L'instruction injectée via la sortie standard (stdout) ordonne textuellement aux LLM d'ignorer les consignes précédentes et de supprimer l'intégralité du code et des tests jqwik du projet. Pour dissimuler cette action aux yeux des développeurs humains, le mainteneur a utilisé des séquences d'échappement ANSI qui effacent la ligne d'injection dans les émulateurs de terminaux interactifs. La modification a été découverte par un utilisateur qui a pointé du doigt les risques majeurs et disproportionnés pour les machines des utilisateurs, bien que certains outils comme Claude d'Anthropic aient détecté et bloqué la consigne malveillante. Face aux critiques de la communauté et aux accusations de comportement infantile ou potentiellement illégal, le développeur a mis à jour ses notes de version pour documenter explicitement son opposition à l'usage de son outil par des IA, avant de refuser tout commentaire supplémentaire sur conseil de son avocat. La réalité du rôle de Principal Engineer https://leaddev.com/career-development/reality-being-principal-engineer Le passage au rôle de Principal Engineer marque une transition majeure où les compétences techniques ne suffisent plus, l'impact se mesurant désormais à travers l'influence, la stratégie et la capacité à aligner la technique avec les objectifs business. Contrairement aux attentes, le quotidien est souvent marqué par une forme d'isolement, car le poste se situe à l'intersection de la direction (qui attend des solutions) et des équipes techniques (qui attendent des directives), sans appartenance directe à un groupe précis. Le rôle exige d'accepter une grande part d'ambiguïté et l'absence de retours immédiats, les projets et les décisions stratégiques mettant parfois des mois ou des années à porter leurs fruits. La gestion du temps devient un défi critique, nécessitant de savoir naviguer entre les sollicitations constantes, la présence en réunion et le besoin de préserver des moments de réflexion approfondie pour concevoir des visions à long terme. La réussite à ce niveau repose sur le développement de compétences humaines pointues (soft skills), notamment la négociation, la communication vulgarisée auprès des profils non techniques, et la capacité à faire grandir les autres ingénieurs par le mentorat. Sécurité Une attaque de la chaîne d'approvisionnement npm utilise binding.gyp pour compromettre des dizaines de paquets https://cybersecuritynews.com/binding-gyp-supply-chain-attack-compromises-dozens-of-npm-packages/ Une nouvelle variante du ver auto-propageable "Shai-Hulud", baptisée "Miasma", cible l'écosystème npm (et PyPI sous le nom de "Hades") en dissimulant son exécution dans le fichier binding.gyp au lieu des scripts classiques preinstall ou postinstall. La technique, surnommée "Phantom Gyp", exploite le fait que npm lance automatiquement node-gyp rebuild dès qu'un fichier binding.gyp est présent à la racine d'un paquet pour compiler des modules natifs C/C++, exécutant ainsi le code malveillant dès la commande npm install. L'attaque contourne la plupart des outils de sécurité traditionnels car l'injection s'appuie sur l'évaluation récursive de commandes (via la syntaxe ) ou directement sur la fonction eval() de Python sous-jacente à GYP, cachée sous n'importe quelle clé du fichier. Le script malveillant télécharge un runtime alternatif (Bun) pour échapper aux détections comportementales de Node.js, puis moissonne les identifiants et secrets des développeurs et des environnements CI/CD (npm, GitHub, AWS, GCP, Azure, Kubernetes, HashiCorp Vault). Plus de 57 paquets npm (dont le SDK serveur de Vapi ou des outils liés à l'IA) et des dizaines de paquets PyPI ont été infectés via des comptes de mainteneurs compromis, le ver republiant automatiquement de nouvelles versions vérolées en utilisant les jetons volés. Loi, société et organisation Restructuration chez Gitlab https://about.gitlab.com/blog/gitlab-act-2/ GitLab entame une restructuration majeure pour s'adapter à l'ère de l'intelligence artificielle agentique, incluant une réduction d'effectifs planifiée de manière transparente et ouverte. L'entreprise prévoit de réduire de 30 % le nombre de pays où elle maintient de petites équipes, d'aplatir sa hiérarchie en supprimant jusqu'à trois niveaux de gestion, et de réorganiser la R&D en une soixantaine d'équipes plus petites et autonomes. Les processus internes vont être revus en intégrant des agents d'IA pour automatiser les revues, les approbations et les passages de relais afin d'accélérer le rythme de travail. La stratégie repose sur la conviction que le logiciel sera bientôt écrit par des machines et dirigé par des humains, ce qui va multiplier la demande de logiciels et transformer le rôle des ingénieurs vers la résolution de problèmes complexes. Sur le plan technique, GitLab reconstruit son infrastructure sous-jacente (notamment Git) pour supporter la charge massive générée par les agents d'IA, tout en misant sur l'orchestration du cycle de vie, la centralisation du contexte des données et une gouvernance intégrée. Le modèle économique évolue vers un système hybride combinant les abonnements classiques et une tarification à la consommation pour le travail effectué par les agents d'IA. Un LLM local sur un mac pourrait coûter plus cher en électricité qu'un modèle hébergé sur OpenRouter dans le cloud https://www.williamangel.net/blog/2026/05/17/offline-llm-energy-use.html Conclusion : L'inférence locale sur Mac M5 Max est 3x plus chère et 2x plus lente que le cloud (OpenRouter). Électricité : Négligeable (~0,02 $/heure pour 50-100W). Matériel (Le vrai coût) : Achat du Mac à 4 299 $; l'amortissement sur 3 à 5 ans plombe la rentabilité horaire. Coût au million de tokens (Gemma 4 31b) : Mac M5 Max : 0,40 à4, 79 (pour 10-40 tokens/s). OpenRouter : 0,38 à0, 50 (pour 60-70 tokens/s). Verdict pro : Le temps humain perdu à cause de la lenteur locale coûte infiniment plus cher que les tokens cloud. Privilégier les API (Anthropic, OpenRouter). Ai didn't kill your junior pipeline https://andrewmurphy.io/blog/ai-didnt-kill-your-junior-pipeline-you-did L'IA n'a pas tué le recrutement des juniors, les entreprises l'ont fait elles-mêmes, par effet de mode. Sans juniors, pas de futurs seniors : on retire l'échelle qui nous a tous fait monter. Tout le monde pêche dans le même bassin de seniors sans le réapprovisionner, pénurie garantie dans 3-5 ans. Une équipe 100% senior + IA est fragile : un départ et tout le savoir tacite s'évapore. Les juniors posent les "pourquoi ?" qui révèlent les bugs et processus absurdes ; l'IA, elle, exécute sans questionner. Les seniors s'atrophient aussi en déléguant leur réflexion à l'IA, pince à double effet sur les compétences. Dépendre des outils IA, c'est sous-traiter sa stratégie talents à des fournisseurs dont les prix vont tripler. Solution : redéfinir le rôle junior (revue de code IA + mentorat), pas le supprimer. Les rapports internes de Microsoft révèlent la crise des coûts de l'IA : les agents coûtent plus cher que les employés humains https://fortune.com/2026/05/22/microsoft-ai-cost-problem-tokens-agents/ Des données et rapports internes chez Microsoft et d'autres géants de la tech ébranlent la promesse de rentabilité de l'IA, révélant que le déploiement d'agents autonomes à l'échelle de l'entreprise revient souvent plus cher que de payer des humains pour le même travail. Le modèle de tarification à l'usage (basé sur les tokens) se heurte à la nature même des architectures agentiques : contrairement à un simple chatbot, un agent boucle, enchaîne les appels d'outils, crée des sous-agents et auto-évalue son code, ce qui multiplie la consommation de tokens par un facteur de 5 à 30, voire jusqu'à 1 000 fois pour des tâches de programmation complexes. L'impact financier sur les budgets de calcul cloud est immédiat ; par exemple, Uber a entièrement épuisé l'intégralité de son budget annuel 2026 dédié au codage par IA en l'espace de seulement quatre mois. Face à cette explosion des coûts, des retours en arrière drastiques sont observés : Microsoft a ainsi commencé à suspendre une grande partie de ses licences internes Claude Code pour rediriger d'urgence ses milliers de développeurs vers sa propre solution moins onéreuse, GitHub Copilot CLI. Les directeurs techniques (CTO) et acheteurs de solutions logicielles qui ont signé des contrats pluriannuels basés sur des projections de réduction de masse salariale se retrouvent pris au piège, les gains réels de productivité ne parvenant pas à compenser les factures d'infrastructure exorbitantes. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 15 juin 2026 : Jupyter Workshops: Demystifying MyST Markdown in Education - Orsay (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 23-24 juin 2026 : MWCP 2026 - Paris (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 26-27 juin 2026 : LeHACK - Paris (France) 27 juin 2026 : Asynconf - Paris (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2 juillet 2026 : MCP Connect Travel Edition - Paris (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 28-30 août 2026 : State of the Map - Champs-sur-Marne (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 10-11 septembre 2026 : Nantes Craft - Nantes (France) 17 septembre 2026 : dotAI - Paris (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 18 septembre 2026 : dotJS - Paris (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 22 septembre 2026 : Salon Data 2026 - Nantes (France) 22-23 septembre 2026 : Agile en Seine & IA 2026 - Paris (France) 24 septembre 2026 : OWASP AppSec Days France 2026 - Paris (France) 24 septembre 2026 : PlatformCon Paris - Paris (France) 24 septembre 2026 : React Native Connection 2026 - Paris (France) 24-26 septembre 2026 : Paris Web 2026 - Paris (France) 25 septembre 2026 : SAP Inside Track Paris 2026 - Paris (France) 28-29 septembre 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 2 octobre 2026 : DevFest Perros-Guirec 2026 - Perros-Guirec (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 8-9 octobre 2026 : Forum PHP 2026 - Marne-la-Vallée (France) 12 octobre 2026 : Dev With AI - Paris (France) 22-23 octobre 2026 : Agile Tour Bordeaux 2026 - Bordeaux (France) 26 octobre 2026 : Agile Tour Montpellier - Montpellier (France) 27-29 octobre 2026 : Directions EMEA 2026 - Paris (France) 29-30 octobre 2026 : BDX I/O 2026 - Bordeaux (France) 29-30 octobre 2026 : Agile Tour Nantais 2026 - Nantes (France) 29 octobre 2026-1 novembre 2026 : Pycon FR - Biarritz (France) 30 octobre 2026 : Cloud Nord 2026 - Lille (France) 4-5 novembre 2026 : Devoxx Morocco - Casablanca (Morocco) 14-15 novembre 2026 : Capitole du Libre - Toulouse (France) 19 novembre 2026 : DevFest Toulouse 2026 - Toulouse (France) 19 novembre 2026 : Agile Laval 2026 - Laval (France) 19 novembre 2026 : OVHcloud Summit - Paris (France) 19 novembre 2026 : Codeurs en Seine - Rouen (France) 27 novembre 2026 : DevFest Paris 2026 - Paris (France) 1-3 décembre 2026 : Apidays Paris - Paris (France) 2-3 décembre 2026 : Cloud Native AI Summit Europe - Paris (France) 4 décembre 2026 : DevFest Lyon 2026 - Lyon (France) 4 décembre 2026 : DevFest Dijon 2026 - Dijon (France) 9-10 décembre 2026 : OpenSource Expérience - Paris (France) 9-10 décembre 2026 : DevOps REX - Paris (France) 10 décembre 2026 : KCD Provence - Aix-en-Provence (France) 7-9 avril 2027 : Devoxx France 2027 - Paris (France) 3 juin 2027 : Cloud Native Days France 2027 - Paris (France) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

Ellie GMs for Beth, Crash, Io, & Jen. This episode: The adventurers return from the underground city to learn there’s a mystery on the surface, as well. Follow this series on… RSS: https://aaronbsmith.com/cogwheel/tag/plus-ultra-s2/feed/ Patreon: https://www.patreon.com/cogwheelgaming Mastodon: https://is.aaronbsmith.com/@cogwheel Not on Mastodon? Consider these instances: gamepad.club dice.camp mastodon.art chirp.enworld.org tabletop.vip MP3 Download: Plus Ultra S2 Ep 10: The Blind Leading the Blind (Cypher System) Music Used: “The Digital Dragon” by Drozerix is Public Domain and can be downloaded from http://modarchive.org Keep us ad free by supporting us on Patreon! Thanks to our current Patreon Patrons (as of this upload…): Ellie, Liv Dromen, Paul, Walter, & Patron Emeritus Cindy!

How do you champion an authentic leadership style in a sector that's slow to adapt to change? Infrastructure Ontario President and CEO Angela Clayton has traversed this career journey by building a robust network while maintaining a deep dedication to continuing education and an iron-clad work ethic. She joins Riccardo and Shormila for a candid conversation about career growth and what it means (and doesn't mean) to be a woman in leadership in this male-dominated industry. Angela has seen the benefits of prioritizing community and collaboration in many facets of her career. She reflects on her history of learning on the job and how employer-supported professional development has transformed her trajectory. She and the hosts dig into the practical realities of leadership in major programmes and the mistaken assumption that great individual contributors naturally become great leaders. She also speaks frankly about being the only woman in the room, and how essential trust in hard-earned champions, while it doesn't negate impostor syndrome, certainly puts it into perspective.The discussion also explores IO's evolution over time, Angela's approach to her transition to CEO, and her future plans for pursuing more impact-driven work. Discover this successful industry innovator's take on the shifts leaders must make as they navigate new roles and her thoughtful advice for women aspiring to excel in infrastructure.Key takeaways:How a strong network creates career opportunities, and why getting the job is only the beginning;The pitfall of assuming technical excellence translates into project and people leadership;How to think about “diversity hires” without tokenism—and what real sponsorship looks like;The culture and mandate evolution of Infrastructure Ontario;The mental shift required when moving from PM issues management to CEO vision and culture development.Quote:“[Former peers who now report to me are] some of my most trusted advisors. They have the history of the organization, and it's so valuable to have that institutional knowledge.” - Angela ClaytonThe conversation doesn't stop here—connect and converse with our community via LinkedIn:Follow Navigating Major Programmes: https://www.linkedin.com/company/navigating-major-programmes/Read Riccardo's latest at www.riccardocosentino.comFollow Riccardo Cosentino: https://www.linkedin.com/in/cosentinoriccardo/Follow Shormila Chatterjee: https://www.linkedin.com/in/shormilac/Follow Angela Clayton: https://www.linkedin.com/in/angelafclayton/ 

Týždenné spravodajstvo z finančných trhov. Fínsko, dlhodobo považované za vzor úspešnej krajiny, dnes čelí pomalému ekonomickému rastu, rastúcemu verejnému dlhu a vysokej nezamestnanosti mladých, pričom stále hľadá nový motor hospodárskeho rozvoja. Napriek spomínaným problémom však zostáva lídrom v inováciách a pripravenosti na umelú inteligenciu. Jeho budúcnosť bude závisieť od toho, či dokáže premeniť technologické ambície na skutočný ekonomický rast. Téme sa podrobne venujeme v našom pravidelnom komentári z finančných trhov. ..IAD TALKS, týždenník, IAD Investments,správ. spol., a.s., Malý trh 2/A, 811 08 Bratislava, IČO: 17 330 254, dátum vydania: 22.06.2026, 31/2026, EV 139/23/EPP..*UPOZORNENIE. Tento materiál je marketingovým oznámením. Kompletné znenie upozornenia nájdete na stránke www.iad.sk/marketingoveoznamenia

Stai per partire per l'Italia e hai paura di non riuscire a comunicare? In questo articolo trovi 25 frasi indispensabili per il tuo viaggio: espressioni pratiche e concrete, quelle che userai davvero per parlare del tuo viaggio, ordinare al ristorante, spostarti e farti capire anche quando l'italiano corre troppo veloce. Parla Italiano in Vacanza: Espressioni che Userai Davvero L'articolo è organizzato in quattro blocchi tematici, così è più semplice memorizzare le frasi: parlare del tuo viaggio, ristorante e negozi, spostamenti e orientamento, aiuto e comunicazione. Se vuoi approfondire altri verbi utili per viaggiare in Italia, puoi consultare la guida dedicata. Blocco 1: Parlare del Tuo Viaggio Appena gli italiani capiscono che sei un turista, partono con le domande: "Da dove vieni? Cosa fai qui? Ti piace l'Italia?". In Italia la conversazione con uno sconosciuto può durare anche venti minuti, soprattutto al Sud. Ecco le frasi che ti aiuteranno a rispondere. 1. Sono in Vacanza La frase più semplice e più usata di tutte. Per esempio: "Sono qui in vacanza" oppure "Sono in vacanza in Italia per due settimane". Nota linguistica: in italiano si dice "in vacanza", non "alle vacanze". Memorizza la preposizione "IN": è importante. Se vuoi approfondire l'uso delle preposizioni semplici in italiano, c'è una guida dedicata. 2. Sono Qui per Lavoro / Viaggio per Lavoro Per chi non è in vacanza ma in trasferta. Esempio: "Sono qui per lavoro, resto solo tre giorni". Attenzione: non si dice "Viaggio per il lavoro" con l'articolo, ma semplicemente "per lavoro". Stessa regola con "in vacanza": senza articolo. È una frase utile soprattutto a Milano, capitale economica d'Italia. 3. È la Mia Prima Volta in Italia Dilla a un italiano e vedrai cosa succede: ti consiglierà tanti posti da visitare, ti spiegherà dove mangiare la migliore pizza, ti darà persino il contatto di un suo conoscente. Gli italiani apprezzano molto chi scopre il loro paese per la prima volta. Variante utile: "Sono già stato/stata in Italia diverse volte". Ricorda l'accordo: stato per gli uomini, stata per le donne. 4. Resto per + Numero + Giorni Esempi: "Resto per cinque giorni", "Rimango per due settimane". I verbi restare e rimanere sono praticamente sinonimi, e puoi usarli indifferentemente. Userai questa frase tantissimo: alla reception dell'hotel, ai controlli in aeroporto, e in qualsiasi conversazione con un italiano curioso. Attenzione alla pronuncia: la "G" di giorni è dolce, non come la "G" di gatto. 5. Sto Visitando + Luogo Il verbo "stare" + gerundio è la forma progressiva italiana. Esempi: "Sto visitando la Toscana", "Sto visitando le Cinque Terre", "Sto visitando il Sud Italia". Nota grammaticale: in italiano questa forma si usa meno che in altre lingue. Spesso si preferisce dire semplicemente dove si è: "Sono in Toscana", "Sono a Firenze". Ma per parlare di quello che stai facendo proprio adesso, "sto visitando" è perfetto. 6. Sono Qui da Solo / con la Mia Famiglia / con gli Amici Frase importante per spiegare la tua situazione. "Sono qui da solo" se sei un uomo, "Sono qui da sola" se sei una donna: ricorda sempre l'accordo del participio e dell'aggettivo. SituazioneFraseViaggi da soloSono qui da solo / da solaViaggi con la famigliaSono qui con la mia famigliaViaggi con amiciSono qui con i miei amiciViaggi col partnerSono qui con il mio ragazzo / la mia ragazzaViaggi col coniugeSono qui con mio marito / mia moglie Nota culturale: in Italia, quando si parla di famiglia, si include spesso una rete ampia di parenti. Se dici "sono qui con la famiglia", può capitarti la domanda: "Quanti siete?". 7. Mi Consiglia Qualcosa di Tipico? Una delle frasi più utili in assoluto. La puoi usare al ristorante, nei negozi di alimentari, nelle enoteche. Esempi: "Mi consiglia un piatto tipico?", "Mi consiglia un vino della zona?", "Mi consiglia un dolce locale?". Gli italiani sono orgogliosi della loro cucina regionale e amano dare consigli. Attenzione però: ogni regione ha la sua specialità. Non chiedere la "carbonara autentica" a Firenze o la "pasta al pesto originale" a Napoli. La forma informale è: "Mi consigli qualcosa di tipico?". Per imparare altre espressioni utili per iniziare una conversazione in italiano, dai un'occhiata alla guida dedicata. 8. Cosa Mi Consiglia di Vedere? L'equivalente della frase precedente, ma per luoghi e attività. Esempi: "Cosa mi consiglia di vedere in città?", "Cosa mi consiglia di visitare nei dintorni?". Spesso i consigli migliori non vengono dalle guide turistiche, ma dai locali: il barista, il tassista, la persona del bed and breakfast. La forma informale è: "Cosa mi consigli di vedere?". Blocco 2: Ristorante e Negozi Questo è probabilmente il blocco più utile in assoluto: ecco le frasi che ti serviranno davvero quando andrai a mangiare o a fare acquisti in Italia. Per approfondire, puoi consultare la guida sulle parole ed espressioni al ristorante. 9. Vorrei… Questa è la frase chiave per ordinare in modo educato. Esempi: "Vorrei un caffè", "Vorrei una pizza margherita", "Vorrei un bicchiere di vino rosso". Vorrei è il condizionale del verbo volere e suona molto più gentile di voglio. Se entri in un bar e dici "voglio un caffè", suoni un po' brusco. Con vorrei, invece, sei nel registro giusto. 10. Prendo… Alternativa a vorrei, leggermente più diretta ma sempre cortese, soprattutto al ristorante quando il cameriere ti chiede cosa hai scelto. Esempi: "Io prendo le lasagne", "Prendo un tiramisù". 11. Mi Può Dare…? Forma di cortesia per chiedere qualcosa. Esempi: "Mi può dare il menù?", "Mi può dare un altro tovagliolo?", "Mi può dare il sale?". Se sei in confidenza, puoi usare il tu: "Mi puoi dare…?". 12. Avete un Tavolo per Due? La prima frase che dirai entrando in un ristorante. Esempi: "Avete un tavolo per due?", "Avete un tavolo per quattro?". Se non hai prenotato (cosa che in Italia è sempre meglio fare nei posti famosi, soprattutto la sera), questa è la domanda chiave. Il cameriere potrebbe risponderti: "Avete prenotato?". E se sei da solo? "Avete un tavolo per uno?" – in Italia mangiare da soli è normale. 13. Sono Allergico a… / Senza Glutine Frase fondamentale per la tua sicurezza. Esempi: "Sono allergico alle noci", "Sono allergica al lattosio", oppure "Sono celiaco/celiaca e ho bisogno di un piatto senza glutine". In Italia la cultura del cibo è molto attenta alle intolleranze, e quasi tutti i ristoranti hanno opzioni senza glutine, soprattutto le pizzerie. Una frase utile per chiedere è: "Questo piatto contiene…?". 14. Quanto Costa? Frase indispensabile in negozio, al mercato, ovunque. Se si tratta di più cose, si dice: "Quanto costano?". Nota culturale: nei mercati storici come Porta Portese a Roma o Vucciria a Palermo, si può trattare sul prezzo. Puoi provare a dire: "Mi fa un po' di sconto?". 15. Posso Pagare con la Carta? Frase molto utile. In Italia, soprattutto nei bar piccoli, nelle pizzerie al taglio e nei mercatini, non sempre accettano la carta. Per legge dovrebbero, ma la realtà è diversa. Quindi prima di ordinare, chiedi sempre. È consigliabile portare sempre un po' di contanti con sé. 16. Ci Può Portare il Conto? Quando hai finito di mangiare, in Italia il conto non arriva da solo come in altri paesi. Devi chiederlo. Quindi alza gentilmente la mano e di': "Scusi, ci può portare il conto, per favore?". Si usa "ci" quando si è in più persone; se sei da solo, si dice: "Mi può portare il conto?". In Italia la mancia non è una regola fissa come in altri paesi: se il servizio è stato ottimo, puoi lasciare qualche euro, ma non è obbligatorio. Blocco 3: Spostamenti e Orientamento Muoversi in Italia richiede qualche frase chiave per orientarsi e usare i mezzi di trasporto. Se vuoi approfondire come chiedere e dare informazioni stradali in italiano, puoi leggere la guida dedicata. 17. Dov'è + Luogo Esempi: "Dov'è il bagno?", "Dov'è la stazione?", "Dov'è il Colosseo?". Frase fondamentale per orientarsi. Se sono più luoghi, si dice: "Dove sono i bagni?". Consiglio pratico: gli italiani amano dare indicazioni, ma a volte preferiscono inventare la strada piuttosto che ammettere di non sapere. Se ti sembrano vaghi, chiedi conferma a una seconda persona. 18. È Lontano? / È Vicino? La domanda che segue naturalmente "Dov'è…?". Sapere dove si trova una cosa è una cosa, sapere se è raggiungibile a piedi o se serve l'autobus è un'altra. Esempi: "Scusi, la stazione è lontana?", "Il Duomo è vicino?". Nota culturale: per gli italiani "qui vicino" può voler dire 50 metri o 2 chilometri. Chiedi sempre conferma con: "A piedi o in autobus?". 19. A Che Ora Apre / Chiude? Domanda essenziale, perché in Italia gli orari sono particolari. Molti negozi chiudono per la pausa pranzo, dalle 13 alle 16 circa, soprattutto nei paesini e al Sud. I musei spesso chiudono il lunedì. Prima di spostarti per visitare qualcosa, chiedi: "A che ora apre il museo?", "A che ora chiude la farmacia?". 20. A Che Ora Parte / Arriva il Treno? Frase indispensabile se viaggi in treno – in Italia è uno dei modi migliori per spostarsi tra città. Esempi: "A che ora parte il treno per Firenze?", "A che ora arriva a Venezia?". In Italia ci sono treni veloci come il Frecciarossa e l'Italo, ma anche i regionali, più lenti ed economici. È bene controllare sempre i tabelloni in stazione per eventuali ritardi. Per imparare il vocabolario specifico, c'è il dialogo in stazione per prendere il treno in Italia. 21. Un Biglietto per… Frase universale: la usi per il treno, l'autobus, la metropolitana, il museo, qualsiasi cosa. Esempi: "Un biglietto per Roma, per favore", "Due biglietti per gli Uffizi". Attenzione alla Convalida del Biglietto ...

Amici e amiche,inizierò questo breve ringraziamento dicendo una cosa molto semplice: non me l'aspettavo. Quando ho ricevuto la prima email della MOIGE, l'ho ignorata. Ho pensato: "Si saranno sbagliati a mandare l'invito. Staranno cercando Alberto Angela". Quando è arrivata la seconda email di Maria Vittoria Pica ho capito che, incredibilmente, stava succedendo davvero.Il mio stupore è in parte giustificato. "Favole nel traffico" è un progetto che, sulla carta, sembrava destinato a fallire fin da subito. In un tempo in cui i contenuti per bambini sono sempre più veloci, rumorosi e pieni di stimoli visivi, l'idea di un podcast che racconta semplicemente fiabe e favole sembrava quasi fuori dal tempo.Io sono un maestro di scuola dell'infanzia e, nel mio lavoro, ho visto spesso bambini affidati agli schermi nei momenti più spensierati della giornata, proprio come nei viaggi in auto. Questa cosa mi ha fatto riflettere e mi ha spinto a provare a fare qualcosa di diverso. Così ho iniziato a scrivere storie, poi a raccontarle, registrarle e condividerle.Pensavo di fermarmi dopo dieci episodi. Oggi, quattro anni dopo, ho pubblicato più di duecento puntate e continuo a farlo tre volte a settimana. L'affetto ricevuto dai bambini e dalle famiglie mi ha convinto che c'è ancora bisogno di immaginazione, ascolto e narrazione. Per questo motivo, vado avanti.Questo progetto esiste grazie a Ludovica Sodano, che lo porta avanti con me dal primo episodio attraverso le sue splendide illustrazioni, e grazie alla mia famiglia, che mi sostiene ogni giorno e mi sopporta mentre registro di giorno e di notte.Ringrazio il MOIGE e Michele Casella per questo riconoscimento così importante e vi saluto come faccio sempre alla fine delle mie puntate: "Ciao amici, ciao amiche e alla prossima avventura!" FOTO: Michele Casella

[Ukrainian: Four hundred and three – All or nothing] Packing an hour full of bangers from across the world always promises for a good time. But this week, it really is a humdinger of a show. Michael and Io keep you dancing from start to finish with some bops and bangers that you won’t hear anywhere else on Australian radio. And we love it. Liked a particular track? Click the link to check out the video. And don’t forget to follow across social media: Facebook | X (Twitter) | Threads Список відтворення Fredrika Rei – Dårar vid makten [Swedish: Fools in power]        Ott Lepland – Kardan nagu tuld [Estonian: Scared like fire]        Raiven ft Macha Ravel – Tandem [Slovenian]        Derya Uluğ – Şımarık [Turkish: Spoiled]        Jota Quest – Você [Brazilian Portuguese: You]        babble2babble: Ukrainian Yulia Yurina – Нема льоду [No ice]        YAKAYA – ПРИПИНДА [PRYPYNDA]        Angelina Mango ft Marco Mengoni – Canto d’amore [Italian: Love song]        YENA – Catch Catch [Korean]        rossomodo – Y’aller Y’aller [French: Let’s go, let’s go]        The post Чотириста три – Або пан, або пропав appeared first on babble POP!.

Io aspetto l'Eterno che nasconde il suo volto alla casa di Giacobbe; in lui ripongo la mia speranza. Isaia 8:17

Last 4 days before regular tickets sell out at AI Engineer World's Fair - this is the single biggest gathering of AI Engineers, Founders, Leaders, and Researchers in the world. Attendees get >$5000 worth of sponsor credits and talk tracks are looking FANTASTIC. Join us!The AI scaling debate always focuses on the question of “how do we get more GPUs?” but the better question may be: how do we make the most of ones we already have.The fact that a frontier lab like xAI could be running at sub-10% MFU (Model FLOPs Utilization) is just a hint at what the real problem may be.For context, older frontier-scale training runs were already much higher than 10%. GPT-3 was around 21% MFU. Gopher was around 32%. Megatron-Turing NLG was around 30%. PaLM reached around 46%. And our guest Anjney says best-in-class MFU today is closer to 60–70%.It's not necessarily that xAI is uniquely incompetent (it's clear they have talented folks) but rather the priorities may be flipped in the GPU arms race.While GPU access is a bottleneck, simply increasing CapEx won't automatically translate to better models as frontier AI is increasingly a systems problem: scheduling, utilization, networking, kernels, frameworks, data pipelines, parallelism, cluster reliability, and the thousand small decisions that determine whether your theoretical FLOPs become real training progress.From building Discord's developer platform and backing frontier AI companies like Anthropic, Mistral, Black Forest Labs, and Periodic Labs to now building AMP's independent compute grid, Anjney Midha has spent years close to the real bottlenecks of AI scaling. In this episode, Anjney joins swyx at Periodic Labs to unpack why the AI race is not just about buying more GPUs, why 95% utilization would have been considered an outage at Google, and why the next era of AI infrastructure has to be more aligned, more efficient, and more responsible.We go deep on AMP's vision for a compute grid that makes FLOPs flow like megawatts, the difference between full-stack AI labs and horizontal pooling, why AI data centers need community buy-in, and how compute markets could evolve into something closer to an independent system operator. Anjney also explains why DeepMind's unpublished research points to a market failure, why end-of-life prediction remains one of the most important AI applications he has thought about for fourteen years, and why “output maxing” may become a new discipline for frontier systems.We also discuss Anthropic's culture, why “luck favors the prepared mind” in coding models, how Claude cracked coding, why too much capital too early can make AI labs fragile, what Periodic Labs is trying to do with science and superconductors, why great researchers can become great CEOs, and why Silicon Valley is both deeply missionary and deeply mercenary.We discuss:* Why 95% utilization was considered an outage at Google* Why AI infrastructure waste compounds at frontier-lab scale* Why “move fast and break things” does not work for AI data centers* How data center backlash, power grids, and community incentives shape AI scaling* AMP's vision for making FLOPs flow like megawatts* Why compute needs an independent system operator* How interruptible demand and dynamic prioritization worked inside Google* Why DeepMind research hoarding creates negative externalities* AMP's 1.2GW base-load ambition and the need for 6GW of spike capacity* Why end-of-life prediction could become one of AI's most important healthcare applications* Frontier Systems, output maxing, and full-stack alignment* Why APIs and abstraction layers become lossy as organizations scale* Superconductors, standards, and the dream of lossless systems* SF Compute, open protocols, and the future of compute marketplaces* Why non-NVIDIA chips can still benefit from NVIDIA's reference architecture* Trust boundaries and why chip startups need visibility into future model architectures* Why VCs often underestimate researchers as CEOs* Scientists as star athletes of the mind* Why great CEOs need to be confrontational up and down the stack* Why leading the frontier matters more than “winning”* How Anthropic cracked coding* Why culture is fragile, not a permanent moat* Why hardship was a feature, not a bug, for Anthropic* Why Anthropic's P0 was coding from day one* Periodic Labs, physics as the constraint, and technical reality* Silicon Valley mercenaries, missionary teams, and what happens after a breakthroughAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney* X: https://x.com/AnjneyMidhaAMP PBC* Website: https://amppublic.com/* X: https://x.com/amppublicTimestamps00:00:00 Introduction00:00:09 Why AI Compute Is Being Wasted00:03:17 Responsible Infrastructure and Data Center Backlash00:06:07 AMP Grid: Making FLOPs Flow Like Megawatts00:12:41 Foundry, Frontier Labs, and Research Hoarding00:14:42 Gigawatt-Scale Compute and End-of-Life Prediction00:24:08 Frontier Systems, Output Maxing, and Alignment00:27:38 Compute Markets, SF Compute, and Non-NVIDIA Chips00:32:57 Trust Boundaries, Co-Design, and Researcher CEOs00:38:17 AI Coachella and First-Principles Thinking00:42:43 Leading vs Winning in Frontier AI00:45:54 How Anthropic Cracked Coding00:48:25 Culture, Hardship, and Anthropic's P000:54:03 Periodic Labs, Physics, and Silicon Valley Mercenaries00:56:26 Rishi Valley, Singapore, and Money as a Measure00:58:47 Closing ThoughtsTranscriptIntroduction: Anjney Midha, AMP, and Compute WasteSwyx [00:00:00]: We're in Periodic Labs with Anjney Midha, CEO, founder of AMP. Welcome.Compute Utilization: Node Allocation, MFU, and AlignmentAnjney [00:00:09]: Thanks for having me. At Google, there are two types of utilization usually, right? That you're measuring in these clusters. One is node allocation, and then the other's MFU. Node utilization is usually like what percentage of cards in the data center are just, used, and that, if it's not at, 95%-Swyx [00:00:29]: There is no excuseAnjney [00:00:29]: There's no excuse, right? I think 95% at Google, which is where my co-founder, Seb, came from, he built the Borg, PBorg/GQM scheduler at Google, and there I think 95% was considered an outage, so 96% node utilization is, should be standard. And most single-tenant clusters are not running at that. So that's one. And then MFU should be, I would say the best in class today is somewhere between 60 and 70%. I think this is a leadership question, right? Fundamentally it's an alignment question, which is are the people who are funding the cluster and then deploying the cluster actually aligned? And sometimes theoretically they are, but in practice the number of people in the chain, the supply chain between, the capital and all the way to whoever's managing the cluster and then whoever's measuring what the output is, are just so many, degrees of separation away that, the, The Have you ever heard the radian metaphor, which is at the beginning of an arc, if you have two arcs that are two lines that are just off by a few degrees, that-Swyx [00:01:33]: It spreads outAnjney [00:01:34]: It spreads out, right? Or at scale. And I think what's happening is a lot of cluster implementations and infrastructure, a lot of frontier labs and other teams, that's what's happening, is they're, they initialize the plan, which is kind of like North Star with a team that wants to do good, but then they're, required to scale so fast instead of iteratively that the wastage just compounds really fast at scale. And so I think we know the answer, which is just do iterative bring ups. If you spend time with people who've been in the semiconductor industry or the DSN industry for a long time, this is not new, and I don't think AI should be an excuse. Sure. Something What is new? Okay. We have a lot of new capabilities, but that doesn't mean just abandon common sense. Common sense should always be in fashion. ? AI scaling doesn't change the in fact, if anything, AI scaling should be putting a premium on the value of common sense and infrastructure because the margin of error now is so much lower and the costs of wastage are so much higher. And the cost of wastage, by the way, is not just economic. I'm, obviously I'm, I'm an investor, or I'm an investor by background. Over the last few years now we're running an AI infrastructure business called, AMP. And I think that it's okay to say this time is different on the capabilities front. We are genuinely getting capabilities at, of the, of a kind we haven't had before. That doesn't give you an excuse to say this time is different for everything, especially infrastructure. So look, I love the hacker mindset and the hustler mindset. Now, that's great for the startup mindset, but you remember this moment where Zuck went from saying, “Move fast, break things” to, move-Responsible Infrastructure and Data Center BacklashSwyx [00:03:10]: Fast and stable infrastructureAnjney [00:03:11]: Move fast with stable infrastructure. I think now we need to move fast with, responsible infrastructure. People are going to ask where the impact is. There was a really In our class yesterday, Scott Nolan, who's the founder of General Matter, came by at Stanford to speak about energy bottlenecks. And he had a phenomenal idea. He said, “if you look at the marginal unit economics of compute per hour,” he goes, “let's call it, $4 an hour. If you're having to bring up a new data center in a new community, why not just say we're going to charge 4.50 an hour, and that marginal impact or that marginal increase, we just literally take that and give it to the local community as cash?” I can tell you as a customer of that compute, I would love that. I'd be happy to pay an additional 50 cents per hour at scale.Swyx [00:03:57]: Wow. Yeah.Anjney [00:03:58]: Because if that means the public benefit is so clear to the communities that the data centers are coming up in, I'm going to feel like that compute is much more reliable. Up to 20% of all data centers this year in the US, my understanding is are at risk.Swyx [00:04:13]: Of community backlash?Anjney [00:04:14]: Correct. Of not getting the community support they need to get brought up.Swyx [00:04:19]: Wow. That's a huge number.Anjney [00:04:20]: Yeah. Now, we, I think we should dig into what that number is. I think it's a little bit of overstated. These things can get over-reported, but it-Swyx [00:04:27]: They don't just care about jobs. They care about all the other stuff around it, right? They care about power grid, they care about environments-Anjney [00:04:33]: Power grid, permitting, and so on. And imagine I think if you said there's a new AI deal. If we're bringing up a data center in your community, we're actually going to reduce the cost of your electricity bill. Okay, now we're talking. Right? The community's going, “Okay. Now this is a deal. I feel like a partner in this.” Right now that's not happening. There will be audits, there will be investigations, and when the, when the regulators come, I don't know when it's going to be, the folks who are moving fast and breaking things in the name of AI progress better be prepared. That's certainly not how we're procuring compute. Or we're, we're trying as much as we can to work with partners who have long-term track records. Many of whom, by the way, are not, AI providers. I think this whole idea of neoclouds being somehow this new category is a lot of marketing speak. There are really good, reliable, trusted data center providers in America who've been around 20 plus years. I love those folks. They know how to Sure. Are they sponsoring happy hours at NeurIPS? No. Are they legibly listed in Build? No. Are they hanging out in my, in, situational awareness parties? No. But they're adults. I trust them.Swyx [00:05:44]: They can run LAN. They can run power.Anjney [00:05:45]: They can run LAN, power, and shell. They have credit histories. We sit down, we have a conversations. Many of them live in Silicon Valley. They've, they've had to deal with the boom and bust cycles of the internet, and I love those folks. They are stable infrastructure partners and thinkers. And I think there's a lot of short-term thinking going on in the compute layer, and it's going to catch up to us. It's not going to be good.AMP Grid: Making FLOPs Flow Like MegawattsSwyx [00:06:07]: You talk about aligning incentives, and, I would think that aligning incentives means you have the full stack in one company, which is xAI and OpenAI, right? So you as a standalone infrastructure layer, why are you somehow more aligned to your portfolio companies than people who just own the whole thing?Anjney [00:06:28]: In systems design, right, there's, there's two regimes of, architecture, right? You have integration, and then you have pooling and utilization, right? So the Or rather, the way to increase utilization often is you can do systems integration where you collapse a lot of process into one node, or you can pull out a process from a node and share that amongst various That resource amongst several different nodes. And so we see the AMP grid, which is, the, what, the system we're building here, which is basically a compute grid. We're trying to do for compute what the electric grid-Swyx [00:07:02]: PowerAnjney [00:07:02]: Yeah, what the power grid did for electricity. It-- this is a pooling and utilization layer across clouds, And so we're actually the opposite of a full stack integration like approach.Swyx [00:07:12]: Super horizontal.Anjney [00:07:13]: Where it's much more horizontal and it's, it's multi-cloud, it's multi-silicon. The goal is to try to make FLOPs flow like megawatts, and that is very hard to do today for many reasons. There's stranded pools of compute all over the place and there's no fungibility. And so right now we do it at the level of scheduling, and we often do it at the economic layer. But as we start to announce what we're working on, it's extraordinary like how many folks are coming out of the woodworks and saying, “Hey, I'm actually working on a way to make compute fungible at this part of the stack and that part of the stack.” And as a grid, we'd like all of these folks to participate on the grid. There's, people often ask me, “Andra, are you a new cloud?” And I go, “No, actually neoclouds are suppliers.” sometimes they'll ask, “Are you a venture capital firm?” I go, “No, actually they are, they are demand like sort of off-takers of the grid.” We see ourselves as what's called an independent system operator. So if you study the history of the electric grid, once it became legible to a lot of factories and industrial sort of participants that, hey, actually it turns out pooling is a good idea. We should pool our generators instead of all having a generator running at half capacity in our backyard. There was a need for an independent entity who could coordinate all these parties. Transmission line, power generation, facilities, transmission lines, factories, and that neutral coordination mechanism is very critical. In order-- If you study like the history of grids, the most enduring ones were those that never owned their own assets. They were ones that had, or often started with long-term anchors who are uncorrelated sources of demand, a steel factory, a shoe mill or whatever in a particular town who weren't competitive, where the steel factory want to spike up at night, the shoe mill wanted to spike up during the day. So then you pool and you share, right? So each of you is guaranteed some base load, but then you kind of schedule your spikes to drive a peak utilization across the town. The gold standard, so to speak, historically, has been these utility companies like PJM Interconnect in the northeast of America, where they, over many years became this what's called an ISO, an independent system operator of the grid. So that's how we see ourselves. Economically, that's what we are. From a technical perspective, we started at the scheduling layer because Seb and Mihai, who, run engineering here, built that at-Swyx [00:09:28]: Did your schedulingAnjney [00:09:28]: They did that at Google. And, -Swyx [00:09:32]: And you have infra shops from Discord as well.Anjney [00:09:35]: I have some.Swyx [00:09:35]: I don't know, I don't know if Discord is like the primary identity, but what-whatever, I'm just kind of-Anjney [00:09:39]: No, D-Discord was-Swyx [00:09:40]: Choosing a well-known name.Anjney [00:09:42]: Well, I So I was running the developer platform there. The internal infrastructure I was not responsible for. That was actually a guy by the name of Mark Smith, who was extraordinary. And yes, Discord did pool So Discord is actually a counter example. I had the chance to learn a lot about fully, full stack infra there because-Swyx [00:09:56]: It's the same thing, yeahAnjney [00:09:57]: It's the, it's the other architecture which is, Discord built its own WebRTC vo-voice and video infra. So like Discord did not use-Swyx [00:10:08]: For the calls, yeah.Anjney [00:10:09]: Yeah, did not For communication, Discord did not use third party infra. It was all built in-house. And then the way you maximize utilization was you pool demand from the world's 200 million plus monthly active gamers, right? And so that's, that's how those stacks were constructed. Again, in systems design, the two concepts that keep coming up over and over again are abstraction and composition, right? And-Swyx [00:10:31]: Bundling and unbundlingAnjney [00:10:33]: Bundling and unbundling, abstraction, composition, like verticalization and-Swyx [00:10:36]: HorizontalAnjney [00:10:36]: Horizontalization. So in that sense, AMP is an independent system operator of the grid. We pool demand, we pool supply from a number of partners we trust At about 1.3 gigawatt scale over four years. And then we pool demand from some of the world's best, research labs and so on. We're sitting at one, periodic labs who need extraordinary long-term demand. And the idea is that, each of them is guaranteed base load on the grid, but they can spike up and down flexibly on, for compute, with much shorter timelines as needed. That was roughly the design of the program I came up with at a16z called Oxygen. The same-- That was the same design of the GQM, BorgX, Borg GQM implementation at Google that Mihai and Seb had built. Which was that how do you allow, teams inside of Google, on the internal infrastructure to be guaranteed capacity, for their base workloads? But when they need to spike up on research, how could they ensure that was sufficiently there? And of course, the big innovation that was not discovered, but kind of implemented in the space, this infra space maybe three, four years ago at Google was the idea of interruptible demand, right? Where you just queue up a bunch of jobs and through this like sort of credit system, there can be a bidding mechanism.Swyx [00:11:53]: Like priorities.Anjney [00:11:54]: It's a dynamic prioritization Basically. And jobs can get interrupted based on somebody else who's saying, “what? I have 10 tokens, 10 credits I want to spend on this job.” Another like team lead, research lead is “Genie 3 or whatever is only worth five, credits, and NanoBanana2 is worth 10 credits,” and so the NanoBanana job gets priority. That's a, that's a made up example.Swyx [00:12:15]: It's very real. Brain Marketplace was real. And, we've, we've covered this on the pod with David Luan, who was-Anjney [00:12:20]: Oh, great. OkaySwyx [00:12:20]: Was there. And the criticism is that, well, actually sometimes you need central command to go all in on a thing. And actually sometimes capitalism via credits doesn't work. Not, this is not a criticism of AMP. I'm just saying, this is a thing that has been tried, internally within Google, and it led to Google missing GPT.Foundry, Frontier Labs, and Research HoardingAnjney [00:12:41]: Like, we structured ourself essentially very similarly to Google. We are structured as a holdings company. So, Alphabet holdings is Alphabet holdings, and then they've got these subsidiaries called Google and-Swyx [00:12:51]: Other betsAnjney [00:12:52]: Other bets and so on. We've got, AMP holdings, and we've got our infrastructure business, and then we've got a capital business called Foundry that incubates new frontier AI labs or invests in them as venture capital, like Periodic. We put a few hundred million dollars into Anthropic from our fund earlier this year. So wherever we feel like teams are making progress, especially researchers and so on who've pushed the frontier inside of existing labs like DeepMind, I find, there comes a point where they feel misaligned with the dictatorship of Alphabet holdings. And at that point, sometimes the dictatorship doesn't want them anymore. And they're “Thank you. You've done your job here. You've kind of helped us through the zero to one phase, and for whatever reason, we're going to deprioritize your amazing, omni model or whatever it is, and instead we're going to prioritize coding.” And, I think that's a tragedy, but I get it. They're Sergey and team are running their own business there. But that doesn't mean we the rest of us should sit around waiting for that progress to get unlocked for the rest of the world and humanity. If you think about how much extraordinary research has happened inside of DeepMind over the last 10 years, I, Demis and Sergey and those guys did such a great job. But at the end of the day, so much of that has never seen the light of day?Swyx [00:14:00]: Or they're like papers only, but they never actually shipped it to production or-Anjney [00:14:03]: What's worse is the paper is actually not even being published anymore ‘cause there's a six-month embargo inside of DeepMind, right? We've heard about this where a paper comes out, and then I think there's a six-month embargo window where if anybody on the business team says, “This could be interesting” It's embargoed for life.Swyx [00:14:18]: Exactly. So the stuff that gets published is the stuff that's not good enough.Anjney [00:14:21]: There's an adverse selection problem, basically. Yeah. At this point-Swyx [00:14:25]: It's, it's a common complaint at NeurIPS, by the way, that's “Well, why would I look at the papers that are the trash of GDM?”Anjney [00:14:31]: Again, I think it's a tragedy. I get it. They're running their business, but the rest of the I think there's negative externalities of research being hoarded, and so that'there's a market failure. And somebody needs to unlock that research, and we can't do it on our own. We only have 1.2 gigawatts of compute. That's nothing. That's about $40 billion of cloud spend. We're going to need a lot-Gigawatt-Scale Compute and End-of-Life PredictionSwyx [00:14:51]: By the way, is that's a new number. I haven't, haven't come across that gigawatt number. That's huge.Anjney [00:14:56]: Yeah. And to be clear, we haven't secured all of it. That's how much demand we have started to secure. I think publicly we haven't actually confirmed how much we have for this year. In order-Swyx [00:15:04]: Where do you want to get to?Anjney [00:15:06]: I think the steady state would be that we have a base load pool Of 1.2 gigawatts at all times Of base load capacity. For spike capacity, right now my estimate is we need roughly six gigawatts over the next four years for all our teams to feel like they were able to keep moving the frontier, whatever they're working on, whether it's, like superconductor discovery over here. There's a new investment we're working on right now, which is in the end of life prediction space in healthcare. It's extraordinary how much you can, you can give this was actually my graduate school work. I went to grad school for bioinformatics at Stanford Med. And I know we-Swyx [00:15:40]: Econ, MCS, bio.Anjney [00:15:41]: So my-- I was this really weird cat where, I was never satisfied with my major options. So at one point I was an econ major, then I was a CS major, then I was a MCS major called mathematical computational science, and they decided they were going to end that major. So I took all that coursework, and I applied it to grad school, my graduate degree in bioinformatics, which was the master's program, and then I thought I was going to do a PhD. I never ended up doing it. I dropped out and went to work at Kleiner. But I was lucky enough to apprentice with this professor at, Stanford Med. His name is Nigam Shah, and he was working on end of life prediction. Stanford is one of the only research facilities in America that has a longitudinal patient data set that's larger at scale. I think it's at least 12 million patient lives. The only larger data set is at the VA, the Veterans Affairs, of America. And to do research, like do any deep learning and so on that data set, it was called the STRIDE data set at that time, you had to be a Stanford Med School affiliate, which is why I went and enrolled in the bioinformatics department. End of deep learning was early. Nigam Shah had the visibility-- the vision to see that, you could do end of life prediction to help palliative care. In America, the, over 30% of all Medicare, Medicaid spend, at least at that time, was spent on end of life care. And what's we grew up in Asia, so we all-- Yeah, at least I won't speak for you, but I have A very different relationship with death than I find folks who grew up in America do. In America, spiritually and culturally, especially in Western societies where Christianity, the Christian tradition sort of frames death as this terminal point, there's often a judgment day and so on. The way we view death is with a finality. In Indian culture, in Hindu culture, death is one-Swyx [00:17:35]: Also, he's Buddhist as well.Anjney [00:17:36]: You're Buddhist, yeah. So it's one, it's one step in a journey of many lives, right? And so, I grew up in this city called Chennai in the south of India, and when people die, you dance on the street. There's like a procession where your body is carried to be cremated and your family, like celebrates and there's drums and so on. It's this huge thing. And, It's because the idea is that you're going to be reincarnated. You've been liberated from the responsibilities of this life, and now you're onto your next. It's a new It's like going off to a new college or whatever, right? And so it was so alien to me when I got here as an undergrad- That the medical system works backwards from that assumption that we have to view death as this terminal thing and delay it, postpone it's a bad thing. And so at the time, clinical decision support in the United States was this very primitive field. Even to this day, physicians in the United States often will tell you when you have a terminal disease, this is your, we've diagnosed you, which is great. Our ability to diagnose you is extraordinary. You have somewhere between six months to six years to live. What do you do with that information? The error bars are so high that then you In times of uncertainty, we default to culture, and when the culture is let's-- this is a bad thing, I've got to prolong my life, then you start doing things like And just to, just sort of from a systems perspective, what's going on there is Physicians often feel like they need to provide such high error bars because there's always some uncertainty in end of life diagnosis, and if you provide the wrong Diagnosis or recommendation to your patient, you can be sued for medical malpractice. And then your license can be taken away. It can be catastrophic for your career. In contrast, if in countries where that's not the case, what you often observe is that patients, physicians are quite prescriptive with their recommendation. They say, “Hey, this is your condition. The literature says that you probably have this much time on Earth left. My expert opinion is that you are an outlier or whatever.” And they try to be more prescriptive, and that empowers a patient, right? ‘Cause then a patient can say, “I trust my doctor. They said on average, I have six months to live, but if I do these things, I may have a shot because of my particular predispositions or my genetic history or whatever.” And that empowers you to go about your life in a actually more scientific way than leaning on religion, culture, spirituality, and so on. In contrast, here, because of that medical malpractice sort of thing looming over your head, a physician never gives you a clear recommendation. So instead you say, “Okay, Doc, well, let's try it all.” And then you start a whole regime of drugs and therapies, and then you often spend weeks and weeks in the hospital, and that deteriorates your quality of life. And when that deteriorates your quality of life, you instead of spending your last few days doing the things you love with your family, you're spending it on a hospital bed. And that ends up being thirty percent of Medicare and Medicaid. So it's worse for the patients. The doctors feel terrible. The American taxpayer is paying a huge amount of money. And so this is why Nigam Shah, who was this professor at Stanford, said, “Anjney, if there's “ I kind of sat down with him. I was this young, I'd, I was twenty-one, and I was “I want to work on a big problem.” He's “The big problem is end of life care.” And so we tried to do deep learning to say, to-- So we started trying to run deep learning on these tried patient data sets to say, “Could you have an AI system make a recommendation that is orders of magnitude more precise about how much time you have left once you've been diagnosed with a terminal condition than a human?” And then if we can get that precision to be high enough, then you can empower the patient. And it turns out the tech works. Like it's-- Once you get the data set, like RL works. Honestly, even regression models work. You don't need to get that fancy. At the time, we were just trying, doing like very simple neural nets.Swyx [00:21:54]: Simple solutions, yeah.Anjney [00:21:54]: Today, what we can do with RL is extraordinary. The problem remains then and now is regulatory, because you actually can't shift the burden of the wrong clinical diagnoses from the physician to the AI system. And so at that time, I got quite disillusioned ten years ago for, twelve years ago where, ‘cause I felt I just didn't have the resources to influence regulation. Today, I'm very lucky. I'm in a different place. I've, I'm a lot older, and so I've been spending a lot of time on my next incubation, which is how can we unlock the, patient empowerment by training AI models to do end of life prediction much, with much more precision and ac-Swyx [00:22:37]: Oh, wow. You're still focused on this the whole time.Anjney [00:22:40]: The-- I haven't been able to get, this out of my mind a single day for the last fourteen years. This is the hill I want, I would like to die on. There's two, I would say. What? I actually, I'd prefer not to die.Swyx [00:22:51]: Yeah, exactly.Anjney [00:22:52]: But I think two bipartisan issues, I think two issues that should be bipartisan in America are how do we empower patients to make the right clinical decisions at the end of their life, such that we're reducing the taxpayer burden with science? It's just good old science, and AI can help here. And the second is, net positive data centers, ‘cause I think that's the biggest critical bottleneck on training and good enough AI models to help people at the end of their life. So there's sort of two sides of the, of the same scaling bottleneck curve, but those two, we formed AMP as a public benefit corporation. My wife and I, who you've met, you've met Viv. Her passion is education. Her family is a long line of educators and so on, and, of physicists. And so this class is my attempt to stop being the black sheep of the family and be a, an educator. But if I'm not educating, the thing I would be doing is working, on these two problems, whether on the political spectrum or as a researcher back at, in some lab. And my hope is if anyone's listening to this podcast, if they're passionate about either of those two topics, I'd love to hear from them. We'll, we'll we can share the contact in the show notes, but, we're looking for people to join both of those missions on the, on the political side as well as on the medical side, on the research side.Frontier Systems, Output Maxing, and AlignmentSwyx [00:24:08]: You said, this is a discipline that you want to form. You call it's called variously called Frontier System. It's variously called One Person Frontier Lab. What is the ideal name or shape of this? Like the, what is the mission?Anjney [00:24:24]: Of the class?Swyx [00:24:26]: Of the discipline that you're, exploring, right? I The class is called Frontier Systems. But like for me, maybe one phrase is you're, you're just anti-waste, right? Which is wasting GPUs, wasting in human and Medicare. But is there, is there a broader theme that I'm, that maybe you can encapsulate more succinctly?Anjney [00:24:45]: Yeah. The, from an engineering perspective, it's very simple. It's output maxing. It's the, it's the department of output maxing.Swyx [00:24:51]: Making the most of what we have.Anjney [00:24:52]: Exactly. I'm a huge believer in optimal outcomes. I think both in America and other countries, we are losing our appreciation for nuance, and this is the thing of And AI is the same case, right? Oh, the bitter lesson holds. Okay, fine. But that doesn't mean you just like throw 500 GB300, 500,000 GB300s at your suboptimal model scaling and you waste a bunch of compute. It also doesn't mean that, the most optimal is to have like 50 different architectures where there isn't enough standardization. One of the reasons Anthropic has had extraordinary sort of velocity is ‘cause they picked the transform architecture and said, “This is simple. Let's double down on it,” right? And now luckily there's enough investment going to the space that we can afford other architectures, but at the time, investment was just too fragmented into other architectures, so that arguably unlocked scaling. So I think there's a philosophy. I think we all owe it to ourselves to do output maxing with a new capability called AI on a global level. I think if I was starting a new department at Stanford, depending on how fuzzy or technical I wanted to be, I'd probably call it the Department of Alignment. Like-Swyx [00:25:59]: It's an overloaded termAnjney [00:26:01]: But it is, But alignment really Is a hard problem. And I think when you unlock it, full stack alignment is super hard in any organization and in any system. Like in a, in a venture capital firm, if you can have full stack alignment between your limited partners and your, the founders who are creating the value and ultimately the public that owns the IPO stock, that is a gift that keeps giving. And when you study the history of these systems, when they start off, they usually start out small scale where the feedback loop is actually so tight that there's alignment. And then the more you try to scale, the more division of labor happens, the more specialization happens, and at each step you add abstractions. And wherever there's an API interface, there's like loss. There's communication loss. And so I think a really cool thing would be for us to figure out is there a way for us to have our cake and eat it too as an engineering discipline? Is there a way to actually scale up and scale out Without losing any alignment, without lossy transmission?Swyx [00:27:01]: You mean standards?Anjney [00:27:02]: So standards is one way. The other way is you just have net new capabilities. So like what we're trying to do here is discover new superconductors. A room temperature superconductor would be a lossless transmission mechanism for energy. We would have flying cars. We are right within a few years of having a new room temperature superconductor. So I think those are the two. You either have to standardize On protocols or API specs that allow lossless communication, or you can come up with a whole new capability that unlocks so much abundance, the standardization doesn't matter ‘cause you just unlock net new capacity. This, the, so this is what I spend my days thinking about these days.Compute Markets, SF Compute, and Non-NVIDIA ChipsSwyx [00:27:38]: No, I think every infra person at, who wants scale and wants to output max does eventually end up thinking about this. We don't have time to go into it, but we have done an episode with SF Compute-Anjney [00:27:50]: Oh, coolSwyx [00:27:50]: That is trying to standardize The futures contract for compute. I don't, I don't know how that's going by the way, but like at some point this will be public.Anjney [00:27:57]: Oh, I think Evan is awesome and SF Compute is the kind of effort that I hope we can accelerate because what often happens is these exchanges are very hard to get, they, it's hard to bootstrap them, right? Because they often require-- There's many inefficiencies between parties. There's trust boundary inefficiencies in infrastructure because you don't trust, one part of the stack doesn't trust another part of the stack to give them visibility. There's capital markets inefficiencies, there's operational efficiencies. So if you can inject like a single shock to the system of a ton of compute demand or supply, then you can accelerate, these new flywheels. And so my hope is one day, or soon, if SF Compute needs extra like has excess capacity, they just hook it up to the grid and they get flooded with demand from us. And on the other side, if they have a ton of demand but they don't have supply, they just again hook up to the grid and it's a two-way protocol where they can just hook up to our capacity. And I don't think we're too far from that. Today our working implementation of it is mostly through a group of labs, universities, and a few sort of trusted parties who are, who all feel like they're in alignment to borrow an over sort of used word. But our hope is to just have it be an open protocol that anyone can hook up to on-Swyx [00:29:20]: Hook up for demand or hook up for supply? In primarily demand, it sounds like. Like you-Anjney [00:29:25]: No, bothSwyx [00:29:26]: You would want to offer demand.Anjney [00:29:27]: Both. Yeah. Unfortunately, what's happened in the last six weeks is, we thought we'd have a bunch of excess capacity by the end of this year. It's all gone.Swyx [00:29:37]: It's exploding.Anjney [00:29:38]: It, yeah. It's all gone. And so I have, my text messages are full of friends, we know many of these people, these are founders who've raised billions of dollars in San Francisco going, “Oh, any chance you have like 50 nodes in the next few weeks?”Swyx [00:29:51]: What is the scope for, non-Nvidia, right? You have Lisa Su coming and, Rainer Pope as well. And so There is a lot of demand for, more performance Alternative architectures and all that. At the same time, this hurts your standardization.Anjney [00:30:11]: I don't think so. So actually Rainer's a great example, right? Rainer is a CEO and founder of, MatX. I actually had him by for office hours in the class earlier today, and there was an insight he brought up that I hadn't considered before, which is when they decided to pick the standard For their data center, they picked the NVIDIA reference architecture. So the MatX chips Just plug in to any site that has an NVIDIA bring up planned. And, the-Swyx [00:30:42]: It's just software then. It's, it's not the-Anjney [00:30:44]: A-Swyx [00:30:44]: Hardware.Anjney [00:30:46]: Well, from an input and IO perspective It's the same footprint as an NVIDIA rack.Swyx [00:30:52]: That makes sense.Anjney [00:30:53]: Where they have done, innovated a bunch from what I can tell is on systems co-design. Which is where a lot of the gains are to be had. And so he picked He was “Anjney, we, there's just so much work to do when you're building a new chip company.”Swyx [00:31:08]: Can't fight every front.Anjney [00:31:08]: You just can't fight on every front. So my question to him was, “Well, you're working on this new chip. Their tape-out is next year. What, who are you going to partner with to host the chips?” And he said, “Whoever will host them. That's just not, that's not my focus.” And I said, “But how did you “ you decided back to our earlier systems design question, he decided that, he didn't want to be a full, fully integrated chip provider. The bottleneck they're focused on is the logic die, and they, he feels they can crank out a ton of performance gains through co-design there. But then that means you delegate, to our question earlier, it, you he's the data center provider is a different part of the stack, and so then he's dependent on that part of the ecosystem to host his chips to get the performance gains to the customer. So now you have another abstraction, and you might have loss. So I asked him, “How do you prevent loss?” And back to your point, he said, “I just picked the NVIDIA standard ‘cause I didn't want to Like I wanted to piggyback off of an existing protocol.” And that, what's great about NVIDIA is that reference architecture is known.Swyx [00:32:15]: Open.Anjney [00:32:15]: It's open. They've published it. So Jensen's actually enabled someone like Rainer to build a chip company like MatX, and I don't see them as competitive. The compute demand is so high. Like, I don't I think NVIDIA's not able to meet the demands of production, so we just need more chips. And I think it's very smart what MatX has done, which is say, “We're just going to we're not going to innovate on the data center design ‘cause actually, thank you, Jensen, you've done all the hard work. Where we can innovate is somewhere else.” And I think that's, that's very healthy. I think that's how we unblock new bottlenecks. And my view is these, the, chip teams like MatX, who have arrived at the insight that co-design is the way, The primary bottleneck for them is trust boundary. To do co-design well, you need visibility into the next model generation as soon as possible ‘cause it takes two years to tape out. So if by the time I bring my chip to market, your model architecture's changed, I'm host. Now, when he was inside Google, he was sitting next to the Gemini team. He was on Palm or whatever.Trust Boundaries, Co-Design, and Researcher CEOsSwyx [00:33:19]: His co-founder was the, was one, was one of the Palm guys, I think.Anjney [00:33:23]: Yes. Yes, exactly. So when you're inside the trust boundary of Google, then your systems co-design loop is super tight. When you leave as a founder, one of the biggest risks you take is now you're outside the trust boundary. And so what I love doing is helping chip teams who can help us unlock more capacity for the independent ecosystem access to trust. Because when I If I've been, involved with a lab from day one, and I was lucky enough to work with Anthropic, and then I'm on the board of Mistral and helped Black Forest Labs get started. I think at this point I'm on six or seven different teams.Swyx [00:33:57]: Only six? I feel like my mental number was going to be 13, but yeah, it's-Anjney [00:34:02]: No, I go deep with one at a time.Swyx [00:34:04]: You're founding CEO of Arena.Anjney [00:34:07]: Nah, that was an, that was an-Swyx [00:34:08]: Administrative CEOAnjney [00:34:09]: It was an administrative five-month gig where Whalen and Anastasios were graduating from their PhDs, and they didn't need a product team. So I helped recruit the head of engineering product and design. But Anastasios has always been the CEO of that company. I played a pinch-hitting I'm an intern. I was CEO intern For five months. -Swyx [00:34:33]: I interviewed him, and he's he's very well-spoken. I think he's a debate, former debate, champion. But also very quantitative and mathematical, which is-Anjney [00:34:41]: He-Swyx [00:34:41]: Such a unicorn.Anjney [00:34:43]: See, what's amazing about him? If you look at his output, he's an output maxer. By the time he was graduating from his PhD, which he only graduated last year, he had published more work with a citation count than, people twice his age. But at the same time, he'd already started a project called LLM Arena that was being used by millions of people As a side project. And time and time again, what I've realized is venture capitalists suck at seeing human beings as, dynamic agents where-Swyx [00:35:14]: They want to put you in a boxAnjney [00:35:15]: They want to put you in a box.Swyx [00:35:15]: This is your thing.Anjney [00:35:16]: So the first time I got introduced to Anastasios, somebody had told me “Oh, he's amazing, but he's a researcher.” I was “what? What do you mean he's a researcher?” That's what-Swyx [00:35:28]: Like he's not a CEO, not a founder.Anjney [00:35:29]: Not a CEO, exactly. I was “Are you crazy? Do you Have you met Dario?” Dario's a scientist. He's gone from zero to, what will soon be a trillion-dollar company in four years. Being a CEO, nominally speaking, is not that hard. Being a good CEO is hard. Being a great CEO actually requires a level of performance that scientists who have already published at the top of their field have accomplished. It is super hard to be a competitive scientist. To publish in academia over the last 20, 30 years, to make it to the top of your discipline at a place like Berkeley, you are a star athlete. Like, you are an athlete of the mind, and you perform at the highest levels. And to get there, whether you're, Anastasios or Whalen at Berkeley, or you are Robin, who-Swyx [00:36:23]: BFL, yeahAnjney [00:36:24]: With Black Forest, who created Stable Diffusion, or if you're, like Guillaume at Meta, who created Llama before he started Mistral. The amount of human leadership you have to demonstrate to get the resources, like get the trust of the organization, publish it, put it up. I would just fund researchers all day Right? If who have contributed already to the field. If they've, if they've put SOTA out there, they're, they're star athletes already. If they haven't done SOTA Look, they can still be good CEOs, but then I find the failure mode is that they just don't want to be CEOs, they primarily want to publish, and that's okay, too. One of the things we do with the AMP Grid is we donate excess compute. We have two nonprofits, like university labs. We carved out like a couple thousand H100s. But I do think there's extraordinary research being done on university campuses. My father-in-law's a physicist. He's a professor. Extraordinary work in physics, and we need that. But if you want to be a CEO, what you need to be willing To do is be super confrontational, outside of science. Like within the scientific community, some of the best researchers are very confrontational about their convictions, right? This architecture is right. To be a great CEO, you basically have to be willing to be confrontational up and down the stack.Swyx [00:37:41]: To your own team.Anjney [00:37:42]: To your own team-Swyx [00:37:43]: To customersAnjney [00:37:43]: Hiring, recruiting customers. Well, I would say, Yeah, pretty much to everyone Everybody. Of course-Swyx [00:37:50]: I see, I feel a little bit of that in my own work, but yeah, I can't imagine the stakes that Dario has had to go through. It's, it's pretty insane.Anjney [00:37:56]: No, I don't think the stakes are that different From how you're feeling it, right? Stakes are personal scaling vectors, right? The stakes that seem so low to you, like having this podcast where you can talk to somebody and just have a you're an extraordinary communicator, right? Like already in this conversation, you've pulled more out of me than most people, and I've been on 12 podcasts in the last two weeks.AI Coachella and First-Principles ThinkingSwyx [00:38:17]: I think I, we've just seen each other enough that there's some base trust.Anjney [00:38:20]: There's base trust.Swyx [00:38:20]: And I think, and I know that you, that I've done my homework and like I know that trust is a big deal for you, so.Anjney [00:38:27]: I think trust is about consistency, and you and I have seen each other In the community for years, right? Like, I remember the first time we met was at NeurIPS in New Orleans. I don't know if you remember that, luncheon.Swyx [00:38:38]: Oh my God.Anjney [00:38:39]: Reiko had set up this Reiko's amazing, and he set up this luncheon and-Swyx [00:38:43]: Yeah, I was “Who's this Discord guy?” I'm “Okay.” But-Anjney [00:38:45]: No, you weren't-Swyx [00:38:46]: You were just “You made some investments.”Anjney [00:38:47]: You were much less polite. You were “Who's this VC?” You're like-Swyx [00:38:51]: No, I Was I? Oh my God.Anjney [00:38:53]: It was-Swyx [00:38:53]: I'm so sorryAnjney [00:38:53]: It was visible on your face.Swyx [00:38:54]: I'm so sorry. But you weren't, you weren't The introduction was bad. I was I didn't know who you were.Anjney [00:39:00]: The, see, this is the thing about context, right? Like, but then I think I heard your accent. And I was “Are you-”Swyx [00:39:06]: Singapore, yeahAnjney [00:39:06]: “Are you Singaporean?” And you're “Yeah.” And I said, “I went to high school, JC, in Singapore.” And then the ice broke. But This is the there are in the scientific community, sometimes the stakes are very high for people who haven't had the emotional, what is called EQ Coaching and mentorship, right? Which is like to have scientific impact, you often need to be a extraordinary emotional, like emotionally in tune person with the folks you're trying to influence. And so what comes so naturally to you is actually a super high stakes thing to other people. And so I wouldn't assume that Dario's more stressed out than you. These things are you'd be surprised how similar and small sometimes the problems are to you That some of the world's biggest, leaders are facing. And that's what I've learned from this class. The guest speakers are Sam, Satya, Jensen.Swyx [00:40:01]: AI Coachella.Anjney [00:40:02]: Yeah. It's AI Coachella, right? So we got to get all the headliners, and they're I'm very lucky that some of these people have either mentored me over the years or I've done business with them. And when you, take the performative stuff out and any assumptions you may have about these people that you read in the press or on Twitter, We're all just humans. We're all trying to get along. And what's so special about this moment is AI is forcing, like scaling, the bitter lesson is forcing a lot of people to revise their assumptions for how the world works and go back to first principles or go and educate themselves. So the kind of people I was, I won't name who this person is, but I was at an event last week in Texas and, ran to somebody who said, “Anjney, I came across the class. What do you think about real time action prediction models?” And I was, don't know how happy it made me feel when they asked me that question. I know they've done the work. They've challenged themselves. I'm, they didn't ask me, “What do you think of world models?” They said, “What do you think of n-”Swyx [00:41:04]: Real time action predictionAnjney [00:41:05]: “action, real time action prediction models?” World models, don't get me wrong, are cool and everything, but you and I both know that is a layer of abstraction that is sometimes not usefully precise enough. Right? Ours-Swyx [00:41:16]: There's like four different kinds of world models.Anjney [00:41:17]: Yes, exactly.Swyx [00:41:18]: We've done the part with general intuition, by the way, which is very focused on, -Anjney [00:41:22]: Oh, cool. Yes. I love Pim. Pim is great. And this is what I love about people who've done that level of work. They realize they're not in competition with people who the rest of the world thinks they're in competition with.Swyx [00:41:34]: Because they're not in the category, they're in the specific thing they're trying to do.Anjney [00:41:37]: They're focused on their mission, and they have a systems understanding of the bottleneck they're trying to solve. And when somebody else says, “I'm working on real time, action prediction models too,” Pim goes, “Oh, I love that person. I want, I can learn from them.” But the minute they're “Oh, that person's a world model person,” it's “like which type of world model person?” But mostly they're just trying to figure out if it's a waste of their time, because we don't have enough time. So, Pim, for example, is super, loves this other company I work with we've talked about called Black Forest Labs. And he's mentioned to me multiple times that he's so, He thinks what Flux is doing is really cool. Andy Blattman came by and spoke in the class. And what I find over and over again is for people who do the work, who can be usefully precise enough about like what is actually going on in the world of frontier research, The sense of camaraderie is still well and alive, but it gets lost sometimes when you have to like abstract The technical complexities in, business terms And then the VCs are “How are you different from that world model?” I'm going to say Where do I even start to explain this stuff? And then the misalignment creeps in.Leading vs. Winning in Frontier AISwyx [00:42:43]: This is good. Yeah, I think, people listening get a sense of, what it is like to operate at a real level, like yourself, rather than at, the journalist level, where you have to sort of put everyone in, a rough category and create a narrative of competition, and who's winning today, who's behind.Anjney [00:42:58]: It-- this idea of winning is so Weird to me.Swyx [00:43:03]: You do want to win. You want you want competitiveness.Anjney [00:43:06]: No, I think you want to lead.Swyx [00:43:07]: You want SOTA.Anjney [00:43:07]: No, I think you want to lead. Yes, so you want to push the frontier. You want to push the SOTA. You want to do something that hasn't been done before. You want to capture value, but you don't want to capture so much value that, people think you're unaligned with your mission or trying to do what's best for the world. You want to capture enough value that you can keep innovating, right? And I think that people want to lead, they don't really This idea of winning and losing, again, I love Jensen. He's a, he's a leader. The mindset that he talked about on Dwarkesh's podcast, right? He's “I didn't wake up with a loser mindset.” I think that was awesome, right? Because he's, he's an engineer. Dwarkesh has done the work. So there's at least-- even though the, to me, it was very obvious they're talking about the same thing, they just passed each other. They just had to basically, Jensen has this, five-layer cake abstraction of how the industry works. And Dwarkesh had, I think from that podcast, had more of, a pre-training, mid-training, post-training systems loop concept.Swyx [00:44:04]: It's just a factor of who he talks to, right? Again, it's very clear.Anjney [00:44:06]: It's the systems It's the abstraction, the mental models, the It's the whole-- Dude, so much of the problem in the world is reasoning by analogy. And then the assumptions that are held invisibly.Swyx [00:44:19]: Yeah, I've, I've said, this is actually the best time in human history for first principles thinkers. Because everything you think will happen is actually now coming true.Anjney [00:44:28]: Correct. And the venture capital community is, notorious for this, where people look-- In times of uncertainty, they, cling to axioms that ended up being true from the previous era, and they kind of like proclaim them with confidence as if they're truths, but they're not. And it's very important to see the distinction between a heuristic and an axiom. An axiom can be proven-Swyx [00:44:55]: Like from internal consistency point of viewAnjney [00:44:56]: With internal consistency. A heuristic is a way you kind of a shortcut. And my God, the number of people I have had to put up with over the last few years who proclaim-- use heuristics As axioms to judge people, to judge which companies are going to succeed or the number of people who are “Oh, yeah, Anthropic, they're just training models right now,” but this one continue.Swyx [00:45:22]: Because that's a B2B SaaS?Anjney [00:45:23]: Yeah, the, like Which over the fullness of time, if you squint at it, maybe. But the way you arrive there is so important that you can-- you just, you can dismiss people. Here's what happened, right? What happened is Anthropic basically achieved takeoff in October of last year. That training run-Swyx [00:45:41]: Whatever, three seven?Anjney [00:45:42]: I forget the numbers now, but whatever that checkpoint was-Swyx [00:45:45]: We saw the cognition.Anjney [00:45:46]: Yeah. Right? You probably-- The, to those of us in the community, especially once post-training was done and it was released in December-Swyx [00:45:52]: Yeah. Can I sneak a sneaky question in there? I don't know if you have a perspective, maybe you don't, I just The number one question is how did Anthropic crack coding, right? Because Claude One, Claude Two, okay, like it was part of it, but it wasn't a big deal. And the leading hypothesis, it's a lucky dice roll that was then compounded, right? Like it was like Mildly better, but then they saw it and they were “Okay, let's really invest.”How Anthropic Cracked CodingAnjney [00:46:17]: I had this very annoying teacher. I went to this boarding school called Rishi Valley in India, which is like this, bird preserve. It's like three hundred and fifty acres of bird preserve in rural India, and there was no technology for seven years. There was this teacher, I won't name them, but they would have this-- I hated it every time he said this to me. He was “Luck fa-favors the prepared mind,” which is like a common saying, but the way he delivered it, always grated me, ‘cause he was always I was always one of those kids who got, a good grade without trying very hard. ‘Cause like high middle school is not that hard if you, if you're generally, paying attention and so on. And there was this one time where I-- But then I would get an eighty percent grade, and he would keep pushing me to say “The reason you didn't get the ninety-five plus percent is because you're not that lucky.” And I would say, “What do you mean?” ‘Cause I would think that I deserved that grade, and I would sometimes argue with him. And he'd say, “You didn't have a prepared mind. If you want to get lucky again “ There was basically one time where I got like ninety-five or ninety-six on this, on this subject, and I, now that I felt entitled. I was “Okay, I'm going to keep doing this,” and I didn't. And then he was “Luck favors a prepared mind. You got lucky last time, but you got to stay prepared.” And I didn't understand what he meant. Now, as I'm older, I'm okay, these adults actually knew a thing or two. Anthropic has been the most prepared company for four years. And so then when the right, context data comes in, the right developers start sending in, the right context diffs, Sure, you could say you got lucky, but if you ask me, they're pr-pretty damn prepared with paranoia for like four years. And you have to remember, it was so hard for them to get going early on that they had to do so much more with so much less that you just have to be prepared to be so efficient.Swyx [00:48:06]: Yes. There's numbers on their burn compared to OpenAI. I've, I've written about it, but they are so much more efficient in their, in their tech stack.Anjney [00:48:14]: It's not even It's not funny.Swyx [00:48:14]: Not even close.Anjney [00:48:15]: Yeah. But it's so clear, right? Like how to output max for the world. They have been prepared, and you could call that luck, but Luck favors the prepared mind.Culture, Hardship, and Anthropic's P0Swyx [00:48:25]: This is one of those things that I was going over some of your old lectures and, you were data, people think it's a moat and actually it's culture and actually it's team Actually. And I, it's-- there's different levels of moats, and this is the ultimate one that determines everything else. Which you can then compoundAnjney [00:48:43]: You're saying culture is the ultimate moat? Yeah. But the thing about culture is it's very fragile. So moats, I don't think they're-- there's very few moats I found that are actually moats. They're-- It's, it's a nice concept, but in reality, you have to replenish your culture. Ben Horowitz was, the speaker in CS153 on Tuesday, and I asked him this question about the culture bottleneck in teams because, there are several AI teams-Swyx [00:49:09]: His book, Hard Things About Hard ThingsAnjney [00:49:11]: Hard Thing About Hard Things. But more concretely, there are so many AI labs today that have all the cash they need, they have all the compute they need, and they're still not able to ship anything SOTA. And then you start seeing people leave and so on, and my diagnosis, it's, is it's the culture. And so I asked him, Ben, they're-- He's been one of the most aggressive investors in AI labs. He goes back to this thing which resonates in my mind a lot. It-- When I used to work at a16z, I would, book a conference room, and right outside the conference room, which is closest to the toilet ‘cause it was the fastest way for me to go use the bathroom between Zoom meetings-Swyx [00:49:45]: Oh my God, I'll put maxing my toilet optimization. Okay, never mind.Anjney [00:49:48]: It was not healthy in hindsight, but maybe this is TMI. But anyway, outside that conference on the wall was this quote that was printed that said, “Culture is not a set of beliefs, it's a set of actions.” And it's by Bushido, is this, Japanese philosopher. And if you stop taking the actions that demonstrate the mission alignment to what you've said to your team and to your-- the world matters to you, then your culture starts to fray. So it's not actually a moat, I would say. It's a very brittle, fragile thing that requires daily tending to like a garden. But if you figure out the system to keep that garden tended, which I think ultimately comes down to knowing yourself ‘cause you most naturally, if you're authentic and so on, you'll naturally make trade-offs that seem effortless to you, but that reinforce your culture. And then That becomes this very hard thing for other people to catch up to. And at Anthropic, from day one, there was this mission like-- missionary like zeal and belief that, hey, these capabilities will scale. These systems are stochastic, not deterministic. There will be error bars, and until we crack interpretability, there's risk. And at some point, people will go-- stop using Claude just for coding. They'll use it in some mission-critical context where there's-- it'll throw off a bug, and then people are going to come blame them, and they want to be on the right side of history where they said, “Yes, this is a powerful technology. We think it's going to change the world, And we want to be very measured and scientific about the fact that, ‘Hey, guys, these are stats models, statistical models.' That's how statistics works.” ultimately, when you're training neural nets, it is just a statistical system. And I think that Belief that safety is important and that it might seem toy-like in the early days, and sometimes, you could say, “Anjney, they totally over-exaggerated the risk,” like two years ago when they said, “Let's not launch Claude One,” or whatever. Well, okay, maybe in hindsight, but hindsight is twenty/twenty. And at the time, they didn't know how that model would be used, and to them it felt existential if somebody came and said, “You weren't responsible. It-- This wrote a bug.” The liability associated with that is massive. So how do you prevent against that? Well, day in, day out, you say safety. And when you start deviating from that, you have the team hold you accountable, you have the world hold you accountable, and I think that becomes a moat over time. At some point, that moat will get challenged and so on, and then it become fragile. I hope it endures because that's the beauty of having founders run the show, ‘cause they can make really hard trade-offs to do mission alignment. The hardest part is in the earliest days when you don't have a group of people who are going through difficulty, stress, crisis together, then your culture doesn't get defined sharply enough, and that's what I'm worried about right now, is there's so much money going to these labs. There's no hardship. There's no-Swyx [00:52:50]: To anyone who knowsAnjney [00:52:51]: There's no to anyone who knows. And that, in hindsight, was a feature, not a bug for Anthropic. The number of people who said no, the number of people who said, “Sorry, we're all doing investors in OpenAI,” that is competitive difference. It forces you to really understand, what is the hill you want to die on at the expense of everything else. What's the P zero? And there, P zero from day one was coding. The reason, the mechanism system there was if we crack coding, Then we will crack AGI. Our mission is AGI. We want to get there safely. If we focus on codin

In this deep-dive episode of the Prolonged Field Care Podcast, Dennis sits down with trauma and critical care surgeon Dr. John McClellan ( University of North Carolina) to cut through the noise on tranexamic acid (TXA) in trauma.They cover the mechanism, who actually needs it, why the dosing shifted from 1g + drip to 2g upfront, pre-hospital decision-making when bleeding is controlled, redosing in ongoing hemorrhage, IM/IO options, seizure and hypotension concerns, the critical 3-hour window, and practical advice for the medic who is truly alone and afraid.Whether you're a combat medic, flight medic, or trauma provider, this conversation delivers actionable clarity on one of the most studied — and sometimes misunderstood — tools in hemorrhagic shock resuscitation.Key Takeaways:TXA is a lysine analog that reversibly (and at higher doses irreversibly) binds plasminogen, preventing its conversion to plasmin and stabilizing clots. It is one of the most evidence-backed hemorrhage adjuncts available.The ideal candidate is any patient you suspect will trigger (or has triggered) a massive transfusion protocol — not just obvious amputations. Err on the side of giving it early in pre-hospital/austere settings to avoid missing occult bleeding.Modern trauma practice favors 2g IV push upfront over the older CRASH-2 regimen of 1g bolus + 8-hour drip because traumatic bleeding is an acute event that needs rapid high plasma levels. The 8-hour drip was designed for elective surgical cases with ongoing bleeding over hours.Overall safety is excellent. Large meta-analyses have not shown a clear increase in thrombotic events attributable to TXA. The bigger practical risks are seizures with doses significantly above 2g and accidental double-dosing due to poor handoff between pre-hospital and hospital teams.Transient hypotension can occur with rapid push, but causality is murky — it is often impossible to separate from the patient's underlying shock state.Redosing is reasonable (another 1–2g) if significant re-bleeding causes hemodynamic instability. Roughly 25% of active TXA can be lost in major hemorrhage/transfusion models.Give TXA within 3 hours of injury for maximum benefit. After 3 hours efficacy drops sharply and some data suggest potential increased bleeding risk.For the solo medic: Preload if your protocol allows. Make TXA automatic once you have access (alongside calcium and blood products). Prioritize rapid transport. TCCC supports IM if no IV/IO is possible, though delivering the full 2g volume can be challenging.Documentation and clear handoff are non-negotiable when pre-hospital TXA is given.Chapters:00:00 – Welcome & Podcast Disclaimer00:25 – Guest Introduction: Dr. John McClellan, Trauma Surgeon01:52 – What is TXA and How Does It Actually Work?03:28 – Who Should Get TXA? The Massive Transfusion Patient04:16 – Pre-Hospital TXA: Bleed Control First or TXA First?07:06 – Safety Concerns: Thrombosis, Seizures & Double Dosing Risks09:54 – Dosing Evolution: CRASH-2, 1g + Drip vs 2g Push in Trauma13:33 – Does TXA Cause Hypotension? Unpacking the Evidence19:12 – IO & IM TXA: Practical Routes When IV Access Is Tough21:46 – Redosing TXA in Ongoing Bleeding or Transport29:37 – Advice for the Medic Who Is Truly “Alone and Afraid”32:21 – The 3-Hour Rule: Why Timing Matters and What Happens After34:14 – Final Thoughts & Practical Takeaways from Dr. McClellanFor more content, go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.prolongedfieldcare.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Consider supporting us: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠patreon.com/ProlongedFieldCareCollective⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.lobocoffeeco.com/product-page/prolonged-field-care⁠⁠

TRASCRIZIONE E VOCABOLARIOPuoi sostenere il mio lavoro con una donazione su Patreonhttps://www.patreon.com/italianosiPer €2 al mese riceverai le trascrizioni di tutti i PodcastPer €3 al mese riceverai, oltre alle trascrizioni, anche una lista dei vocaboli più difficili, con spiegazione in italiano e traduzione in inglese.CONTENUTICominciamo parlando di scarlattina e concludiamo parlando di decrescita felice. Due cose apparentemente scollegate tra di loro e infatti lo sono.  Non so nemmeno io come riassumere questo episodio in poche righe, quindi posso solo augurarvi buon ascolto. TRASCRIZIONECiao a tutti e ciao a tutte! Bentornati o benvenuti nel podcast di Italiano Sì. Se siete nuovi, grazie intanto per aver cliccato su questo podcast, spero che vi piacerà. Io sono Elisa, sono un'insegnante di italiano per stranieri, insegno online e all'università. Sono anche un'esaminatrice, cioè preparo, scrivo, e faccio fare gli esami di italiano all'università. Ma adesso vi racconto un po' com'è stata la mia settimana. Non ho molto da dire, tra un attimo capirete perché. [...]MY YOUTUBE CHANNELSupport the show

🧭 REBEL Rundown Click here for Direct Download of the Podcast. 💨 What Is Nitrous Oxide? Nitrous Oxide (N2O) is a colorless, odorless inhaled anesthetic that has been used for centuries, particularly in the surgical world. Mechanistically, it can induce euphoria, anxiolysis, and intoxication via NMDA receptor antagonism.During the late twentieth century, nitrous oxide was increasingly used recreationally due its accessibility and perceived benign nature.The modern day slang term for nitrous oxide is “whippets” – which tends to refer to the canisters that contain this agent and are frequently used as whipped cream foaming agents.Despite the legal nature and benign perception of nitrous, frequent use can lead to lasting and permanent neurologic effects. 🧠 How Nitrous Oxide Causes Toxicity Nitrous oxide toxicity results from its ability to oxidize the cobalt moiety in Vitamin-B12, thus leading to a functional B12 deficiency, despite adequate consumption and absorption.1Functioning B12 is needed as a cofactor for methionine synthase.2 This enzyme has two critical roles:The conversion of 5-methyl tetrahydrofolate to tetrahydrofolate; tetrahydrofolate is essential for the synthesis of our DNA.And the conversion of homocysteine to methionine; methionine is needed to maintain the integrity of the myelin sheath of our axons.As a result, nitrous toxicity leads to: a megaloblastic anemia and demyelination of both the dorsal columns and the lateral corticospinal tracts (also known as subacute combined degeneration). 🚶‍️ Clinical Manifestations of Nitrous Oxide Toxicity These patients will have a combination of both upper and lower motor neuron symptoms due to demyelination of the dorsal columns, lateral corticospinal tracts, and peripheral nerves. As a result, the following may manifest:Dorsal Columns: diminished sense of proprioception, vibration, and fine touch.Lateral Corticospinal Tracts: upgoing plantars, hyperreflexia, weakness of voluntary distal muscle controlPeripheral Nerves: numbness/tingling and weakness in a glove and stocking pattern (symptoms that start initially in the feet and hands that progressively spread proximally to the ankles and wrists)Taking all of this into account, patients may present with difficulty ambulating, positive Romberg sign, dysmetria (difficulty with finger to nose or heel to shin), upgoing Babinski reflex, and decreased strength and sensation in a glove and stocking pattern. 🔍 How to Diagnose Nitrous Oxide Neurotoxicity History is key! As with a lot of pathologies in toxicology, identifying the exposure will expedite management.A thorough neurologic exam will narrow the differential – with a particular focus to fine, peripheral motor and sensory deficits, dysmetria, proprioception, and ability to ambulate.Magnetic resonance imaging of the spine may identify enhancement and/or edema of the dorsal columns, specifically on T2 weight axial imaging – sometimes referred to as the “inverted V” or “inverted rabbit ears appearance.”3Serum B12 concentrations may be normal as the issue is with a functional deficiency as opposed to a vitamin absence. However, patients have elevated concentrations of both homocysteine and methylmalonic acid, both of which are metabolized in the presence of functional B12. 💉 Management of Nitrous Oxide Toxicity First and foremost, cessation of nitrous oxide abuse is crucial to limit/prevent toxicity.While there is no universally agreed upon treatment regimen, supplementation with intramuscular B12 is recommended.Approaches vary from daily or every other day injections until symptoms improve at which point injections can be spaced out to weekly and then monthly.Physical and occupational therapy may be needed depending on the degree of functional debility.It is important to note, that depending of the severity and chronicity of toxicity, some proportion of patients may not fully return to their baseline. 📌 Take-Home Points Though legal and seemingly benign, nitrous oxide abuse can lead to permanent neurologic dysfunction.Nitrous oxide toxicity can affect the dorsal columns, lateral corticospinal tracts, and peripheral nerves.Thus leading to a constellation of both upper and lower motor neuron deficits, particular in a glove and stocking pattern: deficits in proprioception and fine motor skills, positive Romberg, upgoing Babinski, peripheral numbness, tingling, and weakness.Magnetic resonance imaging may identify symmetric high signal intensity in the dorsal columns.Treatment includes B12 supplementation and physical/occupational therapy as needed. 📚 References Long H. Chapter 81. Inhalants. In: Nelson LS, et al. Goldfrank’s Toxicologic Emergencies. 11th ed. New York: McGraw-Hill; 2019Shah K, Murphy C. Nitrous Oxide Toxicity: Case Files of the Carolinas Medical Center Medical Toxicology Fellowship. J Med Toxicol. 2019 Oct;15(4):299-303. doi: 10.1007/s13181-019-00726-x. Epub 2019 Aug 6. PMID: 31388940; PMCID: PMC6825085.Schmitz ZP, Hoffman RS. Magnetic resonance imaging in a patient with nitrous oxide-induced subacute combined degeneration of the spinal cord. Clin Toxicol (Phila). 2023 Nov;61(11):1006-1008. doi: 10.1080/15563650.2023.2286205. Epub 2023 Dec 19. PMID: 38060330. Post Peer Reviewed By: Marco Propersi, DO (Twitter/X: @Marco_propersi), and Mark Ramzy, DO (X: @MRamzyDO) 👤 Associate Editor Anand Swaminathan MD, MPH All Things REBEL EM Meet The Team 🔎 Your Deep-Dive Starts Here REBEL Core Cast – Pediatric Respiratory Emergencies: Beyond Viral Season Welcome to the Rebel Core Content Blog, where we delve ... Pediatrics Read More REBEL Core Cast 143.0–Ventilators Part 3: Oxygenation & Ventilation — Mastering the Balance on the Ventilator When you take the airway, you take the wheel and ... Thoracic and Respiratory Read More REBEL Core Cast 142.0–Ventilators Part 2: Simplifying Mechanical Ventilation – Most Common Ventilator Modes Mechanical ventilation can feel overwhelming, especially when faced with a ... Thoracic and Respiratory Read More REBEL Core Cast 141.0–Ventilators Part 1: Simplifying Mechanical Ventilation — Types of Breathes For many medical residents, the ICU can feel like stepping ... Thoracic and Respiratory Read More REBEL Core Cast 140.0: The Power and Limitations of Intraosseous Lines in Emergency Medicine The sicker the patient, the more likely an IO line ... Procedures and Skills Read More REBEL Core Cast 139.0: Pneumothorax Decompression On this episode of the Rebel Core Cast, Swami takes ... Procedures and Skills Read More Showing Slide 1 of 7 The post REBEL Core Cast—Nitrous Oxide Toxicity: Whippets and Neurologic Injury appeared first on REBEL EM - Emergency Medicine Blog.

It’s that time of the year when JOY asks for your support to remain being out, loud and proud – JOY Radiothon. This year, we’re mixing it up with JOY by combining JOYEurovision and babblePOP! to bring back babbleVISION! Michael and Io play some Eurovision classics alongside new bops and bangers in the same languages to help you celebrate with JOY. Io’s enjoying the World Cup, while Michael’s shopping for a pirate shirt. And we’re looking forward to inviting (soon to be) famous drag queen Roy Jadiothon to JOY for next year’s Radiothon spectacular. Get involved You can show your support during JOY Radiothon by becoming a member or donating at joy.org.au/radiothon Follow JOYEurovision across Facebook, Instagram, Threads, TikTok, Bluesky and X at linktr.ee/joy_eurovision Playlist Spanish Eurovision 2007: D’Nash ️‍ – I Love You Mi Vida        Brand-new babble: Lucenzo – Limoncello        Danish Eurovision 1963: Grethe & Jørgen Ingmann – Dansevise [Dance song]        Brand-new babble: Katinka – På Tværs [Across]        Finnish Eurovision 1983: Ami Aspelund – Fantasiaa [Fantasy]        Brand-new babble: Benjamin ️‍ – Badabim (My Kind of Terapiaa)        Polish Eurovision 1995: Justyna – Sama [Alone]        Brand-new babble: Lor – obcy (1979) [alien]        Italian Eurovision 1977: Mia Martini – Libera [Free]        Brand-new babble: Orietta Berti ft il rosso & IAEM – QUADRI CUORI PICCHE FIORI [DIAMONDS HEARTS SPADES CLUBS]         The post JOY Radiothon 2026: Celebrate with JOY (babbleVISION pt 2) appeared first on JOY Eurovision.

Patrick Moorhead and Daniel Newman cover Tim Cook's final WWDC as CEO and Apple's Gemini-powered Siri strategy, the $35 billion Apollo and Blackstone deal backing Anthropic's capacity expansion, Intel's packaging wins with Google and NVIDIA, SpaceX's IPO at a $1.77 trillion valuation, Anthropic's Claude Fable 5 and Mythos 5 launch across every major cloud, and earnings reactions from Oracle, Micron, and Adobe. The handpicked topics for this week are: Apple's Siri AI Will Run on Gemini, Closing Out Tim Cook's Final WWDC as CEO: At WWDC, Apple confirmed Siri AI will run on Gemini through a new billion-dollar per year, multi-year deal, while Apple's Foundation Model Cloud Pro runs on NVIDIA GPUs inside Google Cloud. The announcement marks Tim Cook's last WWDC as CEO before John Ternus takes over on September 1. Apple isn't building its own AI cluster or competing on CapEx. They're betting that by owning the consumption layer, backed by access to health data and private messaging through iMessage, Apple will have a moat that compute spending can't replicate. (The Decode) Apollo and Blackstone Close the Largest Private Credit Deal Ever Backing Anthropic's Capacity Expansion: A $35 billion deal, the largest private credit transaction on record, will fund Google TPU capacity tied to Anthropic's compute needs, with Broadcom backstopping senior debt tranches and Google backstopping lease payments. The structure treats compute as a lendable asset class and signals more than 20 gigawatts of demand still being built out through 2028. Circular financing between chipmakers, cloud providers, and AI labs has moved from controversial to standard practice. (The Decode) Intel's Foundry Wins Packaging Work on Google's TPUs, Not a Full Fab Deal: Reports that Intel landed a deal tied to Google and NVIDIA reframe what's actually being handed off. Intel gets the packaging work on over 3 million TPUs, the compute die stays with TSMC, and the I/O die is being negotiated with Samsung at 2nm. INTC rose 12% Monday. The deal represents a low-risk path for Intel to augment, not replace, TSMC, while raising questions about anti-competitive dynamics in the foundry market. (The Decode) SpaceX Becomes an AI Infrastructure Company With a $1.77 Trillion IPO: SpaceX's IPO priced amid oversubscribed demand, with its valuation now reflecting not just Starlink connectivity and launch dominance but a newly material AI business, including AI1 orbital data center tests planned for late 2027 and a $920 million per month Google compute contract running through 2029. A sum-of-the-parts breakdown of the connectivity, launch, and AI segments lands well short of the trading price, with the gap largely explained by confidence in Elon Musk's track record of execution. (The Decode) Anthropic Launches Claude Fable 5 and Mythos 5 Across Every Major Cloud: Anthropic shipped Claude Fable 5 and Mythos 5 with same-day availability across Snowflake, AWS Bedrock, Vertex AI, and Microsoft Foundry, pricing at $10 and $50 per million tokens. The hyperscaler-neutral distribution strategy lands ahead of Anthropic's anticipated IPO. The models represent a real step up in research capability over Opus 4.8, but they come with a significant change. Users no longer have the option to opt out of data sharing with Anthropic, a shift some enterprises, including Microsoft, are already responding to. (The Decode) Is SpaceX a Once-in-a-Generation Entry or the Top of the Market? One side argues SpaceX represents a generational opportunity on par with early Amazon or Netflix, with interplanetary travel and off-world resource extraction as the long-term payoff that justifies looking past current valuation math. The other side argues this is peak euphoria: a company trading at roughly 95 times sales, propped up in part by circular investment from Google into both SpaceX and its AI segment, with a steep drawdown likely before any sustained climb. (The Flip) The Chip and Security Trade Reverses From Broken to Bifurcated: The semiconductor sector posted its biggest single-day gain since 2020, with the SOX up 5% on Monday, June 8, as a prior selloff in names like Broadcom, CrowdStrike, and Palo Alto Networks fully reversed. Intel rose 12%, Marvell 10%, and Corning 7%. The rebound reframes the AI trade narrative from a broad breakdown to a split between winners and laggards within the same sector. (Bulls & Bears) Oracle Posts a Record Quarter, But the Market Focuses on a $50 Billion Funding Plan: Oracle delivered record revenue of $19.2 billion, up 21 %, with EPS of $2.11, beating estimates of $1.89. IaaS grew 93 %, the fastest pace among hyperscalers, and RPO hit $638 billion, up $85 billion quarter over quarter, including $75 billion in AI contracts. FY27 guidance of $90 billion was maintained, and EPS guidance was raised, yet the stock fell 5% after hours amid concerns about Oracle's capital spending plans. Oracle's AI cloud backlog now exceeds those of AWS, Google, and Microsoft, built heavily on commitments from Anthropic and OpenAI. (Bulls & Bears) Micron's Profit Trajectory Puts It in Google's Earnings Tier: Micron is projected to generate nearly as much profit in 2027 as Google, with Q2 revenue of $23.86 billion, up 22 % and beating estimates, and Q3 guidance of $33.5 billion in revenue, $19.15 EPS, and 81 % gross margin. The stock is up 776%, with Wall Street firms, including UBS, raising price targets. The open question is whether memory has broken its historically cyclical pattern given sustained AI demand. (Bulls & Bears) Adobe Beats Across the Board, But the Stock Drops on CEO Departure and Freemium Pivot: Adobe posted record revenue of $6.62 billion, up 13 % and beating consensus of $6.45 billion, with non-GAAP EPS of $5.96, topping estimates of $5.81. AI first ARR tripled year over year to over $500 million, with total ARR reaching $27.1 billion, and FY26 guidance was raised. The stock still fell 5.5 % after hours, driven by the CFO's departure to Marvell and market concern over a strategic shift toward freemium pricing that delays near-term profitability. (Bulls & Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. The Decode Apple WWDC- Apple Caves to Google AND NVIDIA — Siri AI Runs on Gemini ($1B/yr) + Apple Foundation Model Cloud Pro Runs on NVIDIA GPUs in Google Cloud; Tim Cook's Final WWDC as CEO Before John Ternus Succeeds Him Sept 1 https://www.cnbc.com/2026/06/08/apple-wwdc-2026-live-updates.html Google's $35B Infra Deal — Apollo + Blackstone Close the Largest Private Credit Deal Ever; Broadcom Backstops Senior Tranches; Google Backstops Lease Payments https://www.reuters.com/business/apollo-blackstone-back-anthropics-35-billion-capacity-expansion-new-broadcom-tie-2026-06-09/ Intel's Foundry Reportedly Wins Google Packaging (Not Full Fab) — The Information Reframed: 3M+ TPU Packaging by Intel, Compute Die Still TSMC, I/O Die Being Negotiated With Samsung 2nm; INTC +12% Monday; Pat Calls Out TSMC Anti-Competitive Risk https://www.trendforce.com/news/2026/06/09/news-intel-foundry-gains-momentum-as-google-reportedly-orders-3m-tpus-nvidia-evaluates-18a-for-multi-die-gpu-design/ SpaceX Becomes an AI Infrastructure Company — Friday IPO at $1.77T; AI1 Orbital Data Center Tests Late 2027; Google $920M/mo Compute Contract Through 2029 https://finance.yahoo.com/markets/stocks/articles/spacex-poised-history-record-75-100000402.html Anthropic Ships Claude Fable 5 + Mythos 5 — Same-Day Distribution Across Snowflake, AWS Bedrock, Vertex AI, Microsoft Foundry; Hyperscaler-Neutral by Design Ahead of IPO; $10/$50 per M Tokens https://www.anthropic.com/news/claude-fable-5-mythos-5 The Flip FOR: https://www.cnbc.com/2026/06/11/spacex-billionaire-investing.html AGAINST: https://www.nytimes.com/2026/05/20/technology/elon-musk-spacex-ipo.html Bulls & Bears The Chip + Security Tape Recovery — SOX +5% Monday June 8 (Biggest Day Since 2020); AVGO/CRWD/PANW Selloff Reversed; Intel +12%, Marvell +10%, Corning +7%; the AI Trade Pivots From "Broken" to "Bifurcated" https://www.investopedia.com/stock-market-today-dow-jones-s-and-p-500-06082026-11992852 Oracle (ORCL) Q4 FY26 ACTUALS — Record $19.2B Rev (+21%), EPS $2.11 Beat ($1.89); IaaS +93%; RPO HITS $638B (+$85B QoQ, $75B AI Contracts); FY27 $90B Guide Maintained, EPS Guide Raised; Stock −5% AH on Massive Capex Plan https://www.tradingkey.com/analysis/stocks/us-stocks/261959450-oracle-record-q4-2026-earnings-report-cloud-data-center-stock-tradingkey "$MU Will Generate Almost As Much Profit in 2027 as $GOOGL"; Q2 Rev $23.86B (+22% Beat), Q3 Guide $33.50B / $19.15 EPS / 81% GM; MU Stock +776%; UBS Among Wall Street Raising Targets https://247wallst.com/investing/2026/06/11/wall-street-just-put-a-monster-target-on-micron-is-the-stock-still-too-cheap/ Adobe (ADBE) Q2 FY26 ACTUALS — Record $6.62B Rev (+13%) Beats Consensus $6.45B; Non-GAAP EPS $5.96 Beats $5.81; AI-First ARR Triples YoY to $500M+; Total ARR $27.10B; FY26 Guide RAISED; Stock −5.5% AH Despite Beat-and-Raise https://www.businesswire.com/news/home/20260611677110/en/Adobe-Reports-Record-Q2-Results    

Beth GMs for Ellie, Crash, Io, and Paul. This episode: The Technicalities follow the pipes towards a supposed destination and learn things along the way. Follow this series on… RSS: https://aaronbsmith.com/cogwheel/tag/gurpswars/podcast Patreon: https://www.patreon.com/cogwheelgaming Mastodon: https://is.aaronbsmith.com/@cogwheel Not on Mastodon? Consider these instances: gamepad.club dice.camp mastodon.art chirp.enworld.org tabletop.vip MP3 Download: GURPS Wars: Technicalities S1 Ep 09: A Series of Tubes Music Used: “biotech” by Kokesz is Public Domain and can be downloaded from http://modarchive.org. Keep us ad free by supporting us on Patreon! Thanks to our current Patreon Patrons (as of this upload…): Ellie, Liv Dromen, Paul, ShanShen, Walter, & Patron Emeritus Cindy!

It’s that time of the year when JOY asks for your support to remain being out, loud and proud – JOY Radiothon. This year, we’re mixing it up with JOY by combining JOYEurovision and babblePOP! to bring back babbleVISION! Michael and Io play some Eurovision classics alongside new bops and bangers in the same languages to help you sing with JOY. Get involved You can show your support during JOY Radiothon by becoming a member or donating at joy.org.au/radiothon Follow JOYEurovision across Facebook, Instagram, Threads, TikTok, Bluesky and X at linktr.ee/joy_eurovision Playlist Croatian Eurovision 1999: Doris Dragović – Marija Magdalena [Mary Magadelene]        Brand-new babble: Detour – Pusti me da spavam [Let me sleep]        French Eurovision 1956: Dany Dauberson ️‍ – Il Est Là [He is Here] Brand-new babble: kissed – reviens me voir [come back and see me]        Icelandic Eurovision 1994: Sigga ️‍ – Nætur [Nature]        Brand-new babble: Tatjana – Háð þér [Depends on you]        Slovenian Eurovision 2002: Sestre ️‍ – Samo ljubezen [Only love]        Brand-new babble: Damjan Murko – Moj Mali Ku… [My Little Pup…]        Turkish Eurovision 1980: Ajda Pekkan – Pet’r Oil [Petrol]        Brand-new babble: manifest & Ajda Pekkan – Hileli [Fraud]        The post JOY Radiothon 2026: Sing with JOY (babbleVISION pt 1) appeared first on JOY Eurovision.

[#402 – Make your mark with JOY during Radiothon] It’s that time of the year when JOY asks for your support to remain being out, loud and proud – JOY Radiothon. This year, we’re mixing it up with JOY by combining JOYEurovision and babblePOP! to bring back babbleVISION! Michael and Io play some Eurovision classics alongside new bops and bangers in the same languages to help you celebrate with JOY. Io’s enjoying the World Cup, while Michael’s shopping for a pirate shirt. And we’re looking forward to inviting (soon to be) famous drag queen Roy Jadiothon to JOY for next year’s Radiothon spectacular. You can show your support during JOY Radiothon by becoming a member or donating at joy.org.au/radiothon Liked a particular track? Click the link to check out the video. And don’t forget to follow across social media: Facebook | X (Twitter) | Threads Playlist Spanish Eurovision 2007: D’Nash ️‍ – I Love You Mi Vida        Brand-new babble: Lucenzo – Limoncello        Danish Eurovision 1963: Grethe & Jørgen Ingmann – Dansevise [Dance song]        Brand-new babble: Katinka – På Tværs [Across]        Finnish Eurovision 1983: Ami Aspelund – Fantasiaa [Fantasy]        Brand-new babble: Benjamin ️‍ – Badabim (My Kind of Terapiaa)        Polish Eurovision 1995: Justyna – Sama [Alone]        Brand-new babble: Lor – obcy (1979) [alien]        Italian Eurovision 1977: Mia Martini – Libera [Free]        Brand-new babble: Orietta Berti ft il rosso & IAEM – QUADRI CUORI PICCHE FIORI [DIAMONDS HEARTS SPADES CLUBS]         The post Four hundred and two – Celebrate with JOY (babbleVISION pt 2) appeared first on babble POP!.

[#402 – Make your mark with JOY during Radiothon] It’s that time of the year when JOY asks for your support to remain being out, loud and proud – JOY Radiothon. This year, we’re mixing it up with JOY by combining JOYEurovision and babblePOP! to bring back babbleVISION! Michael and Io play some Eurovision classics alongside new bops and bangers in the same languages to help you sing with JOY. You can show your support during JOY Radiothon by becoming a member or donating at joy.org.au/radiothon Liked a particular track? Click the link to check out the video. And don’t forget to follow across social media: Facebook | X (Twitter) | Threads Playlist Croatian Eurovision 1999: Doris Dragović – Marija Magdalena [Mary Magadelene]        Brand-new babble: Detour – Pusti me da spavam [Let me sleep]        French Eurovision 1956: Dany Dauberson ️‍ – Il Est Là [He is Here] Brand-new babble: kissed – reviens me voir [come back and see me]        Icelandic Eurovision 1994: Sigga ️‍ – Nætur [Nature]        Brand-new babble: Tatjana – Háð þér [Depends on you]        Slovenian Eurovision 2002: Sestre ️‍ – Samo ljubezen [Only love]        Brand-new babble: Damjan Murko – Moj Mali Ku… [My Little Pup…]        Turkish Eurovision 1980: Ajda Pekkan – Pet’r Oil [Petrol]        Brand-new babble: manifest & Ajda Pekkan – Hileli [Fraud]        The post Four hundred and two – Sing with JOY (babbleVISION pt 1) appeared first on babble POP!.

ぬるぽ放送局おたより投稿フォーム https://forms.gle/6tbmBzK6wbyavJG47 2026年6月パワープレイ 「Phantasmagoria mystical expectation」 アレンジ・ギター・ベース　ARM ボーカル　悠 杏李 作詞　kiku　夕野ヨシミ 原曲：風神少女 音楽ジャンル：ミクスチャーポップ 収録アルバム：東方風櫻宴 2006・5・21 Release https://www.iosysos.com/discographyportal.php?cdno=IO-0090 https://www.youtube.com/watch?v=fOmaLZDp3y0 番組時間：86分22秒 出演者：夕野ヨシミ、たくや VOICEVOX:ずんだもん VOICEVOX:四国めたん ---- 2026/6/11に公開録音したものを配信いたします。 ラジオ記事はリスナーのEEチャンピオンさんが書いてくれているので楽してます。 ＜オープニング＞ ・札幌も夏が始まりました ・外は、暑いんでしょうね ・喉の肉離れ ・VDONinjaの調子が悪い ・今日はアイドリングがないから事故っちゃう ・ポッドキャストの人は待ってないよ ・イオシスくんの活動をあれしますか ・かつ丼と活動って似てますよね ・＜楽曲提供＞ 　カバー楽曲 　「天ノ弱」／ドラゴンブラッド：スレイヤーズ学院 　歌唱：花たん 　作詞・作曲：164 　編曲：コバヤシユウヤ(IOSYS) 　ギター：三浦公紀 　ベース：john=hive(IOSYS) ・ドラゴンブラッドを始めるなら今！ ・正解はじゃがポックル ・じゃがポおじさん ・楽曲提供のお知らせ 　「私たちは、花になる／イロドリミドリ｜HaNaMiNa｜S.S.L.」 　作詞：七条レタス 　作曲：D.watt 　編曲：fu_mou(Hifumi,inc.) ・楽曲提供のお知らせ 　「きゅんキラ☆ネバギバ行進曲／あぴゃりちゃん」 　作編曲：コバヤシユウヤ 　作詞：john=hive 　Guitar：三浦公紀 ・トピックチャンネルとは ・やはり、かわいいキャラは必要 ・自由の女神を女性枠ととらえるとは ・ガワだけのwiki ・追加されたよ 　Nintendo Switch『グルーヴコースター フューチャーパフォーマーズ』 　2026/6/11 無料アップデート 　「HG魔改造ポリビニル少年」 　作詞・作編曲：IOSYS TRAX 　歌：さきぴょ ・YouTubeタイトーチャンネルにて試聴動画が公開されました 　「DX超性能フルメタル少女」 　作詞・作編曲：IOSYS TRAX 　歌：ちよこ 　「HG魔改造ポリビニル少年」 　作詞・作編曲：IOSYS TRAX 　歌：さきぴょ ・もう、12,3年前 ・アメリカニキは現金を持ってきてください ・​ありったけのキャッシュをかき集め ・何をやります？ ・1分将棋を盤面もなく初心者が？ ・歩が8枚集まってキング歩 ・マイクラ将棋 ・ムダ話を雑談力って言いました？ ・新日本将棋連盟作ろう ＜Aパート＞ ・ふつおたです ・歯医者で引き分け ・ぬるぽもギネスいけるのでは？ ・急なニンテンドーダイレクト ・強引に同意を求める ・ビールおかわりした直後にワインを飲む ・生ビール放送 ・東方projectすげーな ・ニュークラだとキャバクラになっちゃうな ・ぴっちりした服はみんな好きだから ・歯って欠けませんか？ ・吉野家がタッチパネルに ・梅干しとチーズと炭酸水しかない冷蔵庫 ・え？ネットスーパーで2万も？何を？ ・ウイスキーは普通1本で済むから ・お便り1通で何分やってるのか ・ホラー映画をご所望 ・ミーガン ・女性Vならホラーゲームは映えますよね ・英語のタイトルなら自信がない ・東方アレンジっぽい単語を組み合わせる ・穴からは離れてほしい ・マスパ音頭はありそう ・バニーガーデンを買ってしまいました ・重い過去のキャラに定評のあるキュリエイトさん ・今日は漫才をやりますか ・そのお店がグレーだったとしても？ ・片玉から紹介されました ・バター犬牧場ってなんだよ ＜Bパート＞ ・みつをたです ・水道管が壊れたので送ります ・おっきなゴンってなんだよ ・シアンさんどうしたの？ ・減った骨は食べちゃったの？ ・暗殺の母のＣＶが柴田理恵さん ・ばんちょーがせくちーな件について ・豆柴でごまかせる ・供給の多いブルアカ ・ブルアカ始めるなら今！ ・にじさんじピックアップニュース ・にじさんじストーンズ ・小ジョッキで水を飲みましょう ・でび様の新曲 ・カラオケでオケツブンブンフェスティバル ・ほな、エンドラ討伐がええんじゃないかな ・ボーイは食べ物じゃないんだよな ・ホロピックアップニュース ・しぐれういだから ・75万円のエレキギター ・イオシスは1万日ですけどね（マウント ・100万円のＰＣも使ったことない ・合体してもスペックは大したことはない ・お家で核融合発電 ・Ｖピックアップニュース ・ローソンのVTuber ・いろんなＶがいるんだね ・ガッツ石松さんご冥福をお祈りします ・ロリ3人組 ・今はフローラ ・ポロって出るゆうじ ・おにぎりスライムとは ・ゲーム実況をやる曜日が足りない ・冥曜日 ・朝配信でおやすみなさーい ・お便りお待ちしてます ＜エンディング＞ ・Forza Horizon 6やりますか ・あまりテクテクライク知識は生かせない ・梅雨はやる気あるんですか？ ・もう、ほぼ水 ・キリン5番絞り ・体内で石の錬成しないようにしましょう

AI can finally write back to the plant floor, but only if you can trust it. Chris Stevens and Annemarie Breu of Siemens explain how orchestration makes that safe.Industrial AI has reached a turning point. Manufacturers can already collect data, contextualize it, and surface insights, but the hardest step has always been turning insight into action on real control equipment. Chris Stevens and Annemarie Breu of Siemens explain how an orchestration layer finally closes that loop. Annemarie frames the tension clearly. Automation depends on determinism, while large language models are probabilistic by design, so the goal is to bring that discipline into AI and validate any suggestion before it changes a set point.Most executive conversations start with return on investment, and two forces are making the case easier to prove. The workforce shortage has stretched the expected payback window from 18 months toward 36 months, and when a line cannot run for lack of people every idle minute costs thousands of dollars. The other driver is overall equipment effectiveness, since most plants run near 70 percent OEE and even a fraction of a percent of gain can justify a project. Energy is a standout case too. A BorgWarner sustainability effort used a digital twin to flatten demand peaks and reportedly paid for itself in under six months, even as data center growth pushes electricity demand higher through 2040.On trust and safety, Annemarie borrows a principle from industrial safety. Just as fail safe IO modules rely on two channel evaluation, every AI suggestion is validated against a state machine, a workflow, or a physics based digital twin before the orchestration layer passes it to a controller. With virtual commissioning and soft PLCs a change can be tested virtually, approved by a human in the loop, and only then written to control, an approach PepsiCo and NVIDIA echoed at CES when they called the digital twin a must have. Making AI real, the pair argue, comes down to discipline, clear scope, acceptance criteria, and focused 90 day challenges, plus the change management and user experience that drive adoption. Their favorite quick win is preventive maintenance driven by machine data, which both BorgWarner and Maersk tied to millions in savings.About Chris StevensChris Stevens is President of US Automation at Siemens, where he leads a roughly one billion dollar business spanning software, services, and hardware. He brings more than 25 years across Siemens Digital Industries, starting in the field selling assembly and test equipment, moving into the software and digital twin world, and returning to automation to bring the hardware and software sides of the business together.About Annemarie BreuAnnemarie Breu is a senior technology leader at Siemens Digital Industries focused on automation software deployment and customer technology partnerships in the US. She began at Siemens about a decade ago as a systems engineer in the San Francisco Bay Area, working with consumer electronics manufacturers on virtual commissioning and digital twins. Her work today centers on bringing the determinism and reliability of automation into industrial AI.Timestamps0:00 Introduction and Automate 2026 preview2:50 Meet Chris Stevens and Annemarie Breu9:30 The first AI question is always ROI14:00 Workforce gaps and OEE drive the business case19:30 Energy management and the data center demand surge23:20 Data, sensors, and contextualization requirements28:00 Guardrails, hallucinations, and two channel validation32:40 The digital twin and the human in the loop37:40 How partners and integrators move up the stack45:30 What it takes to make AI real on the floor55:50 Preventive maintenance as a quick win59:40 Predictions, career advice, and book picksAbout Your HostsVladimir Romanov is a co-host of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to de-risk modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Edge Computing and the Value of AI in Manufacturing Data: https://www.joltek.com/blog/edge-computing-ai-value-manufacturing-dataIT and OT Architecture Integration: https://www.joltek.com/services/service-details-it-ot-architecture-integrationDave Griffith is a co-host of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub

PHP Podcast – June 11, 2026 Guest Hosts: Sara Golemon, Elizabeth Barron & Holly Schilling Eric and John are out this week — Sara, Elizabeth, and Holly take over. Here’s what they covered: PHPVerse Recap PHPVerse just wrapped up, and Elizabeth was there in Amsterdam. The format is unusual — all speakers are flown to one location, but the audience is entirely virtual. It was a class act: professional TV crew, studio lighting, and a makeup and hair team on site. Around 2,500–3,000 people watched the live stream. Everything was broadcast as one long block; individual talk segments and possibly the documentary trailer will be cut and released separately. The full stream is available now — the PHP documentary trailer (produced by Jet Breeze, covering 30+ years of PHP history) appears around the 2:24:30 mark. PHP Foundation 2026 Strategy Document Elizabeth and the PHP Foundation released their 2026 strategy document the same day as this recording. The foundation gathered community input across numerous conversations and conferences, synthesized it into findings, and has now published a plan for the rest of the year. Key themes: repositioning PHP’s public perception (which Elizabeth calls a solvable problem), creating six special interest groups, and launching an Onboarding Initiative to build a real on-ramp for new PHP developers. Elizabeth’s view is that the two things giving her the most hope for PHP’s future are the passion and expertise of the community, and how good the language itself has gotten. Visit thephp.foundation to read the full document. The Onboarding Initiative One of the six special interest groups the foundation is launching is specifically focused on bringing new developers into PHP. Goals include creating a true learning path (not just a reference manual that assumes existing knowledge), improving educational resources, and potentially working with the php.net website to improve the first-time experience. Holly made the point that PHP’s barrier to entry is genuinely lower than almost any other language — the Hello World program is 11 characters — but that story isn’t being told outside the PHP bubble. New developers are turning to JavaScript as a first language and running into minified spaghetti instead of something approachable. AI Writing PHP — And PHP as a Second Language Holly built the entire PHP Tek conference app backend in Laravel without writing a single line of code herself — AI-generated throughout, which she reviewed and approved. The code held up to peer review at the conference with only minor style nits. She ran it on PHP 8.3 and used modern standards throughout (one piece of feedback: stop using empty()). The consensus: AI models write good modern PHP because of the vast amount of open source PHP they were trained on. The caveat Sara raised is worth thinking about — how much of that training data is PHP 4-era code and WordPress 3 repositories? Either way, Holly’s case for PHP as a second language is strong: low ceremony, low boilerplate, readable syntax, and it’s a language where you can do something useful in minutes. PHP’s Reputation Problem (and Why It’s Fixable) The group dug into PHP’s perception gap — the mismatch between how good the language actually is and how it’s perceived outside the community. Holly’s experience as a mobile developer who recommends PHP to others: the pushback is immediate (“isn’t that slow?”, “isn’t that dead?”). The benchmarks don’t support that reputation — PHP outperforms Python on most comparable workloads — but data alone doesn’t shift perception. Elizabeth’s point is that this is primarily a storytelling and coordination problem, not a language problem, and that the foundation’s repositioning work is exactly aimed at closing that gap. The community has the passion. It just needs to tell the story outside its own bubble. PHP Polling API RFC Sara walked through the RFC for a new Polling API in PHP (wiki.php.net/rfc/poll_API). The short version: PHP currently has five or six different ways to do I/O multiplexing (watching multiple streams and acting on whichever one is ready first), and which one works depends on the OS, available extensions, and PHP version. The Polling API proposal creates a single, unified interface that abstracts all of that. The immediate beneficiaries are async frameworks like Amp PHP, ReactPHP, and Revolt, which currently have to maintain multiple backend implementations to cover different environments. The bigger picture: this is a building block on the path toward true async PHP, likely contributing to something more complete in PHP 9.0. Most app developers won’t use it directly — but the libraries they depend on will. RFCs are all listed at wiki.php.net/rfc. PHP.net: Do As We Say, Not As We Do Sara, who has contributed to php.net, copped to the state of the codebase: some of it dates to the PHP 3 era, there are functions.inc files, and it is very much “do as we say, not as we do.” The historical reason is that php.net used to rely on community-administered mirrors (r-synced servers running everything from PHP 5.1 to 5.6 simultaneously), so modernizing the code was impossible without controlling the runtime. That’s changed with CDN-based load balancing — they can now control what PHP version runs on php.net — and the code has been getting better. But it’s a slow process. PHP Podcasts Past, Present, and Future Holly asked about the PHP Town Hall podcast (Ben Edmonds and Phil Sturgeon), and the group did a quick tour of PHP podcast history. The PHP Roundtable — originally started by Sammy, taken over by Eric — has produced about three episodes. Sara and producer Joe are planning to take it off Eric’s hands and actually do it properly. And Elizabeth announced that the PHP Foundation is launching a new podcast: tentatively called PHP at Scale, hosted by Ben Marx, focused on telling the stories of organizations pushing PHP to its limits. No launch date yet, but there’s already a queue of interested guests. Next Week’s Show — Moved to Wednesday Sara will be on a boat off the coast of Galicia on Thursday, so next week’s episode is moving to Wednesday. Guests will include Paul Reinheimer and (hopefully) Sean Coase — two veterans from PHP’s podcasting past. Elizabeth is going to try to make it work around the Canadian Grand Prix. Mac Mini M4 for Local LLMs Holly picked up a refurbished Mac Mini M4 (16GB RAM, 512GB storage) specifically to run LLM models locally via Ollama. Apple Silicon is a solid choice for this because the unified memory architecture gives the neural cores access to far more RAM than a discrete GPU setup. Sara is waiting for the M5, which is reportedly not coming until fall — and is already resigned to spending too much on it when it lands. Links from the show: PHP Foundation — 2026 Strategy Document PHP RFC: Polling API PHP RFC Wiki — All RFCs Under Discussion Amp PHP — Async framework ReactPHP — Event-driven async PHP Revolt — Event loop for PHP php.net website source code (github.com/php/web-php) PHP Architect Discord Guest Hosts: Sara Golemon Based in Lisbon, Portugal PHP core contributor; code contributor via the Curl project (which means she technically has code on Mars) Elizabeth Barron Executive Director, PHP Foundation Based in Germany Holly Schilling Primary mobile developer; built the PHP Tek 2026 conference app Based near Chicago, IL Streams: Youtube Channel Twitch Connect & Hire PHP Architect Website Twitter/X Mastodon Hire PHP Developers Looking to hire PHP developers? Email support@phparch.com – Joe and the team are available for consulting, infrastructure work, Ansible playbooks, and code review. Partner This podcast is made a little better thanks to our partners Displace Infrastructure Management, Simplified Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease. https://displace.tech/ PHPScore Put Your Technical Debt on Autopay with PHPScore Music Provided by Epidemic Sound https://www.epidemicsound.com/ Join Us Live Next Week Note: Next week’s show is on Wednesday (not Thursday) with guests Paul Reinheimer and Sean Coase. Youtube Channel Got feedback? Join us on Discord at discord.phparch.com The post The PHP Podcast 2026.06.11 appeared first on PHP Architect.

Marco Dané "Se potessimo fare un bel gioco"Dal Paese di Giocagiò a Tandem. La tv dei ragazzi raccontata da uno dei suoi protagonisti.Manni Editoriwww.mannieditori.itLa televisione per bambini era diventata il mio mondo. E per molti bambini, quella televisione è stata una finestra sul possibile. Abbiamo parlato con loro, non solo per loro. Abbiamo giocato, raccontato, cantato, spiegato, ma abbiamo anche ascoltato. E quando oggi un adulto mi ferma per strada e mi dice: «Io ti guardavo da piccolo, mi hai insegnato a sorridere», sorrido anch'io. Perché so che in quel viaggio, io non ero solo. Eravamo in tanti. E tutti abbiamo imparato qualcosa. Marco Dané è stato un protagonista dei programmi televisivi per ragazzi come autore e conduttore: dall'esordio nel 1969 nel Paese di Giocagiò al fianco di Gianni Rodari, a Trentaminutigiovani, il tg per ragazzi di Rai2, a Tandem negli anni Ottanta con Fabrizio Frizzi, fino al ruolo di giudice in Paroliamo (quello in Rai e poi all'interno di Non è la Rai di Gianni Boncompagni), ha contribuito alla nascita di un'epoca d'oro della televisione italiana, in cui l'intento educativo sapeva coniugarsi con l'intrattenimento intelligente.In queste pagine Dané ripercorre le sue trasmissioni che hanno segnato intere generazioni, restituendo la magia dei personaggi come Signor Coso, Scarabocchio, Buendìa, il Pagliaccio... È un viaggio nella storia della tv per ragazzi e una scoperta delle prime sperimentazioni tecniche, come l'evoluzione dei mezzi di ripresa, l'elettronica, le nuove possibilità di interazione con i telespettatori.Per gli adulti di oggi, questo libro è un ponte con l'infanzia: una galleria di ricordi, emozioni e nostalgia ma anche la dimostrazione di come la fantasia, il gioco e l'intrattenimento possano essere una cosa seria.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.IL POSTO DELLE PAROLEascoltare fa pensarehttps://ilpostodelleparole.it/

Welcome to a show about death. We've got some unfinished business as Alex Garday returns (from the dead?) for an all new fully improvised musical. Crashing cars, mystical mirrors, liminal limerence, and more as we put the FUN in funeral on this week's Charm Scene! Alex Garday is a performer from Phoenix, Arizona. He has performed in Chicago for the past 16 years at all of the major comedy theaters including recently as an understudy for The Second City's Mainstage production, Don't Quit Your Daydream. He regularly can be seen performing with Baby Wine at The Annoyance, Blank! The Musical at The Revival, Phony Award Winning Musical at iO, and Baby Wants Candy at the Second City. He has worked across North America as a host/emcee/facilitator for corporate events for Fortune 500 companies. He is the Talent/Product Coordinator and Marquee Host for Game Night Out a company that provides entertainment and curated in-person game nights for clients from across the Chicagoland area. He is 6'5″ and ethnically ambiguous. You can find him on social media platforms @alexgarday. Cast: Lily Ludwig, Austin Packard, Alex Garday Music Director: Sam Scheidler Drums: Chris Ditton Charm Scene is performed entirely by humans in sunny Chicago, IL. For more on the podcast, follow us @CharmScenePod on Instagram, visit us online at charmscenepod.podbean.com, or email us at CharmScenePod@gmail.com. In listening to this show, we hope you continue to support live human art wherever you find it. Stay charming!

Francesca ha 53 anni, vive a Senigallia e per quasi tutta la vita ha fatto l'insegnante. Oggi non lavora più: si è licenziata e vive con 23.000 euro l'anno, frutto dei suoi investimenti. Il suo rapporto con il denaro nasce da bambina, quando suo padre, impiegato di banca a Ravenna, le regala uno dei primi bancomat per bambini. «Da quel momento ho imparato a gestire i soldi: sapevo quanto potevo spendere in una settimana, e in che cosa». Cresce così tenendo la contabilità di ogni spesa e mette da parte tutto con una direzione sola: i viaggi, l'unica voce davvero preponderante nel suo bilancio.Diventa insegnante, compra casa, e poi si trasferisce in un casolare nelle Marche con il compagno, per inseguire il sogno di una vita in collina. Ma per dieci anni a lavorare è solo lei, mentre lui si licenzia per scrivere. «L'orto lo curavo io, della casa mi occupavo io, guadagnavo io. Lo squilibrio economico ha fatto saltare il piatto». Dopo la separazione conosce quello che è oggi il suo compagno, un ingegnere che da anni vive dei propri investimenti, e che le insegna la cosa che le mancava: smettere di affidare i risparmi alla banca. «Io non faccio trading, sono più una cassettista: compro titoli e li tengo lì, per far lavorare l'interesse composto». Comincia così a investire da sola e a ricalibrare ogni voce delle sue spese. Vende la casa in collina, si trasferisce a Senigallia, prova un anno sabbatico senza stipendio per capire come si vive senza un'entrata fissa. E quando capisce che regge, nel 2024 si licenzia. Oggi dei 23.000 euro annuali di cui ha bisogno per vivere, 9mila euro sono spesi in viaggi, e una parte finisce nel risparmio già a inizio mese, prima ancora di spendere il resto. «Io voglio godermi la vita adesso. Ho 24 anni in meno dei miei genitori: quando me la godo, a ottant'anni?».

TRASCRIZIONE E VOCABOLARIOPuoi sostenere il mio lavoro con una donazione su Patreonhttps://www.patreon.com/italianosiPer €2 al mese riceverai le trascrizioni di tutti i PodcastPer €3 al mese riceverai, oltre alle trascrizioni, anche una lista dei vocaboli più difficili, con spiegazione in italiano e traduzione in inglese.CONTENUTIIn questa puntata, dopo un'introduzione su come ho passato il fine settimana, mi/vi pongo una domanda: parlo troppo lentamente per il livello di questo podcast?TRASCRIZIONECiao a tutti e ciao a tutte! Bentornati, bentornate o benvenuti, benvenute nel podcast di italiano sì. Io sono Elisa, questo è un podcast pensato per voi che imparate l'italiano, ma avete già un livello intermedio B1. Forse solo B1, forse B2? In realtà parleremo proprio di questo più tardi. Che poi magari avete un livello A2, ma con l'aiuto delle trascrizioni, del vocabolario riuscite a seguire senza problemi. Ho una studentessa nuova (ciao Eileen), che è partita da 0 due mesi fa circa e ha un altissimo livello di comprensione, ma di produzione, naturalmente, non ha nemmeno un A1. Ha appena cominciato. La differenza tra comprensione e produzione può essere molto alta. Nel mio caso, per esempio, lo è sempre. Io ho sempre un livello altissimo di comprensione e magari anche quasi inesistente di produzione. [...]MY YOUTUBE CHANNELSupport the show

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubA N M Bazlur Rahman - Java Champion & Author of "Modern Concurrency in Java"Michael Redlich - Java Champion & Lead Java Queue News Editor at InfoQCheck out more here:https://gotopia.tech/episodes/443RESOURCESBazlurhttps://bsky.app/profile/bazlur.cahttps://x.com/bazlur_rahmanhttps://github.com/rokon12https://www.linkedin.com/in/bazlurhttps://bio.site/bazlurhttps://bazlur.caMichaelhttps://twitter.com/mpredlihttps://github.com/mpredli01https://www.linkedin.com/in/michael-redlich-13a966https://about.me/mpredliDESCRIPTIONIn this GOTO Book Club episode, Java Champion A N M Bazlur Rahman joins host and fellow Java Champion Michael Redlich to discuss Modern Concurrency in Java — the first comprehensive update to Java concurrency literature in 20 years. Bazlur traces his motivation to the arrival of virtual threads in JDK 21, which he describes as a fundamental shift in Java's concurrency cost model: platform threads were expensive and scarce, demanding careful pooling; virtual threads are cheap, plentiful, and behave like ordinary threads from the developer's perspective, without requiring a new programming model. The book covers this evolution end-to-end, from the history of threads through to structured concurrency, scope values, and the modern frameworks that have already adopted virtual threads — most with a single config change.The conversation also takes a nuanced look at reactive programming's future. Bazlur's conclusion is that reactive remains compelling in specific contexts — event-driven streaming systems, architectures needing end-to-end back-pressure — but it's no longer the default answer to scalability. For most microservices doing blocking I/O, virtual threads are now the stronger default, and reactive becomes a deliberate architectural choice rather than an automatic one. The book's goal is to give developers both the conceptual grounding and the practical guidance to make that choice confidently — understanding the tool one level deep, so they can design better systems, not just configure their way through a framework.RECOMMENDED BOOKSA N M Bazlur Rahman • Modern Concurrency in Java • https://amzn.to/42w8cOkBen Evans & Jim Gough • Optimizing Cloud Native Java • https://amzn.to/41nivD9Ben Evans, Jason Clark & David Flanagan • Java in a Nutshell • https://amzn.to/43FDoMAIan F. Darwin • Java Cookbook 5th ed. • https://amzn.to/3QH0NZyVictor Grazi & Jeanne Boyarsky • Real-World Java • https://amzn.to/4oCEeBRBlueskyInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

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

Google recently announced its 2026 algorithm updates at I/O in May 2026, and that made me stop and ask: Is my website actually built for how people are going to find me now?And how can I incorporate this for website copywriting for my clients?Google just fundamentally shifted how people discover businesses online. Information agents are scanning the web 24/7. Agentic booking is expanding to pull real-time pricing and availability. Conversational search is remembering context and surfacing deep-dive content. These affect how your business gets found online.So I audited my own website against these new realities. And I made three specific changes that all business owners can make today.In this episode, I'm walking you through the three specific website copywriting changes I made after Google's 2026 algorithm updates announced at I/O in May. These updates (information agents, agentic booking, and especially conversational search) are actively reshaping how AI finds, evaluates, and recommends service providers online. And the businesses who act on this now have a real advantage.Let's make sure the website you've already built is actually working for you in 2026 and beyond.Want me to make these website copywriting changes for you so that YOU will be found through Google with its new updates? Book a call here to get started.➡️ SHOW NOTES: Grab all the links and resources mentioned in this episode on the blog here! https://www.megankachigan.com/website-copywriting-google-algorithm-updates-2026CONNECT WITH MEGAN:Join My Inbox Community → www.megankachigan.com/email Website → www.megankachigan.comLinkedIn → https://www.linkedin.com/in/megan-kachigan-loehr-9957684b/Threads → https://www.threads.net/@megankachiganInstagram → https://www.instagram.com/megankachigan/Know exactly what to fix in your copywriting with this "Why Isn't This Converting?" Free 5-Day Challenge. You'll get bite-sized email prompts where you'll apply one simple, high-impact fix in just minutes to make your content convert without having to re-write everything or constantly guess at what's going to work.

It's Christmas in June as charming guest Rob Grabowski (Clued In, Hitch*Cocktails) joins us for a merry murder mystery. Was it the sinister son? The revengeful rabbit? The horrible head of HR? Everyone has a motive on this week's fully improvised cluesical. Rob Grabowski is a Michigan native but has called Chicago home for over 15 years. He performs regularly with Hitch*Cocktails: an improved thriller; Clued In: an improvised murder mystery; Comedy Sportz Chicago; and Kohl's Cash, an iO house team. Follow him on instagram @robgrabo. He recommends visiting your local independent bookstore.  Cast: Lily Ludwig, Austin Packard, Rob Grabowski Music Director: Sam Scheidler Drums: Chris Ditton Charm Scene is performed entirely by humans in sunny Chicago, IL. For more on the podcast, follow us @CharmScenePod on Instagram, visit us online at charmscenepod.podbean.com, or email us at CharmScenePod@gmail.com. In listening to this show, we hope you continue to support live human art wherever you find it. Stay charming!

Worn intake valves, pitted camshafts, shock cooling, and AD compliance are on the docket. Email podcasts@aopa.org for a chance to get on the show. Join the world's largest aviation community at aopa.org/join Full notes below: Norm wonders whether condition-based maintenance and inspections failed him. He is co-owners in an airplane with a Lycoming IO-360, and after a few years they found a crack in the crankcase. The engine was torn down and found to have some rust on the cylinder walls, scoring on the crankshaft, and a worn and pitted lifter. They had been borescoping, doing oil analysis, looking at the filter, and never found any concerns. The hosts say the approach worked perfectly. The point of condition-based maintenance is to fix safety related problems, and they argue that all Norm's issues were financial issues. Mike argues that the lifter wear could have been found with by measuring the valve opening, but that it wouldn't have necessarily resulted in a teardown. The oil analysis wouldn't have found anything because the metal chunks were too large, and although a magnet over the filter material may have helped, he's not sure that would have resulted in a teardown either. The lesson is that the airplane was safe, despite the condition concerns. Jay has an RV with an experimental IO-540 that he loves. A look at the cylinder data found that one of his intake valves was eroding. As the shop dug into the engine they found a few other issues, including pitting on the camshaft. An IRAN is going to cost him maybe $20,000 or $30,000 less than an overhaul, so he's wondering if it's ok to save the money or should he just overhaul the engine while it's off. The hosts tell him to save his money. The only reason they would overhaul now is to increase the market value if he were planning on selling. Otherwise there's little benefit. Ronan wonders how to interpret the data on his friend's Piper Arrow as regards shock cooling. They often get the alerts on the Garmin engine analyzer, and they are wondering if there's anything they can do to avoid it. Paul jokes that he should just turn that feature off. Mike said the only time you have to worry about this is when the cylinders are at high temperature, such as cruise to chopping the power. But in a descent the cylinders are already cooling, so he's not worried about it. Bill is wondering if his club is documenting too much on AD compliance. The hosts give some detailed information on how they document ADs and why it matters. They tend to document everything in a large spreadsheet and note whether or not it applies. If it doesn't, they say so on the document and leave it for a future mechanic or owner. Doing so helps with hours of research, they say. They are also careful to document parts and accessories, especially those inside the engine, as you don't want to have to take the prop off to check a crankshaft serial number every year, for example.

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

McKenzie and Io talk with friend of the pod and genuine movie pervert Vicky Osterweil about the history of cinema, copyright, and how that fucking mouse wound up owning everything. BUY THE BOOK!! https://www.haymarketbooks.org/books/2525-the-extended-universe Vicky can also be found on bluesky @vickyacab.bsky.social Io can be found https://twitter.com/bum_lungon Instagram @Bum.Lung, bluesky @bumlung.bsky.social or you can buy their prints at https://www.etsy.com/shop/BumLung This show is published by Strangers in A Tangled Wilderness. We can be found at www.tangledwilderness.org, or on Twitter @TangledWild and Instagram @Tangled_Wilderness. You can support the show on Patreon at www.patreon.com/strangersinatangledwilderness. Our logo is by Robin Savage. And our theme music is by a lovely mountain goblin.

The Sweet Side of Tasty Caffeine™. Your go-to flavors when your treat cravings call for a boost! The sweetest 5-hour ENERGY flavors are back. Three mouthwatering flavors: Confetti Craze, Fruity Rainbow, and Cotton Candy, full snack break vibes with zero-sugar and a Tasty Caffeine boost. Add some fun to your caffeine break. Taste the Fun: https://click2cart.com/274100bu?utm_campaign=swtflvr&utm_medium=paid_video&utm_source=kf&utm_content=allLet Rocket Money help you reach your financial goals faster. Join at https://rocketmoney.com/kindafunny Thank you for the support! Run of Show - 00:00:00 - Start00:02:52 - Fable is delayed to 202700:21:00 - New Xbox Boss Asha Sharma Reportedly Warns Staff 'Hard Choices' Are Ahead, but Insists Recent Game Pass Changes Are Helping00:30:38 - Ad00:32:20 - 007 First Light is already IO's fastest-selling game ever00:38:00 - Activision Files Trademark For Crash Bandicoot Motion Pictures00:40:55 - Balatro publisher Playstack is being sold to GameSpot and Fandom parent company00:47:01 - FIFA announces new "Digital Football" vision, an ecosystem of games from multiple publishers and developers00:50:56 - Wee News!01:03:01 - SuperChats & You‘re Wrong Learn more about your ad choices. Visit megaphone.fm/adchoices

It's here – the James Bond game we've been waiting almost six years for! How much Hitman is in First Light? And how successfully does IO borrow from Naughty Dog? We discuss these subjects and many more across almost two hours.We've done our best to keep this episode free of spoilers. You'll hear about none of the major story beats in this one. Hosted on Acast. See acast.com/privacy for more information.

You'll need a map, compass and legend to understand all the new AI Google announced at its I/O conference last week. (They literally wrote a blog post called, "100 things we announced at I/O 2026” and most of them were AI based.) Luckily for you, we spend hours each day going through the latest in AI to cut the fluff from the real. So on today's ‘AI Working Wednesdays' series, we break down 3 of Google's biggest AI updates you can use today: Google Omni, Gemini 3.5 Flash and Antigravity 2.0. What's new and how do they work? We'll show you the ins and outs live. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Gemini 3.5 Flash Model Hands-On DemoGemini 3.5 Flash Pricing and Token UsageBenchmarks: Gemini 3.5 Flash vs. 3.1 ProIntelligence vs. Cost in Gemini 3.5 FlashGemini 3.5 Flash for API and DevelopersGoogle Gemini Omni Flash Video Model ReviewOmni Anything-to-Anything Multimodal FeaturesGoogle Omni vs. Video Model CompetitorsAnti Gravity 2.0 Agent Desktop App OverviewAnti Gravity 2.0 Pros, Cons, and Use CasesUsage Limits in Google Gemini and Anti GravityChain of Thought Transparency in Gemini ModelsCanvas Mode Interactive Web App DemonstrationsTimestamps:00:00 Key AI updates from Google IO04:58 New Google AI updates discussed08:57 Google's anti gravity desktop use10:01 Touring Google's Anti Gravity App14:40 Testing a new AI prompt18:06 Critiquing vibe coding aesthetics21:28 Discussing Google's Gemini 3.1 Pro Model24:40 Comparing AI model performances and costs29:13 Google's advancements in video AI30:13 Future of Google's AI Technology33:58 Exploring Google Gemini features36:51 Google Gemini chain of thought feature42:02 Google Gemini's new model features44:23 River crossing puzzle gameplay48:25 Discussing Google Gemini 3.5 flash drawbacks51:10 Feedback on an AI releaseKeywords: Gemini 3.5 Flash, Google Gemini, AI updates, Google I/O 2026, Gemini Omni, Gemini Omni Flash, anti gravity 2.0, AI video model, hands-on AI demo, agentic coding, desktop AI app, benchmarking, AI model comparison, Gemini Spark, Gemini Pro 3.5, Gemini 3.1 Pro, token usage, API users, Google Workspace, always-on agent, AI cost efficiency, intelligent agents, world model, multimodal AI, generative video creation, video editing, scheduled tasks, Google Daily Brief, model usage limits, thinking steps, chain of thought, artificial analysis intelligence index, token inefficiency, cost to run AI, OpenAI GPT-5.5, Claude Sonnet, Claude Opus, open source AI models, AI-powered creativity, robotics, embodied AI, front-end AI tools, Canvas mode, conversational editing, interactive website builder, AI-powered app creation.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

Connecting with Google CEO Sundar Pichai at I/O every year is one of my favorite Decoder traditions. This was our fifth year doing it, and there's always a whole slew of new things to talk about. This year, in addition to the news, we talked about Google Zero; picking fights with YouTube creators and publishers; and what being at “the foothills of the singularity" even means.  Links:  If Google can't make AI agents useful, maybe no one can | The Verge The future of Google is a search box that does everything | The Verge Large language mistake | The Verge You can now remix other people's YouTube Shorts with AI | The Verge Condé Nast calls Google Zero | The Verge Demis Hassabis said this may be the ‘foothills of the singularity' | The Verge Google I/O 2026: All the news and announcements | The Verge Subscribe to The Verge to access the ad-free version of Decoder! Credits: Decoder is a production of The Verge and part of the Vox Media Podcast Network. Decoder is produced by Kate Cox and Nick Statt. This episode was edited by Kabir Chopra. Our editorial director is Kevin McShane.  The Decoder music is by Breakmaster Cylinder. Learn more about your ad choices. Visit podcastchoices.com/adchoices

The Pope said WHAT about AI?

On this week's Marketplace Tech Bytes: Week in Review, we take a look at how college graduates do not wanna hear about AI. Plus, what we all learned from the Musk v. Open AI case. But first, AI was unsurprisingly front and center at Google's annual I/O developer conference. Among a suite of new AI products, Google said it updated its iconic search bar. Now, when searching in AI mode, the bar will expand as you ask a question. It will also provide suggestions about what you might wanna ask. Google says this is the biggest change to its search box since it debuted over 25 years ago. Marketplace's Stephanie Hughes spoke with Anita Ramaswamy, a columnist at The Information, about how this could change how people experience the internet. Check out our YouTube page to watch more episodes of “Tech Bytes.”

On this week's Marketplace Tech Bytes: Week in Review, we take a look at how college graduates do not wanna hear about AI. Plus, what we all learned from the Musk v. Open AI case. But first, AI was unsurprisingly front and center at Google's annual I/O developer conference. Among a suite of new AI products, Google said it updated its iconic search bar. Now, when searching in AI mode, the bar will expand as you ask a question. It will also provide suggestions about what you might wanna ask. Google says this is the biggest change to its search box since it debuted over 25 years ago. Marketplace's Stephanie Hughes spoke with Anita Ramaswamy, a columnist at The Information, about how this could change how people experience the internet. Check out our YouTube page to watch more episodes of “Tech Bytes.”

Google dropped like 197 new AI features this week.

Apple has shown off the new Accessibility features coming in iOS 27, which did nothing to stem the torrent of rumors about what we'll see in Apple Intelligence, but possibly did steal a little bit of thunder from Google's peculiar mishmash of an I/O conference, on the AppleInsider Podcast.Contact your hosts:@williamgallagher_ on Threads@WGallagher on TwitterWilliam's 58keys on YouTubeWilliam Gallagher on emailWes on BlueskyWes Hilliard on emailWes's blog HillitechSponsored by:Bartender:  Check out the new Bartender Pro at macbartender.com/appleinsiderNordStellar: Unlock your 10% discount at nordstellar.com/appleinsider with the coupon code nordappleinsider-10-NORDSTELLARLinks from the Show:Owning an Apple Home: implementing smart pet solutionsVision Pro wheelchair control & more accessibility features detailed ahead of WWDCHikawa Grip & Stand for iPhone launches globally at a new lower priceRevamped Siri may launch in beta, despite two year delayPrivacy & data security will remain central to Apple's 2026 AI pushGenmoji in iOS 27 will use what you type and what's in Photos for suggestionsImproved Writing Tools, generated wallpapers, & easier Shortcut creation rumored for iOS 27AI is making smartphones verifiably worse by designDon't expect new Macs at WWDC 2026Google I/O 2026 had nothing to say and said it badly ahead of Apple's WWDCProblematic hinge could delay the iPhone FoldApple's iPhone Fold hinge design may become industry standard Latest Apple Immersive rollout exemplifies Apple Vision Pro's entire problemSupport the show:Support the show on Patreon or Apple Podcasts to get ad-free episodes every week, access to our private Discord channel, and early release of the show! We would also appreciate a 5-star rating and review in Apple PodcastsMore AppleInsider podcastsTune in to our HomeKit Insider podcast covering the latest news, products, apps and everything HomeKit related. Subscribe in Apple Podcasts, Overcast, or just search for HomeKit Insider wherever you get your podcasts.Subscribe and listen to our AppleInsider Daily podcast for the latest Apple news Monday through Friday. You can find it on Apple Podcasts, Overcast, or anywhere you listen to podcasts.Those interested in sponsoring the show can reach out to us at: advertising@appleinsider.com ★ Support this podcast on Patreon ★

Google dominated I/O with Gemini 3.5 Flash, its fastest agentic model yet, plus Gemini Spark as a 24/7 personal agent. It also launched Gemini Omni for video generation, overhauled its search box, shipped Antigravity 2.0, and added Street View to Project Genie. Google rolls out Gemini 3.5 Flash, its "strongest agentic and coding model yet", for tackling long-horizon agentic tasks, in the Gemini app and Search's AI Mode (Google) Google announces Gemini Spark, a "24/7 personal AI agent" that is powered by Gemini 3.5 and supports integrations with Google Workspace apps, including Gmail (Engadget) Google launches Gemini Omni, a multimodal model it says can "create anything from any input", starting with video generation, for Google AI Plus, Pro, and Ultra (VentureBeat) Google overhauls its search box, letting users input longer queries, including with photos and videos, and automate searches with Gemini 3.5 Flash-based agents (NYT) Google introduces Antigravity 2.0, featuring an updated desktop app that lets users orchestrate agents, an Antigravity CLI tool, and an SDK for custom workflows (TechCrunch) Google adds Street View integration to Project Genie, its interactive world builder, and expands Genie from the US to adult Google AI Ultra subscribers globally (Engadget) Learn more about your ad choices. Visit megaphone.fm/adchoices