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Tue, 10 Mar 2026 13:00:00 GMT http://relay.fm/upgrade/606 http://relay.fm/upgrade/606 Photogenic Lemon 606 Jason Snell and Myke Hurley This week, Jason reviews the MacBook Neo! Plus: Draft results, Jason is (back) in print, and new MacBook Pros and Studio Displays. But it's mostly about MacBook Neo! This week, Jason reviews the MacBook Neo! Plus: Draft results, Jason is (back) in print, and new MacBook Pros and Studio Displays. But it's mostly about MacBook Neo! clean 6432 This week, Jason reviews the MacBook Neo! Plus: Draft results, Jason is (back) in print, and new MacBook Pros and Studio Displays. But it's mostly about MacBook Neo! This episode of Upgrade is sponsored by: Claude: Ready to tackle bigger problems? Get started with Claude today. DeleteMe: Get 20% off your plan when you use this link and code UPGRADE20. Sentry: Mobile crash reporting and app monitoring. New users get $100 in Sentry credits with code upgrade26. 1Password: Take the first step to better security by securing your team's credentials. Links and Show Notes: Get Upgrade+. More content, no ads. Submit Feedback The Upgrade March 2025 Experience Draft Scorecard ‘Apple' by David Pogue | Book Review - WSJ ‘Apple' by David Pogue | Book Review - WSJ (Apple News) Mac Studio 512GB RAM Option Disappears Amid Global DRAM Shortage - MacRumors Apple adds Steve Lemay and Molly Anderson to its leadership page - 9to5Mac Jason's MacBook Neo review The Technical Differences Between the MacBook Neo and MacBook Air - 512 Pixels Riding the Press Release Tidal Wave – The Enthusiast Apple gives in to temptation and renames its CPU cores – Six Colors MacBook Neo review: Fresh-squeezed laptop – Six Colors M5 Pro MacBook Pro review: Fast, familiar friend – Six Colors

Podcast ONE: 6 de marzo de 2026 CoPaw (IA local sin nube), GPT‑5.4 con millón de tokens, la nueva MacBook Neo “económica”, la guerra Irán‑Israel amplificada por desinformación de IA y todo lo que dejó el #MWC2026. Escucha el nuevo episodio de #PodcastONE en One Digital. Escucha aquí el Podcast ONE: 6 de marzo de 2026 Facebook Live One Digital: CoPaw, GPT-5.4, MacBook Neo y el caos geopolítico de marzo 2026 En este episodio del viernes 6 de marzo de 2026, transmitido en vivo desde São Paulo (Brasil) y Ciudad de México, Vincent Quezada y Pablo Berruecos analizan una semana explosiva: herramientas de inteligencia artificial local (CoPaw), el lanzamiento de GPT‑5.4 con contexto de un millón de tokens, la MacBook Neo (la laptop Apple más económica de su historia), el conflicto geopolítico Irán‑Israel amplificado por desinformación de IA en redes sociales y el Mobile World Congress 2026, que redefinió privacidad, seguridad y conectividad móvil. Un episodio que resume el estado actual de la tecnología, la geopolítica y la ética digital en 2026. ¿Qué es CoPaw? Un agente de IA completamente local sin dependencias en la nube Vincent abre el episodio presentando CoPaw (Co‑Personal Agent Workstation), un agente de inteligencia artificial que funciona completamente en tu equipo local, sin procesar datos en servidores externos como ChatGPT o Gemini. La arquitectura es una evolución directa de los agentes COD (marco multiagente de Alibaba). La diferencia crítica: toda la información permanece dentro de tu máquina, lo que garantiza privacidad total y funcionamiento sin internet una vez instalado el proyecto. “CoPaw no es simplemente un cliente de chat para modelos locales. Es un orquestador de tareas que puede navegar por internet, leer PDFs, generar documentos Word, enviar mensajes por Telegram y ejecutar acciones programadas de forma automática sin intervención humana”. — Vincent Quezada Requisitos técnicos de CoPaw: hardware y software RAM mínima: 8 GB (16 GB ideales para multitarea). Almacenamiento: 10 GB mínimos (20 GB recomendados para modelos grandes). Software: Python 3.10, Node.js v18. GPU opcional pero recomendada: una tarjeta NVIDIA con CUDA acelera respuestas de 15‑40 segundos a 3‑8 segundos. Compatibilidad: Windows, macOS y Linux; la instalación automática gestiona todas las dependencias. Motor de modelos: Ollama (descargable desde ollama.com), disponible para Windows, macOS, Ubuntu y Debian. Modelos de lenguaje local según necesidad y RAM disponible La elección del modelo depende de tu hardware y de tu caso de uso. Vincent explica que el número al final del nombre (3B, 7B, 8B, 14B) representa los miles de millones de parámetros que maneja; a mayor número, mayor precisión, pero también más RAM requerida. Phi 3 Mini (4 GB RAM): respuestas cortas, equipos básicos, uso introductorio. Llama 2 8B (8 GB RAM): velocidad media (15‑40 segundos), ideal para redacción general, análisis de textos y resúmenes. Mistral 7B (8 GB RAM): especializado en escritura creativa y resúmenes de contenido largo. DeepSeek 8B (8 GB RAM): razonamiento lógico, análisis de código y debugging. Qwen 3 (14B) (16 GB RAM): tareas complejas y análisis extenso de datos; es lento sin GPU. “No uses un modelo de 20 gigabytes para una simple traducción. Es como manejar un camión de carga para ir a la tienda. Elige según tu tarea real”. — Vincent Quezada Módulos especializados que llevan CoPaw más allá del chat básico CoPaw incluye módulos independientes que se activan automáticamente según el contexto de tu tarea. Cada uno requiere cierta configuración específica. Browser Reissable: navegador web autónomo que busca información en tiempo real; requiere la instalación de Playwright. News Module: búsqueda y resumen automático de noticias; requiere una clave API de Tavily (gratuita con 1,000 búsquedas mensuales). File Reader: lee archivos locales (.txt, .csv, .json) sin configuración adicional. PDF Module: extrae, analiza y resume PDFs complejos. DOCX Module: crea y edita documentos Word de forma automática. XLSX Module: manipula hojas de cálculo y calcula promedios, máximos y mínimos de columnas. PPTX Module: genera presentaciones de PowerPoint de forma automática. Cron Jobs (automatización): programa tareas para ejecutarse en intervalos específicos (diarios, semanales, cada N horas) sin intervención del usuario. Email Manager (Himalaya): gestión automática de correos; Vincent lo recomienda solo para usuarios avanzados. Casos de uso prácticos según nivel de experiencia Principiante: “Busca las noticias más importantes de inteligencia artificial de hoy”. “Explica la diferencia entre aprendizaje autónomo y aprendizaje profundo con ejemplos prácticos”. “Redacta un correo formal para solicitar una reunión con un cliente importante”. Intermedio: “Lee el archivo C:UsuariosDocumentosreporte.pdf y genera un resumen ejecutivo de máximo 500 palabras”. “Abre ventas_2025.xlsx, identifica los tres meses con mayor crecimiento entre enero y marzo y muestra los porcentajes”. “Navega a Amazon.com.mx, busca auriculares inalámbricos menores a 1,500 pesos y lista las cinco mejores opciones con precio y enlace”. Avanzado: “Busca las cinco noticias tecnológicas más importantes de hoy, redacta un párrafo de 150 palabras para cada una y guarda el resultado en noticiashoy.docx”. “Lee todos los archivos .csv de C:datos, combínalos en uno solo y calcula el promedio, máximo y mínimo de cada columna numérica”. “Navega a LinkedIn, busca vacantes de redactor de contenido publicadas esta semana en Ciudad de México, extrae títulos, empresas, enlaces y guarda todo en empleos.xlsx”. Automatización con tareas programadas: el verdadero diferenciador de CoPaw La función más poderosa es la capacidad de programar ejecuciones automáticas sin que el usuario esté presente. Esto convierte a CoPaw de una simple herramienta de chat en un asistente de productividad genuino. Resumen diario de noticias: “Configura una tarea que se ejecute todos los días a las 8:00 a. m.: busca las principales noticias de tecnología e IA y guarda el resultado en noticiasdiarias.txt”. Monitoreo de precio de criptomonedas: “Crea una tarea cada seis horas: registra la cotización actual de Bitcoin con fecha y hora en precio.txt”. Reporte semanal consolidado: “Programa una tarea cada lunes a las 9:00 a. m.: lee todos los archivos .txt de C:reportes, genera un resumen ejecutivo y guarda el documento como reportesemanal.docx”. Limpieza automática de archivos: “Configura una tarea cada viernes a las 11:00 p. m.: mueve todos los archivos .log con más de 30 días de antigüedad a la carpeta archivos_antiguos”. Estas variables (frecuencia, horarios, tiempos de latido o heartbeat) se controlan en el archivo config.json. Vincent subraya la importancia de probar con cuidado antes de automatizar procesos críticos. ¿CoPaw requiere internet? Solución de errores comunes CoPaw funciona completamente sin conexión una vez instalado con su modelo descargado. Solo requiere internet para búsquedas web mediante Tavily y si configuras APIs externas (OpenAI, Anthropic). Los errores más frecuentes que Vincent encontró durante sus pruebas son: “No es posible conectar con servidor CoPaw”: verifica que ejecutaste copaw start y que el puerto 8088 está disponible. “Comando copaw no reconocido”: el directorio de ejecución no está en el PATH del sistema; asigna la ruta manualmente o usa el script completo. “Ollama no disponible”: la dirección debe ser exactamente localhost:11434 sin sufijos; revisa el archivo de configuración. CoPaw vs. OpenCloud: ¿cuál es mejor? “CoPaw fue más útil que OpenCloud en mis pruebas. Mientras OpenCloud es muy potente, CoPaw ofrece instalación más rápida, una interfaz más accesible y documentación más clara. Ambas son de código abierto bajo licencia Apache 2.0. CoPaw es completamente gratis; solo la clave de Tavily tiene un costo opcional (unos 10 dólares mensuales)”. — Vincent Quezada MacBook Neo: la primera laptop Apple verdaderamente económica (599 dólares) Apple lanzó la MacBook Neo, un quiebre histórico en su estrategia de precios. Por primera vez en la historia de Macintosh existe una laptop Apple genuinamente accesible: 599 dólares (499 dólares para educación). Dirigida a estudiantes y nuevos usuarios, representa un cambio radical en la democratización del ecosistema Apple. Especificaciones técnicas de la MacBook Neo Procesador: chip A18 Pro; seis núcleos (dos de rendimiento y cuatro de eficiencia); GPU de cinco núcleos; Neural Engine de seis núcleos para tareas de inteligencia artificial. Rendimiento en IA: hasta tres veces más rápido en cargas de trabajo de inteligencia artificial que la competencia; acceso completo a Apple Intelligence manteniendo la privacidad de los datos. Pantalla Liquid Retina: 13 pulgadas, 2,408 × 1,506 píxeles, 510 nits de brillo, soporte para mil millones de colores; una de las pantallas más brillantes en su rango de precio. Batería: 36,5 Wh, hasta 16 horas de autonomía en uso mixto; dos puertos USB‑C para carga rápida. Diseño y construcción: carcasa de aluminio resistente, peso de solo 1,23 kg; colores disponibles: Blush, Indigo, Plata y Eléctrico. Conectividad: Wi‑Fi 6E, Bluetooth 6, entrada de audio de 3,5 mm (rara hoy en día), cámara FaceTime HD 1080p, micrófono dual y audio espacial Dolby Atmos. Almacenamiento: 256 GB base (Vincent cuestiona esta especificación a ese precio, pues alternativas con Windows ofrecen 512 GB por menos dinero). Software: macOS preinstalado con integración completa de Apple Intelligence. Disponibilidad: envíos a partir del 11 de marzo de 2026. “La pantalla es realmente excepcional. Es una de las mejores que he visto comparada con iPads y monitores tradicionales. Solo por ese aspecto la MacBook Neo se justifica”. — Vincent Quezada ¿Para quién es la MacBook Neo? Estudiantes: necesitan un equipo potente, ligero y con batería para todo el día; el precio educativo (499 dólares) es especialmente atractivo. Nuevos usuarios de Mac: quienes buscan una introducción asequible al ecosistema Apple sin gastar más de 1,200 dólares. Profesionales de tareas cotidianas: navegación web, edición de documentos, videollamadas y productividad básica. Usuarios preocupados por la sostenibilidad: está fabricada con un 60% de material reciclado. Vincent lanza una advertencia: el almacenamiento base de 256 GB a 599 dólares es cuestionable, ya que por ese mismo precio se encuentran laptops Windows con 512 GB que ofrecen mejor valor a corto plazo. Sin embargo, el diseño, la pantalla y la autonomía de la MacBook Neo compiten favorablemente. GPT‑5.4 de OpenAI: millón de tokens, automatización y 33% menos errores OpenAI lanzó GPT‑5.4 el 5 de marzo de 2026, apenas un día antes de este episodio. Durante la conversación, ChatGPT (participando en diálogo con Vincent) explicó las novedades clave que marcan diferencia en el mercado: contexto de hasta un millón de tokens, mejora del 33% en reducción de errores respecto a la versión previa, herramientas de automatización más profundas y mayor integración con flujos de trabajo profesionales. (Los detalles técnicos completos se abordan con más calma en el programa, pero el foco del episodio está en el impacto práctico y geopolítico.) Irán ataca infraestructura crítica: desinformación de IA amplifica el caos geopolítico A mitad del episodio, la conversación gira hacia el conflicto que explota sobre el planeta: Irán lanzó ataques contra bases militares estadounidenses, centros de datos (incluyendo instalaciones de Microsoft Azure en el Golfo Pérsico) y sistemas de desalinización en Oriente Medio. Vincent y Pablo enmarcan este escalamiento dentro de una historia más amplia: Estados Unidos, en apenas 250 años de existencia, ha estado en paz solo 16 años; el resto ha sido conflicto bélico constante. Irán, durante cuatro décadas, ha acumulado una capacidad defensiva nacional inmensa. Cuando se lanzan misiles de un millón de dólares para destruir drones de 20,000 dólares, la economía de la guerra revela su irracionalidad inherente. “Estamos viendo una operación quirúrgica de un país que lleva décadas preparándose para un momento así. No es improvisado; es cálculo estratégico. El problema es que genera nacionalismo extremo, no revolución interna”. — Vincent Quezada ¿Cuántos países están realmente involucrados? Expansión del conflicto más allá de Irán e Israel Lo que inicialmente parecía ser un conflicto bilateral Irán‑Israel se ha expandido a entre 16 y 17 países. No se trata solo de ataques entre naciones, sino también de: Ataques a bases militares de Estados Unidos en múltiples naciones del Golfo Pérsico. Infraestructura civil crítica comprometida, como plantas desalinizadoras que suministran agua a millones de personas. Centros de datos de Microsoft Azure, que gestionan sistemas de la OTAN, la defensa estadounidense y grandes instituciones financieras. Sistemas GPS degradados o bloqueados en las zonas del conflicto. Pablo subraya que una planta desalinizadora comprometida en el Golfo Pérsico afecta a millones de civiles. No se trata solo de un conflicto militar, sino de un ataque sistémico a la supervivencia civil. “La estrategia inicial que leí era que, después de matar al líder, habría revolución interna y cambio de gobierno. No funciona así. No puedes cambiar 40 años de dominación, creencia popular y cultura con un bombardeo. Generó nacionalismo extremo, justo lo contrario”. — Pablo Berruecos Gasto económico diario: más de mil millones de dólares en conflicto activo La cifra de gasto militar diario es casi incomprensible. Según el monitoreo de cuentas en X (Twitter) que rastrean gasto militar en tiempo real, el conflicto cuesta más de mil millones de dólares al día. Comparado con las pérdidas bursátiles simultáneas en Estados Unidos (Nvidia ‑1,55%, Google en rojo, Apple ‑1,42%, Visa ‑0,69%, Amazon ‑0,48%, Tesla ‑2,33%), el costo económico global es catastrófico. Desglose de los primeros días de ataques Día 1 (primer ataque de Irán): 500 misiles lanzados hacia Israel y bases estadounidenses. Día 2: 200 misiles. Día 3: 100 misiles. Día 4: 50 misiles. Día 5 y posteriores: 15‑20 misiles, pero con intensificación del uso de drones y sistemas más sofisticados. En cuanto a municiones, para interceptar cada misil lanzado Estados Unidos empleó entre 10 y 20 misiles Tomahawk, cuyo coste ronda los 4‑5 millones de dólares cada uno. La matemática es devastadora: para defenderse de 500 misiles, se gastaron entre 5,000 y 10,000 millones de dólares solo en defensa. Irán, con un presupuesto militar inferior, amplifica su impacto usando drones de bajo coste que replican la capacidad de misiles mucho más caros. ¿Por qué Dubái está en pánico? Crisis de confianza en los paraísos fiscales Pablo narra una anécdota inquietante: una influencer española se mudó a Dubái explícitamente para no pagar impuestos. Cuando comenzó el bombardeo, pidió al gobierno español que la rescatara. Las redes sociales reaccionaron con dureza: “Te fuiste para evitar impuestos, pero esperas que nuestros impuestos te salven”. Más allá del drama mediático, esto revela una crisis de confianza más profunda. Dubái representa la opulencia extrema (albercas en cada piso, derroche de dinero). Al mismo tiempo es una ciudad vulnerable: construida en medio del desierto sin recursos naturales, depende de agua desalinizada y petróleo importado. Una planta desalinizadora comprometida deja a millones de personas sin acceso a agua potable. Las embajadas no pueden evacuar a todos; la capacidad del aeropuerto es limitada. Los depósitos de oro de países del Golfo plantean preguntas: ¿quién los controla si hay invasión? ¿Se pierde la credibilidad de esa moneda? “Dubái te da una ilusión de seguridad. Luego descubres que estás tan vulnerable como en cualquier otro sitio. Si pierdes acceso a agua, dinero y energía, la opulencia desaparece en cuestión de horas”. — Pablo Berruecos ¿Es una tercera guerra mundial? La respuesta compleja de Vincent y Pablo La gran pregunta: ¿es esto la tercera guerra mundial? Vincent y Pablo responden que no, pero sí se trata de un conflicto multinacional sin precedentes recientes. Factores que empujan hacia un conflicto total: múltiples frentes (tecnológico, energético, cibernético), riesgo de escalamiento incalculable y poder nuclear en equilibrio inestable. Factores limitantes: China no quiere involucrarse (si lo hace, el “game over” planetario); Rusia comenta desde la banda; la diplomacia existe, pero parece ficción. Realidad actual: es una guerra sin declaración formal, sin límites claros y sin un final visible. Es un conflicto mayor que podría convertirse en guerra mundial si alguien toma la decisión equivocada. Censura en redes sociales: TikTok, Grok y ChatGPT eliminan realidad selectivamente Vincent lanza una acusación central: las plataformas de redes sociales están censurando el conflicto real mientras amplifican la desinformación generada con IA. Se forma así un mecanismo de control dual. Censura selectiva. TikTok, Grok y ChatGPT han censurado términos como “Palestina libre”, bloquean videos de ataques verificables y silencian reportajes de bombardeos reales. El resultado es que los usuarios no ven la magnitud real del conflicto. Amplificación de desinformación. Al mismo tiempo, videos falsos generados con IA se replican masivamente. Un ejemplo documentado es un video de un misil impactando un portaaviones, con barcos salvavidas saliendo disparados de forma físicamente imposible. Medios internacionales lo replicaron como si fuera un evento real. “Mucha gente salió de ChatGPT esta semana no por problemas técnicos, sino porque OpenAI dijo ‘sí' a participar en la guerra cuando Anthropic dijo ‘no'. Unos 1,5 millones de usuarios migraron por cuestiones éticas”. — Vincent Quezada El parque “Policía” de Teherán: cómo la IA comete atrocidades sin intención Un detalle sintetiza la tragedia: en Teherán existe un parque público llamado Parque Policía. Sistemas de IA estadounidenses lo detectaron como “base militar de policía” y lo bombardearon. No había policías, solo civiles. Se destruyó infraestructura pública sin valor militar. Esto ilustra una crisis existencial: si los sistemas de IA se usan para identificar blancos y esos sistemas cometen errores de clasificación, ¿quién es responsable? La respuesta legal suele ser que nadie, porque “fue una máquina”. El patrón se repite: Hospitales destruidos. Escuelas destruidas. Iglesias destruidas. Cada error (Con o sin intención) se traduce en más víctimas civiles. ¿Qué porcentaje de lo que ves es real y qué parte es generado por IA? Esta es la pregunta que obsesiona a Pablo al final de la sección. En redes sociales, el feed está contaminado: videos viejos del año pasado, videos recientes manipulados con IA, análisis en tiempo real legítimos, campañas de desinformación coordinada y censura selectiva, todo mezclado. Pablo cita un reportaje de un canal europeo (disponible vía Roku) que analizaba la cantidad masiva de videos falsos que circulan. La conclusión es aterradora: no sabes en qué creer. “Entre no ver nada (porque está censurado) y ver todo falso (porque es IA), terminas paralizado. La verdad deja de importar cuando ya no sabes identificarla”. — Pablo Berruecos Impacto tecnológico real: Microsoft Azure y la columna vertebral digital del conflicto Un detalle merece su propio análisis: Irán atacó centros de datos de Microsoft en el Golfo Pérsico. No se trata de servicios comerciales como AWS, sino de infraestructura Azure que soporta: La columna vertebral operativa de la OTAN. El Departamento de Defensa de Estados Unidos. Grandes instituciones financieras occidentales. Infraestructura militar 5G. Zonas de disponibilidad Azure con clasificación FedRAMP High, la más alta que puede obtener un proveedor comercial. Si estos centros de datos llegaran a caer (algo aún no confirmado oficialmente), el impacto sería catastrófico para la estructura de defensa y las finanzas occidentales. Pablo subraya que esto no es un ataque comercial, sino un ataque al tejido conectivo digital que une la arquitectura de defensa con las ambiciones soberanas de IA en el Golfo Pérsico. Conclusión parcial. El conflicto Irán‑EU – Israel ya no es solo militar; es digital, económico y tecnológico. La desinformación generada con IA amplifica el caos mientras la censura selectiva paraliza la comprensión pública. El resultado es un planeta sin ley en el que la verdad es tan escasa como la paz. Mobile World Congress 2026: privacidad, seguridad y conectividad satelital Tras el análisis geopolítico, Vincent y Pablo redirigen la conversación hacia el Mobile World Congress 2026 en Barcelona, el evento más importante de la industria móvil global. Este año marca un punto de inflexión: privacidad y seguridad dejan de ser características opcionales para convertirse en pilares competitivos. Motorola abandona el Android tradicional por GrapheneOS; múltiples fabricantes lanzan teléfonos con Linux exclusivos para Europa; MediaTek integra conectividad satelital 5G; Nothing presenta el Phone 4 con diseño transparente Glyph Matrix. Pablo y Vincent diseccionan cada lanzamiento con detalle técnico. Nothing Phone 4: diseño Glyph Matrix transparente Nothing lanzó el Phone 4 con una propuesta radical: mantener el diseño transparente icónico y añadir Glyph Matrix, una matriz de 137,000 mini‑LEDs que cubren el 57% de la parte trasera del dispositivo y que brillan un 100% más que en generaciones anteriores. Estos LEDs generan iconos personalizables (batería, temporizador, reloj digital, espejo Glyph, camino solar) que transforman la cámara trasera en una interfaz háptica y visual única. Especificaciones técnicas del Nothing Phone 4 Diseño Glyph Lift Matrix: fusión de un cuerpo unibody de metal con refracciones de luz, acabados suaves sin fisuras y un diseño retrofuturista inspirado en cámaras de cine vintage y consolas clásicas. Colores: plata, negro y rosa metálico (poco común en 2026 y distintivo a simple vista). Cámara trasera principal: sensor Sony Exmor 700c de gran tamaño, 50 megapíxeles, zoom óptico 3,5x. Cámara gran angular: sensor Sony de 32 megapíxeles para captura de contexto amplio. Motor Lens Engine 4: compatible con fotos y video 4K Ultra HDR, efectos HDR Flex y Dolby Vision integrado. Pantalla AMOLED de 6,83 pulgadas: resolución 1,5K (2,408 × 1,506 píxeles), 450 ppp, tasa de refresco de 144 Hz (ideal para videojuegos) y brillo máximo de 5,000 nits. Protección: cristal Corning Gorilla Glass 7i con resistencia mejorada a caídas y rasguños. Procesador: Snapdragon 7 Serie Gen 4; CPU un 27% más rápida y GPU un 30% más potente que la generación anterior; capacidades de IA un 65% superiores. Memoria y almacenamiento: RAM LPDDR5X y almacenamiento UFS 3.1, con velocidades de lectura y escritura elevadas. Batería: 5,080 mAh, carga rápida de 50 W y más de 17 horas documentadas de uso mixto. Software: Nothing OS 4.1 basado en Android 16, con AI Dashboard para control de funciones de IA, Essential AI para organización de calendario y vida diaria, Essential Search (acceso multiplataforma inmediato), Essential Memory (personalización según actividad), Playground (creación de apps sin código) y Essential Space (sincronización en la nube multiplataforma). Precio y disponibilidad: la revelación oficial se programa para el 18 de marzo de 2026. Vincent confirma invitación al evento, pero con conflicto de agenda; espera recibir unidades de prueba. “El diseño transparente de Nothing no es solo estética; es filosofía. Muestran lo que todas las demás marcas ocultan. Es una declaración sobre privacidad y accesibilidad”. — Vincent Quezada Pruebas de cámara con el Honor Magic 8 Lite Vincent comparte sus pruebas de cámara con el Honor Magic 8 Lite realizadas durante un fin de semana en Chapultepec (Ciudad de México). Sus conclusiones son claras: la fotografía es excelente, el video es aceptable pero presenta limitaciones de estabilización al usar el zoom máximo. La batería del Honor duró desde el domingo hasta el viernes con un 82% restante al momento de grabar, algo que Vincent califica de “maravilla” frente a la competencia. La carga rápida también impresiona: del 15% al 80% en menos de 30 minutos. MediaTek M90: primer chip 5G con conectividad satelital integrada MediaTek presentó el M90, el primer chip móvil 5G con conectividad satelital integrada de fábrica. Esto permite que los dispositivos accedan a redes como Starlink Mobile incluso sin infraestructura celular terrestre. En contextos críticos —terremotos, conflictos armados, zonas rurales remotas—, esta conectividad híbrida 5G‑satelital es infraestructura de supervivencia, no un lujo tecnológico. ¿Por qué la conectividad satelital es crítica? Vincent comparte evidencia directa: durante simulacros de alerta sísmica y terremotos reales de 2026 en México, solo dos de sus cuatro teléfonos recibieron la alerta de emergencia. Los que tenían Wi‑Fi permanente activo y chips compatibles con conectividad satelital sí captaron la señal; los otros, no. La conclusión es inequívoca: la redundancia de conectividad puede literalmente salvar vidas. Casos de uso estratégicos: comunicaciones militares sin depender de operadores civiles comprometidos, navegación precisa en regiones sin torres celulares, transmisión de datos en vehículos autónomos en autopistas remotas y alertas de emergencia en zonas sísmicas o bajo ataque. Implicación geopolítica: gobiernos y fuerzas de seguridad pueden operar de forma independiente a los monopolios de conectividad nacional y los ciudadanos en zonas de conflicto pueden comunicarse sin censura de proveedores locales. Velocidad: no es la más alta (la latencia es mayor que la del 5G terrestre), pero garantiza conectividad donde no hay alternativas viables. “La conectividad satelital no es un lujo; es infraestructura crítica de supervivencia. Si no recibiste la alerta sísmica porque tu teléfono no tenía redundancia, la tecnología fracasó”. — Vincent Quezada Motorola abandona Android tradicional: apuesta por GrapheneOS Motorola anunció oficialmente el fin de su línea de dispositivos con Android estándar y su migración hacia GrapheneOS, un sistema operativo de código cerrado pero obsesionado con la privacidad. GrapheneOS implementa un aislamiento extremo a nivel granular: una aplicación de mensajería no puede acceder a micrófono, cámara o ubicación a menos que el usuario lo autorice explícitamente en cada sesión. Esta decisión responde a una demanda corporativa creciente de teléfonos resistentes a la vigilancia masiva, a ciberataques y a la exfiltración de datos. El mercado objetivo son empresas multinacionales, gobiernos, periodistas en contextos de riesgo y usuarios muy conscientes de la privacidad. Ventajas de GrapheneOS: aislamiento estricto por aplicación, permisos granulares que expiran por sesión, resistencia a puertas traseras corporativas o gubernamentales y actualizaciones de seguridad más rápidas que en Android AOSP. Desventajas: fragmentación de aplicaciones, compatibilidad limitada con Google Play Services, ecosistema menos maduro y curva de aprendizaje más pronunciada para usuarios no técnicos. Precio estimado: no se ha revelado oficialmente, pero se espera un sobreprecio de entre el 15% y el 20% respecto a modelos Android estándar. “Android abierto es poderoso pero vulnerable. GrapheneOS es Android cerrado, paranoico y centrado en la privacidad. La elección depende de si valoras más la conveniencia o el control absoluto de tus datos”. — Pablo Berruecos Teléfonos con Linux: código abierto verificable y seguridad auditada Varios fabricantes presentaron prototipos de teléfonos basados completamente en Linux, con lanzamiento inicial exclusivo en Europa. Linux ofrece transparencia total de código fuente, auditoría comunitaria constante y resistencia natural a puertas traseras corporativas o gubernamentales. Aunque el mercado se limita, de momento, a Europa por las estrictas regulaciones del RGPD, las proyecciones apuntan a una expansión global alrededor de 2027. Ventaja clave: código abierto 100% verificable, auditoría de seguridad comunitaria permanente, ausencia de telemetría corporativa oculta y actualizaciones controladas por el usuario. Desafío principal: enorme fragmentación de aplicaciones, compatibilidad casi nula con Google Play Store, ecosistema de apps menos maduro e interfaces menos pulidas que Android o iOS. Público objetivo: gobiernos europeos con requisitos de soberanía digital, periodistas de investigación, disidentes políticos y profesionales de sectores de seguridad crítica (finanzas, defensa, salud). Otros lanzamientos destacados del Mobile World Congress 2026 Smartphones con innovación radical en diseño y modularidad Honor Robot Phone: cámara de 200 megapíxeles montada en un brazo gimbal motorizado que se despliega desde el chasis, permitiendo ángulos de captura profesionales imposibles en teléfonos convencionales (autorretratos sin distorsión, videografía con estabilización tipo cine, panorámicas sin cortes digitales). Motorola Razr y Edge (FIFA World Cup 26 Collection): ediciones especiales con logotipo oficial del torneo, interfaz personalizada del evento y colores temáticos. Xiaomi 17 Ultra: presentación europea con especificaciones de gama alta, precio por anunciar pero competitivo frente al Samsung Galaxy S26 Ultra. Nothing Phone 4A: versión más accesible del Phone 4 con colores llamativos (destaca el rosa metálico) y un Glyph Matrix reducido pero funcional. Unihertz Titan Elite 2: teclado físico completo (nostalgia BlackBerry) en un formato moderno con Android 16. Vivo X300 Ultra: cámara de 200 megapíxeles y lanzamiento global fuera de China, la primera vez que Vivo lleva un buque insignia de este tipo a mercados occidentales. Tecno Atom (modular magnético): sistema de accesorios magnéticos intercambiables inspirado en los antiguos Moto Mods (proyectores, cámaras adicionales, baterías extendidas) sin sacrificar portabilidad diaria. Tecno Power Neon: incorpora iluminación neón real usando tecnología de gas inerte de baja tensión; diseño retrofuturista cyberpunk; primer teléfono con neón físico desde 2003. Legion Gold Fold (concepto): teléfono plegable centrado en videojuegos, con pantalla de 240 Hz y gatillos ultrasónicos integrados. Laptops y tablets con pantallas modulares e IA integrada Lenovo ThinkBook módulo IPC: puertos intercambiables magnéticos para conectar una segunda pantalla portátil; extensión dinámica del espacio de trabajo sin cables. Lenovo Yoga Book Pro D: doble pantalla con visualización 3D sin necesidad de gafas de realidad virtual, productividad multitarea reforzada y reconocimiento de gestos en el aire. Asus VivoBook Pad XPS: tablet estilo laptop con pantalla OLED más grande (15,6 pulgadas) y teclado mecánico desmontable mejorado. Chips y conectividad avanzada: preparación para 6G Qualcomm FastConnect 8800: módulo Wi‑Fi 7 con IA integrada para optimizar el ancho de banda automáticamente según el tipo de contenido. Qualcomm X105 5G: módem un 15% más rápido, un 20% más pequeño y un 30% más eficiente que el X100, pensado como puente hacia 5G Advanced (5G‑A). Snapdragon Wear Elite: chip orientado a wearables y robótica, con procesamiento de baja latencia (por debajo de 10 ms), ideal para relojes inteligentes, audífonos con IA y robots de servicio. Samsung y la pantalla anti‑espionaje Samsung presentó una tecnología de pantalla que impide que las personas situadas a los lados del usuario vean el contenido. La innovación cambia la forma en que los píxeles emiten luz: se coloca un “aro óptico” alrededor de cada píxel que nubla la imagen cuando se observa desde ángulos laterales. Desde el frente, la imagen es perfectamente clara; desde cualquier otro ángulo, se ve borrosa e ilegible. “Esto resuelve el problema de privacidad en transporte público, oficinas compartidas y aeropuertos. Finalmente puedes trabajar con información sensible sin preocuparte de quién mira por encima de tu hombro”. — Pablo Berruecos Conclusión parcial. El Mobile World Congress 2026 consolidó privacidad, seguridad y conectividad satelital como pilares no negociables de la telefonía móvil. Nothing Phone 4 democratiza el diseño transparente; MediaTek integra satelital en chips 5G; Motorola apuesta por GrapheneOS; Europa lidera con teléfonos Linux. La pregunta ya no es “qué tan rápido es tu teléfono”, sino “qué tan privado y resiliente es”. Robots humanoides y audífonos inteligentes: la IA se vuelve física El Mobile World Congress 2026 no giró solo en torno a teléfonos. La inteligencia artificial se materializó en hardware físico: robots humanoides capaces de bailar moonwalk, audífonos que analizan la geometría del canal auditivo para prevenir pérdida de audición, dispositivos para mascotas con llamadas bidireccionales mediante gestos y gafas de realidad extendida con traducción en tiempo real. Vincent y Pablo exploran estas innovaciones con mirada crítica. Honor Robot Humanoid: bípedo capaz de bailar y servir Honor presentó un robot humanoide bípedo completamente funcional, capaz de bailar (incluyendo un moonwalk que se volvió viral), mantener el equilibrio en superficies irregulares y ejecutar tareas de servicio básicas. Pablo recuerda un momento particularmente comentado: un robot humanoide propinando un “golpe bajo” a un boxeador durante una demostración, probablemente por un error de calibración, que generó memes instantáneos. Capacidades motoras: caminar de forma estable, correr a baja velocidad, subir escaleras y bailar coreografías preprogramadas. Casos de uso previstos: servicio hotelero, asistencia en hospitales, limpieza industrial y entretenimiento en eventos. Limitaciones actuales: velocidad de procesamiento de IA para decisiones complejas, autonomía de batería de entre cuatro y seis horas en operación continua y costo prohibitivo para el consumidor final (por encima de 50,000 dólares). PetFoam: comunicación bidireccional para mascotas PetFoam es un dispositivo que permite a las mascotas “llamar” a sus dueños mediante gestos reconocidos por IA. Por ejemplo, un perro que rasca un sensor específico puede activar una videollamada al dueño. Este, a su vez, puede responder con voz, mientras la mascota ve la imagen en una pequeña pantalla integrada. El caso de uso central es claro: mascotas en una posible emergencia (heridas, atrapadas) pueden alertar sin que haya intervención directa de otra persona. Google Iris XR: gafas de realidad extendida con traducción simultánea Google presentó el prototipo Iris XR, unas gafas de realidad extendida —no realidad virtual completa— con traducción en tiempo real integrada mediante IA. Sus casos de uso incluyen viajes internacionales, reuniones multilingües y accesibilidad para personas sordas (con subtítulos en tiempo real de las conversaciones). De momento no tienen fecha de lanzamiento comercial y solo están disponibles en demos controladas del MWC. Audífonos inteligentes que analizan tu oído: riesgos y beneficios Los audífonos evolucionan de meros accesorios pasivos a dispositivos de bioacústica avanzada. En el MWC 2026 se mostraron modelos capaces de analizar la geometría única del canal auditivo del usuario para ajustar de forma dinámica la cancelación de ruido, la ecualización personalizada y la exposición a decibeles. Esto crea un perfil acústico único por oído, minimizando la fatiga auditiva acumulativa y el riesgo de pérdida de audición permanente. Características técnicas de estos audífonos Cancelación de ruido adaptativa: detecta frecuencias específicas del entorno (motor de autobús, viento, multitudes, maquinaria industrial) y las atenúa selectivamente sin aislar por completo. Medición de decibeles en tiempo real: emite alertas visuales o hápticas si el volumen excede los 85 dB durante más de 30 minutos, siguiendo el límite seguro sugerido por la OMS. Análisis de la forma del oído: ajusta la presión en el canal auditivo y modifica el ancho de banda según la morfología individual, reduciendo la fatiga en usos prolongados de más de ocho horas diarias. Ecualización personalizada: compensa las deficiencias auditivas naturales de cada usuario en determinadas frecuencias. Riesgos para la salud auditiva: la presión en el tubo de Eustaquio Vincent advierte sobre un riesgo poco mencionado por los fabricantes: la cancelación de ruido total crea un sello hermético que genera presión en el canal auditivo. Esta presión activa el tubo de Eustaquio, responsable de regular la presión en el oído medio. El uso prolongado con sellado hermético puede: Comprometer la capacidad natural del oído para regular la presión (similar a lo que ocurre en un avión). Crear dependencia de una presión artificial para “escuchar correctamente”. Generar fatiga auditiva acumulativa por exceso de vibraciones internas. Aumentar el riesgo de infecciones de oído medio por retención de humedad. “La cancelación de ruido total te aísla del mundo. Una cancelación inteligente te mantiene conectado a tu entorno mientras disfrutas la música. La diferencia es literal entre la vida y un accidente”. — Vincent Quezada Caso práctico en Chapultepec: ceguera auditiva y casi choque Pablo cuenta una experiencia personal: caminaba en Chapultepec, en Ciudad de México, con audífonos con cancelación activa total. No escuchó a una persona que le gritaba para evitar un choque. Cuando finalmente la vio, ya era tarde y terminaron chocando. Reflexiona que, si hubiera estado en bicicleta y no escuchara la campanilla del trenecito turístico —que avisa su paso—, podría haber frenado de golpe y causar un accidente. Su recomendación es clara: nunca uses cancelación de ruido total en espacios públicos como calles, ciclovías o transporte. Actívala solo en entornos controlados y seguros (oficina, casa, avión). Mantén siempre un nivel medio de cancelación que permita escuchar alertas críticas del entorno (claxon, sirenas, gritos de advertencia). “Tengan cuidado. Si vas en el camión o en transporte público y te toca sentarte atrás del motor, el ruido se vuelve insoportable. Los filtros te dejan solo con la música y con el entorno realmente importante. Pero si te aíslas por completo, no sabes si alguien te está alertando de un peligro real”. — Pablo Berruecos Alianzas estratégicas hacia 6G: Nokia, NTT, Vodafone y más El MWC 2026 no solo presentó dispositivos, sino alianzas estratégicas que definen la ruta hacia un 6G nativo en inteligencia artificial. Nokia, NVIDIA, NTT, NTT Docomo, Vodafone, BT, Elisa y otros operadores anunciaron colaboraciones para adoptar tecnologías AI‑RAN (inteligencia artificial en redes de acceso radio) que mejoran el rendimiento de la red y soportan el crecimiento exponencial de la IA móvil. ¿Qué es 6G y cuándo llegará? Vincent y Pablo aclaran una confusión común: 5G Advanced (5G‑A) no es una nueva generación, sino un refinamiento del 5G existente con más velocidad, menor latencia y mejor eficiencia energética. El verdadero salto generacional será 6G, proyectado para 2030‑2032 según el consenso de los operadores presentes en el MWC. Características esperadas de 6G: velocidades teóricas 100 veces más rápidas que 5G (hasta 1 Tbps), latencias de menos de 0,1 ms (frente a 1 ms en 5G), conectividad híbrida 5G‑satelital como estándar, orquestación de IA de forma nativa en la red y uso de fotónica óptica para reducir el consumo energético. Infraestructura necesaria: inversión estimada de 100,000 millones de euros a nivel global, renovación completa de torres celulares e integración de computación cuántica en los núcleos de red. Casos de uso diferenciales: vehículos autónomos de nivel 5 (sin intervención humana), cirugías remotas en tiempo real con robótica, realidad extendida persistente (un metaverso funcional) y ciudades inteligentes con millones de sensores de IoT sincronizados. “6G no será mejor solo por ser 6G. Será mejor porque será inteligente, consciente del contexto y capaz de auto‑optimizarse en tiempo real sin intervención humana”. — Vincent Quezada Financiamiento y fotónica óptica: la apuesta de NTT Group AWS anunció la expansión de su infraestructura en mercados emergentes (India, Indonesia, Nigeria). Vodafone, la GSMA y otros organismos de telecomunicaciones aseguraron financiamiento de hasta 100 millones de euros específicamente para el desarrollo de estándares 6G con IA integrada desde el diseño. Esta inversión señala un cambio: actores privados financian estándares que antes estaban bajo control casi exclusivo de gobiernos. Por su parte, NTT Group (Japón) presentó sus avances en fotónica óptica y redes ópticas inalámbricas (ION: Innovative Optical and Wireless Network). El objetivo es reducir el consumo energético de los centros de datos, disparado por el uso intensivo de inteligencia artificial. Entre los proyectos destacados se encuentran: Convergencia fotónico‑electrónica: mejora la eficiencia energética de los centros de datos hasta un 60% respecto a la electrónica tradicional. Computación cuántica óptica: cálculos a gran escala con menor espacio físico, más velocidad y menores costes a largo plazo. Infraestructura resiliente con IA: redes autorreparables que detectan y resuelven fallos sin intervención humana. Ya no se trata solo de lanzar productos, sino de redefinir cómo se integran telecomunicaciones, movilidad y tecnología para sostener la explosión de la IA sin colapsar redes eléctricas a nivel global. Conclusión general: hacia una tecnología más consciente El episodio del 6 de marzo de 2026 captura un momento bisagra. La inteligencia artificial local (CoPaw) permite privacidad sin sacrificar productividad; GPT‑5.4 amplía el contexto a niveles impensables hace apenas un año; la MacBook Neo democratiza el acceso a macOS; el conflicto Irán‑Israel muestra cómo la desinformación generada por IA paraliza la comprensión pública mientras la censura selectiva oculta la realidad; y el Mobile World Congress 2026 consagra la privacidad, la seguridad satelital y el 6G como pilares del futuro móvil. Motorola abandona Android por GrapheneOS. Llegan teléfonos con Linux a Europa. MediaTek integra la conectividad satelital en chips 5G. Audífonos inteligentes analizan la geometría auditiva. Robots humanoides bailan moonwalk. Nokia y NVIDIA sientan las bases para 6G. De forma simultánea, la geopolítica y la desinformación revelan que una IA sin restricciones éticas se convierte en arma de control masivo. El desafío de 2026 no es tecnológico, sino humano: elegir entre la conveniencia monitoreada y la privacidad consciente. Las alianzas hacia 6G establecerán quién controla la infraestructura digital del planeta. La censura en redes sociales demuestra que la verdad es tan escasa como la paz. Y herramientas como CoPaw ofrecen una alternativa: control total de tus datos sin depender de corporaciones dispuestas a negociar su ética a cambio de contratos militares. Escucha el episodio completo en One Digital y únete a la conversación con los hashtags #PodcastONE, #OneDigital y #MWC2026. El cargo Podcast ONE: 6 de marzo de 2026 apareció primero en OneDigital.

Google lowers cut to 20% in the Play Store, Sony cancels plans to bring future single-player PlayStation titles to PC, Apple renames CPU cores in the M5 Pro and M5 Max chips. MP3 Please SUBSCRIBE HERE for free or get DTNS Live ad-free. A special thanks to all our supporters–without you, none of this wouldContinue reading "Apple Announces MacBook Neo- DTH"

Everything Apple announced this week including the new M5 lineup, Studio Displays, compare the MacBook Neo vs Air, answer if Apple really did raise prices, plus Anthropic's tense Pentagon negotiations, Netflix is out of the Warner Bros. deal, and Jason finally tries Claude.Ad-Free + Bonus EpisodesShow Notes via EmailCreative Effort - Jason's PodcastWatch on YouTube!Join the CommunityEmail Us: podcast@primarytech.fm@stephenrobles on Threads@jasonaten on Threads------------------------------Sponsors:Claude AI: Sign up for Claude Pro today at: claude.ai/primaryQuo: Try QUO for free PLUS get 20% off your first 6 months when you go to: Quo.com/primary------------------------------Links from the showEverything Apple Released This Week: M5 Lineup, Displays, iPad Air - YouTubeApple gives in to temptation and renames its CPU cores - Six ColorsDisplays - AppleBenQ Computer Monitor 27"MacBook Neo vs MacBook Air: Every Difference Explained - YouTubeThe MacBook Neo May Be Apple's Most Consequential New Product in a Decade - Inc.comEveryone Expected Apple to Raise Prices on Its New Macs. What It Did Instead Was Much Smarter - Inc.comNothing is finally covering up with the slim, metal Phone 4A Pro | The VergeNothing Headphone A review: something worth considering | The VergeHeadphone (a) | NothingAnthropic makes last-ditch effort to salvage deal with Pentagon after blowup | The VergeOpenAI releases GPT-5.3 Instant update to make ChatGPT less 'cringe' - 9to5MacHBO Max-Paramount+ to Combine Streaming Services‎Watch Knife Edge: Chasing Michelin Stars - Apple TVAI-generated art can't be copyrighted after Supreme Court declines to review the rule | The VergeTim Sweeney signed away his right to criticize Google until 2032 | The VergeStephen Robles: Gemini is like the eager assistant - Mastodon ★ Support this podcast ★

●YouTube影片● https://user322571.psee.ly/8sjw4z ●FB粉專影片 ● https://user322571.pse.is/8sjw6s 本集主題：建築與室內設計 訪問：周先勤 學經歷： 私立中原大學建築研究所畢業 1999年專技人員建築師高考及格 崑山科技大學空間設計系講師 東南科技大學室內設計系講師 孫德鴻建築師事務所 建築師 林健成美術製作工程有限公司 設計部主任 佑得營造有限公司 主任建築師 現任： Ai建築及室內設計工作室 主持人 綠色點子生活創意有限公司 設計總監 主要作品及專案： 建築設計 台北縣立十三行博物館新建工程 皇鼎建設萊特科技中心廠辦大樓 日泰建設中和市集合住宅新建工程 蘭陽博物館競圖案第一名 中壢新屋基督長老教會新建案 展示設計 蘭陽博物館展示設計細部設計 台北縣立黃金博物館展示設計及製作 金門總兵署展示設計細部設計及製作 世界宗教博物館「戀戀雙和-中和庄八景」特展設計製作 淡水古蹟園區「在福爾摩沙遇見西班牙」特展設計製作 室內設計 商業空間：台北玩彩格商務旅館設計案 竹北酒窩(葡萄酒專賣店) 竹北諾曼第生活館展示空間 桃園饗象泰式火鍋店 覓燒烤桃園二店 小南風-咖啡廳 抓抓-寵物專賣店 喵喵有約-貓咪咖啡廳 住宅：約150戶 其他 幸孺企業有限公司CPU散熱風扇包裝設計 上海華東師範大學出版社出版品－高考英語900插畫繪製 杉隆工業股份有限公司氣動工具握把設計 世界宗教博物館商品設計開發 #李基銘 #李基銘主持人#fb新鮮事#快樂玩童軍 #廣播之神#廣播之神李基銘 ●YouTube節目採訪頻道● https://voh.pse.is/83c4sg podcast平台，可以收聽 SoundOn　https://bit.ly/3oXSlmF Spotify　https://spoti.fi/2TXxH7V Apple　https://apple.co/2I7NYVc KKBOX　https://bit.ly/2JlI3wC Firstory　https://bit.ly/3lCHDPi 請支持粉絲頁 廣播之神：  / voh.god  李基銘主持人粉絲頁：  / voh.lee  李基銘-主持人-節目採訪頻道 ：  / voh.video  漢聲廣播電臺「fb新鮮事」節目 ：  / voh.vhbn  -- Hosting provided by SoundOn

MacBook Neo cuesta 599 dólares y usa chip A18 Pro en apuesta educativa masivaPor Félix Riaño @LocutorCoApple presentó el MacBook Neo como su portátil más económico. Parte desde 599 dólares y 499 dólares para estudiantes. Usa el chip A18 Pro del iPhone y promete hasta 16 horas de batería. Llega con 8 GB de memoria y 256 GB de almacenamiento. La pregunta es: ¿es una ganga real o un anzuelo para entrar al ecosistema?Apple decidió entrar de frente al terreno de los portátiles de 599 dólares. Lo hizo con el nuevo MacBook Neo. Es un equipo de 13 pulgadas, con pantalla Liquid Retina de 2.408 por 1.506 píxeles y brillo de 500 nits. Pesa 1,2 kilogramos y mide 1,27 centímetros de grosor. Tiene dos puertos USB-C, con una diferencia incómoda: uno es USB 3 de hasta 10 gigabits por segundo y el otro es USB 2 de 480 megabits por segundo.El procesador es el A18 Pro, el mismo que usa el iPhone 16 Pro. Viene con 8 GB de memoria unificada y 256 GB de almacenamiento en su versión base. La batería promete hasta 16 horas de reproducción de video y 11 horas de navegación web. El precio oficial es 599 dólares, y con descuento educativo baja a 499 dólares.Apple afirma que es hasta 50 por ciento más rápido en tareas cotidianas que el portátil más vendido con Intel Core Ultra 5, según pruebas con el benchmark Speedometer. Pero aquí viene la pregunta: ¿estamos ante un nuevo estándar de valor o ante un Mac recortado con buen marketing?Un Mac accesible… con recortesApple no solía competir en esta franja. El MacBook Air más reciente con chip M5 parte desde 1.099 dólares. El salto hasta 599 dólares es grande. La diferencia son 500 dólares. Eso cambia el público. Ahora hablamos de estudiantes, familias y personas que antes miraban un Chromebook o un portátil con Windows.El MacBook Neo mantiene el chasis de aluminio. Se siente como un Mac. Viene en colores como Citrus, Blush, Indigo y plata. Esa decisión recuerda al iBook G3 de principios de los años 2000. Apple está enviando un mensaje: este es el Mac juvenil.La pantalla conserva buena resolución y brillo. Tiene cámara de 1080p. Tiene altavoces con Dolby Atmos. Pero empiezan los ajustes: el teclado no tiene retroiluminación. El trackpad es mecánico, no háptico. Solo admite un monitor externo en 4K a 60 hercios. No tiene puerto MagSafe. Y el Touch ID solo aparece en el modelo de 512 GB que cuesta 699 dólares.Apple no está escondiendo que hubo concesiones. Está diciendo que el precio lo justifica. ¿Te parece suficiente?Aquí está el punto delicado. El MacBook Neo usa un chip de iPhone, no un chip de la serie M. Eso rompe la lógica que Apple venía construyendo desde 2020, cuando migró todos sus Mac a Apple Silicon con arquitectura pensada para computadores.El A18 Pro tiene seis núcleos de CPU. Dos de alto rendimiento y cuatro de eficiencia. Tiene cinco núcleos de GPU y soporte para trazado de rayos. En tareas ligeras como navegar, escribir y ver video, va a rendir bien. Pero en edición de video 4K, en modelado 3D o en grandes proyectos de programación, puede quedarse corto frente a un MacBook Air con chip M.Además, los 8 GB de memoria son el límite. No hay opción de 16 GB. En 2026, muchos usuarios ya consideran 8 GB como el mínimo justo. Si abres muchas pestañas, videollamadas y apps al mismo tiempo, vas a notar presión en el sistema.Otro detalle: solo uno de los puertos USB-C es USB 3. El otro es USB 2. Eso significa que puedes conectar un monitor o tener transferencia rápida, pero no todo a la vez con la misma velocidad. Para un equipo pensado para estudiantes, puede ser suficiente. Para alguien que quiere crecer con el equipo, puede sentirse limitado.Entonces surge la duda real: ¿es una puerta de entrada inteligente o una forma de segmentar más el mercado para empujar después al usuario hacia modelos más caros?Apple no improvisó este movimiento. El mercado de portátiles económicos estaba dominado por Chromebook y por equipos Windows de menos de 700 dólares. Muchos de ellos ofrecen buena batería y rendimiento aceptable. Lo que Apple aporta aquí es construcción premium, integración con iPhone y acceso completo a macOS Tahoe.El MacBook Neo permite copiar y pegar entre iPhone y Mac. Permite usar apps del ecosistema. Está preparado para Apple Intelligence. Eso significa que Apple quiere que el usuario joven entre al ecosistema temprano y luego, cuando necesite más potencia, suba a un Air o a un Pro.Desde el punto de vista estratégico, tiene lógica. Desde el punto de vista técnico, hay límites claros. Si eres estudiante que escribe, navega y hace trabajos en la nube, este equipo puede ser suficiente durante varios años. Si eres creador de contenido, diseñador o desarrollador exigente, probablemente vas a necesitar un modelo con chip M y más memoria.El precio de 599 dólares lo convierte en el Mac más accesible de la historia en lanzamiento oficial. Eso cambia la conversación. Pero también redefine qué entendemos por “Mac completo”.La decisión final no es emocional. Es práctica. ¿Qué vas a hacer con él todos los días?El lanzamiento ocurrió junto a otros anuncios como el iPhone 17e y los nuevos MacBook Pro con chip M5 Pro y M5 Max. El contraste es fuerte. Mientras el Neo baja a 599 dólares, el MacBook Pro de 16 pulgadas puede superar los 7.000 dólares en configuraciones altas.El Neo pesa 1,2 kilogramos. Es el mismo peso que el MacBook Air. Su batería es de 36,5 vatios hora. Apple afirma hasta 16 horas de video. Esa cifra suele medirse en condiciones controladas, con brillo moderado y aplicaciones optimizadas. En uso real puede variar.En Reino Unido y la Unión Europea, el cargador no viene incluido en la caja. Solo el cable USB-C. En Estados Unidos sí incluye adaptador de 20 vatios. Ese detalle reduce costos logísticos y ambientales, pero también puede generar molestias.El descuento educativo baja el precio a 499 dólares. Eso lo pone en territorio de iPad Air. Apple está compitiendo contra su propio catálogo. Si alguien duda entre un iPad con teclado y un MacBook Neo, ahora la diferencia es menor.Y algo más: solo soporta un monitor externo. Para quien usa dos pantallas, esto es un límite concreto. No es un detalle menor.Todo esto configura un producto atractivo, pero muy medido. Apple calculó cada concesión.El MacBook Neo abre la puerta de entrada al ecosistema Mac desde 599 dólares. Ofrece buen diseño y rendimiento suficiente para tareas básicas. Tiene límites claros en memoria y puertos. Antes de comprar, revisa qué uso real le vas a dar.Cuéntame qué opinas y sígueme en Flash Diario.Resumen para TikTok (20 palabras)MacBook Neo cuesta 599 dólares, usa chip de iPhone y apunta a estudiantes. Buen precio, pero con límites claros.BibliografíaWallpaperWiredThe TelegraphTechRadarMacRumorsPCMagMacworldCreative BloqConviértete en un supporter de este podcast: https://www.spreaker.com/podcast/flash-diario-de-el-siglo-21-es-hoy--5835407/support.Apoya el Flash Diario y escúchalo sin publicidad en el Club de Supporters. 

Dive into the heart of the AI revolution with Gary Brode from Deep Knowledge Investing. In this episode, we unravel the complex world of the semiconductors that power AI. Nvidia's GPU dominance to ARM-based innovations, Intel and AMD's CPU roles, and the massive energy demands of data centres. Learn about key deals like Nvidia-Meta's collaboration, investment risks in hyperscalers, and opportunities in nuclear energy and uranium. Perfect for investors navigating the AI boom.

Dive into the heart of the AI revolution with Gary Brode from Deep Knowledge Investing. In this episode, we unravel the complex world of the semiconductors that power AI. Nvidia's GPU dominance to ARM-based innovations, Intel and AMD's CPU roles, and the massive energy demands of data centres. Learn about key deals like Nvidia-Meta's collaboration, investment risks in hyperscalers, and opportunities in nuclear energy and uranium. Perfect for investors navigating the AI boom.

Hey Retro Fans!

Pausing a product roadmap for an entire month to point 700 engineers at a single goal is a significant structural shift, but it transformed monday.com. Andrew sits down with VP of R&D Sergei Liakhovetsky to uncover how fixing core infrastructure and adopting a cell-based architecture paved the way for platform scale. Sergei details the exact framework his leadership team used during their 30-day pause to launch user solutions while maintaining a strict zero-bureaucracy policy. The conversation also explores the new realities of reliability as platforms transition from being CPU-bound to heavily GPU-bound under the weight of automated agents.Follow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest:monday magic: A tool for generating initial work solutions and boards using simple prompts.monday vibe: An app builder that allows users to create custom applications on top of the monday.com platform.Sidekick: The horizontal AI assistant/copilot that works across the entire platform to help with tasks like data management and content generation.Agent Factory: A platform for building vertical, specialized agents that can handle specific workflows and roles.Connect with Sergei Liakhovetsky on LinkedInOFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

Apple presenta la nueva Studio Display XDR, un monitor profesional 5K Mini LED de 27 pulgadas que mejora en brillo, frecuencia y prestaciones… pero recorta tamaño respecto a la Pro Display XDR de 32 pulgadas, y va a contracorriente de un mercado que empuja hacia pantallas cada vez más grandes. El MacBook Air estrena chip M5 y se pone todavía más serio: CPU de 10 núcleos, salto grande en rendimiento de IA y SSD el doble de rápida que en el modelo anterior. El diseño no cambia, pero ahora parte de 512 GB de almacenamiento y puede llegar hasta 4 TB, con Wi‑Fi 7 y Bluetooth 6 de serie. A cambio, sube el precio: el modelo de 13 pulgadas arranca en 1.199 euros y el de 15 en 1.499, consolidándose como el portátil ligero ‘para casi todo' dentro del catálogo de Apple.” #StudioDisplayXDR #Apple #MacStudio #ProDisplayXDR #Monitor5K #MiniLED #EdiciónDeVídeo #FotografíaProfesional #SetupMac #AppleFan #MacBookAirM5 #MacBookAir #AppleM5 #Mac2026 #AppleMac #AppleEspañol #ReviewMacBook #PodcastTecnología #PortátilApple #AppleFans https://seoxan.es/crear_pedido_hosting Codigo Cupon "APPLE" PATROCINADO POR SEOXAN Optimización SEO profesional para tu negocio https://seoxan.es https://uptime.urtix.es PARTICIPA EN DIRECTO Deja tu opinión en los comentarios, haz preguntas y sé parte de la charla más importante sobre el futuro del iPad y del ecosistema Apple. ¡Tu voz cuenta! ¿TE GUSTÓ EL EPISODIO? ✨ Dale LIKE SUSCRÍBETE y activa la campanita para no perderte nada COMENTA COMPARTE con tus amigos applelianos SÍGUENOS EN TODAS NUESTRAS PLATAFORMAS: YouTube: https://www.youtube.com/@Applelianos Telegram: https://t.me/+Jm8IE4n3xtI2Zjdk X (Twitter): https://x.com/ApplelianosPod Facebook: https://www.facebook.com/applelianos Apple Podcasts: https://apple.co/39QoPbO

QuitGPT Claims Surge, NVIDIA's Vera Rubin 10x Efficiency, Remote Work Pay Premium & Brain Cells Play Doom | Hashtag Trending Jim Love covers claims from QuitGPT.org that 1.5 million people have taken action against ChatGPT, noting the figure mixes signups, shares, and cancellations and that substantiated numbers remain unclear amid negative OpenAI headlines and a possible rise in interest in Anthropic's Claude, which hit #1 on the Apple App Store and saw an outage from "unprecedented demand." NVIDIA announces its next AI platform, Vera Rubin, claiming 10x performance per watt over Grace Blackwell, higher NVLink bandwidth, and a rack-scale 72-GPU/36-CPU system aimed at lowering energy per inference and defending market leadership. A French study finds remote/hybrid workers earn about 12% more (about 6% after controls). Researchers also taught lab-grown human neurons on a chip to play Doom via electrical feedback. Apple updates iPad Air with the M4 chip, and a developer describes being locked out of a premium Google AI account with no clear human support escalation. 00:00 Sponsor Message 00:21 Today's Headlines 01:00 QuitGPT Backlash 04:28 Nvidia Vera Rubin 07:12 Remote Work Pay Premium 09:13 Brain Cells Play Doom 11:02 M4 iPad Air Update 11:35 Locked Out of AI Account 13:25 Wrap Up and Sponsor Thanks

When your business runs on data, even a few seconds of downtime can hurt. That's why this episode focuses on what keeps Oracle Database@AWS running when real-world problems strike.   Hosts Lois Houston and Nikita Abraham are joined by Senior Principal Database Instructor Rashmi Panda, who takes us inside the systems that keep databases resilient through failures, maintenance, and growing workloads.   Oracle Database@AWS Architect Professional: https://mylearn.oracle.com/ou/course/oracle-databaseaws-architect-professional/155574 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption with Customer Success Services, and with me is Nikita Abraham, Team Lead: Editorial Services with Oracle University. Nikita: Hi everyone! In our last episode, we explored the security and migration strengths of Oracle Database@AWS. Today, we're joined once again by Senior Principal Database Instructor Rashmi Panda to look at how the platform keeps your database available and resilient behind the scenes. 01:00 Lois: It's really great to have you with us, Rashmi. As many of you may know, keeping critical business applications running smoothly is essential for success. And that's why it's so important to have deployments that are highly resilient to unexpected failures, whether those failures are hardware-, software-, or network-related. With that in mind, Rashmi, could you tell us about the Oracle technologies that help keep the database available when those kinds of issues occur? Rashmi: Databases deployed in Oracle Database@AWS are built on Oracle's Foundational High Availability Architecture. Oracle Real Application Cluster or Oracle RAC is an Active-Active architecture where multiple database instances are concurrently running on separate servers, all accessing the same physical database stored in a shared storage to simultaneously process various application workloads. Even though each instance runs on a separate server, they collectively appear as a single unified database to the application. As the workload grows and demands additional computing capacity, then new nodes can be added to the cluster to spin up new database instances to support additional computing requirements. This enables you to scale out your database deployments without having to bring down your application and eliminates the need to replace existing servers with high-capacity ones, offering a more cost-effective solution. 02:19 Nikita: That's really interesting, Rashmi. It sounds like Oracle RAC offers both scalability and resilience for mission-critical applications. But of course, even the most robust systems require regular maintenance to keep them running at their best. So, how does planned maintenance affect performance?  Rashmi: Maintenance on databases can take a toll on your application uptime. Database maintenance activities typically include applying of database patches or performing updates. Along with the database updates, there may also be updates to the host operating system. These operations often demand significant downtime for the database, which consequently leads to slightly higher application downtime. Oracle Real Application Cluster provides rolling patching and rolling upgrades feature, enabling patching and upgrades in a rolling fashion without bringing down the entire cluster that significantly reduces the application downtime.  03:10 Lois: And what happens when there's a hardware failure? How does Oracle keep things running smoothly in that situation? Rashmi: In the event of an instance or a hardware failure, Oracle RAC ensures automatic service failover. This means that if one of the instance or node in the cluster goes down, the system transparently failovers the service to an available instance in the cluster, ensuring minimal disruption to your application. This feature enhances the overall availability and resilience of your database.  03:39 Lois: That sounds like a powerful way to handle unexpected issues. But for businesses that need even greater resilience and can't afford any downtime, are there other Oracle solutions designed to address those needs? Rashmi: Oracle Exadata is the maximum availability architecture database platform for Oracle databases. Core design principle of Oracle Exadata is built around redundancy, consisting of networking, power supplies, database, and storage servers and their components. This robust architecture ensures protection against the failure of any individual component, effectively guaranteeing continuous database availability. The scale out architecture of Oracle Exadata allows you to start your deployment with two database servers and three storage servers, having different number of CPU cores and different sizes and types of storage to meet the current business needs. 04:26 Lois: And if a business suddenly finds demand growing, how does the system handle that? Is it able to keep up with increased needs without disruptions? Rashmi: As the demand increases, the system can be easily expanded by adding more servers, ensuring that the performance and capacity grow with your business requirements. Exadata Database Service deployment in Oracle Database@AWS leverages this foundational technologies to provide high availability of database system. This is achieved by provisioning databases using Oracle Real Application Cluster, hosted on the redundant infrastructure provided by Oracle Exadata Infrastructure Platform. This deployment architecture provides the ability to scale compute and storage to growing resource demands without the need for downtime. You can scale up the number of enabled CPUs symmetrically in each node of the cluster when there is a need for higher processing power or you can scale out the infrastructure by adding more database and storage servers up to the Exadata Infrastructure model limit, which in itself is huge enough to support any large workloads. The Exadata Database Service running on Oracle RAC instances enables any maintenance on individual nodes or patching of the database to be performed with zero or negligible downtime. The rolling feature allows patching one instance at a time, while services seamlessly failover to the available instance, ensuring that the application experienced little to no disruption during maintenance. Oracle RAC, coupled with Oracle Exadata redundant infrastructure, protects the Database Service from any single point of failure. This fault-tolerant architecture features redundant networking and mirrored disk, enabling automatic failover in the event of a component failure. Additionally, if any node in the cluster fails, there is zero or negligible disruption to the dependent applications. 06:09 Nikita: That's really impressive, having such strong protection against failures and so little disruption, even during scaling and maintenance. But let's say a company wants those high-availability benefits in a fully managed environment, so they don't have to worry about maintaining the infrastructure themselves. Is there an option for that? Rashmi: Similar to Oracle Exadata Database Service, Oracle Autonomous Database Service on dedicated infrastructure in Oracle Database@AWS also offers the same feature, with the key difference being that it's a fully managed service. This means customers have zero responsibility for maintaining and managing the Database Service. This again, uses the same Oracle RAC technology and Oracle Exadata infrastructure to host the Database Service, where most of the activities of the database are fully automated, providing you a highly available database with extreme performance capability. It provides an elastic database deployment platform that can scale up storage and CPU online or can be enabled to autoscale storage and compute. Maintenance activities on the database like database updates are performed automatically without customer intervention and without the need of downtime, ensuring seamless operation of applications. 07:20 Lois: Can we shift gears a bit, Rashmi? Let's talk about protecting data and recovering from the unexpected. What Oracle technologies help guard against data loss and support disaster recovery for databases? Rashmi: Oracle Database Autonomous Recovery Service is a centralized backup management solution for Oracle Database services in Oracle Cloud Infrastructure. It automatically takes backup of your Oracle databases and securely stores them in the cloud. It ensures seamless data protection and rapid recovery for your database. It is a fully managed solution that eliminates the need for any manual database backup management, freeing you from associated overhead. It implements an incremental forever backup strategy, a highly efficient approach where only the changes since the last backup are identified and backed up. This approach drastically reduces the time and storage space needed for backup, as the size of the incremental changes is significantly lower than the full database backup. 08:17 Nikita: And what's the benefit of using this backup approach? Rashmi: The benefit of this approach is that your backups are completed faster, with much lesser compute and network resources, while still guaranteeing the full recoverability of your database in the event of a failure. You can achieve zero data loss with this backup service by enabling the real-time protection option, while minimizing the data loss by recovering data up to the last subsecond. It is highly recommended to enable this option for mission-critical databases that cannot tolerate any data loss, whether due to a ransomware attack or due to an unplanned outage. The protection policy can retain the protected database backups for a minimum of 14 days to a maximum of 95 days. The recovery service requires and enforces the backups are encrypted. These backups are compressed and encrypted during the backup process. The integrity of the backups is continuously validated without placing a burden on the production database. This ensures that the stored backup data is consistent and recoverable when needed. This protects against malicious user activity or any ransomware attack. With strict policy-based retention strategy, it prevents modification or deletion of backup data by malicious users. 09:30 Lois: Now, let's look at the next layer of protection. Rashmi, can you tell us about Oracle Active Data Guard? Rashmi: Oracle Active Data Guard provides highly available data protection and disaster recovery for Enterprise Oracle Databases. It creates and manages one or more transactionally consistent standby copies of production database, which is the active primary. The standby database is isolated from production environment located miles away in a distance data center, ensuring the standby remains protected and unaffected, even if the primary is impacted by a disaster. In the event of a disaster or data corruption occurring at the primary, the standby can take over the role as new primary, thus allowing business to continue its operations uninterrupted. It keeps the standby database in sync with the production database by continuously applying change logs from production. 10:25 Do you want to stay ahead in today's fast-paced world? Check out our New Features courses for Oracle Fusion Cloud Applications. Each quarter brings new updates and hands-on training to keep your skills sharp and your knowledge current. Head over to mylearn.oracle.com to dive into the latest advancements! 10:45 Nikita: Welcome back! Rashmi, how does Oracle Active Data Guard operate in practice? Rashmi: It uses the knowledge of Oracle Database block format to continuously validate physical blocks or logical intrablock corruption during redo transport and change apply. With automatic block repair feature, whenever any corrupt block is detected in the primary or the standby database, then it is automatically repaired by transferring a good copy of the block from other destination that holds it. This is handled transparently without any error being reported in the application. It enables you to upload the read-only workloads and backup operations to the standby database, reducing the load on the production database. You can achieve zero data loss at any distance by configuring a special synchronization mechanism known as parsing. File systems form the attack surface for ransomware. Since Active Data Guard replicates the data at the memory level, any ransomware attack on the primary database will never be replicated to the standby database. This allows for a safe failover to the standby without any data loss, and shielding the database from effects of the attack. You can enable automatic failover of the primary database to a chosen standby database without any manual intervention by configuring a Data Guard Broker. The Data Guard Broker continuously monitors the primary database and automatically performs a failover to the standby when the predefined failover conditions are met. Active Data Guard enables you to perform database maintenance or database software upgrades with almost zero or minimal downtime. 12:18 Lois: And how does disaster recovery work for Exadata Database Service in Oracle Database@AWS? Rashmi: Exadata Database Service, by design, are already protected against local failures by use of technologies like Oracle RAC and Oracle Exadata. Now, by deploying Exadata Database Service across multiple availability zones in an AWS region, it can ensure that your database services remain resilient to site failures. It leverages Oracle Active Data Guard to create standby in a separate availability zone such that if the primary availability zone is affected, then all application traffic can be routed to the database services in the secondary availability zone, restoring business continuity of the application back to normal. Through continuous validation of the data blocks at both the primary and the standby database, any potential corruption is detected and prevented. This ensures data integrity and protection across the entire database service. By leveraging zero data loss Autonomous Recovery Service, the database ensures that the backup remains secure and unaffected by ransomware. This enables rapid restoration of clean, uncompromised data in the event of an attack. Periodic patching and upgrades are performed online in a rolling fashion with little to no impact on the application uptime using a combination of Oracle RAC and Oracle Active Data Guard technologies. For all resource-intensive workloads like database backup or generating monthly reports, which are read-only in nature, they can be uploaded to the standby, reducing the load on the production database. In the cross-availability zone DR setup, you have the flexibility to configure Active Data Guard to use either the AWS network or the OCI network for keeping database redo logs to the standby database. Choosing which network to use for the traffic is entirely at the enterprise discretion. However, both are Oracle maximum availability–compliant and the setup is pretty simple. If the network traffic being used is OCI network or AWS network, then respective cloud provider is responsible for ensuring the reliability. You have to take into account the different charges that each cloud provider may have. And you can provision multiple standby databases using the console. Optionally, you may set up a broker manually to enable automatic failover capability. 14:30 Nikita: We just covered cross-availability-zone protection. But what if an entire AWS region goes down? Rashmi: This is where we can provide an additional level of protection by provisioning cross-region disaster recovery for your Exadata Database Service in Oracle Database@AWS.  This deployment protects your database against regional disasters. You can provision another DR environment in a different AWS region that supports Oracle Database@AWS. This deployment, together with the cross-availability zone deployment, complements your highly available and protected database service deployment in Oracle Database@AWS. Under the hood, it uses the same Oracle Database technologies that include Oracle Active Data Guard, OCI Autonomous Recovery Service, Oracle Exadata, Oracle RAC to provide the same capabilities as in case of cross-availability zone deployment. Here too, you have the flexibility to configure Oracle Active Data Guard to use either AWS network or OCI network for shipping database redo logs to the standby. And for the network traffic options, the feature remains the same, except a small difference with respect to chargeback. When using OCI Network for cross-region deployment, there is no charge for the first 10 TB of data transfer per month. Beyond that, standard OCI charges would apply. When using AWS network, you may refer to AWS charging sheet for the cross-region traffic. 15:49 Nikita: Thank you so much, Rashmi, for this insightful episode. Lois: Yes, thank you! And if you want to dive deeper into the topics we covered today, go to mylearn.oracle.com and search for the Oracle Database@AWS Architect Professional course. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 16:13 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Vandaag aan tafel:Barend BaarssenKarel van der WoudeRuben van der ZwaanPaul HaggTimeline:0:00 Intro0:29 Introducties + wie is MavenBlue2:26 Wat heeft MavenBlue in de IBM Cloud gebouwd?7:00 Waarom IBM Cloud?7:54 Pizzadozen vs Serverless Fleets11:27 Serverless Fleets voordelen17:32 Wat doet MavenBlue nog meer20:45 Cloud Opslag24:10 Cloud BeveiligingShownotes:We duiken in de wereld van extreem rekenintensieve software met Ruben van der Zwaan (Application Owner van de Economische Scenario Generator) en Paul Hagg (eindverantwoordelijk voor alle software en cloud‑hosting) van MavenBlue.Barend ontmoette Paul vorig jaar op IBM TechXchange in Orlando, waar MavenBlue in een demo liet zien hoe zij hun Economische Scenario Generator hebben gebouwd op de IBM Cloud. Wat Barend toen zag? Een oplossing die slim en dynamisch omgaat met CPU's en GPU's, waardoor duizenden tot miljoenen scenario's tegelijkertijd kunnen worden doorgerekend. Geen wachttijden meer — gewoon pure, schaalbare rekenkracht.MavenBlue begon in 2017 en verhuisden in 2020 de “pizzadozen” naar de cloud, waar ze nog steeds een cruciale rol spelen — maar dan volledig virtueel. Moderne uitdagingen, zoals piekbelasting aan het begin van elke maand, worden nu opgelost met Serverless Fleets, waardoor hardware alleen wordt ingeschakeld wanneer het écht nodig is. GPUs worden automatisch opgeschaald, draaien volle bak, en verdwijnen weer zodra de klus klaar is. Efficiënt, snel en kostenbewust.Met hun Docker‑gebaseerde aanpak houdt IBM alle infrastructuur “saai”, zodat MavenBlue zich volledig kan focussen op wat ze het beste doen: hoogwaardige, parallelle berekeningen bouwen voor de verzekeringswereld. En ondertussen werken ze verder aan hun cloud‑storage‑architectuur om ook daar de volgende stap te zetten.Het thema van onze podcast – “Of Je Stopt De Stekker Er In” – past deze aflevering misschien wel beter dan ooit. Bij MavenBlue gaat de virtuele stekker er alleen in wanneer de rekencapaciteit nodig is. En zodra het werk gedaan is? Dan gaat die er net zo snel weer uit.Links:MavenBlue: https://mavenblue.comEconomic Scenario Generation: https://mavenblue.com/solutions-esg/LinkedIn Paul Hagg: https://www.linkedin.com/in/paul-hagg-7b8bbb1/LinkedIn Ruben van der Zwaan: https://www.linkedin.com/in/ruben-van-der-zwaan-19017295/Op- en aanmerkingen kunnen gestuurd worden naar: ofjestoptdestekkererin@nl.ibm.com

AI funding rounds are getting bigger. Infrastructure bets are getting steeper. And the SaaS model is back under pressure. On episode 294 of The Six Five Pod, Patrick Moorhead and Daniel Newman break down the $110B OpenAI raise, Amazon's expanded role, AMD's $100B Meta deal, sovereign cloud momentum, and whether or not the SaaS premium is being permanently eroded. The handpicked topics for this week are: OpenAI's $110B Funding Round & Amazon's $50B Commitment: OpenAI secured a $110B round backed by Amazon, NVIDIA, and SoftBank. Amazon committed $50B over eight years, including Tranium capacity, co-development, Bedrock integration, and custom model initiatives. Microsoft remains the exclusive API cloud provider, but the competitive cloud dynamics are shifting. Anthropic, the Pentagon & the AI Safety Line: Anthropic risks a $200M DoD contract over refusing to drop safety restrictions related to mass surveillance and automated weapons. Pat and Dan explore the ethics and competitive positioning of this, and what happens if another lab steps in. Model Distillation & IP Risk: Anthropic cited 24,000 fraudulent accounts generating 16 million interactions to distill model capabilities. The episode examines IP theft, enforcement gaps, and global competition. DeepSeek & NVIDIA Blackwell Reports: Recent reports suggest DeepSeek leveraged NVIDIA Blackwell chips. The hosts discuss export controls, enforcement realities, and whether this was ever realistically in doubt. Microsoft Sovereign Cloud Goes GA: Microsoft introduced full-stack Azure sovereign cloud capabilities with support for disconnected operations. Sovereignty, regulatory compliance, and latency management are becoming core enterprise and government requirements. AMD's $100B Meta AI Infrastructure Deal: AMD secured a massive multi-gigawatt inference-focused deal with Meta using MI450. The discussion centers on competitive dynamics with NVIDIA, scale-up architecture, and whether AMD can materially shift market share. Intel & SambaNova Alignment: Intel Capital invested in SambaNova's Series E. The hosts examine inference strategy, CPU resurgence, and how Intel rounds out its AI positioning while advancing its GPU roadmap. The Flip: Is SaaS Permanently Repriced? Are enterprise SaaS multiples structurally resetting due to AI agents and consumption models, or is the market misreading enterprise AI adoption speed? Nuance emerges around consolidation, consumption pricing, and the durability of complex enterprise platforms. Bulls & Bears: NVIDIA, Salesforce, Synopsys, Dell, Snowflake, IBM, Everpure, HP Strong earnings across several big tech companies met with mixed market reactions. Terminal value concerns, consumption transitions, stock-based compensation, and memory constraints shape sentiment more than raw performance. For a deeper dive into each topic, subscribe to The Six Five Pod so you never miss an episode.

Bryan Cantrill is the co-founder and CTO of Oxide Computer Company. We discuss why the biggest cloud providers don't use off the shelf hardware, how scaling data centers at samsung's scale exposed problems with hard drive firmware, how the values of NodeJS are in conflict with robust systems, choosing Rust, and the benefits of Oxide Computer's rack scale approach. This is an extended version of an interview posted on Software Engineering Radio. Related links Oxide Computer Oxide and Friends Illumos Platform as a Reflection of Values RFD 26 bhyve CockroachDB Heterogeneous Computing with Raja Koduri Transcript You can help correct transcripts on GitHub. Intro [00:00:00] Jeremy: Today I am talking to Bryan Cantrill. He's the co-founder and CTO of Oxide computer company, and he was previously the CTO of Joyent and he also co-authored the DTrace Tracing framework while he was at Sun Microsystems. [00:00:14] Jeremy: Bryan, welcome to Software Engineering radio. [00:00:17] Bryan: Uh, awesome. Thanks for having me. It's great to be here. [00:00:20] Jeremy: You're the CTO of a company that makes computers. But I think before we get into that, a lot of people who built software, now that the actual computer is abstracted away, they're using AWS or they're using some kind of cloud service. So I thought we could start by talking about, data centers. [00:00:41] Jeremy: 'cause you were. Previously working at Joyent, and I believe you got bought by Samsung and you've previously talked about how you had to figure out, how do I run things at Samsung's scale. So how, how, how was your experience with that? What, what were the challenges there? Samsung scale and migrating off the cloud [00:01:01] Bryan: Yeah, I mean, so at Joyent, and so Joyent was a cloud computing pioneer. Uh, we competed with the likes of AWS and then later GCP and Azure. Uh, and we, I mean, we were operating at a scale, right? We had a bunch of machines, a bunch of dcs, but ultimately we know we were a VC backed company and, you know, a small company by the standards of, certainly by Samsung standards. [00:01:25] Bryan: And so when, when Samsung bought the company, I mean, the reason by the way that Samsung bought Joyent is Samsung's. Cloud Bill was, uh, let's just say it was extremely large. They were spending an enormous amount of money every year on, on the public cloud. And they realized that in order to secure their fate economically, they had to be running on their own infrastructure. [00:01:51] Bryan: It did not make sense. And there's not, was not really a product that Samsung could go buy that would give them that on-prem cloud. Uh, I mean in that, in that regard, like the state of the market was really no different. And so they went looking for a company, uh, and bought, bought Joyent. And when we were on the inside of Samsung. [00:02:11] Bryan: That we learned about Samsung scale. And Samsung loves to talk about Samsung scale. And I gotta tell you, it is more than just chest thumping. Like Samsung Scale really is, I mean, just the, the sheer, the number of devices, the number of customers, just this absolute size. they really wanted to take us out to, to levels of scale, certainly that we had not seen. [00:02:31] Bryan: The reason for buying Joyent was to be able to stand up on their own infrastructure so that we were gonna go buy, we did go buy a bunch of hardware. Problems with server hardware at scale [00:02:40] Bryan: And I remember just thinking, God, I hope Dell is somehow magically better. I hope the problems that we have seen in the small, we just. You know, I just remember hoping and hope is hope. It was of course, a terrible strategy and it was a terrible strategy here too. Uh, and the we that the problems that we saw at the large were, and when you scale out the problems that you see kind of once or twice, you now see all the time and they become absolutely debilitating. [00:03:12] Bryan: And we saw a whole series of really debilitating problems. I mean, many ways, like comically debilitating, uh, in terms of, of showing just how bad the state-of-the-art. Yes. And we had, I mean, it should be said, we had great software and great software expertise, um, and we were controlling our own system software. [00:03:35] Bryan: But even controlling your own system software, your own host OS, your own control plane, which is what we had at Joyent, ultimately, you're pretty limited. You go, I mean, you got the problems that you can obviously solve, the ones that are in your own software, but the problems that are beneath you, the, the problems that are in the hardware platform, the problems that are in the componentry beneath you become the problems that are in the firmware. IO latency due to hard drive firmware [00:04:00] Bryan: Those problems become unresolvable and they are deeply, deeply frustrating. Um, and we just saw a bunch of 'em again, they were. Comical in retrospect, and I'll give you like a, a couple of concrete examples just to give, give you an idea of what kinda what you're looking at. one of the, our data centers had really pathological IO latency. [00:04:23] Bryan: we had a very, uh, database heavy workload. And this was kind of right at the period where you were still deploying on rotating media on hard drives. So this is like, so. An all flash buy did not make economic sense when we did this in, in 2016. This probably, it'd be interesting to know like when was the, the kind of the last time that that actual hard drives made sense? [00:04:50] Bryan: 'cause I feel this was close to it. So we had a, a bunch of, of a pathological IO problems, but we had one data center in which the outliers were actually quite a bit worse and there was so much going on in that system. It took us a long time to figure out like why. And because when, when you, when you're io when you're seeing worse io I mean you're naturally, you wanna understand like what's the workload doing? [00:05:14] Bryan: You're trying to take a first principles approach. What's the workload doing? So this is a very intensive database workload to support the, the object storage system that we had built called Manta. And that the, the metadata tier was stored and uh, was we were using Postgres for that. And that was just getting absolutely slaughtered. [00:05:34] Bryan: Um, and ultimately very IO bound with these kind of pathological IO latencies. Uh, and as we, you know, trying to like peel away the layers to figure out what was going on. And I finally had this thing. So it's like, okay, we are seeing at the, at the device layer, at the at, at the disc layer, we are seeing pathological outliers in this data center that we're not seeing anywhere else. [00:06:00] Bryan: And that does not make any sense. And the thought occurred to me. I'm like, well, maybe we are. Do we have like different. Different rev of firmware on our HGST drives, HGST. Now part of WD Western Digital were the drives that we had everywhere. And, um, so maybe we had a different, maybe I had a firmware bug. [00:06:20] Bryan: I, this would not be the first time in my life at all that I would have a drive firmware issue. Uh, and I went to go pull the firmware, rev, and I'm like, Toshiba makes hard drives? So we had, I mean. I had no idea that Toshiba even made hard drives, let alone that they were our, they were in our data center. [00:06:38] Bryan: I'm like, what is this? And as it turns out, and this is, you know, part of the, the challenge when you don't have an integrated system, which not to pick on them, but Dell doesn't, and what Dell would routinely put just sub make substitutes, and they make substitutes that they, you know, it's kind of like you're going to like, I don't know, Instacart or whatever, and they're out of the thing that you want. [00:07:03] Bryan: So, you know, you're, someone makes a substitute and like sometimes that's okay, but it's really not okay in a data center. And you really want to develop and validate a, an end-to-end integrated system. And in this case, like Toshiba doesn't, I mean, Toshiba does make hard drives, but they are a, or the data they did, uh, they basically were, uh, not competitive and they were not competitive in part for the reasons that we were discovering. [00:07:29] Bryan: They had really serious firmware issues. So the, these were drives that would just simply stop a, a stop acknowledging any reads from the order of 2,700 milliseconds. Long time, 2.7 seconds. Um. And that was a, it was a drive firmware issue, but it was highlighted like a much deeper issue, which was the simple lack of control that we had over our own destiny. [00:07:53] Bryan: Um, and it's an, it's, it's an example among many where Dell is making a decision. That lowers the cost of what they are providing you marginally, but it is then giving you a system that they shouldn't have any confidence in because it's not one that they've actually designed and they leave it to the customer, the end user, to make these discoveries. [00:08:18] Bryan: And these things happen up and down the stack. And for every, for whether it's, and, and not just to pick on Dell because it's, it's true for HPE, it's true for super micro, uh, it's true for your switch vendors. It's, it's true for storage vendors where the, the, the, the one that is left actually integrating these things and trying to make the the whole thing work is the end user sitting in their data center. AWS / Google are not buying off the shelf hardware but you can't use it [00:08:42] Bryan: There's not a product that they can buy that gives them elastic infrastructure, a cloud in their own DC The, the product that you buy is the public cloud. Like when you go in the public cloud, you don't worry about the stuff because that it's, it's AWS's issue or it's GCP's issue. And they are the ones that get this to ground. [00:09:02] Bryan: And they, and this was kind of, you know, the eye-opening moment. Not a surprise. Uh, they are not Dell customers. They're not HPE customers. They're not super micro customers. They have designed their own machines. And to varying degrees, depending on which one you're looking at. But they've taken the clean sheet of paper and the frustration that we had kind of at Joyent and beginning to wonder and then Samsung and kind of wondering what was next, uh, is that, that what they built was not available for purchase in the data center. [00:09:35] Bryan: You could only rent it in the public cloud. And our big belief is that public cloud computing is a really important revolution in infrastructure. Doesn't feel like a different, a deep thought, but cloud computing is a really important revolution. It shouldn't only be available to rent. You should be able to actually buy it. [00:09:53] Bryan: And there are a bunch of reasons for doing that. Uh, one in the one we we saw at Samsung is economics, which I think is still the dominant reason where it just does not make sense to rent all of your compute in perpetuity. But there are other reasons too. There's security, there's risk management, there's latency. [00:10:07] Bryan: There are a bunch of reasons why one might wanna to own one's own infrastructure. But, uh, that was very much the, the, so the, the genesis for oxide was coming out of this very painful experience and a painful experience that, because, I mean, a long answer to your question about like what was it like to be at Samsung scale? [00:10:27] Bryan: Those are the kinds of things that we, I mean, in our other data centers, we didn't have Toshiba drives. We only had the HDSC drives, but it's only when you get to this larger scale that you begin to see some of these pathologies. But these pathologies then are really debilitating in terms of those who are trying to develop a service on top of them. [00:10:45] Bryan: So it was, it was very educational in, in that regard. And you're very grateful for the experience at Samsung in terms of opening our eyes to the challenge of running at that kind of scale. [00:10:57] Jeremy: Yeah, because I, I think as software engineers, a lot of times we, we treat the hardware as a, as a given where, [00:11:08] Bryan: Yeah. [00:11:08] Bryan: Yeah. There's software in chard drives [00:11:09] Jeremy: It sounds like in, in this case, I mean, maybe the issue is not so much that. Dell or HP as a company doesn't own every single piece that they're providing you, but rather the fact that they're swapping pieces in and out without advertising them, and then when it becomes a problem, they're not necessarily willing to, to deal with the, the consequences of that. [00:11:34] Bryan: They just don't know. I mean, I think they just genuinely don't know. I mean, I think that they, it's not like they're making a deliberate decision to kind of ship garbage. It's just that they are making, I mean, I think it's exactly what you said about like, not thinking about the hardware. It's like, what's a hard drive? [00:11:47] Bryan: Like what's it, I mean, it's a hard drive. It's got the same specs as this other hard drive and Intel. You know, it's a little bit cheaper, so why not? It's like, well, like there's some reasons why not, and one of the reasons why not is like, uh, even a hard drive, whether it's rotating media or, or flash, like that's not just hardware. [00:12:05] Bryan: There's software in there. And that the software's like not the same. I mean, there are components where it's like, there's actually, whether, you know, if, if you're looking at like a resistor or a capacitor or something like this Yeah. If you've got two, two parts that are within the same tolerance. Yeah. [00:12:19] Bryan: Like sure. Maybe, although even the EEs I think would be, would be, uh, objecting that a little bit. But the, the, the more complicated you get, and certainly once you get to the, the, the, the kind of the hardware that we think of like a, a, a microprocessor, a a network interface card, a a, a hard driver, an NVME drive. [00:12:38] Bryan: Those things are super complicated and there's a whole bunch of software inside of those things, the firmware, and that's the stuff that, that you can't, I mean, you say that software engineers don't think about that. It's like you, no one can really think about that because it's proprietary that's kinda welded shut and you've got this abstraction into it. [00:12:55] Bryan: But the, the way that thing operates is very core to how the thing in aggregate will behave. And I think that you, the, the kind of, the, the fundamental difference between Oxide's approach and the approach that you get at a Dell HP Supermicro, wherever, is really thinking holistically in terms of hardware and software together in a system that, that ultimately delivers cloud computing to a user. [00:13:22] Bryan: And there's a lot of software at many, many, many, many different layers. And it's very important to think about, about that software and that hardware holistically as a single system. [00:13:34] Jeremy: And during that time at Joyent, when you experienced some of these issues, was it more of a case of you didn't have enough servers experiencing this? So if it would happen, you might say like, well, this one's not working, so maybe we'll just replace the hardware. What, what was the thought process when you were working at that smaller scale and, and how did these issues affect you? UEFI / Baseboard Management Controller [00:13:58] Bryan: Yeah, at the smaller scale, you, uh, you see fewer of them, right? You just see it's like, okay, we, you know, what you might see is like, that's weird. We kinda saw this in one machine versus seeing it in a hundred or a thousand or 10,000. Um, so you just, you just see them, uh, less frequently as a result, they are less debilitating. [00:14:16] Bryan: Um, I, I think that it's, when you go to that larger scale, those things that become, that were unusual now become routine and they become debilitating. Um, so it, it really is in many regards a function of scale. Uh, and then I think it was also, you know, it was a little bit dispiriting that kind of the substrate we were building on really had not improved. [00:14:39] Bryan: Um, and if you look at, you know, the, if you buy a computer server, buy an x86 server. There is a very low layer of firmware, the BIOS, the basic input output system, the UEFI BIOS, and this is like an abstraction layer that has, has existed since the eighties and hasn't really meaningfully improved. Um, the, the kind of the transition to UEFI happened with, I mean, I, I ironically with Itanium, um, you know, two decades ago. [00:15:08] Bryan: but beyond that, like this low layer, this lowest layer of platform enablement software is really only impeding the operability of the system. Um, you look at the baseboard management controller, which is the kind of the computer within the computer, there is a, uh, there is an element in the machine that needs to handle environmentals, that needs to handle, uh, operate the fans and so on. [00:15:31] Bryan: Uh, and that traditionally has this, the space board management controller, and that architecturally just hasn't improved in the last two decades. And, you know, that's, it's a proprietary piece of silicon. Generally from a company that no one's ever heard of called a Speed, uh, which has to be, is written all on caps, so I guess it needs to be screamed. [00:15:50] Bryan: Um, a speed has a proprietary part that has a, there is a root password infamously there, is there, the root password is encoded effectively in silicon. So, uh, which is just, and for, um, anyone who kind of goes deep into these things, like, oh my God, are you kidding me? Um, when we first started oxide, the wifi password was a fraction of the a speed root password for the bmc. [00:16:16] Bryan: It's kinda like a little, little BMC humor. Um, but those things, it was just dispiriting that, that the, the state-of-the-art was still basically personal computers running in the data center. Um, and that's part of what, what was the motivation for doing something new? [00:16:32] Jeremy: And for the people using these systems, whether it's the baseboard management controller or it's the The BIOS or UF UEFI component, what are the actual problems that people are seeing seen? Security vulnerabilities and poor practices in the BMC [00:16:51] Bryan: Oh man, I, the, you are going to have like some fraction of your listeners, maybe a big fraction where like, yeah, like what are the problems? That's a good question. And then you're gonna have the people that actually deal with these things who are, did like their heads already hit the desk being like, what are the problems? [00:17:06] Bryan: Like what are the non problems? Like what, what works? Actually, that's like a shorter answer. Um, I mean, there are so many problems and a lot of it is just like, I mean, there are problems just architecturally these things are just so, I mean, and you could, they're the problems spread to the horizon, so you can kind of start wherever you want. [00:17:24] Bryan: But I mean, as like, as a really concrete example. Okay, so the, the BMCs that, that the computer within the computer that needs to be on its own network. So you now have like not one network, you got two networks that, and that network, by the way, it, that's the network that you're gonna log into to like reset the machine when it's otherwise unresponsive. [00:17:44] Bryan: So that going into the BMC, you can are, you're able to control the entire machine. Well it's like, alright, so now I've got a second net network that I need to manage. What is running on the BMC? Well, it's running some. Ancient, ancient version of Linux it that you got. It's like, well how do I, how do I patch that? [00:18:02] Bryan: How do I like manage the vulnerabilities with that? Because if someone is able to root your BMC, they control the system. So it's like, this is not you've, and now you've gotta go deal with all of the operational hair around that. How do you upgrade that system updating the BMC? I mean, it's like you've got this like second shadow bad infrastructure that you have to go manage. [00:18:23] Bryan: Generally not open source. There's something called open BMC, um, which, um, you people use to varying degrees, but you're generally stuck with the proprietary BMC, so you're generally stuck with, with iLO from HPE or iDRAC from Dell or, or, uh, the, uh, su super micros, BMC, that H-P-B-M-C, and you are, uh, it is just excruciating pain. [00:18:49] Bryan: Um, and that this is assuming that by the way, that everything is behaving correctly. The, the problem is that these things often don't behave correctly, and then the consequence of them not behaving correctly. It's really dire because it's at that lowest layer of the system. So, I mean, I'll give you a concrete example. [00:19:07] Bryan: a customer of theirs reported to me, so I won't disclose the vendor, but let's just say that a well-known vendor had an issue with their, their temperature sensors were broken. Um, and the thing would always read basically the wrong value. So it was the BMC that had to like, invent its own ki a different kind of thermal control loop. [00:19:28] Bryan: And it would index on the, on the, the, the, the actual inrush current. It would, they would look at that at the current that's going into the CPU to adjust the fan speed. That's a great example of something like that's a, that's an interesting idea. That doesn't work. 'cause that's actually not the temperature. [00:19:45] Bryan: So like that software would crank the fans whenever you had an inrush of current and this customer had a workload that would spike the current and by it, when it would spike the current, the, the, the fans would kick up and then they would slowly degrade over time. Well, this workload was spiking the current faster than the fans would degrade, but not fast enough to actually heat up the part. [00:20:08] Bryan: And ultimately over a very long time, in a very painful investigation, it's customer determined that like my fans are cranked in my data center for no reason. We're blowing cold air. And it's like that, this is on the order of like a hundred watts, a server of, of energy that you shouldn't be spending and like that ultimately what that go comes down to this kind of broken software hardware interface at the lowest layer that has real meaningful consequence, uh, in terms of hundreds of kilowatts, um, across a data center. So this stuff has, has very, very, very real consequence and it's such a shadowy world. Part of the reason that, that your listeners that have dealt with this, that our heads will hit the desk is because it is really aggravating to deal with problems with this layer. [00:21:01] Bryan: You, you feel powerless. You don't control or really see the software that's on them. It's generally proprietary. You are relying on your vendor. Your vendor is telling you that like, boy, I don't know. You're the only customer seeing this. I mean, the number of times I have heard that for, and I, I have pledged that we're, we're not gonna say that at oxide because it's such an unaskable thing to say like, you're the only customer saying this. [00:21:25] Bryan: It's like, it feels like, are you blaming me for my problem? Feels like you're blaming me for my problem? Um, and what you begin to realize is that to a degree, these folks are speaking their own truth because the, the folks that are running at real scale at Hyperscale, those folks aren't Dell, HP super micro customers. [00:21:46] Bryan: They're actually, they've done their own thing. So it's like, yeah, Dell's not seeing that problem, um, because they're not running at the same scale. Um, but when you do run, you only have to run at modest scale before these things just become. Overwhelming in terms of the, the headwind that they present to people that wanna deploy infrastructure. The problem is felt with just a few racks [00:22:05] Jeremy: Yeah, so maybe to help people get some perspective at, at what point do you think that people start noticing or start feeling these problems? Because I imagine that if you're just have a few racks or [00:22:22] Bryan: do you have a couple racks or the, or do you wonder or just wondering because No, no, no. I would think, I think anyone who deploys any number of servers, especially now, especially if your experience is only in the cloud, you're gonna be like, what the hell is this? I mean, just again, just to get this thing working at all. [00:22:39] Bryan: It is so it, it's so hairy and so congealed, right? It's not designed. Um, and it, it, it, it's accreted it and it's so obviously accreted that you are, I mean, nobody who is setting up a rack of servers is gonna think to themselves like, yes, this is the right way to go do it. This all makes sense because it's, it's just not, it, I, it feels like the kit, I mean, kit car's almost too generous because it implies that there's like a set of plans to work to in the end. [00:23:08] Bryan: Uh, I mean, it, it, it's a bag of bolts. It's a bunch of parts that you're putting together. And so even at the smallest scales, that stuff is painful. Just architecturally, it's painful at the small scale then, but at least you can get it working. I think the stuff that then becomes debilitating at larger scale are the things that are, are worse than just like, I can't, like this thing is a mess to get working. [00:23:31] Bryan: It's like the, the, the fan issue that, um, where you are now seeing this over, you know, hundreds of machines or thousands of machines. Um, so I, it is painful at more or less all levels of scale. There's, there is no level at which the, the, the pc, which is really what this is, this is a, the, the personal computer architecture from the 1980s and there is really no level of scale where that's the right unit. Running elastic infrastructure is the hardware but also, hypervisor, distributed database, api, etc [00:23:57] Bryan: I mean, where that's the right thing to go deploy, especially if what you are trying to run. Is elastic infrastructure, a cloud. Because the other thing is like we, we've kinda been talking a lot about that hardware layer. Like hardware is, is just the start. Like you actually gotta go put software on that and actually run that as elastic infrastructure. [00:24:16] Bryan: So you need a hypervisor. Yes. But you need a lot more than that. You, you need to actually, you, you need a distributed database, you need web endpoints. You need, you need a CLI, you need all the stuff that you need to actually go run an actual service of compute or networking or storage. I mean, and for, for compute, even for compute, there's a ton of work to be done. [00:24:39] Bryan: And compute is by far, I would say the simplest of the, of the three. When you look at like networks, network services, storage services, there's a whole bunch of stuff that you need to go build in terms of distributed systems to actually offer that as a cloud. So it, I mean, it is painful at more or less every LE level if you are trying to deploy cloud computing on. What's a control plane? [00:25:00] Jeremy: And for someone who doesn't have experience building or working with this type of infrastructure, when you talk about a control plane, what, what does that do in the context of this system? [00:25:16] Bryan: So control plane is the thing that is, that is everything between your API request and that infrastructure actually being acted upon. So you go say, Hey, I, I want a provision, a vm. Okay, great. We've got a whole bunch of things we're gonna provision with that. We're gonna provision a vm, we're gonna get some storage that's gonna go along with that, that's got a network storage service that's gonna come out of, uh, we've got a virtual network that we're gonna either create or attach to. [00:25:39] Bryan: We've got a, a whole bunch of things we need to go do for that. For all of these things, there are metadata components that need, we need to keep track of this thing that, beyond the actual infrastructure that we create. And then we need to go actually, like act on the actual compute elements, the hostos, what have you, the switches, what have you, and actually go. [00:25:56] Bryan: Create these underlying things and then connect them. And there's of course, the challenge of just getting that working is a big challenge. Um, but getting that working robustly, getting that working is, you know, when you go to provision of vm, um, the, all the, the, the steps that need to happen and what happens if one of those steps fails along the way? [00:26:17] Bryan: What happens if, you know, one thing we're very mindful of is these kind of, you get these long tails of like, why, you know, generally our VM provisioning happened within this time, but we get these long tails where it takes much longer. What's going on? What, where in this process are we, are we actually spending time? [00:26:33] Bryan: Uh, and there's a whole lot of complexity that you need to go deal with that. There's a lot of complexity that you need to go deal with this effectively, this workflow that's gonna go create these things and manage them. Um, we use a, a pattern that we call, that are called sagas, actually is a, is a database pattern from the eighties. [00:26:51] Bryan: Uh, Katie McCaffrey is a, is a database reCrcher who, who, uh, I, I think, uh, reintroduce the idea of, of sagas, um, in the last kind of decade. Um, and this is something that we picked up, um, and I've done a lot of really interesting things with, um, to allow for, to this kind of, these workflows to be, to be managed and done so robustly in a way that you can restart them and so on. [00:27:16] Bryan: Uh, and then you guys, you get this whole distributed system that can do all this. That whole distributed system, that itself needs to be reliable and available. So if you, you know, you need to be able to, what happens if you, if you pull a sled or if a sled fails, how does the system deal with that? [00:27:33] Bryan: How does the system deal with getting an another sled added to the system? Like how do you actually grow this distributed system? And then how do you update it? How do you actually go from one version to the next? And all of that has to happen across an air gap where this is gonna run as part of the computer. [00:27:49] Bryan: So there are, it, it is fractally complicated. There, there is a lot of complexity here in, in software, in the software system and all of that. We kind of, we call the control plane. Um, and it, this is the what exists at AWS at GCP, at Azure. When you are hitting an endpoint that's provisioning an EC2 instance for you. [00:28:10] Bryan: There is an AWS control plane that is, is doing all of this and has, uh, some of these similar aspects and certainly some of these similar challenges. Are vSphere / Proxmox / Hyper-V in the same category? [00:28:20] Jeremy: And for people who have run their own servers with something like say VMware or Hyper V or Proxmox, are those in the same category? [00:28:32] Bryan: Yeah, I mean a little bit. I mean, it kind of like vSphere Yes. Via VMware. No. So it's like you, uh, VMware ESX is, is kind of a key building block upon which you can build something that is a more meaningful distributed system. When it's just like a machine that you're provisioning VMs on, it's like, okay, well that's actually, you as the human might be the control plane. [00:28:52] Bryan: Like, that's, that, that's, that's a much easier problem. Um, but when you've got, you know, tens, hundreds, thousands of machines, you need to do it robustly. You need something to coordinate that activity and you know, you need to pick which sled you land on. You need to be able to move these things. You need to be able to update that whole system. [00:29:06] Bryan: That's when you're getting into a control plane. So, you know, some of these things have kind of edged into a control plane, certainly VMware. Um, now Broadcom, um, has delivered something that's kind of cloudish. Um, I think that for folks that are truly born on the cloud, it, it still feels somewhat, uh, like you're going backwards in time when you, when you look at these kind of on-prem offerings. [00:29:29] Bryan: Um, but, but it, it, it's got these aspects to it for sure. Um, and I think that we're, um, some of these other things when you're just looking at KVM or just looks looking at Proxmox you kind of need to, to connect it to other broader things to turn it into something that really looks like manageable infrastructure. [00:29:47] Bryan: And then many of those projects are really, they're either proprietary projects, uh, proprietary products like vSphere, um, or you are really dealing with open source projects that are. Not necessarily aimed at the same level of scale. Um, you know, you look at a, again, Proxmox or, uh, um, you'll get an OpenStack. [00:30:05] Bryan: Um, and you know, OpenStack is just a lot of things, right? I mean, OpenStack has got so many, the OpenStack was kind of a, a free for all, for every infrastructure vendor. Um, and I, you know, there was a time people were like, don't you, aren't you worried about all these companies together that, you know, are coming together for OpenStack? [00:30:24] Bryan: I'm like, haven't you ever worked for like a company? Like, companies don't get along. By the way, it's like having multiple companies work together on a thing that's bad news, not good news. And I think, you know, one of the things that OpenStack has definitely struggled with, kind of with what, actually the, the, there's so many different kind of vendor elements in there that it's, it's very much not a product, it's a project that you're trying to run. [00:30:47] Bryan: But that's, but that very much is in, I mean, that's, that's similar certainly in spirit. [00:30:53] Jeremy: And so I think this is kind of like you're alluding to earlier, the piece that allows you to allocate, compute, storage, manage networking, gives you that experience of I can go to a web console or I can use an API and I can spin up machines, get them all connected. At the end of the day, the control plane. Is allowing you to do that in hopefully a user-friendly way. [00:31:21] Bryan: That's right. Yep. And in the, I mean, in order to do that in a modern way, it's not just like a user-friendly way. You really need to have a CLI and a web UI and an API. Those all need to be drawn from the same kind of single ground truth. Like you don't wanna have any of those be an afterthought for the other. [00:31:39] Bryan: You wanna have the same way of generating all of those different endpoints and, and entries into the system. Building a control plane now has better tools (Rust, CockroachDB) [00:31:46] Jeremy: And if you take your time at Joyent as an example. What kind of tools existed for that versus how much did you have to build in-house for as far as the hypervisor and managing the compute and all that? [00:32:02] Bryan: Yeah, so we built more or less everything in house. I mean, what you have is, um, and I think, you know, over time we've gotten slightly better tools. Um, I think, and, and maybe it's a little bit easier to talk about the, kind of the tools we started at Oxide because we kind of started with a, with a clean sheet of paper at oxide. [00:32:16] Bryan: We wanted to, knew we wanted to go build a control plane, but we were able to kind of go revisit some of the components. So actually, and maybe I'll, I'll talk about some of those changes. So when we, at, For example, at Joyent, when we were building a cloud at Joyent, there wasn't really a good distributed database. [00:32:34] Bryan: Um, so we were using Postgres as our database for metadata and there were a lot of challenges. And Postgres is not a distributed database. It's running. With a primary secondary architecture, and there's a bunch of issues there, many of which we discovered the hard way. Um, when we were coming to oxide, you have much better options to pick from in terms of distributed databases. [00:32:57] Bryan: You know, we, there was a period that now seems maybe potentially brief in hindsight, but of a really high quality open source distributed databases. So there were really some good ones to, to pick from. Um, we, we built on CockroachDB on CRDB. Um, so that was a really important component. That we had at oxide that we didn't have at Joyent. [00:33:19] Bryan: Um, so we were, I wouldn't say we were rolling our own distributed database, we were just using Postgres and uh, and, and dealing with an enormous amount of pain there in terms of the surround. Um, on top of that, and, and, you know, a, a control plane is much more than a database, obviously. Uh, and you've gotta deal with, uh, there's a whole bunch of software that you need to go, right. [00:33:40] Bryan: Um, to be able to, to transform these kind of API requests into something that is reliable infrastructure, right? And there, there's a lot to that. Uh, especially when networking gets in the mix, when storage gets in the mix, uh, there are a whole bunch of like complicated steps that need to be done, um, at Joyent. [00:33:59] Bryan: Um, we, in part because of the history of the company and like, look. This, this just is not gonna sound good, but it just is what it is and I'm just gonna own it. We did it all in Node, um, at Joyent, which I, I, I know it sounds really right now, just sounds like, well, you, you built it with Tinker Toys. You Okay. [00:34:18] Bryan: Uh, did, did you think it was, you built the skyscraper with Tinker Toys? Uh, it's like, well, okay. We actually, we had greater aspirations for the Tinker Toys once upon a time, and it was better than, you know, than Twisted Python and Event Machine from Ruby, and we weren't gonna do it in Java. All right. [00:34:32] Bryan: So, but let's just say that that experiment, uh, that experiment did ultimately end in a predictable fashion. Um, and, uh, we, we decided that maybe Node was not gonna be the best decision long term. Um, Joyent was the company behind node js. Uh, back in the day, Ryan Dahl worked for Joyent. Uh, and then, uh, then we, we, we. [00:34:53] Bryan: Uh, landed that in a foundation in about, uh, what, 2015, something like that. Um, and began to consider our world beyond, uh, beyond Node. Rust at Oxide [00:35:04] Bryan: A big tool that we had in the arsenal when we started Oxide is Rust. Um, and so indeed the name of the company is, is a tip of the hat to the language that we were pretty sure we were gonna be building a lot of stuff in. [00:35:16] Bryan: Namely Rust. And, uh, rust is, uh, has been huge for us, a very important revolution in programming languages. you know, there, there, there have been different people kind of coming in at different times and I kinda came to Rust in what I, I think is like this big kind of second expansion of rust in 2018 when a lot of technologists were think, uh, sick of Node and also sick of Go. [00:35:43] Bryan: And, uh, also sick of C++. And wondering is there gonna be something that gives me the, the, the performance, of that I get outta C. The, the robustness that I can get out of a C program but is is often difficult to achieve. but can I get that with kind of some, some of the velocity of development, although I hate that term, some of the speed of development that you get out of a more interpreted language. [00:36:08] Bryan: Um, and then by the way, can I actually have types, I think types would be a good idea? Uh, and rust obviously hits the sweet spot of all of that. Um, it has been absolutely huge for us. I mean, we knew when we started the company again, oxide, uh, we were gonna be using rust in, in quite a, quite a. Few places, but we weren't doing it by fiat. [00:36:27] Bryan: Um, we wanted to actually make sure we're making the right decision, um, at, at every different, at every layer. Uh, I think what has been surprising is the sheer number of layers at which we use rust in terms of, we've done our own embedded firmware in rust. We've done, um, in, in the host operating system, which is still largely in C, but very big components are in rust. [00:36:47] Bryan: The hypervisor Propolis is all in rust. Uh, and then of course the control plane, that distributed system on that is all in rust. So that was a very important thing that we very much did not need to build ourselves. We were able to really leverage, uh, a terrific community. Um. We were able to use, uh, and we've done this at Joyent as well, but at Oxide, we've used Illumos as a hostos component, which, uh, our variant is called Helios. [00:37:11] Bryan: Um, we've used, uh, bhyve um, as a, as as that kind of internal hypervisor component. we've made use of a bunch of different open source components to build this thing, um, which has been really, really important for us. Uh, and open source components that didn't exist even like five years prior. [00:37:28] Bryan: That's part of why we felt that 2019 was the right time to start the company. And so we started Oxide. The problems building a control plane in Node [00:37:34] Jeremy: You had mentioned that at Joyent, you had tried to build this in, in Node. What were the, what were the, the issues or the, the challenges that you had doing that? [00:37:46] Bryan: Oh boy. Yeah. again, we, I kind of had higher hopes in 2010, I would say. When we, we set on this, um, the, the, the problem that we had just writ large, um. JavaScript is really designed to allow as many people on earth to write a program as possible, which is good. I mean, I, I, that's a, that's a laudable goal. [00:38:09] Bryan: That is the goal ultimately of such as it is of JavaScript. It's actually hard to know what the goal of JavaScript is, unfortunately, because Brendan Ike never actually wrote a book. so that there is not a canonical, you've got kind of Doug Crockford and other people who've written things on JavaScript, but it's hard to know kind of what the original intent of JavaScript is. [00:38:27] Bryan: The name doesn't even express original intent, right? It was called Live Script, and it was kind of renamed to JavaScript during the Java Frenzy of the late nineties. A name that makes no sense. There is no Java in JavaScript. that is kind of, I think, revealing to kind of the, uh, the unprincipled mess that is JavaScript. [00:38:47] Bryan: It, it, it's very pragmatic at some level, um, and allows anyone to, it makes it very easy to write software. The problem is it's much more difficult to write really rigorous software. So, uh, and this is what I should differentiate JavaScript from TypeScript. This is really what TypeScript is trying to solve. [00:39:07] Bryan: TypeScript is like. How can, I think TypeScript is a, is a great step forward because TypeScript is like, how can we bring some rigor to this? Like, yes, it's great that it's easy to write JavaScript, but that's not, we, we don't wanna do that for Absolutely. I mean that, that's not the only problem we solve. [00:39:23] Bryan: We actually wanna be able to write rigorous software and it's actually okay if it's a little harder to write rigorous software that's actually okay if it gets leads to, to more rigorous artifacts. Um, but in JavaScript, I mean, just a concrete example. You know, there's nothing to prevent you from referencing a property that doesn't actually exist in JavaScript. [00:39:43] Bryan: So if you fat finger a property name, you are relying on something to tell you. By the way, I think you've misspelled this because there is no type definition for this thing. And I don't know that you've got one that's spelled correctly, one that's spelled incorrectly, that's often undefined. And then the, when you actually go, you say you've got this typo that is lurking in your what you want to be rigorous software. [00:40:07] Bryan: And if you don't execute that code, like you won't know that's there. And then you do execute that code. And now you've got a, you've got an undefined object. And now that's either gonna be an exception or it can, again, depends on how that's handled. It can be really difficult to determine the origin of that, of, of that error, of that programming. [00:40:26] Bryan: And that is a programmer error. And one of the big challenges that we had with Node is that programmer errors and operational errors, like, you know, I'm out of disk space as an operational error. Those get conflated and it becomes really hard. And in fact, I think the, the language wanted to make it easier to just kind of, uh, drive on in the event of all errors. [00:40:53] Bryan: And it's like, actually not what you wanna do if you're trying to build a reliable, robust system. So we had. No end of issues. [00:41:01] Bryan: We've got a lot of experience developing rigorous systems, um, again coming out of operating systems development and so on. And we want, we brought some of that rigor, if strangely, to JavaScript. So one of the things that we did is we brought a lot of postmortem, diagnos ability and observability to node. [00:41:18] Bryan: And so if, if one of our node processes. Died in production, we would actually get a core dump from that process, a core dump that we could actually meaningfully process. So we did a bunch of kind of wild stuff. I mean, actually wild stuff where we could actually make sense of the JavaScript objects in a binary core dump. JavaScript values ease of getting started over robustness [00:41:41] Bryan: Um, and things that we thought were really important, and this is the, the rest of the world just looks at this being like, what the hell is this? I mean, it's so out of step with it. The problem is that we were trying to bridge two disconnected cultures of one developing really. Rigorous software and really designing it for production, diagnosability and the other, really designing it to software to run in the browser and for anyone to be able to like, you know, kind of liven up a webpage, right? [00:42:10] Bryan: Is kinda the origin of, of live script and then JavaScript. And we were kind of the only ones sitting at the intersection of that. And you begin when you are the only ones sitting at that kind of intersection. You just are, you're, you're kind of fighting a community all the time. And we just realized that we are, there were so many things that the community wanted to do that we felt are like, no, no, this is gonna make software less diagnosable. It's gonna make it less robust. The NodeJS split and why people left [00:42:36] Bryan: And then you realize like, I'm, we're the only voice in the room because we have got, we have got desires for this language that it doesn't have for itself. And this is when you realize you're in a bad relationship with software. It's time to actually move on. And in fact, actually several years after, we'd already kind of broken up with node. [00:42:55] Bryan: Um, and it was like, it was a bit of an acrimonious breakup. there was a, uh, famous slash infamous fork of node called IoJS Um, and this was viewed because people, the community, thought that Joyent was being what was not being an appropriate steward of node js and was, uh, not allowing more things to come into to, to node. [00:43:19] Bryan: And of course, the reason that we of course, felt that we were being a careful steward and we were actively resisting those things that would cut against its fitness for a production system. But it's some way the community saw it and they, and forked, um, and, and I think the, we knew before the fork that's like, this is not working and we need to get this thing out of our hands. Platform is a reflection of values node summit talk [00:43:43] Bryan: And we're are the wrong hands for this? This needs to be in a foundation. Uh, and so we kind of gone through that breakup, uh, and maybe it was two years after that. That, uh, friend of mine who was um, was running the, uh, the node summit was actually, it's unfortunately now passed away. Charles er, um, but Charles' venture capitalist great guy, and Charles was running Node Summit and came to me in 2017. [00:44:07] Bryan: He is like, I really want you to keynote Node Summit. And I'm like, Charles, I'm not gonna do that. I've got nothing nice to say. Like, this is the, the, you don't want, I'm the last person you wanna keynote. He's like, oh, if you have nothing nice to say, you should definitely keynote. You're like, oh God, okay, here we go. [00:44:22] Bryan: He's like, no, I really want you to talk about, like, you should talk about the Joyent breakup with NodeJS. I'm like, oh man. [00:44:29] Bryan: And that led to a talk that I'm really happy that I gave, 'cause it was a very important talk for me personally. Uh, called Platform is a reflection of values and really looking at the values that we had for Node and the values that Node had for itself. And they didn't line up. [00:44:49] Bryan: And the problem is that the values that Node had for itself and the values that we had for Node are all kind of positives, right? Like there's nobody in the node community who's like, I don't want rigor, I hate rigor. It's just that if they had the choose between rigor and making the language approachable. [00:45:09] Bryan: They would choose approachability every single time. They would never choose rigor. And, you know, that was a, that was a big eye-opener. I do, I would say, if you watch this talk. [00:45:20] Bryan: because I knew that there's, like, the audience was gonna be filled with, with people who, had been a part of the fork in 2014, I think was the, the, the, the fork, the IOJS fork. And I knew that there, there were, there were some, you know, some people that were, um, had been there for the fork and. [00:45:41] Bryan: I said a little bit of a trap for the audience. But the, and the trap, I said, you know what, I, I kind of talked about the values that we had and the aspirations we had for Node, the aspirations that Node had for itself and how they were different. [00:45:53] Bryan: And, you know, and I'm like, look in, in, in hindsight, like a fracture was inevitable. And in 2014 there was finally a fracture. And do people know what happened in 2014? And if you, if you, you could listen to that talk, everyone almost says in unison, like IOJS. I'm like, oh right. IOJS. Right. That's actually not what I was thinking of. [00:46:19] Bryan: And I go to the next slide and is a tweet from a guy named TJ Holloway, Chuck, who was the most prolific contributor to Node. And it was his tweet also in 2014 before the fork, before the IOJS fork explaining that he was leaving Node and that he was going to go. And you, if you turn the volume all the way up, you can hear the audience gasp. [00:46:41] Bryan: And it's just delicious because the community had never really come, had never really confronted why TJ left. Um, there. And I went through a couple folks, Felix, bunch of other folks, early Node folks. That were there in 2010, were leaving in 2014, and they were going to go primarily, and they were going to go because they were sick of the same things that we were sick of. [00:47:09] Bryan: They, they, they had hit the same things that we had hit and they were frustrated. I I really do believe this, that platforms do reflect their own values. And when you are making a software decision, you are selecting value. [00:47:26] Bryan: You should select values that align with the values that you have for that software. That is, those are, that's way more important than other things that people look at. I think people look at, for example, quote unquote community size way too frequently, community size is like. Eh, maybe it can be fine. [00:47:44] Bryan: I've been in very large communities, node. I've been in super small open source communities like AUMs and RAs, a bunch of others. there are strengths and weaknesses to both approaches just as like there's a strength to being in a big city versus a small town. Me personally, I'll take the small community more or less every time because the small community is almost always self-selecting based on values and just for the same reason that I like working at small companies or small teams. [00:48:11] Bryan: There's a lot of value to be had in a small community. It's not to say that large communities are valueless, but again, long answer to your question of kind of where did things go south with Joyent and node. They went south because the, the values that we had and the values the community had didn't line up and that was a very educational experience, as you might imagine. [00:48:33] Jeremy: Yeah. And, and given that you mentioned how, because of those values, some people moved from Node to go, and in the end for much of what oxide is building. You ended up using rust. What, what would you say are the, the values of go and and rust, and how did you end up choosing Rust given that. Go's decisions regarding generics, versioning, compilation speed priority [00:48:56] Bryan: Yeah, I mean, well, so the value for, yeah. And so go, I mean, I understand why people move from Node to Go, go to me was kind of a lateral move. Um, there were a bunch of things that I, uh, go was still garbage collected, um, which I didn't like. Um, go also is very strange in terms of there are these kind of like. [00:49:17] Bryan: These autocratic kind of decisions that are very bizarre. Um, there, I mean, generics is kind of a famous one, right? Where go kind of as a point of principle didn't have generics, even though go itself actually the innards of go did have generics. It's just that you a go user weren't allowed to have them. [00:49:35] Bryan: And you know, it's kind of, there was, there was an old cartoon years and years ago about like when a, when a technologist is telling you that something is technically impossible, that actually means I don't feel like it. Uh, and there was a certain degree of like, generics are technically impossible and go, it's like, Hey, actually there are. [00:49:51] Bryan: And so there was, and I just think that the arguments against generics were kind of disingenuous. Um, and indeed, like they ended up adopting generics and then there's like some super weird stuff around like, they're very anti-assertion, which is like, what, how are you? Why are you, how is someone against assertions, it doesn't even make any sense, but it's like, oh, nope. [00:50:10] Bryan: Okay. There's a whole scree on it. Nope, we're against assertions and the, you know, against versioning. There was another thing like, you know, the Rob Pike has kind of famously been like, you should always just run on the way to commit. And you're like, does that, is that, does that make sense? I mean this, we actually built it. [00:50:26] Bryan: And so there are a bunch of things like that. You're just like, okay, this is just exhausting and. I mean, there's some things about Go that are great and, uh, plenty of other things that I just, I'm not a fan of. Um, I think that the, in the end, like Go cares a lot about like compile time. It's super important for Go Right? [00:50:44] Bryan: Is very quick, compile time. I'm like, okay. But that's like compile time is not like, it's not unimportant, it's doesn't have zero importance. But I've got other things that are like lots more important than that. Um, what I really care about is I want a high performing artifact. I wanted garbage collection outta my life. Don't think garbage collection has good trade offs [00:51:00] Bryan: I, I gotta tell you, I, I like garbage collection to me is an embodiment of this like, larger problem of where do you put cognitive load in the software development process. And what garbage collection is saying to me it is right for plenty of other people and the software that they wanna develop. [00:51:21] Bryan: But for me and the software that I wanna develop, infrastructure software, I don't want garbage collection because I can solve the memory allocation problem. I know when I'm like, done with something or not. I mean, it's like I, whether that's in, in C with, I mean it's actually like, it's really not that hard to not leak memory in, in a C base system. [00:51:44] Bryan: And you can. give yourself a lot of tooling that allows you to diagnose where memory leaks are coming from. So it's like that is a solvable problem. There are other challenges with that, but like, when you are developing a really sophisticated system that has garbage collection is using garbage collection. [00:51:59] Bryan: You spend as much time trying to dork with the garbage collector to convince it to collect the thing that you know is garbage. You are like, I've got this thing. I know it's garbage. Now I need to use these like tips and tricks to get the garbage collector. I mean, it's like, it feels like every Java performance issue goes to like minus xx call and use the other garbage collector, whatever one you're using, use a different one and using a different, a different approach. [00:52:23] Bryan: It's like, so you're, you're in this, to me, it's like you're in the worst of all worlds where. the reason that garbage collection is helpful is because the programmer doesn't have to think at all about this problem. But now you're actually dealing with these long pauses in production. [00:52:38] Bryan: You're dealing with all these other issues where actually you need to think a lot about it. And it's kind of, it, it it's witchcraft. It, it, it's this black box that you can't see into. So it's like, what problem have we solved exactly? And I mean, so the fact that go had garbage collection, it's like, eh, no, I, I do not want, like, and then you get all the other like weird fatwahs and you know, everything else. [00:52:57] Bryan: I'm like, no, thank you. Go is a no thank you for me, I, I get it why people like it or use it, but it's, it's just, that was not gonna be it. Choosing Rust [00:53:04] Bryan: I'm like, I want C. but I, there are things I didn't like about C too. I was looking for something that was gonna give me the deterministic kind of artifact that I got outta C. But I wanted library support and C is tough because there's, it's all convention. you know, there's just a bunch of other things that are just thorny. And I remember thinking vividly in 2018, I'm like, well, it's rust or bust. Ownership model, algebraic types, error handling [00:53:28] Bryan: I'm gonna go into rust. And, uh, I hope I like it because if it's not this, it's gonna like, I'm gonna go back to C I'm like literally trying to figure out what the language is for the back half of my career. Um, and when I, you know, did what a lot of people were doing at that time and people have been doing since of, you know, really getting into rust and really learning it, appreciating the difference in the, the model for sure, the ownership model people talk about. [00:53:54] Bryan: That's also obviously very important. It was the error handling that blew me away. And the idea of like algebraic types, I never really had algebraic types. Um, and the ability to, to have. And for error handling is one of these really, uh, you, you really appreciate these things where it's like, how do you deal with a, with a function that can either succeed and return something or it can fail, and the way c deals with that is bad with these kind of sentinels for errors. [00:54:27] Bryan: And, you know, does negative one mean success? Does negative one mean failure? Does zero mean failure? Some C functions, zero means failure. Traditionally in Unix, zero means success. And like, what if you wanna return a file descriptor, you know, it's like, oh. And then it's like, okay, then it'll be like zero through positive N will be a valid result. [00:54:44] Bryan: Negative numbers will be, and like, was it negative one and I said airo, or is it a negative number that did not, I mean, it's like, and that's all convention, right? People do all, all those different things and it's all convention and it's easy to get wrong, easy to have bugs, can't be statically checked and so on. Um, and then what Go says is like, well, you're gonna have like two return values and then you're gonna have to like, just like constantly check all of these all the time. Um, which is also kind of gross. Um, JavaScript is like, Hey, let's toss an exception. If, if we don't like something, if we see an error, we'll, we'll throw an exception. [00:55:15] Bryan: There are a bunch of reasons I don't like that. Um, and you look, you'll get what Rust does, where it's like, no, no, no. We're gonna have these algebra types, which is to say this thing can be a this thing or that thing, but it, but it has to be one of these. And by the way, you don't get to process this thing until you conditionally match on one of these things. [00:55:35] Bryan: You're gonna have to have a, a pattern match on this thing to determine if it's a this or a that, and if it in, in the result type that you, the result is a generic where it's like, it's gonna be either the thing that you wanna return. It's gonna be an okay that contains the thing you wanna return, or it's gonna be an error that contains your error and it forces your code to deal with that. [00:55:57] Bryan: And what that does is it shifts the cognitive load from the person that is operating this thing in production to the, the actual developer that is in development. And I think that that, that to me is like, I, I love that shift. Um, and that shift to me is really important. Um, and that's what I was missing, that that's what Rust gives you. [00:56:23] Bryan: Rust forces you to think about your code as you write it, but as a result, you have an artifact that is much more supportable, much more sustainable, and much faster. Prefer to frontload cognitive load during development instead of at runtime [00:56:34] Jeremy: Yeah, it sounds like you would rather take the time during the development to think about these issues because whether it's garbage collection or it's error handling at runtime when you're trying to solve a problem, then it's much more difficult than having dealt with it to start with. [00:56:57] Bryan: Yeah, absolutely. I, and I just think that like, why also, like if it's software, if it's, again, if it's infrastructure software, I mean the kinda the question that you, you should have when you're writing software is how long is this software gonna live? How many people are gonna use this software? Uh, and if you are writing an operating system, the answer for this thing that you're gonna write, it's gonna live for a long time. [00:57:18] Bryan: Like, if we just look at plenty of aspects of the system that have been around for a, for decades, it's gonna live for a long time and many, many, many people are gonna use it. Why would we not expect people writing that software to have more cognitive load when they're writing it to give us something that's gonna be a better artifact? [00:57:38] Bryan: Now conversely, you're like, Hey, I kind of don't care about this. And like, I don't know, I'm just like, I wanna see if this whole thing works. I've got, I like, I'm just stringing this together. I don't like, no, the software like will be lucky if it survives until tonight, but then like, who cares? Yeah. Yeah. [00:57:52] Bryan: Gar garbage clock. You know, if you're prototyping something, whatever. And this is why you really do get like, you know, different choices, different technology choices, depending on the way that you wanna solve the problem at hand. And for the software that I wanna write, I do like that cognitive load that is upfront. With LLMs maybe you can get the benefit of the robust artifact with less cognitive load [00:58:10] Bryan: Um, and although I think, I think the thing that is really wild that is the twist that I don't think anyone really saw coming is that in a, in an LLM age. That like the cognitive load upfront almost needs an asterisk on it because so much of that can be assisted by an LLM. And now, I mean, I would like to believe, and maybe this is me being optimistic, that the the, in the LLM age, we will see, I mean, rust is a great fit for the LLMH because the LLM itself can get a lot of feedback about whether the software that's written is correct or not. [00:58:44] Bryan: Much more so than you can for other environments. [00:58:48] Jeremy: Yeah, that is a interesting point in that I think when people first started trying out the LLMs to code, it was really good at these maybe looser languages like Python or JavaScript, and initially wasn't so good at something like Rust. But it sounds like as that improves, if. It can write it then because of the rigor or the memory management or the error handling that the language is forcing you to do, it might actually end up being a better choice for people using LLMs. [00:59:27] Bryan: absolutely. I, it, it gives you more certainty in the artifact that you've delivered. I mean, you know a lot about a Rust program that compiles correctly. I mean, th there are certain classes of errors that you don't have, um, that you actually don't know on a C program or a GO program or a, a JavaScript program. [00:59:46] Bryan: I think that's gonna be really important. I think we are on the cusp. Maybe we've already seen it, this kind of great bifurcation in the software that we writ

Thu, 26 Feb 2026 21:45:00 GMT http://relay.fm/connected/592 http://relay.fm/connected/592 The Rickies (March 2026) 592 Federico Viticci, Stephen Hackett, and Myke Hurley Apple is hosting a mysterious media experience next week, and in anticipation of new products, Stephen, Myke, and Federico make predictions about what is coming. Apple is hosting a mysterious media experience next week, and in anticipation of new products, Stephen, Myke, and Federico make predictions about what is coming. clean 3983 Subtitle: Lil' ChippyApple is hosting a mysterious media experience next week, and in anticipation of new products, Stephen, Myke, and Federico make predictions about what is coming. This episode of Connected is sponsored by: Insta360: Introducing the Insta360 Wave and the Link 2 Pro. Sentry: Mobile crash reporting and app monitoring. New users get $100 in Sentry credits with code connected26. Squarespace: Save 10% off your first purchase of a website or domain using code CONNECTED. Links and Show Notes: Get Connected Pro: Preshow, postshow, no ads. Submit Feedback Apple in 2025: The Six Colors report card – Six Colors Six Colors' Apple in 2025 Report Card - MacStories My Full Responses for the 2025 Six Colors Report Card - 512 Pixels Upgrade #604: The Shifting Sands of Liquid Glass - Relay Samsung Galaxy S26/Ultra Impressions: 1 Crazy Display Feature! - MKBHD - YouTube Samsung Galaxy Unpacked 2026 in 12 minutes - The Verge - YouTube Introducing Perplexity Computer 2026 March Keynote Rickies – Rickies.net Keynote Rickies, March 2026 – Rickies.co Wood Blocks | Nintendo The MacBook Air's wedge is truly gone — and I miss it already | The Verge Leaker Says Apple's Lower-Cost MacBook Will Have These 8 Limitations - MacRumors M5 Pro chip could separate CPU and GPU in 'server grade' chips - 9to5Mac 2.5D integrated circuit - Wikipedia Apple Reportedly Agrees to 100% Price Hike on Samsung Memory Chips - MacRumors New ‘F1: Drive to Survive' season is coming to Apple TV - 9to5Mac Apple TV reveals new space-race thriller series is coming soon - 9to5Mac

Thu, 26 Feb 2026 21:45:00 GMT http://relay.fm/connected/592 http://relay.fm/connected/592 Federico Viticci, Stephen Hackett, and Myke Hurley Apple is hosting a mysterious media experience next week, and in anticipation of new products, Stephen, Myke, and Federico make predictions about what is coming. Apple is hosting a mysterious media experience next week, and in anticipation of new products, Stephen, Myke, and Federico make predictions about what is coming. clean 3983 Subtitle: Lil' ChippyApple is hosting a mysterious media experience next week, and in anticipation of new products, Stephen, Myke, and Federico make predictions about what is coming. This episode of Connected is sponsored by: Insta360: Introducing the Insta360 Wave and the Link 2 Pro. Sentry: Mobile crash reporting and app monitoring. New users get $100 in Sentry credits with code connected26. Squarespace: Save 10% off your first purchase of a website or domain using code CONNECTED. Links and Show Notes: Get Connected Pro: Preshow, postshow, no ads. Submit Feedback Apple in 2025: The Six Colors report card – Six Colors Six Colors' Apple in 2025 Report Card - MacStories My Full Responses for the 2025 Six Colors Report Card - 512 Pixels Upgrade #604: The Shifting Sands of Liquid Glass - Relay Samsung Galaxy S26/Ultra Impressions: 1 Crazy Display Feature! - MKBHD - YouTube Samsung Galaxy Unpacked 2026 in 12 minutes - The Verge - YouTube Introducing Perplexity Computer 2026 March Keynote Rickies – Rickies.net Keynote Rickies, March 2026 – Rickies.co Wood Blocks | Nintendo The MacBook Air's wedge is truly gone — and I miss it already | The Verge Leaker Says Apple's Lower-Cost MacBook Will Have These 8 Limitations - MacRumors M5 Pro chip could separate CPU and GPU in 'server grade' chips - 9to5Mac 2.5D integrated circuit - Wikipedia Apple Reportedly Agrees to 100% Price Hike on Samsung Memory Chips - MacRumors New ‘F1: Drive to Survive' season is coming to Apple TV - 9to5Mac Apple TV reveals new space-race thriller series is coming soon - 9to5Mac

This week on the podcast we go over our reviews of the ASRock Phantom Gaming 360 LCD Liquid CPU Cooler and the KLEVV CRAS C925G Gen4 Solid State Drive.  We also discuss Intel rethinking CPU design, the drama around the Ryzen Z1 updates, DDR5 prices going down, and much more!

U svetu softvera, grešku rešavate jednostavnim patch-om. Ali kada razvijate hardver, svaka greška koju pronađete pre proizvodnje je besplatna, dok ona koju otkrijete tek na gotovom čipu košta milione dolara i mesece bačenog vremena. Kako izgleda raditi u industriji gde pravo na grešku praktično ne postoji? U trećoj epizodi Pojačalo specijala Next Silicon, Ivan razgovara sa Vladimirom Miloševićem, liderom tima za verifikaciju hardvera u ovoj kompaniji. Kroz razgovor otkrivamo fascinantan i kompleksan svet razvoja čipova - od početne ideje i arhitekture, preko rigoroznog testiranja pre proizvodnje, pa sve do finalne fizičke realizacije. Vladimir objašnjava zašto je verifikacija presudan korak u industriji gde je svaka greška izuzetno skupa i demistifikuje činjenicu da je Srbija, sa svojim centrima u Beogradu, Novom Sadu i Nišu, postala ozbiljan globalni "powerhouse" za razvoj najsavremenijeg hardvera. Fokus priče je na revolucionarnoj tehnologiji koju razvija Next Silicon, posebno na njihovom „Maverick 2“ čipu koji menja pravila igre u svetu superračunara i high-performance computinga (HPC). Saznaćete kako izgleda inženjerska avantura kreiranja hardvera koji se dinamički prilagođava softveru, rešavajući probleme energetske efikasnosti i brzine koje tradicionalni procesori (CPU i GPU) ne mogu da savladaju. Podržite nas na BuyMeACoffee: https://bit.ly/3uSBmoa Pročitajte transkript ove epizode: https://bit.ly/4cNdB9T Posetite naš sajt i prijavite se na našu mailing listu: http://bit.ly/2LUKSBG Prijavite se na naš YouTube kanal: http://bit.ly/2Rgnu7o Pratite Pojačalo na društvenim mrežama: FB: https://www.facebook.com/PojacaloRS/ IG: https://www.instagram.com/pojacalo.rs/ X: https://x.com/PojacaloRS LN: https://www.linkedin.com/company/pojacalo TikTok: https://www.tiktok.com/@pojacalo.rs

Мы уже рассказывали несколько раз про eBPF. И пришло время к нему вернуться. И обсудим мы его самое что ни на есть практическое применение.. в гиперскейлерах. Про что: html Введение в BPF: механика работы, виды хуков (sockops, TC, XDP) и диапазон решаемых задач — от мониторинга до безопасности. XDP глубокого погружения: как устроен балансировщик Katran, можно ли реализовать BGP-роутинг на XDP и особенности работы с единственным хуком в системе. Задачи Traffic Team: L3-балансировка (TCP bypass), кейс с включением Яндекса в мировой NTP-пул и особенности обработки DHCP на высоких скоростях. Стабильность DNS: методы классификации трафика, изоляция «тяжелых» запросов и защита от DoS-атак через BPF socket selection и CPU affinity. Архитектура DNS XDP Offload: перенос формирования ответов в ядро (минуя userspace), роль контроллера и парсинг пакетов «на лету» для экстремальной производительности. Технические вызовы: эволюция от простых A/AAAA записей до сложных ответов, проблемы IP-фрагментации и конвейерная обработка TCP. Результаты внедрения: время обработки менее 100 нс, кратный рост пропускной способности и цена, которую приходится платить CPU за подготовку данных. Острие технологий: новые возможности ядра (bpf_arena, таймеры) и идея создания самообучающегося кеша внутри XDP для отказа от подготовки данных. Оставайтесь на связи Пишите нам: info@linkmeup.ru Канал в телеграме: t.me/linkmeup_podcast Канал на youtube: youtube.com/c/linkmeup-podcast Подкаст доступен в iTunes, Google Подкастах, Яндекс Музыке, Castbox Сообщество в вк: vk.com/linkmeup Группа в фб: www.facebook.com/linkmeup.sdsm Добавить RSS в подкаст-плеер. Пообщаться в общем чате в тг: https://t.me/linkmeup_chat Поддержите проект:

Sono passati quattro anni dall'invasione russa dell'Ucraina iniziata il 24 febbraio 2022, un'offensiva che nelle intenzioni del Cremlino avrebbe dovuto riportare rapidamente Kiev nell'orbita di Mosca e che invece si è trasformata nella più grande guerra in Europa dal secondo dopoguerra. Il bilancio umano resta drammatico: secondo le stime del New York Times circa 1,2 milioni di soldati russi e 600mila ucraini risultano morti, feriti o dispersi, mentre le vittime civili sfiorano quota 15mila e quasi 5,9 milioni di persone hanno lasciato il Paese. Mosca controlla oggi circa il 19,4% del territorio ucraino, segno di un conflitto ormai entrato in una fase di logoramento prolungato.Nel giorno dell'anniversario i vertici dell'Unione europea sono a Kiev e ribadiscono il sostegno politico e militare all'Ucraina, sostenendo che la Russia non abbia raggiunto i suoi obiettivi strategici e accusando Mosca di colpire deliberatamente infrastrutture civili ed energetiche. Sul piano diplomatico emerge però una nuova variabile: secondo Bloomberg, Donald Trump punta a un accordo di pace entro il 4 luglio, data simbolica del 250° anniversario della Dichiarazione d'Indipendenza americana. Anche l'Italia conferma il proprio impegno a favore di una pace definita giusta e duratura, sostenendo il percorso negoziale promosso dagli Stati Uniti e il lavoro della coalizione internazionale sulle garanzie di sicurezza per Kiev. Ne parliamo con Fabrizio Pagani, Partner Vitale&Co e docente a SciencesPo di Parigi.AI, Meta scommette su AMD: accordo oltre i 100 miliardiNella corsa globale all'intelligenza artificiale cambia l'equilibrio tra i giganti dei semiconduttori. Advanced Micro Devices, per anni considerata l'alternativa a Nvidia, firma con Meta Platforms un'intesa strategica destinata a ridisegnare la competizione nell'infrastruttura AI. L'accordo prevede forniture di chip per cinque anni per un valore iniziale fino a 60 miliardi di dollari, con la possibilità per Meta di salire fino al 10% del capitale AMD; considerando hardware, incentivi azionari e sviluppo tecnologico congiunto, il valore complessivo dell'operazione potrebbe superare i 100 miliardi di dollari.AMD fornirà fino a sei gigawatt di capacità di calcolo, a partire dalla nuova piattaforma MI450 prevista nella seconda metà dell'anno, oltre a CPU personalizzate progettate per combinare alte prestazioni e minori consumi energetici nei data center dedicati all'AI. Il mercato ha reagito immediatamente con forti rialzi del titolo AMD, segnale della crescente competizione con Nvidia per il controllo delle infrastrutture dell'intelligenza artificiale globale. Analizziamo le implicazioni tecnologiche e industriali con Biagio Simonetta de Il Sole 24 Ore.Auto europea in frenata: il 2026 parte in salitaIl mercato automobilistico europeo apre il 2026 con il segno meno. Secondo i dati Acea, a gennaio sono state immatricolate 961.382 auto in Europa (Ue27+Efta+Uk), in calo del 3,5% rispetto allo stesso mese del 2025, mentre nella sola Unione europea la flessione raggiunge il 3,9%. Crescono però le alimentazioni a basse emissioni: le auto elettriche salgono del 13,9%, le ibride plug-in del 32,2% e le ibride tradizionali del 6,4%, mentre continuano a crollare benzina e diesel.Il confronto con il periodo pre-pandemia resta però il dato più preoccupante: il mercato europeo è ancora inferiore del 21,6% rispetto al 2019, mentre altre aree globali hanno già recuperato. Germania e Francia arretrano, mentre Italia, Spagna e Regno Unito mostrano solo timidi segnali di crescita. Secondo il Centro Studi Promotor, il settore paga anche le difficoltà della transizione energetica europea, con l'auto elettrica che rappresenta ancora appena il 2,3% del parco circolante. Un quadro che approfondiamo insieme a Gian Primo Quagliano, Direttore generale del Centro Studi Promotor.

professorjrod@gmail.comIn this episode, we explore the 'Pocket Revolution' that transformed not just the phone but the entire technology landscape. Discover how the iPhone's breakthrough in multi-touch science, silicon strategy, and platform economics reshaped IT skills development and technology education. We also discuss the impact of Apple's innovation on enterprise communication and how understanding these shifts can help you in your CompTIA exam prep and tech certification journey. Whether you're studying with a group or using a CompTIA study guide, this episode connects revolutionary tech history with practical IT skills development tips to help you succeed.We dive into the hidden engine of the mobile era: the App Store. By standardizing distribution, payments, security reviews, and SDKs, Apple transformed a device into an ecosystem that seeded ridesharing, mobile banking, creator tools, and on‑demand everything. Security became everyday: sandboxing, code signing, and direct OS updates reduced risk for consumers while biometrics and secure enclaves made cryptography feel effortless. At the same time, attention and data became currency. Push notifications, infinite feeds, and engagement loops pulled us into a new marketplace where design and business models overlapped with our habits and mental health.Underneath the experience, custom silicon changed the game. We break down how Apple's SoCs integrated CPU, GPU, and neural engines to enable on‑device AI, privacy‑first biometrics, and unmatched performance per watt. Then we zoom out: supply chains as geopolitical power, BYOD reshaping workplace control, and regulation arriving as smartphones turn into infrastructure. Finally, we ask where we go from here—AR overlays, wearables, and ambient computing—or a cognitive leap where AI becomes the interface. Subscribe, share with a friend who still misses their keyboard, and leave a review telling us what you think replaces the smartphone next.Support the showArt By Sarah/DesmondMusic by Joakim KarudLittle chacha ProductionsJuan Rodriguez can be reached atTikTok @ProfessorJrodProfessorJRod@gmail.com@Prof_JRodInstagram ProfessorJRod

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How secure are your Chrome extensions and certificate signings really? This episode pulls back the curtain on a massive spyware discovery and exposes the convoluted hoops developers must jump through to prove their identity in 2026. Websites can place high demands upon limited CPU resources. Microsoft appears to back away from its security commitment. What's Windows 11 26H1 and where do I get it. Chrome 145 brings Device Bound Session Credentials. More countries are moving to ban underage social media use. The return of Roskomnadzor. Discord to require proof of adulthood for adult content. Might you still be using WinRAR 7.12 -- I was. Paragon's Graphite can definitely spy on all instant messaging. 30 malicious Chrome Extensions. 287 Chrome extensions from spying on 37.4 million users. The first malicious Outlook add-in steals 4000 user's credentials. Some AI "vibe" coding thoughts. What I just went through to obtain a new code signing certificate Show Notes - https://www.grc.com/sn/SN-1065-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT meter.com/securitynow zscaler.com/security hoxhunt.com/securitynow

How secure are your Chrome extensions and certificate signings really? This episode pulls back the curtain on a massive spyware discovery and exposes the convoluted hoops developers must jump through to prove their identity in 2026. Websites can place high demands upon limited CPU resources. Microsoft appears to back away from its security commitment. What's Windows 11 26H1 and where do I get it. Chrome 145 brings Device Bound Session Credentials. More countries are moving to ban underage social media use. The return of Roskomnadzor. Discord to require proof of adulthood for adult content. Might you still be using WinRAR 7.12 -- I was. Paragon's Graphite can definitely spy on all instant messaging. 30 malicious Chrome Extensions. 287 Chrome extensions from spying on 37.4 million users. The first malicious Outlook add-in steals 4000 user's credentials. Some AI "vibe" coding thoughts. What I just went through to obtain a new code signing certificate Show Notes - https://www.grc.com/sn/SN-1065-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT meter.com/securitynow zscaler.com/security hoxhunt.com/securitynow

How secure are your Chrome extensions and certificate signings really? This episode pulls back the curtain on a massive spyware discovery and exposes the convoluted hoops developers must jump through to prove their identity in 2026. Websites can place high demands upon limited CPU resources. Microsoft appears to back away from its security commitment. What's Windows 11 26H1 and where do I get it. Chrome 145 brings Device Bound Session Credentials. More countries are moving to ban underage social media use. The return of Roskomnadzor. Discord to require proof of adulthood for adult content. Might you still be using WinRAR 7.12 -- I was. Paragon's Graphite can definitely spy on all instant messaging. 30 malicious Chrome Extensions. 287 Chrome extensions from spying on 37.4 million users. The first malicious Outlook add-in steals 4000 user's credentials. Some AI "vibe" coding thoughts. What I just went through to obtain a new code signing certificate Show Notes - https://www.grc.com/sn/SN-1065-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT meter.com/securitynow zscaler.com/security hoxhunt.com/securitynow

How secure are your Chrome extensions and certificate signings really? This episode pulls back the curtain on a massive spyware discovery and exposes the convoluted hoops developers must jump through to prove their identity in 2026. Websites can place high demands upon limited CPU resources. Microsoft appears to back away from its security commitment. What's Windows 11 26H1 and where do I get it. Chrome 145 brings Device Bound Session Credentials. More countries are moving to ban underage social media use. The return of Roskomnadzor. Discord to require proof of adulthood for adult content. Might you still be using WinRAR 7.12 -- I was. Paragon's Graphite can definitely spy on all instant messaging. 30 malicious Chrome Extensions. 287 Chrome extensions from spying on 37.4 million users. The first malicious Outlook add-in steals 4000 user's credentials. Some AI "vibe" coding thoughts. What I just went through to obtain a new code signing certificate Show Notes - https://www.grc.com/sn/SN-1065-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT meter.com/securitynow zscaler.com/security hoxhunt.com/securitynow

How secure are your Chrome extensions and certificate signings really? This episode pulls back the curtain on a massive spyware discovery and exposes the convoluted hoops developers must jump through to prove their identity in 2026. Websites can place high demands upon limited CPU resources. Microsoft appears to back away from its security commitment. What's Windows 11 26H1 and where do I get it. Chrome 145 brings Device Bound Session Credentials. More countries are moving to ban underage social media use. The return of Roskomnadzor. Discord to require proof of adulthood for adult content. Might you still be using WinRAR 7.12 -- I was. Paragon's Graphite can definitely spy on all instant messaging. 30 malicious Chrome Extensions. 287 Chrome extensions from spying on 37.4 million users. The first malicious Outlook add-in steals 4000 user's credentials. Some AI "vibe" coding thoughts. What I just went through to obtain a new code signing certificate Show Notes - https://www.grc.com/sn/SN-1065-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT meter.com/securitynow zscaler.com/security hoxhunt.com/securitynow

How secure are your Chrome extensions and certificate signings really? This episode pulls back the curtain on a massive spyware discovery and exposes the convoluted hoops developers must jump through to prove their identity in 2026. Websites can place high demands upon limited CPU resources. Microsoft appears to back away from its security commitment. What's Windows 11 26H1 and where do I get it. Chrome 145 brings Device Bound Session Credentials. More countries are moving to ban underage social media use. The return of Roskomnadzor. Discord to require proof of adulthood for adult content. Might you still be using WinRAR 7.12 -- I was. Paragon's Graphite can definitely spy on all instant messaging. 30 malicious Chrome Extensions. 287 Chrome extensions from spying on 37.4 million users. The first malicious Outlook add-in steals 4000 user's credentials. Some AI "vibe" coding thoughts. What I just went through to obtain a new code signing certificate Show Notes - https://www.grc.com/sn/SN-1065-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT meter.com/securitynow zscaler.com/security hoxhunt.com/securitynow

"Dok svet priča o ChatGPT-ju, mi otkrivamo hardversku revoluciju iz Beograda koja omogućava da AI uopšte postoji, i to 20 puta brže od svega što ste videli.“ U drugoj epizodi serijala Pojačalo specijala u saradnji sa kompanijom Next Sillicon, Ivan razgovara sa Markom Skakunom, AI Team Leadom u njihovoj beogradskoj kancelariji, o revoluciji u svetu veštačke inteligencije i hardvera koji je pokreće. Marko pruža detaljan istorijski pregled evolucije kompjuterske snage – od generičkih CPU-ova, preko specijalizovanih GPU-ova, pa sve do ultra-efikasnih ASIC čipova. Kroz razgovor se prati i razvoj samog AI-ja, od ranih neuronskih mreža i kompjuterske vizije do "Transformer" arhitekture i "Scaling Laws" fenomena koji su omogućili pojavu masivnih jezičkih modela poput ChatGPT-ja, fundamentalno menjajući zahteve koje postavljamo pred hardver. U drugom delu, fokus se prebacuje na jedinstveni pristup koji NextSilicon primenjuje kako bi odgovorio na ove izazove. Marko detaljno objašnjava inovativnu "dataflow" arhitekturu koja se fundamentalno razlikuje od tradicionalnih rešenja, omogućavajući hardveru da bude fleksibilan, adaptivan i energetski efikasniji. Poseban akcenat je stavljen na beogradsku kancelariju, koja nije samo podrška, već ključni razvojni centar gde timovi rade na najnaprednijim aspektima tehnologije – od dizajna čipa do AI kompajlera. Kroz Markovu ličnu priču, saznajemo zašto je rad na ovakvim "cutting-edge" projektima u Srbiji postao ne samo moguć, već i izuzetno privlačan za vrhunske svetske stručnjake. Podržite nas na BuyMeACoffee: https://bit.ly/3uSBmoa Pročitajte transkript ove epizode: https://bit.ly/4kGroRD Posetite naš sajt i prijavite se na našu mailing listu: http://bit.ly/2LUKSBG Prijavite se na naš YouTube kanal: http://bit.ly/2Rgnu7o Pratite Pojačalo na društvenim mrežama: FB: https://www.facebook.com/PojacaloRS/ IG: https://www.instagram.com/pojacalo.rs/ X: https://x.com/PojacaloRS LN: https://www.linkedin.com/company/pojacalo TikTok: https://www.tiktok.com/@pojacalo.rs

How secure are your Chrome extensions and certificate signings really? This episode pulls back the curtain on a massive spyware discovery and exposes the convoluted hoops developers must jump through to prove their identity in 2026. Websites can place high demands upon limited CPU resources. Microsoft appears to back away from its security commitment. What's Windows 11 26H1 and where do I get it. Chrome 145 brings Device Bound Session Credentials. More countries are moving to ban underage social media use. The return of Roskomnadzor. Discord to require proof of adulthood for adult content. Might you still be using WinRAR 7.12 -- I was. Paragon's Graphite can definitely spy on all instant messaging. 30 malicious Chrome Extensions. 287 Chrome extensions from spying on 37.4 million users. The first malicious Outlook add-in steals 4000 user's credentials. Some AI "vibe" coding thoughts. What I just went through to obtain a new code signing certificate Show Notes - https://www.grc.com/sn/SN-1065-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT meter.com/securitynow zscaler.com/security hoxhunt.com/securitynow

How secure are your Chrome extensions and certificate signings really? This episode pulls back the curtain on a massive spyware discovery and exposes the convoluted hoops developers must jump through to prove their identity in 2026. Websites can place high demands upon limited CPU resources. Microsoft appears to back away from its security commitment. What's Windows 11 26H1 and where do I get it. Chrome 145 brings Device Bound Session Credentials. More countries are moving to ban underage social media use. The return of Roskomnadzor. Discord to require proof of adulthood for adult content. Might you still be using WinRAR 7.12 -- I was. Paragon's Graphite can definitely spy on all instant messaging. 30 malicious Chrome Extensions. 287 Chrome extensions from spying on 37.4 million users. The first malicious Outlook add-in steals 4000 user's credentials. Some AI "vibe" coding thoughts. What I just went through to obtain a new code signing certificate Show Notes - https://www.grc.com/sn/SN-1065-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT meter.com/securitynow zscaler.com/security hoxhunt.com/securitynow

An airhacks.fm conversation with Francesco Nigro (@forked_franz) about: break dancing and basketball including meeting Kobe Bryant in Italy during a dunk competition, using AI coding assistants like Claude Opus 4.5 and GitHub bots for infrastructure setup and CI/CD pipeline configuration, limitations of LLMs for novel performance-sensitive algorithmic work where training data is scarce, branchless IPv4 parsing optimization as a Christmas coding challenge, CPU branch misprediction costs when parsing variable-length IP address octets, converting branching logic into mathematical operations using bit tricks for better CPU pipeline utilization, LLMs excelling at generating enterprise code based on well-documented standards and conventions, providing minimal but precise documentation and annotations to improve LLM code generation quality, the Boundary Control Entity BCE architecture pattern and standards-based development, the core problem of thread handoff between event loops and ForkJoinPool worker threads in frameworks like quarkus Vert.x and Micronaut, mechanical sympathy implications of cross-core memory access when serialized data is allocated on one core and read by another, CPU cache coherency costs and last-level cache penalties when event loop and worker pool run on different cores, the custom virtual thread scheduler project (netty-virtual-thread-scheduler) enabling a single platform thread to handle both networking I/O and virtual thread execution, approximately 50% CPU savings demonstrated by Micronaut when using unified Netty-based scheduling, collaboration with Oracle Loom team including Victor Klang and Alan Bateman on minimal scheduler API design, the scheduler API consisting of just two methods onStart and onContinue plus virtual thread task attachments, work stealing algorithms and their complexity including heuristics similar to Linux CFS scheduler, the importance of being declarative about thread affinity rather than automatic magical binding to avoid issues with lazy class loading and background reaper threads, thread factory based approach for creating virtual threads bound to specific platform threads, stream-based run queues with graceful shutdown semantics that fall back to ForkJoinPool for progress guarantees, thread-local Scoped Values as a hybrid between thread locals and scoped values for efficient context propagation, performance problems with ThreadLocal including lazy ThreadLocalMap allocation overhead on virtual threads and scalability issues with ThreadLocal.remove() and soft reference queues, the impact on reactive programming where back pressure and stream composition still require higher-level abstractions beyond Basic Java concurrency primitives, structured concurrency limitations for back pressure scenarios compared to reactive libraries, deterministic testing possibilities enabled by custom schedulers where execution order can be controlled, the poller mechanism for handling blocking I/O in virtual threads in a non-blocking way, observability improvements possible through virtual thread task attachments for monitoring state changes, cloud cost implications of inefficient thread scheduling and unnecessary CPU wake-up cycles, the distinction between framework developers and application developers as different user personas with different abstraction needs Francesco Nigro on twitter: @forked_franz

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

Another NBA trade deadline has come and gone, and it's inspired us to reflect on some of the most notable midseason swaps. To that end, this week we're reacting to Complex's list of the Top 20 trade deadline deals in league history. We also join the community in recalling memorable deals that our favourite teams have made - or could've made - as well as franchise mode trades that we can't believe we got the CPU to agree to. The post NLSC Podcast #618: Historic Trade Deadline Deals appeared first on NLSC.

Topics covered in this episode: Command Book App uvx.sh: Install Python tools without uv or Python Ending 15 years of subprocess polling monty: A minimal, secure Python interpreter written in Rust for use by AI Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Command Book App New app from Michael Command Book App is a native macOS app for developers, data scientists, AI enthusiasts and more. This is a tool I've been using lately to help build Talk Python, Python Bytes, Talk Python Training, and many more applications. It's a bit like advanced terminal commands or complex shell aliases, but hosted outside of your terminal. This leaves the terminal there for interactive commands, exploration, short actions. Command Book manages commands like "tail this log while I'm developing the app", "Run the dev web server with true auto-reload", and even "Run MongoDB in Docker with exactly the settings I need" I'd love it if you gave it a look, shared it with your team, and send me feedback. Has a free version and paid version. Build with Swift and Swift UI Check it out at https://commandbookapp.com Brian #2: uvx.sh: Install Python tools without uv or Python Tim Hopper Michael #3: Ending 15 years of subprocess polling by Giampaolo Rodola The standard library's subprocess module has relied on a busy-loop polling approach since the timeout parameter was added to Popen.wait() in Python 3.3, around 15 years ago The problem with busy-polling CPU wake-ups: even with exponential backoff (starting at 0.1ms, capping at 40ms), the system constantly wakes up to check process status, wasting CPU cycles and draining batteries. Latency: there's always a gap between when a process actually terminates and when you detect it. Scalability: monitoring many processes simultaneously magnifies all of the above. + L1/L2 CPU cache invalidations It's interesting to note that waiting via poll() (or kqueue()) puts the process into the exact same sleeping state as a plain time.sleep() call. From the kernel's perspective, both are interruptible sleeps. Here is the merged PR for this change. Brian #4: monty: A minimal, secure Python interpreter written in Rust for use by AI Samuel Colvin and others at Pydantic Still experimental “Monty avoids the cost, latency, complexity and general faff of using a full container based sandbox for running LLM generated code. “ “Instead, it lets you safely run Python code written by an LLM embedded in your agent, with startup times measured in single digit microseconds not hundreds of milliseconds.” Extras Brian: Expertise is the art of ignoring - Kevin Renskers You don't need to master the language. You need to master your slice. Learning everything up front is wasted effort. Experience changes what you pay attention to. I hate fish - Rands (Michael Lopp) Really about productivity systems And a nice process for dealing with email Michael: Talk Python now has a CLI New essay: It's not vibe coding - Agentic engineering GitHub is having a day Python 3.14.3 and 3.13.12 are available Wall Street just lost $285 billion because of 13 markdown files Joke: Silence, current side project!

Arrancamos analizando la sorprendente llegada de la programación agéntica a Xcode 26.3, un movimiento inesperado que Apple ha lanzado sin esperar a su conferencia de desarrolladores. Comentamos la velocidad vertiginosa a la que avanza la inteligencia artificial en el sector, permitiendo ahora conectar servicios como Claude o Codex de OpenAI directamente al entorno de desarrollo.Por otro lado, discutimos los detalles de la reciente reunión interna liderada por Tim Cook, donde se abordaron temas delicados como la postura política de la compañía frente a la inmigración y la administración actual, notando una respuesta más tibia por parte del CEO en comparación con años anteriores. Repasamos un variado conjunto de noticias y rumores, destacando el hecho de que la NASA ha certificado oficialmente los iPhone para ser utilizados por astronautas en misiones espaciales y lunares. Examinamos el panorama de los procesadores, con la inminente llegada de los chips M5 y la competencia renovada que presentan los nuevos chips Panther Lake de Intel frente a los de Apple.Cerramos el episodio hablando del despliegue de contenidos del "Apple TV Day", con multitud de nuevas series y temporadas anunciadas, y especulando sobre el inminente lanzamiento del iPhone 17e y las renovaciones de iPad y MacBook Pro. Xcode 26.3 unlocks the power of agentic coding - Apple Apple's Xcode now supports the Claude Agent SDK Anthropic Xcode gets agentic coding Tim Cook talks succession, executive departures during all-hands meeting - 9to5Mac Apple's Cook Talks Immigration, Succession and AI at Meeting The Fallen Apple — Matt Gemmell If Apple is richer than ever, why does it feel so broke? Macworld Apple Reportedly Scaling Back This Long-Rumored iOS 27 Feature - MacRumors NASA will finally allow astronauts to bring their iPhones to space - Ars Technica NASA astronauts can now bring their phones with them on their mission to the moon TechCrunch Apple's Next Launch is 'Imminent' - MacRumors M5 Pro, Max MacBook Pro expected alongside macOS 26.3 Intel Panther Lake Core Ultra review: Intel's best laptop CPU in a very long time - Ars Technica Panther Lake vs Apple M5 benchmarks — 'Intel has done the incredible' | Tom's Guide Apple TV tiene grandes ases en la manga para este año en forma de series y pelis. Y acaba de desvelar los mejores Apple TV sets must-see 2026 lineup of star-studded original series, films and live sports - Apple TV Press

An airhacks.fm conversation with Kabir Khan (@kabirkhan) about: first computer was a ZX Spectrum 48K with rubber keys, playing Bomb Jack as a memorable early game, growing up in Norway near Oslo with lots of outdoor activities including skiing and swimming in warm fjords, discovering multimedia kiosks at Tower Records in Piccadilly Circus as career inspiration, writing a Java applet dissertation visualizing Motorola 68000 CPU instruction processing with animations, early programming in Basic on the ZX spectrum including a hardcoded cookbook application, learning Pascal and the revelation of understanding what files actually are, first job writing an HTTP server in C++ on Windows NT using Winsock, implementing Real-Time Protocol streaming for multimedia content, working at a consultancy learning multiple programming languages including Active Server Pages ASP and Microsoft Transaction Server MTS, going freelance and building a Java-based exhibition industry booking system, using JBoss with EJB3 for the second version of the exhibition system, getting JBoss support and being impressed by their expertise, contributing to JBoss Mail and JBoss AOP as open source contributions, meeting Sacha Labourey at a JBoss partner event in Norway who advised focusing on AOP, joining JBoss in September 2004 when the company had only about 50 people, meeting Marc Fleury and having pizza at his house in Atlanta, the Red Hat acquisition of JBoss in 2006, leading the JBoss AOP project and standardizing interceptor chains, working on the JBoss microcontainer for JBoss 5 which was over-engineered and slow, joining the team that rethought the server architecture leading to Wildfly, working on WildFly core server management and domain management, the recent move of the runtimes division from Red Hat to IBM, current work on Agent-to-Agent (A2A) protocol, quarkus being the Java reference implementation for the A2A specification published by Google, Agent-to-Agent Protocol as a standardized protocol for agent-to-agent communication using JSON-RPC REST and grpc, agent cards as capability advertisements similar to business cards, benefits of smaller specialized agents over monolithic AI applications including better traceability smaller context windows and flexibility with different LLMs, comparison of agent architecture to microservices where smaller agents are preferable unlike traditional services where monoliths can be better, upcoming episode planned to deep-dive into A2A with Quarkus and opentelemetry for agent traceability Kabir Khan on twitter: @kabirkhan

Recorded February 4, 2026. We also cover the upcoming Steam Machine, sad GPU trends, and the arc of the Arc B770. We've got our review of the Thrustmaster T248R and rapidly dive into AMD's glorious financial success, plus a splash of ARM's Q3 results.  Surprise!  There are discussions on memory prices, Nvidia's RTX 50 series supply, and the weeks "best" security breaches.Powered by Clippy.Timestamps:0:00 Intro00:25 Patreon01:16 Food with Josh02:36 AMD Financials08:43 Arm Financials11:45 AMD says Steam Machine still on track for early 2026 (until it isn't)13:30 New memory price outlook has DDR5 doubling again in Q114:48 Low VRAM GPUs reportedly 75 percent of NVIDIA Q1 supply16:45 AMD also in the lower VRAM game19:45 Intel Arc B770 is supposedly canceled22:17 Spinning rust lives on25:33 Qualcomm loses chief CPU architect27:09 PCPer (possibly) influences Microsoft to backpedal on AI features!31:31 5GbE is getting more affordable33:44 (In)Security Corner43:32 Gaming Quick Hits47:56 Josh reviews the Thrustmaster T248R55:45 Picks of the Week1:07:56 Outro ★ Support this podcast on Patreon ★

Thinking about leaving the console life behind for the modded maps and high-frame rates of DayZ PC? This week, Andy and Dave break down the complex world of hardware for the absolute beginner. We know how daunting the switch can be, so we're simplifying what matters most when building or buying your first gaming rig.From CPU bottlenecks to the importance of an SSD, we explain what you should prioritize to get the smoothest experience in Chernarus and beyond!

Apple shatters revenue records, Tim Cook teases new innovations coming this year, Walmart hits $1T market cap, everyone's still pouring money into AI, and OpenClaw's “skills” have serious security concerns.Stephen's Newsletter SignupAd-Free + Bonus EpisodesShow Notes via EmailWatch on YouTube!Join the CommunityEmail Us: podcast@primarytech.fm@stephenrobles on Threads@jasonaten on Threads————————SponsorsShopify: Sign up for your one-dollar-per-month trial and start selling today at: shopify.com/primaryQuo: Try QUO for free PLUS get 20% off your first 6 months when you go to Quo.com/primary————————Links from the showMac Power Users - RelayApple announces all-time record in revenue, iPhone sales – Six ColorsWhile Everyone Else Tries to Replace the iPhone, Apple Just Had Its Best Quarter EverNew Mac configurator may point to separate CPU and GPU options - 9to5MacTim Cook hints at ‘never been seen' innovations coming this year - 9to5MacMeta (META) Q4 2025 earnings185 Billion Reasons Google Isn't Worried AI Will Kill SearchGoogle's subscriptions rise in Q4 as YouTube pulls $60B in yearly revenue | TechCrunchIt Took 64 Years to Build Walmart. It Took 3 Years to Turn It Into a $1 Trillion Tech CompanyXcode moves into agentic coding with deeper OpenAI and Anthropic integrations | TechCrunchOpenClaw's AI ‘skill' extensions are a security nightmare | The VergeHumans are infiltrating the social network for AI bots | The VergeAnthropic's 'Dishonest' Ads Clearly Struck a Nerve With Sam AltmanExpect more upsells and subscription bundles from Apple, Creator Studio was just the start - 9to5MacNow anyone can tap Ring doorbells to search for lost dogs | The VergeAirTag 2 Has Wild Range! #tech #airtag - YouTubeGoogle announces Pixel 10a with completely flat cameraAlexa Plus is now available to everyone in the US | The VergeApple Sports for iPhone updated with PGA, LPGA, and more - 9to5MacThe SpaceX-xAI Merger Isn't About Data Centers in Space. It's About Bailing Out Musk's Biggest GambleShortcuts Team Lead HiringGemini Mac App Tweet ★ Support this podcast ★

AI is changing the data center—but not always in the ways enterprises expect. In this episode, Keith Townsend is joined by Intel's Lynn Comp for Part Two of their conversation, shifting the focus squarely to AI infrastructure realities. They explore why many AI workloads never justify GPUs, how CPU-based deployments often exceed real [...]

In this episode of PING, APNIC Chief Scientist Geoff Huston returns with his annual review of BGP, reflecting on developments across 2025. Geoff has been publishing this year-in-review analysis of BGP dynamics for more than a decade, and this time he has uncovered some genuinely surprising shifts. His 2025 analysis has been published in two parts on the APNIC Blog. Border Gateway Protocol (BGP) is the mechanism by which network operators announce their Internet address space to the rest of the world and, in turn, learn about the addresses announced by others. Operators participating in the global default-free zone receive all publicly announced routes, each expressed as an IP prefix and associated with its originating Autonomous System Number (ASN). Every BGP speaker has a unique ASN, and all routing information is exchanged and interpreted through this fundamental identifier. In effect, the ASN is the basic unit of interdomain routing. BGP also carries path information that describes how routing announcements traverse the network. This data informs routing policy decisions — which paths to prefer, and through which commercial or technical relationships. While the protocol itself is well understood, the system as a whole is anything but simple. When more than 100,000 ASes are continuously exchanging routing information, complexity is unavoidable. Speaking BGP is about telling things and learning things, but it's also about deciding what to do with what has been learned. This is the work behind a router, and involves holding all the information and performing routing decisions on it, so the ‘size' of the information shared and learned has a direct impact on the ‘cost' of operating as a BGP speaker (cost here ultimately means memory and CPU). For most of the Internet's history, BGP growth has been relentless, forcing operators to continually ask whether their current routing infrastructure can accommodate future growth. All technology adoption has a life cycle, and is often referred to as the ‘technology adoption curve'. New technologies start out expensive and scarce, become cheaper and widely adopted, and eventually reach a point of saturation where growth slows and replacement becomes the dominant driver. For much of its existence, the Internet has remained firmly in the rapid growth phase of this curve, with sustained increases in users, networks, and routing information. Geoff has detected changes in the pace of growth for both IPv4 and IPv6, which suggest the underlying economics behind investment in Internet, and growth in customers has reached it's saturation point: We are entering a time where BGP growth may not have the same dynamics we've been used to, and questions about capital investment in BGP routing and underlying Internet Addressing are not the same.

DOLLAR DOOMSDAY - 01.28.2026 - #911 BestPodcastintheMetaverse.com Canary Cry News Talk #911 - 01.28.2026 - Recorded Live to 1s and 0s Deconstructing World Events from a Biblical Worldview Declaring Jesus as Lord amidst the Fifth Generation War! CageRattlerCoffee.com SD/TC email Ike for discount https://CanaryCry.Support   Send address and shirt size updates to canarycrysupplydrop@gmail.com Join the Canary Cry Roundtable This Episode was Produced By:   Executive Producers Sir Jamey Not the Lanister*** Sir LX Protocol Baron of the Berrean Protocol*** Arnold W***   Producers of TREASURE (CanaryCry.Support) Malik, Cage Rattler Coffee, Mrs Tinfoilhatman, Veronica D, Sir Scott Knight of Truth, Sir Casey the Shield Knight   Producers of TIME Timestampers: Jade Bouncerson, Morgan E Clankoniphius Links: JAM   SHOW NOTES:   ARMAGEDDON 7:26 Clip: Doomsday Clock hits 85 seconds to midnight (CBS) →→ US/Russia nuclear treaty to expire next week, Trump "if it expires, it expires" (Reuters)   TRUMP 34:37 Clip: "I've made a lot of people rich" Trump says value of the dollar is 'great', currency hits 4-year low (Reuters)   MONEY/BLACKROCK 48:00 BlackRock says investors can no longer rely on bonds for portfolio safety (CNBC)   AI/BLOCKCHAIN/BIBLICAL 1:03:46 Clip: CEO of Citadel says we need an "AI Savior" (X) Claude reply causing concern for sentient AI and humanity (X)  Note: Essay from CEO Anthropic, says his focus on biology > cyber atm (Dario Modei) ERC-8004 to launch on Ethereum for AI Agents   ENCHANTED/NEW WORLD ORDER 1:27:38 Musk Considers Timing SpaceX IPO With Planetary Alignment, FT Reports (X)  Dev creates astrology-powered CPU scheduler for Linux, makes decisions based on planetary positions and zodiac signs (Tom's Hardware) Clip: Guy uses Numerology and made 8 figures on ZCash (X)   TRANSHUMAN  Clip: Yale prof., survive the next 10 years, we're going to revers aging (X)    ADS 1:45:04 Google agrees to fork over $68MN to settle claims that its Assistant was SECRETLY recording your convos WITHOUT 'Hey Google' & feeding them straight to targeted ads (BBC)     EXECUTIVE PRODUCERS 1:56:52 TALENT/TIME END 2:22:48

This week, the hosts go deep on out-of-band updates, unwanted "innovations," and the uneasy cost of tech's latest gold rush. Plus, securing a Microsoft account is not as hard as some think, and neither are passkeys once you get past the jargon. And for developers, AI Dev Gallery offers a fascinating glimpse at what you can do for free with AI used against a CPU, GPU, or NPU. Windows 11 Microsoft issues an emergency fix for a borked Windows Update. Right. A fix for a fix. Hell freezes over, if only slightly: Microsoft quietly made some positive changes to forced OneDrive Folder Backup. Donʼt worry, itʼs still forced (and appears to be opt-in, but isnʼt). But you can back out more elegantly. So itʼs opt-out, not opt-in, but a step forward. Plus, a new behavior Windows 11 on Arm PCs can now download games from the Xbox app (previously only through the Insider program) Over 85 percent of Xbox games on PC work in WOA now Prism emulator now supports AVX and AVX2 and Epic Anti-Cheat, and there is a new Windows Performance Fit feature offering guidance on which titles should play well. Beta: New 25H2 build with account dialog modernization, Click to Do and desktop background improvements. Not for Dev, suggesting itʼs about to move to 26H1 Notepad and Paint get more features yet again. Notably, these updates are for Dev and Canary only, suggesting these might be 26Hx features (then again, versions don't matter, right?) AI Just say no: To AI, to Copilot, and to Satya Nadella Our national nightmare is over: You can now (easily) hide Copilot in Microsoft Edge ChatGPT Go is now available worldwide, ads are on the way because of course Wikipedia partners with Amazon, Meta, Microsoft, more on AI Xbox & gaming January Xbox Update brings Game Sync Indicator, more Solid second half of January for Xbox Game Pass Microsoft will likely introduce a free, ad-supported Xbox Cloud Gaming tier because of course Tips & picks Tip of the week: Secure your Microsoft account App pick of the week: AI Dev Gallery RunAs Radio this week: Ideation to Implementation with Amber Vandenburg Liquor pick of the week: Estancia Raicilla Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit

This week, the hosts go deep on out-of-band updates, unwanted "innovations," and the uneasy cost of tech's latest gold rush. Plus, securing a Microsoft account is not as hard as some think, and neither are passkeys once you get past the jargon. And for developers, AI Dev Gallery offers a fascinating glimpse at what you can do for free with AI used against a CPU, GPU, or NPU. Windows 11 Microsoft issues an emergency fix for a borked Windows Update. Right. A fix for a fix. Hell freezes over, if only slightly: Microsoft quietly made some positive changes to forced OneDrive Folder Backup. Donʼt worry, itʼs still forced (and appears to be opt-in, but isnʼt). But you can back out more elegantly. So itʼs opt-out, not opt-in, but a step forward. Plus, a new behavior Windows 11 on Arm PCs can now download games from the Xbox app (previously only through the Insider program) Over 85 percent of Xbox games on PC work in WOA now Prism emulator now supports AVX and AVX2 and Epic Anti-Cheat, and there is a new Windows Performance Fit feature offering guidance on which titles should play well. Beta: New 25H2 build with account dialog modernization, Click to Do and desktop background improvements. Not for Dev, suggesting itʼs about to move to 26H1 Notepad and Paint get more features yet again. Notably, these updates are for Dev and Canary only, suggesting these might be 26Hx features (then again, versions don't matter, right?) AI Just say no: To AI, to Copilot, and to Satya Nadella Our national nightmare is over: You can now (easily) hide Copilot in Microsoft Edge ChatGPT Go is now available worldwide, ads are on the way because of course Wikipedia partners with Amazon, Meta, Microsoft, more on AI Xbox & gaming January Xbox Update brings Game Sync Indicator, more Solid second half of January for Xbox Game Pass Microsoft will likely introduce a free, ad-supported Xbox Cloud Gaming tier because of course Tips & picks Tip of the week: Secure your Microsoft account App pick of the week: AI Dev Gallery RunAs Radio this week: Ideation to Implementation with Amber Vandenburg Liquor pick of the week: Estancia Raicilla Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit

This week, the hosts go deep on out-of-band updates, unwanted "innovations," and the uneasy cost of tech's latest gold rush. Plus, securing a Microsoft account is not as hard as some think, and neither are passkeys once you get past the jargon. And for developers, AI Dev Gallery offers a fascinating glimpse at what you can do for free with AI used against a CPU, GPU, or NPU. Windows 11 Microsoft issues an emergency fix for a borked Windows Update. Right. A fix for a fix. Hell freezes over, if only slightly: Microsoft quietly made some positive changes to forced OneDrive Folder Backup. Donʼt worry, itʼs still forced (and appears to be opt-in, but isnʼt). But you can back out more elegantly. So itʼs opt-out, not opt-in, but a step forward. Plus, a new behavior Windows 11 on Arm PCs can now download games from the Xbox app (previously only through the Insider program) Over 85 percent of Xbox games on PC work in WOA now Prism emulator now supports AVX and AVX2 and Epic Anti-Cheat, and there is a new Windows Performance Fit feature offering guidance on which titles should play well. Beta: New 25H2 build with account dialog modernization, Click to Do and desktop background improvements. Not for Dev, suggesting itʼs about to move to 26H1 Notepad and Paint get more features yet again. Notably, these updates are for Dev and Canary only, suggesting these might be 26Hx features (then again, versions don't matter, right?) AI Just say no: To AI, to Copilot, and to Satya Nadella Our national nightmare is over: You can now (easily) hide Copilot in Microsoft Edge ChatGPT Go is now available worldwide, ads are on the way because of course Wikipedia partners with Amazon, Meta, Microsoft, more on AI Xbox & gaming January Xbox Update brings Game Sync Indicator, more Solid second half of January for Xbox Game Pass Microsoft will likely introduce a free, ad-supported Xbox Cloud Gaming tier because of course Tips & picks Tip of the week: Secure your Microsoft account App pick of the week: AI Dev Gallery RunAs Radio this week: Ideation to Implementation with Amber Vandenburg Liquor pick of the week: Estancia Raicilla Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit

We rebuild a small office network around Linux, with an Unplugged twist and real-world constraints. Things don't go quite as expected...Sponsored By:Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love. 1Password Extended Access Management: 1Password Extended Access Management is a device trust solution for companies with Okta, and they ensure that if a device isn't trusted and secure, it can't log into your cloud apps. Support LINUX UnpluggedLinks: