Podcasts about cpu

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

Samsung Galaxy S26 Ultra estrena pantalla antiespías y pone en aprietos a la inteligencia artificialPor Félix Riaño @LocutorCoSamsung presentó la nueva serie Galaxy S26 y, aunque la marca insiste en que estamos ante la era del “teléfono con IA”, la verdadera conversación no gira alrededor de asistentes virtuales. El foco está en una innovación de hardware que, por ahora, solo tiene el modelo más caro: una pantalla con privacidad integrada que oscurece el contenido cuando alguien intenta mirar desde un ángulo lateral.La familia está compuesta por Galaxy S26, S26 Plus y Galaxy S26 Ultra. A primera vista, el diseño cambia muy poco frente a la generación anterior. Pantallas AMOLED de hasta 120 hercios, resistencia al agua IP68 y cámaras de alta resolución siguen presentes. Pero hay diferencias que conviene analizar con calma, sobre todo si alguien está pensando en cambiar de teléfono este año.La función que realmente diferencia al UltraEl Galaxy S26 Ultra mantiene una pantalla de 6,9 pulgadas con resolución de 3.120 x 1.440 píxeles. Integra el procesador Snapdragon 8 Elite Gen 5 en varios mercados y promete mejoras de rendimiento cercanas al 19 % en CPU y 39 % en tareas de inteligencia artificial frente al modelo anterior.Sin embargo, el elemento que más llama la atención es el llamado “Privacy Display”. Se trata de una tecnología integrada en el panel OLED que limita el ángulo de visión. Cuando se activa, la pantalla se ve oscura o casi negra desde los lados, arriba o abajo. Solo quien está frente al dispositivo puede leer con claridad lo que aparece en pantalla.A diferencia de los protectores físicos de privacidad, que reducen el brillo todo el tiempo, esta solución puede activarse o desactivarse desde los ajustes rápidos. Además, se puede configurar para que funcione únicamente en aplicaciones específicas, en notificaciones o cuando el usuario introduce contraseñas.Según Samsung, el desarrollo de esta tecnología tomó cinco años. Para muchos analistas, es el rasgo que realmente diferencia al Ultra en una generación considerada continuista. A diferencia de los protectores físicos de privacidad, que reducen el brillo todo el tiempo, esta solución puede activarse o desactivarse desde los ajustes rápidos. Además, se puede configurar para que funcione únicamente en aplicaciones específicas, en notificaciones o cuando el usuario introduce contraseñas.Según Samsung, el desarrollo de esta tecnología tomó cinco años. Para muchos analistas, es el rasgo que realmente diferencia al Ultra en una generación considerada continuista.Mucha inteligencia artificial, pero ¿qué es realmente nuevo?Samsung centra buena parte de su discurso en la inteligencia artificial. La serie Galaxy S26 incluye herramientas como edición de fotos mediante texto, borrado de ruido en videos incluso en aplicaciones de terceros, organización automática de capturas de pantalla y filtros de llamadas con resúmenes generados por IA.También se refuerza el papel de Bixby, el asistente propio de la marca, que ahora puede entender órdenes más naturales para activar ajustes del sistema sin que el usuario conozca el nombre exacto de cada función.No obstante, muchas de estas mejoras dependen de software y de alianzas con servicios externos, como Gemini de Google o modelos de lenguaje de terceros. Esto abre la puerta a que algunas funciones puedan llegar a modelos anteriores mediante actualizaciones.En otras palabras, no todas las novedades están atadas al nuevo hardware.Precio al alza y almacenamiento base más altoEn Estados Unidos, el Galaxy S26 base arranca en 899,99 dólares, frente a los 799,99 dólares del modelo anterior. El S26 Plus sube a 1.099,99 dólares. El Galaxy S26 Ultra se mantiene en 1.299,99 dólares.En el Reino Unido también se registran aumentos. Samsung atribuye parte del incremento a la presión en el mercado global de memoria, impulsada por la demanda de centros de datos dedicados a inteligencia artificial.Una decisión relevante es la eliminación de la versión de 128 GB. Ahora el almacenamiento base parte de 256 GB en toda la serie. En el caso del Ultra, se ofrecen versiones de 256 GB, 512 GB y 1 TB.Cámaras y batería: ajustes más que revoluciónEl Galaxy S26 Ultra conserva el sensor principal de 200 megapíxeles, acompañado de lentes adicionales que permiten zoom óptico y grabación en 8K a 30 cuadros por segundo. Se anuncian mejoras en apertura y procesamiento para capturar más luz en escenas nocturnas.La batería del Ultra sigue siendo de 5.000 miliamperios hora. La marca promete llegar al 75 % de carga en aproximadamente 30 minutos con un cargador de 60 vatios. El modelo base aumenta su batería a 4.300 miliamperios hora, mientras el Plus se mantiene en 4.900.En comparación con el Galaxy S25, las mejoras en batería y cámara son graduales. No hay cambios drásticos en hardware, sino ajustes en eficiencia y procesamiento.¿Vale la pena cambiar?Para quienes tienen un Galaxy S25, el salto parece limitado. El rendimiento será algo mejor, la batería del modelo base crece ligeramente y la inteligencia artificial es más profunda, pero la experiencia general se mantiene muy cercana.En cambio, quienes usan modelos más antiguos podrían notar un cambio más amplio, especialmente si optan por el Ultra.La pantalla con privacidad integrada es, por ahora, la innovación más concreta y visible. En un mercado donde muchos avances se apoyan en software replicable, esta característica representa una diferencia difícil de copiar mediante una simple actualización.La pregunta final es sencilla: ¿la privacidad en pantalla justifica pagar por el modelo más costoso? Para quienes trabajan con información sensible en espacios públicos, puede ser un argumento sólido. Para otros usuarios, la decisión dependerá más del presupuesto que de la tecnología.BibliografíaCNEThttps://www.cnet.com/tech/mobile/this-one-killer-feature-sets-the-samsung-galaxy-s26-ultra-apart-from-all-other-phones/The Independenthttps://www.independent.co.uk/extras/indybest/gadgets-tech/phones-accessories/samsung-galaxy-s26-ultra-price-pre-order-b2927437.htmlMashablehttps://mashable.com/article/every-samsung-galaxy-unpacked-announcement-s26TechRadarhttps://www.techradar.com/phones/samsung-galaxy-phones/samsung-galaxy-s26-ultra-hands-on-impressionsEngadgethttps://www.engadget.com/mobile/smartphones/samsung-galaxy-s26-vs-galaxy-s25-whats-changed-and-which-one-should-you-buy-181515367.htmlForbeshttps://www.forbes.com/sites/jaymcgregor/2026/02/25/samsung-galaxy-s26-price-specs-features-camera-release-date/The Vergehttps://www.theverge.com/tech/884239/samsung-galaxy-s26-plus-price-specs-geminiWiredhttps://www.wired.com/story/samsung-galaxy-s26-series-galaxy-unpacked/Conviértete 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. 

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

Every six months, we get a new version of Java. Java 26 is just around the corner and will be released soon. But most companies stick to LTS (Long-Term Support) versions, which are maintained and receive security updates for many more years. Versions 8, 11, 17, 21, and 25 are such LTS versions. Hopefully, most of your systems are already on the latest versions and you are not stuck on 8 or earlier. As a reminder, 8 was released in 2014, so much has changed since then.If you are doubting moving from 21 to 25, or even from an earlier version to the latest LTS, this podcast is for you! Together with Jakob Jenkov, we discussed the most important changes, and this episode includes a few quotes from interviews recorded at conferences last year.GuestsJakob Jenkovhttps://www.linkedin.com/in/jakob-jenkov-4a3a8/Jonathan Vilahttps://www.linkedin.com/in/jonathanvila/Ryan Svihlahttps://www.linkedin.com/in/ryan-svihla-096752182/Mary Grygleskihttps://www.linkedin.com/in/mary-grygleski/Anton Arhipovhttps://www.linkedin.com/in/antonarhipov/Ronald Dehuysserhttps://www.linkedin.com/in/ronalddehuysser/Jonathan Ellishttps://www.linkedin.com/in/jbellis/Content00:00 Introduction of topic and guestTutorials by JakobPodcast #89: Quarkus and Agentic Commerce03:30 Bugfixes and performance improvements "under the hoods"Quote Jonathan Vila08:00 Java as a scripting languageQuote Ryan SvihlaCompact Source Files and Instance Main methodsLaunch Multi-File Source-Code Programshttps://www.jbang.dev/Quote Mary Grygleski15:03 GC ImprovementsGenerational ShenandoahTrash Talk - Exploring the JVM memory management by Gerrit GrunwaldWhat Should I Know About Garbage Collection as a Java Developer?19:44 Project Loom: Virtual Threads and Structured ConcurrencyQuote Anton Arhipov29:44 How Java evolves6-months release cycleHow incubator and preview features are used to get feedback from the communityLong-Term Support Short-Term Support versionsFoojay Podcast #28: Java 21 Has Arrived!Foojay Podcast #45: Welcome to Java 22Foojay Podcast #57: Welcome to OpenJDK (Java) 23Foojay Podcast #68: Welcome to OpenJDK (Java) 24Foojay Podcast #78: Welcome to OpenJDK 25!32:15 Project Leyden: Ahead-of-time featuresAhead-of-Time Command-Line ErgonomicsAhead-of-Time Method ProfilingAhead-of-Time Class Loading & Linking39:15 Project BabylonJava on CPU, GPU, FPGA?This is already possible with TornadoVMFoojay Podcast #82: OpenJDK Projects (Leyden, Babylon, Panama) and TornadoVM43:25 Class-File APIQuote Ronald DehuysserJavaFX In Action #22 with Matt Coley, diving into byte code and JARs with Recaf and JavaFX libraries49:20 Foreign Function and Memory APIThe FFM API: How OpenJDK Changed the Game for Native Interactions (And Made Pi4J Better!)jChampions Conference talk 'Foreign Function & Memory (FFM) API on Raspberry Pi'54:26 Vector APIQuote Jonathan Ellis + Ryan Svihla59:59 Removal of String templates01:00:26 Taking a look into the JVM of the future01:03:08 Conclusion

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.

Sample rates: the numbers everyone argues about, few people fully understand, and almost everyone has accidentally overused at least once. In this episode of Inside the Recording Studio, Chris and Jody pull the curtain back on digital audio's favorite bragging rights metric and ask a simple question, why are we even doing this? They start at the beginning, breaking down what a sample rate really is without turning it into a math lecture. From there, they explain why 44.1kHz and 48kHz became the standards they are today, and why jumping straight to higher rates isn't the flex some people think it is. If you've ever felt tempted to crank your session up “just in case,” this episode might save your CPU, and your patience. Jody digs into the practical side effects of higher sample rates: bigger files, heavier processing demands, and fewer plugins running before your system taps out. Chris adds a perfectly on-brand story about someone recording at 192kHz purely to look impressive. The result? A stressed-out system, bloated storage, and absolutely no audible win. Cool story, though. For anyone running home studio gear, this conversation cuts straight to what matters. Chris and Jody explain why upsampling won't fix bad recordings, why converting sample rates mid-session is asking for trouble, and how to choose a rate that fits your actual delivery needs. These recording setup tips aren't theoretical, they're the kind of advice you wish you'd heard before opening that first template. They also touch on how sample rate choices ripple through your workflow, from plugin performance to session compatibility. Whether you're collaborating with others or bouncing between music and video projects, knowing when to stick with a standard rate can keep everything moving smoothly. As usual, there's no gear-snobbery here. Chris and Jody aren't interested in telling you what's “pro”, they're interested in what works. The goal isn't bigger numbers. It's clean audio, stable sessions, and decisions you don't have to second-guess later. Stick around for the Gold Star word, check out this week's Friday Finds, and walk away knowing exactly why your next session doesn't need to run at the highest sample rate your interface allows. Subscribe for next week's studio sanity check. #SampleRates #HomeStudioGear #RecordingSetupTips #DigitalAudioBasics #AudioWorkflow #StudioMistakes #Upsampling #CPUOverload

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

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

Wilder Lopes is the CEO and Founder of Ogre.run, working on AI-driven dependency resolution and reproducible code execution across environments.How Universal Resource Management Transforms AI Infrastructure Economics // MLOps Podcast #357 with Wilder Lopes, CEO / Founder of Ogre.runJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractEnterprise organizations face a critical paradox in AI deployment: while 52% struggle to access needed GPU resources with 6-12 month waitlists, 83% of existing CPU capacity sits idle. This talk introduces an approach to AI infrastructure optimization through universal resource management that reshapes applications to run efficiently on any available hardware—CPUs, GPUs, or accelerators.We explore how code reshaping technology can unlock the untapped potential of enterprise computing infrastructure, enabling organizations to serve 2-3x more workloads while dramatically reducing dependency on scarce GPU resources. The presentation demonstrates why CPUs often outperform GPUs for memory-intensive AI workloads, offering superior cost-effectiveness and immediate availability without architectural complexity.// BioWilder Lopes is a second-time founder, developer, and research engineer focused on building practical infrastructure for developers. He is currently building Ogre.run, an AI agent designed to solve code reproducibility.Ogre enables developers to package source code into fully reproducible environments in seconds. Unlike traditional tools that require extensive manual setup, Ogre uses AI to analyze codebases and automatically generate the artifacts needed to make code run reliably on any machine. The result is faster development workflows and applications that work out of the box, anywhere.// Related LinksWebsite: https://ogre.runhttps://lopes.aihttps://substack.com/@wilderlopes https://youtu.be/YCWkUub5x8c?si=7RPKqRhu0Uf9LTql~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Wilder on LinkedIn: /wilderlopes/Timestamps:[00:00] Secondhand Data Centers Challenges[00:27] AI Hardware Optimization Debate[03:40] LLMs on Older Hardware[07:15] CXL Tradeoffs[12:04] LLM on CPU Constraints[17:07] Leveraging Existing Hardware[22:31] Inference Chips Overview[27:57] Fundamental Innovation in AI[30:22] GPU CPU Combinations[40:19] AI Hardware Challenges[43:21] AI Perception Divide[47:25] Wrap up

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:

Every episode this year just keeps getting better, try it, you'll see.  This food podcast brought to by the tech news!  Also, 9850X3D this month, welcome back 8GB GPUs, and 2026 will be tough for PC hardware enthusiats. Oh, that Micron shut down is really a good thing, trust them.  Enjoy Linux malware, CoPilot exploits, and even a StarCraft based shooter on the way plus so much more!  Seriously, look at the timestamps below.We thank our two sponsors this week:Notion Agent - Bringing all your notes, docs, and projects into one connected space that just works!Zapier - Where tech innovators break the hype cycle and put AI into their workflows - orchestrate it with Zapier!Timestamps0:00 Intro01:54 Patreon03:20 Food with Josh05:17 AMD reportedly launching the 9850X3D this month06:07 NVIDIA to increase 8GB GPU production?08:05 NVIDIA SUPER refresh delay09:32 Micron defends shutting down Crucial12:51 Zoicware is the one true way16:32 SK hynix to build new memory plant17:50 2026 is not going to be a great year for PC builders20:29 Podcast sponsor - Notion21:54 PSUs and CPU coolers are the next to go up in price28:29 Apple's Google Gemini deal31:02 Intel Nova Lake graphics33:06 (In)Security Corner38:37 Podcast sponsor - Zapier40:06 (In)Security Corner, continued46:46 We are not normal57:33 Gaming Quick Hits1:07:38 Picks of the Week1:17:52 Outro ★ Support this podcast on Patreon ★

This is a recap of the top 10 posts on Hacker News on January 15, 2026. This podcast was generated by wondercraft.ai (00:30): The URL shortener that makes your links look as suspicious as possibleOriginal post: https://news.ycombinator.com/item?id=46627652&utm_source=wondercraft_ai(01:57): Apple is fighting for TSMC capacity as Nvidia takes center stageOriginal post: https://news.ycombinator.com/item?id=46633488&utm_source=wondercraft_ai(03:24): Photos capture the breathtaking scale of China's wind and solar buildoutOriginal post: https://news.ycombinator.com/item?id=46630369&utm_source=wondercraft_ai(04:52): The Palantir app helping ICE raids in MinneapolisOriginal post: https://news.ycombinator.com/item?id=46633378&utm_source=wondercraft_ai(06:19): Ask HN: How can we solve the loneliness epidemic?Original post: https://news.ycombinator.com/item?id=46635345&utm_source=wondercraft_ai(07:47): 25 Years of WikipediaOriginal post: https://news.ycombinator.com/item?id=46632023&utm_source=wondercraft_ai(09:14): ‘ELITE': The Palantir app ICE uses to find neighborhoods to raidOriginal post: https://news.ycombinator.com/item?id=46637127&utm_source=wondercraft_ai(10:42): To those who fired or didn't hire tech writers because of AIOriginal post: https://news.ycombinator.com/item?id=46629474&utm_source=wondercraft_ai(12:09): Pocket TTS: A high quality TTS that gives your CPU a voiceOriginal post: https://news.ycombinator.com/item?id=46628329&utm_source=wondercraft_ai(13:37): Raspberry Pi's New AI Hat Adds 8GB of RAM for Local LLMsOriginal post: https://news.ycombinator.com/item?id=46629682&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

professorjrod@gmail.comIn this episode of Technology Tap: CompTIA Study Guide, we explore how proactive detection surpasses reactive troubleshooting in cybersecurity. For those preparing for their CompTIA exam, understanding the subtle clues and quiet anomalies attackers leave behind is essential for developing strong IT skills and excelling in tech exam prep. We dive deep into the critical indicators that help you detect security compromises early, providing practical knowledge essential for your technology education and IT certification journey. Join us as we equip you with expert insights to sharpen your detection abilities and enhance your competence in protecting systems effectively.We walk through the behaviors that matter: viruses that hitch a ride on clicks, worms that paint the network with unexplained traffic, and fileless attacks that live in memory and borrow admin tools like PowerShell and scheduled tasks. You'll learn how to spot spyware by the aftermath of credential misuse, recognize RATs and backdoors by their steady beaconing to unknown IPs, and use contradictions—like tools disagreeing about running processes—as a signal for rootkits. We also draw a sharp line between ransomware's loud chaos and cryptojacking's quiet drain on your CPU and fan.Zooming out, we map network and application signals: certificate warnings and duplicate MACs that hint at man-in-the-middle, DNS mismatches that suggest cache poisoning, and log patterns that betray SQL injection, replay abuse, or directory traversal. Along the way, we talk about building Security+ instincts through scaffolding—A+ for OS and hardware intuition, Network+ for protocol fluency, and Security+ for attacker behavior—so indicators make sense the moment you see them.If you want a sharper eye for subtle threats and a stronger shot at your Security+ exam, this guide will train your attention on the tells adversaries can't fully hide. Subscribe, share with a teammate who handles triage, and leave a review with your favorite indicator to watch—we'll feature the best ones in a future show.Support the showArt By Sarah/DesmondMusic by Joakim KarudLittle chacha ProductionsJuan Rodriguez can be reached atTikTok @ProfessorJrodProfessorJRod@gmail.com@Prof_JRodInstagram ProfessorJRod

What happens when engineering teams can finally see the business impact of every technical decision they make? In this episode of Tech Talks Daily, I sat down with Chris Cooney, Director of Advocacy at Coralogix, to unpack why observability is no longer just an engineering concern, but a strategic lever for the entire business. Chris joined me fresh from AWS re:Invent, where he had been challenging a long-standing assumption that technical signals like CPU usage, error rates, and logs belong only in engineering silos. Instead, he argues that these signals, when enriched and interpreted correctly, can tell a much more powerful story about revenue loss, customer experience, and competitive advantage. We explored Coralogix's Observability Maturity Model, a four-stage framework that takes organizations from basic telemetry collection through to business-level decision making. Chris shared how many teams stall at measuring engineering health, without ever connecting that data to customer impact or financial outcomes. The conversation became especially tangible when he explained how a single failed checkout log can be enriched with product and pricing data to reveal a bug costing thousands of dollars per day. That shift, from "fix this tech debt" to "fix this issue draining revenue," fundamentally changes how priorities are set across teams. Chris also introduced Oli, Coralogix's AI observability agent, and explained why it is designed as an agent rather than a simple assistant. We talked about how Oli can autonomously investigate issues across logs, metrics, traces, alerts, and dashboards, allowing anyone in the organization to ask questions in plain English and receive actionable insights. From diagnosing a complex SQL injection attempt to surfacing downstream customer impact, Oli represents a move toward democratizing observability data far beyond engineering teams. Throughout our discussion, a clear theme emerged. When technical health is directly tied to business health, observability stops being seen as a cost center and starts becoming a competitive advantage. By giving autonomous engineering teams visibility into real-world impact, organizations can make faster, better decisions, foster innovation, and avoid the blind spots that have cost even well-known brands millions. So if observability still feels like a necessary expense rather than a growth driver in your organization, what would change if every technical signal could be translated into clear business impact, and who would make better decisions if they could finally see that connection? Useful LInks Connect with Chris Cooney Learn more about Coralogix Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.

Who are the most dominant players when they're controlled by the CPU in basketball video games? This week, we join the community in discussing some of the most unguardable players on the virtual hardwood, at least when they're in the hands of the AI. We also reflect on the frustration of not always being able to light it up with those same players ourselves, and mention a few players who haven't been as dominant in video games as they really should be. The post NLSC Podcast #614: Dominant CPU Controlled Players in Basketball Video Games appeared first on NLSC.

Logic Pro workflow tips can dramatically speed up music production, and in this episode of Inside The Mix, Marc Matthews breaks down seven practical Logic Pro tweaks that remove friction and help producers finish more music, faster.Designed for beginner to intermediate Logic Pro users, this episode tackles a common frustration: slow sessions that kill creativity. Marc explains why workflow, not plugins, CPU power, or inspiration, is usually the real bottleneck in Logic Pro music production.Listeners learn how to restore creative flow with MIDI Chase, ensuring sustained notes always trigger when playback starts mid-phrase. Marc then shows how to assign a third tool to the right mouse button so essential edits like Gain or Scissors are always one click away. Visual organisation comes next, with auto-colouring tracks, regions, and markers to make large sessions readable at a glance.Timing and arrangement get a boost using Groove Track and Flex, aligning stacked vocals quickly while keeping performances natural. Marc also shares overlooked Logic Pro workflow tips for routing, like instantly revealing the correct aux, and using marker shortcuts to navigate song structure without breaking momentum. The episode wraps with a powerful creative trick: converting Flex Pitch data to MIDI to generate new musical ideas directly from audio.Each tip is explained clearly, with real-world examples and a focus on repeatable systems you can build into your templates.TL;DRSlow Logic Pro sessions aren't about plugins or CPU, they're about workflow friction. Marc Matthews shares 7 beginner-friendly Logic Pro workflow tips that speed up editing, organisation, timing, routing, and creative decision-making so you can stay in flow and finish more music, faster.Subscribe to Inside The Mix for more Logic Pro workflow deep dives, and share which tip sped up your sessions the most.Send me a messageSupport the showWays to connect with Marc: Book your FREE Music Breakthrough Strategy Call Radio-ready mixes start here - get the FREE weekly tips Follow Marc's Socials: Instagram | YouTube | Synth Music Mastering Thanks for listening!! Try Riverside for FREE

Steadicam operator John "Buzz Moyer", Actor Michael Kelly and drummer Gavin Harrison discuss what it takes to accomplish really intricate things and not letting them overwhelm you. A truly fascinating discussion about what it takes to not let your CPU get overloaded and to simply be in the moment. Watch the video recording of this podcast here. Watch Gavin's original CPU Theory video which was the genesis for this discussion. To see pictures and things we discussed in todays episode check out the podcast page of The Op. Please check us out on our website and on instagram and like us and review us if you enjoyed the episode. Theme Music - Tatyana Richaud Theme Mix - Charles Papert

Intel has launched its Core Ultra Series 3 central processing units, utilizing its new 18A process technology, which aims to enhance performance and efficiency across various applications, including gaming and professional workloads. This development is part of Intel's strategy to regain competitiveness in the CPU market, which has faced increasing pressure from rivals. The new processors promise improved performance per watt compared to previous generations, with further specifications expected soon. This advancement in chip technology is significant for Managed Service Providers (MSPs) as it enables the feasibility of edge AI applications, which require careful consideration of workload clarity and governance.Lenovo introduced Cura, an AI assistant designed to operate seamlessly across its computers and Motorola smartphones, emphasizing on-device processing and user privacy. This system-level AI aims to adapt to user habits over time, assisting with tasks such as email drafting and meeting summarization. However, the episode highlights a concerning trend where many users do not fully utilize existing tools, as evidenced by Microsoft's Copilot user statistics. The discussion underscores the importance of governance in AI deployment, as successful enterprise AI implementations, like those from Siemens, demonstrate that explicit authority and responsibility are crucial for effective outcomes.The episode also addresses the ongoing hype surrounding robotics and automation, noting that while advancements are being made, the reality remains that specialized robots are more practical than general-purpose ones. Companies are focusing on single-purpose robots, which contrasts with the expectation of multifunctional robots. The discussion emphasizes that automation in IT should follow a similar path, advocating for narrow automations with explicit authority to avoid misunderstandings and failures that could lead to accountability issues for MSPs.For MSPs and IT service leaders, the key takeaway is the necessity of redefining governance and responsibility in the face of advancing automation and AI technologies. As systems of action become more prevalent, the shift from traditional dashboards to autonomous decision-making systems requires MSPs to update their contracts and governance models accordingly. The opportunity lies not in simply adopting new technologies but in understanding where automation should be limited and ensuring that accountability is clearly defined to mitigate risks associated with automated systems. Three things to know today 00:00 Intel, Lenovo, and Siemens Signal AI Acceleration, Not Automatic Value, for IT Services06:02 CES 2026 Reveals Why Specialized Robotics and Disciplined Automation Deliver ROI Faster Than General AI09:34 Agentic AI, Action-First Platforms, and the End of Forgiving IT Systems Put New Accountability on MSPs This is the Business of Tech.     Supported by: 

PC makers are shaking up CES with wild designs and next-gen chips, but the real story is Microsoft's bold software moves, AI's hardware hunger, and a candid debate over whether any tech company still puts users first. Come for the Windows updates, stay for the whisky warnings and robot bathroom assistants. CES 2026 is here with the 4K hummingbird feeder of your dreams New PCs and more from HP consumers/commercial, HP gamers, Lenovo, others The first official Copilot+ PC desktops Snapdragon X2 Plus joins X2 Elite and X2 Elite Extreme Intel Panther Lake has meaningful CPU and graphics performance gains, but predictable reliability issues AMD Ryzen AI 400 series is a minor bump Windows Paul was the first to report that Microsoft is refactoring it all with Rust A Microsoft distinguished engineer wrote about his desire to refactor all C/C++ code in the company with Rust by 2030 Some mistook this to mean "rewriting Windows with Rust,ˮ so he had to issue a clarification. But I never wrote that. Heads-up: That will happen, but this is really about Azure first and the core underlying code in Microsoftʼs most important platforms Microsoft released hardware-accelerated BitLocker in late 2025 and never told anyone. It requires the latest PC CPUs Copilot app update that adds text editing actions to Copilot Vision across channels Dev and Beta got first previews of AI agents on the Taskbar, starting with the Researcher agent, plus underlying Agent Launchers experience IDC says the global memory shortage (thanks, AI!) could screw up PC and smartphone growth this year AI ChatGPT now has an app store, but it has a ways to go Mozilla Firefox will have a "killswitchˮ for AI Our national nightmare will soon be over, LG will let users remove Copilot app from their smart TVs Xbox and gaming First Xbox Game Pass releases of 2026 include Resident Evil Village and Star Wars Outlaws Xbox Cloud Gaming is coming to Hisense smart TVs and to the latest Fire TV smart TVs GOG goes independent, will continue DRM-free push "Have a blastˮ and other FPS throwbacks from the 1990s Valve quietly killed the LCD Steam Deck model Tips and picks Tip of the week: Itʼs time to give Little AI a look App pick of the week: Bonjourr RunAs Radio this week: What AI can do for SysAdmins in 2026 with Cecilia Wiren Brown liquor pick of the week: The Singleton of Dufftown 12 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 Sponsors: threatlocker.com/twit cachefly.com/twit

PC makers are shaking up CES with wild designs and next-gen chips, but the real story is Microsoft's bold software moves, AI's hardware hunger, and a candid debate over whether any tech company still puts users first. Come for the Windows updates, stay for the whisky warnings and robot bathroom assistants. CES 2026 is here with the 4K hummingbird feeder of your dreams New PCs and more from HP consumers/commercial, HP gamers, Lenovo, others The first official Copilot+ PC desktops Snapdragon X2 Plus joins X2 Elite and X2 Elite Extreme Intel Panther Lake has meaningful CPU and graphics performance gains, but predictable reliability issues AMD Ryzen AI 400 series is a minor bump Windows Paul was the first to report that Microsoft is refactoring it all with Rust A Microsoft distinguished engineer wrote about his desire to refactor all C/C++ code in the company with Rust by 2030 Some mistook this to mean "rewriting Windows with Rust,ˮ so he had to issue a clarification. But I never wrote that. Heads-up: That will happen, but this is really about Azure first and the core underlying code in Microsoftʼs most important platforms Microsoft released hardware-accelerated BitLocker in late 2025 and never told anyone. It requires the latest PC CPUs Copilot app update that adds text editing actions to Copilot Vision across channels Dev and Beta got first previews of AI agents on the Taskbar, starting with the Researcher agent, plus underlying Agent Launchers experience IDC says the global memory shortage (thanks, AI!) could screw up PC and smartphone growth this year AI ChatGPT now has an app store, but it has a ways to go Mozilla Firefox will have a "killswitchˮ for AI Our national nightmare will soon be over, LG will let users remove Copilot app from their smart TVs Xbox and gaming First Xbox Game Pass releases of 2026 include Resident Evil Village and Star Wars Outlaws Xbox Cloud Gaming is coming to Hisense smart TVs and to the latest Fire TV smart TVs GOG goes independent, will continue DRM-free push "Have a blastˮ and other FPS throwbacks from the 1990s Valve quietly killed the LCD Steam Deck model Tips and picks Tip of the week: Itʼs time to give Little AI a look App pick of the week: Bonjourr RunAs Radio this week: What AI can do for SysAdmins in 2026 with Cecilia Wiren Brown liquor pick of the week: The Singleton of Dufftown 12 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 Sponsors: threatlocker.com/twit cachefly.com/twit

PC makers are shaking up CES with wild designs and next-gen chips, but the real story is Microsoft's bold software moves, AI's hardware hunger, and a candid debate over whether any tech company still puts users first. Come for the Windows updates, stay for the whisky warnings and robot bathroom assistants. CES 2026 is here with the 4K hummingbird feeder of your dreams New PCs and more from HP consumers/commercial, HP gamers, Lenovo, others The first official Copilot+ PC desktops Snapdragon X2 Plus joins X2 Elite and X2 Elite Extreme Intel Panther Lake has meaningful CPU and graphics performance gains, but predictable reliability issues AMD Ryzen AI 400 series is a minor bump Windows Paul was the first to report that Microsoft is refactoring it all with Rust A Microsoft distinguished engineer wrote about his desire to refactor all C/C++ code in the company with Rust by 2030 Some mistook this to mean "rewriting Windows with Rust,ˮ so he had to issue a clarification. But I never wrote that. Heads-up: That will happen, but this is really about Azure first and the core underlying code in Microsoftʼs most important platforms Microsoft released hardware-accelerated BitLocker in late 2025 and never told anyone. It requires the latest PC CPUs Copilot app update that adds text editing actions to Copilot Vision across channels Dev and Beta got first previews of AI agents on the Taskbar, starting with the Researcher agent, plus underlying Agent Launchers experience IDC says the global memory shortage (thanks, AI!) could screw up PC and smartphone growth this year AI ChatGPT now has an app store, but it has a ways to go Mozilla Firefox will have a "killswitchˮ for AI Our national nightmare will soon be over, LG will let users remove Copilot app from their smart TVs Xbox and gaming First Xbox Game Pass releases of 2026 include Resident Evil Village and Star Wars Outlaws Xbox Cloud Gaming is coming to Hisense smart TVs and to the latest Fire TV smart TVs GOG goes independent, will continue DRM-free push "Have a blastˮ and other FPS throwbacks from the 1990s Valve quietly killed the LCD Steam Deck model Tips and picks Tip of the week: Itʼs time to give Little AI a look App pick of the week: Bonjourr RunAs Radio this week: What AI can do for SysAdmins in 2026 with Cecilia Wiren Brown liquor pick of the week: The Singleton of Dufftown 12 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 Sponsors: threatlocker.com/twit cachefly.com/twit

PC makers are shaking up CES with wild designs and next-gen chips, but the real story is Microsoft's bold software moves, AI's hardware hunger, and a candid debate over whether any tech company still puts users first. Come for the Windows updates, stay for the whisky warnings and robot bathroom assistants. CES 2026 is here with the 4K hummingbird feeder of your dreams New PCs and more from HP consumers/commercial, HP gamers, Lenovo, others The first official Copilot+ PC desktops Snapdragon X2 Plus joins X2 Elite and X2 Elite Extreme Intel Panther Lake has meaningful CPU and graphics performance gains, but predictable reliability issues AMD Ryzen AI 400 series is a minor bump Windows Paul was the first to report that Microsoft is refactoring it all with Rust A Microsoft distinguished engineer wrote about his desire to refactor all C/C++ code in the company with Rust by 2030 Some mistook this to mean "rewriting Windows with Rust,ˮ so he had to issue a clarification. But I never wrote that. Heads-up: That will happen, but this is really about Azure first and the core underlying code in Microsoftʼs most important platforms Microsoft released hardware-accelerated BitLocker in late 2025 and never told anyone. It requires the latest PC CPUs Copilot app update that adds text editing actions to Copilot Vision across channels Dev and Beta got first previews of AI agents on the Taskbar, starting with the Researcher agent, plus underlying Agent Launchers experience IDC says the global memory shortage (thanks, AI!) could screw up PC and smartphone growth this year AI ChatGPT now has an app store, but it has a ways to go Mozilla Firefox will have a "killswitchˮ for AI Our national nightmare will soon be over, LG will let users remove Copilot app from their smart TVs Xbox and gaming First Xbox Game Pass releases of 2026 include Resident Evil Village and Star Wars Outlaws Xbox Cloud Gaming is coming to Hisense smart TVs and to the latest Fire TV smart TVs GOG goes independent, will continue DRM-free push "Have a blastˮ and other FPS throwbacks from the 1990s Valve quietly killed the LCD Steam Deck model Tips and picks Tip of the week: Itʼs time to give Little AI a look App pick of the week: Bonjourr RunAs Radio this week: What AI can do for SysAdmins in 2026 with Cecilia Wiren Brown liquor pick of the week: The Singleton of Dufftown 12 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 Sponsors: threatlocker.com/twit cachefly.com/twit