Podcasts about Macs

News and Updates: Windows 12: Not Yet: Microsoft has no plans to release Windows 12 in 2026, instead continuing annual updates to Windows 11 to avoid triggering another end-of-support mess like Windows 10's. Windows 12 likely won't require a subscription or higher hardware specs, keeping 8GB RAM support and focusing on agentic AI features that work across all Windows 11 PCs. Instagram Bans Creepy Glasses Content: IG chief Adam Mosseri says Instagram will remove videos filmed with Meta smart glasses that harass strangers, following viral "pickup line" videos targeting women in public. NY Courts Ban Smart Glasses: New York's court system banned smart glasses across all 1,240+ state courthouses starting July 20, citing concerns over secret recording of proceedings, witnesses, and jurors. ASU Launches Influencer Degree: Arizona State University now offers a Bachelor's in Content Creation, training students in video production, branding, and social media strategy for careers in digital influencing and marketing. Apple Launches Device Leasing: Apple's new "Apple Upgrade" program, backed by Klarna, lets customers lease iPhones, iPads, Macs, and Apple Watches monthly instead of buying outright, with plans from $12–$83/month.

Derson Lopes junta-se a Rafael Fischmann e Eduardo Marques para discutir os últimos resultados financeiros da Apple, bem como o restante de temas quentes desta semana. No ar! [Edição: Edu Garcia] 00:00 Introdução 03:56 Apple fatura US$109,4 bilhões no 3º trimestre fiscal de 2026 17:37 Apple Upgrade: programa de leasing é lançado com iPhones, iPads, Macs e Apple Watches 29:04 “Apple Glass” poderá ser revelado na WWDC27 e vir sem câmeras 35:47 Apple Watch deverá passar por grandes mudanças nos próximos anos 43:28 Apple poderá lançar três dispositivos de casa inteligente em breve, incluindo um smart hub 49:19 Epic Games Store está agora disponível para iPhone no Brasil 52:39 Encerramento

Discover how ChromeVox on Chromebooks is transforming accessible computing for blind users. With insights from Charmaine Cole of the Accessible Learning Lounge, Steven Scott and Shaun Preece explore whether Chromebooks can truly replace traditional PCs or Macs for everyday tasks. In this episode of Double Tap, Steven and Shaun dive into the world of Chromebooks and Google's built-in screen reader, ChromeVox. They're joined by Charmaine Cole, who has recently expanded her Accessible Learning Lounge courses to include training for ChromeVox, NVDA, and VoiceOver. Together, they discuss how Chromebooks perform for blind users, the surprising accessibility improvements in ChromeVox, and how these affordable devices could meet daily computing needs like email, Google Docs, and cloud work. Charmaine shares her journey of learning ChromeVox, the benefits of free screen reader tools, and the accessibility challenges still present in areas like Braille support and missing place markers. The conversation also touches on inconsistent hotel accessibility, the rising cost of tech, and why Chromebooks might be the budget-friendly solution for many users. Like what you hear? Share your thoughts in the comments, subscribe for more accessible tech discussions, and check out Charmaine Cole's courses for 10% off with code doubletap. Relevant Links Accessible Learning Lounge: https://accessiblelearning.gumroad.com ----Follow on:YouTube: https://www.doubletaponair.com/youtubeX (formerly Twitter): https://www.doubletaponair.com/xInstagram: https://www.doubletaponair.com/instagramTikTok: https://www.doubletaponair.com/tiktokThreads: https://www.doubletaponair.com/threadsFacebook: https://www.doubletaponair.com/facebookLinkedIn: https://www.doubletaponair.com/linkedinSubscribe to the Podcast:Apple: https://www.doubletaponair.com/appleSpotify: https://www.doubletaponair.com/spotifyRSS: https://www.doubletaponair.com/podcastiHeadRadio: https://www.doubletaponair.com/iheartAbout Double TapHosted by the insightful duo, Steven Scott and Shaun Preece, Double Tap is a treasure trove of information for anyone who's blind or partially sighted and has a passion for tech. Steven and Shaun not only demystify tech, but they also regularly feature interviews and welcome guests from the community, fostering an interactive and engaging environment. Tune in every day of the week, and you'll discover how technology can seamlessly integrate into your life, enhancing daily tasks and experiences, even if your sight is limited."Double Tap" is a registered trademark of Double Tap Productions Inc. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

¿Está la economía de los Grandes Modelos de Lenguaje (LLM) completamente rota y destinada al colapso? Desglosamos por qué los modelos de suscripción actuales no logran cubrir los astronómicos costes de los tokens. Explicamos cómo la fiebre por construir centros de datos ha duplicado el precio de la memoria RAM, encareciendo los dispositivos. Mientras los gigantes tecnológicos gastan más de 650.000 millones en infraestructura, Apple mantiene un gasto mínimo de 14.000 millones. Debatimos si el frenazo tras el lanzamiento de Apple Intelligence es una postura prudente para evitar pérdidas multimillonarias. De la mano de Zitron, evaluamos la tesis de que Apple simplemente se sentará en primera fila a "verlo todo arder" cuando la burbuja estalle. ¿Saldrá Cupertino mejor parada que nadie al no haber sobre-extendido sus recursos en un modelo de negocio no rentable? //Enlaces https://help.revolut.com/help/profile-and-plan/my-plan-benefits/partnership-benefits/question-chatgpt/ #Apple #BurbujaIA #EdZitron #MacRumors #AppleIntelligence #Tecnologia #TimCook #PodcastTech #Innovacion #IA ---------------------------------- Línea de Tiempo para Navegación: Introducción: El demoledor análisis de Ed Zitron en MacRumors La Economía Rota de la IA: Por qué el modelo de tokens y suscripciones no es sostenible El Impacto en el Consumidor: Subida de la memoria RAM y encarecimiento de Macs e iPhones Apple vs. los Gigantes: $14.000M de inversión de Apple frente a los $650.000M de sus competidores El Frenazo de Apple Intelligence: ¿Prudencia estratégica o reacción al rechazo del usuario? "Verlo Todo Arder": Qué ocurrirá con Apple cuando la burbuja de la IA generativa colapse Conclusiones: Por qué no apostar a ciegas puede ser la mayor ventaja de Cupertino ---------------------------------- PARTICIPA EN DIRECTO Deja tu opinión en los comentario ---------------------------------- ¿TE GUSTÓ EL EPISODIO? ✨ Dale LIKE SUSCRÍBETE y activa la campanita para no perderte nada COMENTA COMPARTE con tus amigos Applelianos ---------------------------------- SÍGUENOS EN TODAS NUESTRAS PLATAFORMAS: YouTube: https://www.youtube.com/@Applelianos Telegram: https://t.me/+Jm8IE4n3xtI2Zjdk X (Twitter): https://x.com/ApplelianosPod Facebook: https://www.facebook.com/applelianos Apple Podcasts: https://apple.co/39QoPbO ----------------------------------

Apple has launched a new hardware leasing initiative called Apple Upgrade in partnership with the fintech company Klarna. This program allows consumers to acquire iPhones, iPads, Macs, and Apple Watches for a low monthly fee, with the option to swap for newer models or purchase the device eventually. While the collaboration simplifies the user experience by leveraging Klarna's specialized financial infrastructure, some analysts view it as a response to rising product costs driven by the AI boom. Experts are divided on whether this move signifies a validation of the buy-now, pay-later model or highlights growing financial stress among consumers who can no longer afford upfront prices. Ultimately, the shift helps Apple maintain a steady cycle of product turnover while keeping its expensive ecosystem accessible to a broader audience.

In Nederland zijn vorig jaar 136 ransomware-aanvallen geregistreerd, tegenover 127 een jaar eerder. Dat blijkt uit de jaarlijkse rapportage van de Autoriteit Persoonsgegevens. De toezichthouder waarschuwt ook voor een andere trend: bij de aanvallen wordt steeds vaker data gestolen, waardoor de risico's voor slachtoffers op phishing en fraude toenemen. Verder introduceert Apple een leaseprogramma voor iPhones, iPads, Macs en de Apple Watch. Rosanne Peters vertelt erover in deze Tech Update. Volgens de toezichthouder wordt in de helft van de gevallen naast het versleutelen van bestanden ook data buitgemaakt. In een kwart van de gevallen slaan criminelen het versleutelen helemaal over en stelen ze alleen gegevens, om die zelf te gebruiken of door te verkopen. De AP adviseert organisaties een crisisplan op papier klaar te hebben liggen, dat plan regelmatig te testen, te zorgen voor een goed monitoringsysteem en bij een aanval direct deskundigen in te schakelen. Apple begint leaseprogramma voor hun producten Met Apple Upgrade kunnen klanten in de Verenigde Staten een iPhone, iPad, Mac of Apple Watch leasen in plaats van kopen. Een iPhone begint bij 18 dollar per maand, een Apple Watch bij 12 dollar. Na een looptijd van twaalf of vierentwintig maanden kunnen klanten het toestel teruggeven, inruilen voor een nieuwer model of het resterende bedrag betalen om het te houden. Wie twee jaar leaset, heeft dan nog niet de helft van de verkoopprijs afbetaald. Apple presenteert het programma als een manier om zijn producten voor meer mensen bereikbaar te maken, op een moment dat de prijzen stijgen door een tekort aan geheugenchips. CEO Tim Cook zei eerder dat de prijzen daarom omhoog moeten. Ondertussen ging de beurswaarde van Apple dinsdag voor het eerst door de grens van 5 biljoen dollar. Rapportage ransomware 2025 van de Autoriteit Persoonsgegevens AP roept organisaties op te leren van eerdere ransomwareaanvallen Privacywaakhond waarschuwt dat AI cyberaanvallen aanjaagt Apple gaat iPhones leasen vanaf 17,99 dollar via Klarna Voorwaarden en uitgesloten modellen van het programma Apple Upgrade Apple tweede bedrijf ooit met beurswaarde van 5 biljoen dollar Over de maker:Rosanne Peters is techredacteur en maakt De Grote Tech Show en De Technoloog. Sinds 2025 doet ze redactie- en productiewerk en is zij te horen in de Tech Update tijdens De Ochtend- en Avondspits. See omnystudio.com/listener for privacy information.

Mon, 27 Jul 2026 16:45:00 GMT http://relay.fm/upgrade/630 http://relay.fm/upgrade/630 Repeat 600 Times 630 Jason Snell and Myke Hurley It's a Summer of Fun takeover! Jason asks Myke about the state of his current workflows, and we both highlight our current Macs and current favorite Mac utilities. It's a Summer of Fun takeover! Jason asks Myke about the state of his current workflows, and we both highlight our current Macs and current favorite Mac utilities. clean 7806 It's a Summer of Fun takeover! Jason asks Myke about the state of his current workflows, and we both highlight our current Macs and current favorite Mac utilities. This episode of Upgrade is sponsored by: Backblaze: Unlimited, easy data protection. Try it for free today and get 20% off with code upgrade20. Claude: For problems worth solving — get started with Claude today. Sentry: Mobile crash reporting and app monitoring. New users get $100 in Sentry credits with code upgrade26. Links and Show Notes: Our Essential Mac Utilities: Myke: Blip Dropzone Moom Cleanshot X Screens Tailscale Amphetamine  Granola Popclip Pcalc Switchglass  Timery The Clock iStatMenus SuperAgent ⠀ Jason:  Swiftbar Keyboard Maestro Text Sniper Mic Drop Retrobatch ImageOptim Carbon Copy Cloner Thaw Devonthink Calibre Audio Hijack Loopback Farrago Elsewhen Transmit HomeControl iZotope RX Get Upgrade+. More content, no ads. Check out Upgrade merch! Submit Feedback Thunderbolt 5 Dock – CalDigit Quick Tip: Prevent a disk from auto-mounting – Six Colors Cortex - Relay Personal Retreat PDF - MacSparky

I talk with Ron Johnson, creator of the Apple Store and Genius Bar, former J.C. Penney CEO, and author of Shop Different, about what really made Apple's retail strategy work. We get into his early career at Target, how Steve Jobs recruited him, why the Apple Store was built for the 95% of customers who did not yet use Macs, how the Genius Bar came to life, what he learned from J.C. Penney, and why he remains optimistic about AI, work, and the future of retail.

Mon, 27 Jul 2026 16:45:00 GMT http://relay.fm/upgrade/630 http://relay.fm/upgrade/630 Jason Snell and Myke Hurley It's a Summer of Fun takeover! Jason asks Myke about the state of his current workflows, and we both highlight our current Macs and current favorite Mac utilities. It's a Summer of Fun takeover! Jason asks Myke about the state of his current workflows, and we both highlight our current Macs and current favorite Mac utilities. clean 7806 It's a Summer of Fun takeover! Jason asks Myke about the state of his current workflows, and we both highlight our current Macs and current favorite Mac utilities. This episode of Upgrade is sponsored by: Backblaze: Unlimited, easy data protection. Try it for free today and get 20% off with code upgrade20. Claude: For problems worth solving — get started with Claude today. Sentry: Mobile crash reporting and app monitoring. New users get $100 in Sentry credits with code upgrade26. Links and Show Notes: Our Essential Mac Utilities: Myke: Blip Dropzone Moom Cleanshot X Screens Tailscale Amphetamine  Granola Popclip Pcalc Switchglass  Timery The Clock iStatMenus SuperAgent ⠀ Jason:  Swiftbar Keyboard Maestro Text Sniper Mic Drop Retrobatch ImageOptim Carbon Copy Cloner Thaw Devonthink Calibre Audio Hijack Loopback Farrago Elsewhen Transmit HomeControl iZotope RX Get Upgrade+. More content, no ads. Check out Upgrade merch! Submit Feedback Thunderbolt 5 Dock – CalDigit Quick Tip: Prevent a disk from auto-mounting – Six Colors Cortex - Relay Personal Retreat PDF - MacSparky

Ricki's dream guest finally walked through the studio doors as Australia's number one competitive eater, JWebby, joined the show fresh from another podium finish at the Nathan's Famous Hot Dog Eating Contest. He revealed the brutal training behind competitive eating, why cranberry juice beats water during hot dog contests, and what life is really like after setting world records. Then came the main event as Ricki and Tim each tackled one Big Mac while JWebby demolished six before they could finish. Equal parts fascinating, revolting and impossible to look away from.See omnystudio.com/listener for privacy information.

Google Zero continues to affect website after website, and publisher after publisher. David and Nilay discuss Google's increased focus on AI, the web companies finally starting to fight back, and whether there's a way forward for Google's pact with the open web. After that, they talk about a big week in AI detection software, which sometimes works and frequently doesn't. Then it's time for the Hype Desk, Brendan Carr is a dummy, the Light Flip, and more. Further reading: Google hit with $1 billion fine for breaking EU antitrust rules Reddit might cut ties with Google as traffic drops.  From the NYT: How Google's A.I. Search Is Imperiling the Open Web Google says Gemini now has 950 million monthly users.  Google earnings China delivers a one-two punch to America's AI dominance   OpenAI says it accidentally hacked Hugging Face with a new AI system  Substack adds an AI detector to help spot blogs written by no one  Anthropic's $1.5 billion book piracy settlement approved by judge  Jeff Bezos is getting involved with centering Amazon's AI in Prime Video Meta made its own AI detection system. It should have just used Google's  Ford picks Apple Maps for its next EV platform and updated BlueCruise Apple's rumored ‘Upgrade' program brings lease-to-own pricing for iPhones, Macs, and iPads iOS code could reportedly let Apple cut off apps when users miss iPhone payments  Framework's premium laptop is shipping with less RAM  The higher-end prebuilt Framework Laptop 13 Pro now costs $800 more.  The Light Flip is a minimalist flip phone with a point to prove  Judge pauses Paramount's attempt to buy Warner Bros. Discovery  Paramount's Warner deal gets conditional approval in the EU.  Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed. We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. (Timestamps are approximate.) 00:00:00 Intro 00:01:00 Claude Reads the Meters 00:04:00 Google Zero and Reddit Deal 00:15:00 AI Overviews Death Spiral 00:25:00 OpenAI Hacks Hugging Face 00:32:00 Substack AI Detector Debate 00:39:00 Prime Video Goes All In 00:44:00 Meta Content Seal Cynicism 00:53:00 Avengers Doomsday 01:03:00 Comic Con And Feige Reset 01:04:00 Brendan Carr Is a Dummy 01:12:00 Ramageddon Hits Framework 01:17:00 Apple Leasing Lockdown 01:21:00 Light Flip Phone Trend 01:26:00 Ford Picks Apple Maps 01:35:00 Paramount Merger Mess 01:35:00 Wrap Up And Plugs Learn more about your ad choices. Visit podcastchoices.com/adchoices

The Hide My Email failure is real but nowhere near as significant as it's being portrayed, plus Apple is raising so many prices yet looking at ways to make that palatable, plus Foxconn has begun its annual recruitment drive as it begins producing millions of new iPhones, all on the AppleInsider Podcast.Contact your hosts:@williamgallagher_ on Threads@WGallagher on TwitterWilliam's 58keys on YouTubeWilliam Gallagher on emailWes on BlueskyWes Hilliard on emailWes's blog HillitechSponsored by:MasterClass: Get 15% off annual memberships at MasterClass.comLinks from the Show:Seven years after Apple Card, Samsung leaps into fintech with its own credit cardHide My Email class action lawsuit seeks payout without evidence of any attacksIt's easy to find a real email address behind Hide My Email, but it doesn't really matterHide My Email flaw still worked two weeks after Apple's claimed fixA fake Hide My Email header can expose the address behind your Apple Account -- a different thingApple Music gets first U.S. price hike in four yearsIs Apple One worth it in summer 2026?AAPL capitalization squeaks past NVDA, Apple becomes world's most valuable companyApple's iPhone Upgrade Program was great while it lastedApple Upgrades will let users lease iPhones and Macs with KlarnaIt has begun: Foxconn amassing army of workers for iPhone 18 Pro assemblyUpgraded Mac Mini, Mac Studio, OLED iMac readied, release date hazyApple preps MacBook Pro, MacBook Neo refresh in bigger AI pushApple's $634M payment to Masimo now set in stone after Judge tosses appealCaleb Hammer on YouTubeSupport the show:Support the show on Patreon or Apple Podcasts to get ad-free episodes every week, access to our private Discord channel, and early release of the show! We would also appreciate a 5-star rating and review in Apple PodcastsMore AppleInsider podcastsTune in to our HomeKit Insider podcast covering the latest news, products, apps and everything HomeKit related. Subscribe in Apple Podcasts, Overcast, or just search for HomeKit Insider wherever you get your podcasts.Subscribe and listen to our AppleInsider Daily podcast for the latest Apple news Monday through Friday. You can find it on Apple Podcasts, Overcast, or anywhere you listen to podcasts.Those interested in sponsoring the show can reach out to us at: advertising@appleinsider.com (00:00) - Intro (02:49) - HIde My Email (16:07) - Apple Services and price rises (25:56) - Apple Upgrade (42:09) - Masimo ★ Support this podcast on Patreon ★

O convidado especial Adolfo Bernhard junta-se à dupla Rafael Fischmann e Eduardo Marques para, como sempre, debater os temas mais quentes da semana no mundo Apple. No ar! [Edição: Edu Garcia] 00:00:00 Introdução 00:08:25 Apple Music e Apple One ficam mais caros no Brasil e nos EUA 00:12:32 Apple poderá lançar programa de assinatura de iPhones, iPads, Macs e Apple Watches em breve 00:24:55 Apple deverá lançar diversos novos Macs em breve, incluindo Mac mini com os chips M5 Pro e “M6” 00:33:51 Vazamento confirma abertura variável na câmera do "iPhone 18 Pro Max" 00:41:54 Código do iOS 27 faz referência a iPhone com mais de uma bateria 00:54:32 Jason Sudeikis quer que “Ted Lasso” tenha pelo menos mais 2 temporadas 01:05:23 Encerramento

Benjamin and Chance talk about the last week in Apple News, including the rumored new ‘Apple Upgrade' leasing program, Apple Music price hike, iOS 27 beta 4 changes, an OLED screen roadmap for future Macs and iPads, and next-gen Apple Pencils with replaceable batteries. And in Happy Hour Plus, Apple and Ford announce a new partnership to bring Apple Music directly into the car infotainment system. Subscribe at 9to5mac.com/join. Sponsored by Shopify: All you need is the idea. Shopify handles the rest. Start your free trial at shopify.com/happyhour. Sponsored by Framer: The only free design tool that brings your ideas to the web. Visit framer.com/happyhour for 30% off a Framer Pro annual plan. Sponsored by Keeper: Get 60% off personal and family plans at keepersecurity.com/HAPPYHOUR.

Full Show: Salvador Perez Matches George Brett, KC is a SPORTS CITY, Ben Maller, Smash or Pass, Nate Taylor, Good Luck for Macs Wedding full 10734 Thu, 23 Jul 2026 14:59:46 +0000 ljn02qo9pM6WUJEbaFGnUJcUrstleN6i nfl,mlb,kansas city chiefs,tyreek hill,george brett,kansas city royalas,sports Fescoe & Dusty nfl,mlb,kansas city chiefs,tyreek hill,george brett,kansas city royalas,sports Full Show: Salvador Perez Matches George Brett, KC is a SPORTS CITY, Ben Maller, Smash or Pass, Nate Taylor, Good Luck for Macs Wedding Fescoe & Dusty are huge fans of Kansas City sports and the people of Kansas City who make it the great city it is. Start your morning with us from 6-10 as we talk all things Chiefs, Royals, Jayhawks, and more, with plenty of tangents along the way! 2024 © 2021 Audacy, Inc.

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

Ayer hablábamos de Apple Upgrade y hoy llega Mark Gurman con la segunda parte del plan: una cola de Macs nuevos tan larga que se pierde en el horizonte del multiverso de la locura. Te lo cuento en este capítulo 3014.¿Quieres más de Emilcar Daily? Suscríbete a Emilcar Daily Premium desde https://emilcar.fm/daily y disfruta de capítulos exclusivos los lunes y viernes, además de sonido en HD, acceso anticipado y sin publicidad. Todo esto en tu aplicación de podcasts favorita.

Fresh off Macstock, you get the full debrief on this year’s gathering before diving into a stacked round of Quick Tips. You’ll learn how to disable Summarize Notification Previews to stop needless battery churn, clear every unread Apple Watch notification in one shot, and weigh whether it’s finally time to move to macOS Tahoe. There’s a heads-up that Mac minis and Mac Studios are back in Apple’s Refurbished Store, a slick Control-Tilde trick to reveal formulas in Excel, Google Sheets, and LibreOffice, the case for a USB-only cable when you’re heading into DFU recovery mode, and clever moves like stretching a Kindle book loan with an old iPad, building reusable packing-list templates, and scheduling an automatic weekly reboot to keep your Macs running clean. Then you tackle your questions: setting the default audio app on your iPhone, when prepaid mobile data might be the smarter play (and when it’s not!), and a big Don’t Get Caught warning that macOS 28 is dropping support for encrypted HFS+ drives, so decrypt or reformat those external volumes before you upgrade next year. You’ll also hear how Claude Code can become your new macOS troubleshooting partner, including some honest thoughts on leaning on AI to fix your machine. Cool Stuff Found rounds it out with UDM14 for Google results minus the AI summaries, Backdrop for live macOS wallpaper, Clock Rings for tracking time in decimal, and a cheap camera lens cover that works across your MacBook, iPad, and iPhone. As always, come learn at least five new things. 00:00:00 Mac Geek Gab 1151 for Monday, July 20th, 2026 July 20th: National Fortune Cookie Day MGG Monthly Giveaway – Win a license to Mole 00:02:00 Macstock Debrief Some new-for-the-first-time attendees David Pogue Ken Case Ken Ray Paul Conaway Quick Tips 00:00:01 DLH-QT-Disable Summarize Notification Previews to save battery churn 00:06:16 Pilot Pete-QT How to clear all your unread Apple Watch notifications 00:08:34 Is it time to update to Tahoe? 00:09:25 Mac minis and Mac Studios available again in Apple's Refurb Store 00:12:31 Companies safeguarding your data 00:16:46 Marina-QT-Control-Tilde shows the formulas in Excel (and Google Sheets, and LibreOffice, but not Numbers) 00:20:03 Tony-QT-Use a USB-only cable for DFU Recovery Mode 00:21:49 Harvey-QT-Use your old iPad to extend your Kindle Book loan period 00:24:40 Todd-QT-1150-Create Packing List Templates and Sections 00:27:50 Don-QT-Schedule a weekly reboot for your Macs sudo pmset repeat restart U 05:00:00 (“U” means sUnday) 00:33:40 Pilot Pete-QT-Green light Means the Camera is On Sponsors 00:36:56 SPONSOR: Coveron. One scam can cost you everything – use code “macgeekgab” for up to 76% off at https://coveron.com/macgeekgab to safeguard your identity. 00:38:10 SPONSOR: Shopify. If you're ready to stop putting off your business and start selling, sign up for your free trial and start selling today at https://Shopify.com/MGG Your Questions Answered and Tips Shared! 00:39:33 Dave-How can I set the default audio app on my iPhone? 00:45:30 Doug-Why go prepaid for mobile data? US Mobile (allows network flexibility) Get $25 to join America’s Super Carrier, plus get 30 days free when you transfer your number! 00:53:10 Gary-DGC-macOS 28 to drop support for encrypted HFS+ drives 00:58:10 Dave-QT-Use Claude Code for macOS troubleshooting 01:03:57 Some thoughts on Troubleshooting with AI Cool Stuff Found 01:05:35 Mike-CSF-UDM14 to get Google results sans AI summaries 01:09:34 Javier-CSF-1128–Backdrop – Live MacOS Wallpaper 01:11:34 -n-Eric-CSM-Clock Rings to track time in decimal 01:15:39 Pilot Pete-CSF-Camera Lens Cover Macbook, iPad, iPhone 01:18:48 MGG 1151 Outtro MGG Monthly Giveaway Bandwidth Provided by CacheFly Pilot Pete's Aviation Podcast: So There I Was (for Aviation Enthusiasts) The Debut Film Podcast – Adam's new podcast! Dave's Business Brain (for Entrepreneurs) and Gig Gab (for Working Musicians) Podcasts MGG Merch is Available! Mac Geek Gab iOS app Mac Geek Gab YouTube Page Mac Geek Gab Live Calendar This Week's MGG Premium Contributors MGG Apple Podcasts Reviews feedback@macgeekgab.com 224-888-GEEK Active MGG Sponsors and Coupon Codes List BackBeat Media Podcast Network

The latest In Touch With iOS with Dave Ginsburg is joined by Jeff Gamet, Chuck Joiner, Marty Jencius, Jill McKinley, and Eric Bolden to recap an unforgettable MacStock 10 weekend, including standout presentations, the live In Touch With iOS recording, and the community that makes the event special. The panel also discusses Apple's lawsuit against OpenAI, rumors surrounding Apple's smart home strategy, the latest beta releases, LEGO's iMac G3 proposal, Apple Arcade additions, AirPods Pro 3 hearing assistance, and wraps up with favorite gear picks of the week. The show notes are at InTouchwithiOS.com  Direct Link to Audio  Links to our Show Give us a review on Apple Podcasts! CLICK HERE we would really appreciate it! Click this link Buy me a Coffee to support the show we would really appreciate it. intouchwithios.com/coffee  Another way to support the show is to become a Patreon member patreon.com/intouchwithios Website: In Touch With iOS YouTube Channel In Touch with iOS Magazine on Flipboard Facebook Page BlueSky Mastodon X Instagram Threads Summary   Topics and Links Macstock Recap Fresh off an incredible weekend at MacStock, Dave Ginsburg welcomes Jeff Gamet, Chuck Joiner, Marty Jencius, Jill McKinley, and Eric Bolden for an episode filled with Apple news, technology discussions, and reflections on one of the Apple community's favorite annual events. The panel begins by celebrating MacStock's 10th anniversary, sharing behind-the-scenes stories from the live In Touch With iOS recording, Creator Camp, standout presentations, and the friendships that continue to make MacStock a unique experience for Apple users and creators alike. From Jeff Gamet's interactive drawing workshop and Jill McKinley's thoughtful presentation on Focus Modes to Chuck Joiner's productivity session, Marty Jencius' collaborative troubleshooting discussions, and David Pogue's entertaining keynote, the conversation captures the spirit of the conference. The discussion then turns to Apple's latest software betas across iPhone, iPad, Mac, Apple Watch, Apple TV, and Vision Pro before taking a deep dive into one of the week's biggest stories—Apple's lawsuit against OpenAI over allegations involving confidential hardware development. The panel explores the potential implications for Apple Intelligence, future AI products, and the evolving competitive landscape. The conversation continues with rumors surrounding Apple's next-generation smart home products, including a Home Hub, updated Apple TV, HomePod, and connected home accessories, leading to an engaging discussion about the future of HomeKit and Matter. The episode wraps up with updates on Apple Arcade, AppleCare+, LEGO's proposed Bondi Blue iMac G3, Jeff's first impressions of AirPods Pro 3 as hearing assistance, and another great round of Things We Found. In Touch With Vision Pro this week.  Fifth macOS Tahoe 26.6 Beta Now Available for Developers [Update: Public Beta Available] Im-wizard-ar-magic-combat https://apps.apple.com/us/app/im-wizard-ar-magic-combat/id6747723768 Beta this week.  Public Betas are now released iOS 27 iOS 27 and iPadOS 27 Now Available to Public Beta Testers tvOS 27 and watchOS 27 Now Available to Public Beta Testers Apple Releases New iOS 27 AirPods Firmware For Public Beta Testers Apple Seeds Fifth iOS 26.6 and iPadOS 26.6 Betas to Developers [Update: Public Beta Available] In Touch With Mac this week macOS Golden Gate Public Beta: 10 Features to Try First  Fifth macOS Tahoe 26.6 Beta Now Available for Developers [Update: Public Beta Available] LEGO Considering Bondi Blue iMac G3 Set Other Topics Open AI lawsuit Apple Sues OpenAI for Stealing Trade Secrets to Build AI Hardware OpenAI: No Evidence Apple's Trade Secret Complaint Has Merit OpenAI's First AI Device Will Be a Portable Smart Speaker Apple's 2026 Smart Home Lineup: New Apple TV, HomePod, and Home Hub WIll these be released? News Madden NFL 27 Arcade Edition Coming to Apple Arcade The last full Madden game released for the Mac was Madden NFL 08, released for Mac OS X on September 1, 2007 AppleCare+ for Macs and iPads Just Got More Expensive This weeks "Things we found"  Jeff: AirPods Pro 3 as an assistive hearing device Dave: Spigen for MagSafe Wallet Magnetic Pouch: https://amzn.to/4ftlyky Marty:Anker Prime Foldable Magsafe Charger  https://amzn.to/4bqLWdq Eric: Gray (grey) card, white card, black card for white balance https://amzn.to/3RfPgRU Marty mentioned Osbot Tiny 3 Lite Webcam https://amzn.to/4b1TNhp Jill: Hollyland Lark M2S Microphone Combo Kit https://amzn.to/4hizJuY Chuck: CROWN SHADES 10x10 Pop Up Canopy Tent https://amzn.to/4aVUEAm Announcements Macstock X wrapped up for 2026 what a great event! Stay tuned for the Digital pass coming soon Digital Pass | Macstock Conference & Expo Our Host Dave Ginsburg is an IT professional supporting Mac, iOS and Windows users and shares his wealth of knowledge of iPhone, iPad, Apple Watch, Apple TV and related technologies. Visit the YouTube channel https://youtube.com/intouchwithios follow him on Mastodon @daveg65, , BlueSky @daveg65  and the show @intouchwithios   Our Regular Contributors Jeff Gamet is a podcaster, technology blogger, artist, and author. Previously, he was The Mac Observer's managing editor, and Smile's TextExpander Evangelist. You can find him on his blog Jeff Gamet - Mastadon @jgamet Pixelfed @jgamet@pixelfed.social and Bluesky @jgamet.bsky.social‬ Podcasts The Context Machine Podcast  Retro Rewatch Retro Rewatch His YouTube channel https://youtube.com/jgamet Buy his art! Jeff Gamet Art Marty Jencius, Ph.D., is a professor of counselor education at Kent State University, where he researches, writes, and trains about using technology in teaching and mental health practice. His podcasts include Vision Pro Files, The Tech Savvy Professor and Circular Firing Squad Podcast. Find him at jencius@mastodon.social  https://thepodtalk.net  Eric Bolden is into macOS, plants, sci-fi, food, and is a rural internet supporter. You can connect with him by email at eabolden@mac.com, on Mastodon at @eabolden@techhub.social, on his blog, Trending At Work, and as co-host on The Vision ProFiles podcast.   Jill McKinley works in enterprise software, server administration, and IT A lifelong tech enthusiast, she started her career with Windows but is now an avid Apple fan. Beyond technology, she shares her insights on nature, faith, and personal growth through her podcasts—Buzz Blossom & Squeak, Start with Small Steps, and The Bible in Small Steps. Watch her content on YouTube at @startwithsmallsteps and follow her on X @schmern. Find all her work at http://jillfromthenorthwoods.com  Chuck Joiner is the host of MacVoices and hosts video podcasts with influential members of the Apple community. Make sure to visit macvoices.com and subscribe to his podcast. You can follow him on Twitter @chuckjoiner and join his MacVoices Facebook group. Guy Serle is one of the hosts of the new The Gmen Show along with GazMaz and email GMenshow@icloud.com  @MacParrot and @VertShark on X  Vertshark on YouTube, Google Voice +1 Area code  703-828-4677

The second half of our streaming guide moves from services to the boxes, sticks, smart TVs, antennas, and oddball gadgets that actually get video onto your screen.Roku remains one of the most affordable and widely supported streaming hardware options, with both boxes and sticks available. It began as the original Netflix streamer, but today its biggest advantages are interface preference, ecosystem familiarity, and broad app support.Fire TV sticks and boxes make the most sense for people already deep in Amazon's ecosystem, especially if they use Prime Video and add-on channels heavily. The Fire TV Cube also gets praise as an easy “hotel-like” setup for guests, with strong voice-control features.Apple TV is the easiest recommendation for people already using iPhones, Macs, and iPads. Its ecosystem integration makes passwords, purchases, authentication, and playback smoother, and older Apple TVs can remain useful for years.Chromecast is gone as a hardware brand, replaced by the Google TV Streamer. It is a good fit for people invested in Google, YouTube, YouTube TV, and Android, though Google's TV hardware strategy has been less consistent than Roku, Fire TV, or Apple TV.The NVIDIA Shield TV remains the power-user option for gamers, tinkerers, VPN users, and people who want more control over their streaming box. It has not been substantially updated since 2019, but it still works well, even if it is starting to show its age.Game consoles can still stream video, but Sony and Microsoft have mostly moved away from treating them as primary TV boxes. The crew also gives a farewell nod to retired or fading hardware like TiVo and Microsoft's old TV ambitions.The Xumo Stream Box from Comcast and Charter may be worth considering for internet subscribers who can get it free or discounted. It can replace some traditional cable-box use cases while still offering access to streaming apps.Many viewers simply use the apps built into their smart TVs, and that can work fine as long as performance holds up. Samsung's Tizen, LG's webOS, Roku TV, Fire TV, Google TV, Vizio's SmartCast, and TiVo OS all get a rundown, with the main advice being to consider long-term speed, app support, and whether you may eventually want a separate streaming box.For over-the-air broadcast TV, devices like HDHomeRun and Tablo can turn an antenna into a networked live-TV and DVR system. Amos explains his HDHomeRun Quattro setup feeding Plex for local DVR recordings like Saturday Night Live, NASCAR, major sports, and local news during severe weather.Amos also points out that Alaska's over-the-air TV can sometimes be delayed or degraded because local signals are rebroadcast by satellite. That means streaming apps can occasionally be ahead of the antenna feed, proving once again that every “simple” TV setup has an exception hiding somewhere.The crew closes by talking about how they actually choose what to watch now that almost everything is available somewhere. Brian compares modern TV to a book-club experience, Tom breaks down different household viewing use cases, and Amos describes his viewing as deliberate, focused, and ad-free whenever possible.Website: https://cordkillers.comEmail: cordkillers@gmail.comLive Tuesdays at 5:30 PM Eastern / 2:30 PM Pacific: https://twitch.tv/nightattack https://youtube.com/modernrogue Hosted on Acast. See acast.com/privacy for more information.

The second half of our streaming guide moves from services to the boxes, sticks, smart TVs, antennas, and oddball gadgets that actually get video onto your screen.Roku remains one of the most affordable and widely supported streaming hardware options, with both boxes and sticks available. It began as the original Netflix streamer, but today its biggest advantages are interface preference, ecosystem familiarity, and broad app support.Fire TV sticks and boxes make the most sense for people already deep in Amazon's ecosystem, especially if they use Prime Video and add-on channels heavily. The Fire TV Cube also gets praise as an easy “hotel-like” setup for guests, with strong voice-control features.Apple TV is the easiest recommendation for people already using iPhones, Macs, and iPads. Its ecosystem integration makes passwords, purchases, authentication, and playback smoother, and older Apple TVs can remain useful for years.Chromecast is gone as a hardware brand, replaced by the Google TV Streamer. It is a good fit for people invested in Google, YouTube, YouTube TV, and Android, though Google's TV hardware strategy has been less consistent than Roku, Fire TV, or Apple TV.The NVIDIA Shield TV remains the power-user option for gamers, tinkerers, VPN users, and people who want more control over their streaming box. It has not been substantially updated since 2019, but it still works well, even if it is starting to show its age.Game consoles can still stream video, but Sony and Microsoft have mostly moved away from treating them as primary TV boxes. The crew also gives a farewell nod to retired or fading hardware like TiVo and Microsoft's old TV ambitions.The Xumo Stream Box from Comcast and Charter may be worth considering for internet subscribers who can get it free or discounted. It can replace some traditional cable-box use cases while still offering access to streaming apps.Many viewers simply use the apps built into their smart TVs, and that can work fine as long as performance holds up. Samsung's Tizen, LG's webOS, Roku TV, Fire TV, Google TV, Vizio's SmartCast, and TiVo OS all get a rundown, with the main advice being to consider long-term speed, app support, and whether you may eventually want a separate streaming box.For over-the-air broadcast TV, devices like HDHomeRun and Tablo can turn an antenna into a networked live-TV and DVR system. Amos explains his HDHomeRun Quattro setup feeding Plex for local DVR recordings like Saturday Night Live, NASCAR, major sports, and local news during severe weather.Amos also points out that Alaska's over-the-air TV can sometimes be delayed or degraded because local signals are rebroadcast by satellite. That means streaming apps can occasionally be ahead of the antenna feed, proving once again that every “simple” TV setup has an exception hiding somewhere.The crew closes by talking about how they actually choose what to watch now that almost everything is available somewhere. Brian compares modern TV to a book-club experience, Tom breaks down different household viewing use cases, and Amos describes his viewing as deliberate, focused, and ad-free whenever possible.Website: https://cordkillers.comEmail: cordkillers@gmail.comLive Tuesdays at 5:30 PM Eastern / 2:30 PM Pacific: https://twitch.tv/nightattack https://youtube.com/modernrogue Hosted on Acast. See acast.com/privacy for more information.

The second half of our streaming guide moves from services to the boxes, sticks, smart TVs, antennas, and oddball gadgets that actually get video onto your screen.Roku remains one of the most affordable and widely supported streaming hardware options, with both boxes and sticks available. It began as the original Netflix streamer, but today its biggest advantages are interface preference, ecosystem familiarity, and broad app support.Fire TV sticks and boxes make the most sense for people already deep in Amazon's ecosystem, especially if they use Prime Video and add-on channels heavily. The Fire TV Cube also gets praise as an easy “hotel-like” setup for guests, with strong voice-control features.Apple TV is the easiest recommendation for people already using iPhones, Macs, and iPads. Its ecosystem integration makes passwords, purchases, authentication, and playback smoother, and older Apple TVs can remain useful for years.Chromecast is gone as a hardware brand, replaced by the Google TV Streamer. It is a good fit for people invested in Google, YouTube, YouTube TV, and Android, though Google's TV hardware strategy has been less consistent than Roku, Fire TV, or Apple TV.The NVIDIA Shield TV remains the power-user option for gamers, tinkerers, VPN users, and people who want more control over their streaming box. It has not been substantially updated since 2019, but it still works well, even if it is starting to show its age.Game consoles can still stream video, but Sony and Microsoft have mostly moved away from treating them as primary TV boxes. The crew also gives a farewell nod to retired or fading hardware like TiVo and Microsoft's old TV ambitions.The Xumo Stream Box from Comcast and Charter may be worth considering for internet subscribers who can get it free or discounted. It can replace some traditional cable-box use cases while still offering access to streaming apps.Many viewers simply use the apps built into their smart TVs, and that can work fine as long as performance holds up. Samsung's Tizen, LG's webOS, Roku TV, Fire TV, Google TV, Vizio's SmartCast, and TiVo OS all get a rundown, with the main advice being to consider long-term speed, app support, and whether you may eventually want a separate streaming box.For over-the-air broadcast TV, devices like HDHomeRun and Tablo can turn an antenna into a networked live-TV and DVR system. Amos explains his HDHomeRun Quattro setup feeding Plex for local DVR recordings like Saturday Night Live, NASCAR, major sports, and local news during severe weather.Amos also points out that Alaska's over-the-air TV can sometimes be delayed or degraded because local signals are rebroadcast by satellite. That means streaming apps can occasionally be ahead of the antenna feed, proving once again that every “simple” TV setup has an exception hiding somewhere.The crew closes by talking about how they actually choose what to watch now that almost everything is available somewhere. Brian compares modern TV to a book-club experience, Tom breaks down different household viewing use cases, and Amos describes his viewing as deliberate, focused, and ad-free whenever possible.Website: https://cordkillers.comEmail: cordkillers@gmail.comLive Tuesdays at 5:30 PM Eastern / 2:30 PM Pacific: https://twitch.tv/nightattack https://youtube.com/modernrogue Hosted on Acast. See acast.com/privacy for more information.

- IDC: Apple Was Only Computer Maker to Grow Share in Second Quarter - Omdia: Sub-$400 Smartphones to Shrink by 22% in 2026 - Cook, Cue, and Ternus Turn Up at Billionaire Summer Camp - Lamborghini Launches Apple Vision Pro App - Apple TV Sets "Nocturne" for Late October Premier - Sponsored by Helix Sleep: Get 20% off Sitewide, 25% off Luxe Mattresses, and 30% off Elite Mattresses at helixsleep.com/macosken - Sponsored by Notion: Learn more about Notion's Developer Platform today at notion.com/macosken - Catch Ken on Mastodon - @macosken@mastodon.social - Send Ken an email: info@macosken.com - Chat with us on Patreon for as little as $1 a month. Support the show at Patreon.com/macosken

Thu, 09 Jul 2026 19:15:00 GMT http://relay.fm/connected/611 http://relay.fm/connected/611 My Skin is Not Good for That 611 Federico Viticci, Stephen Hackett, and Myke Hurley Federico is at the beach, Myke questions his iPhone's future, and Stephen wears watches. Then: a whole stack of Q&A items. Federico is at the beach, Myke questions his iPhone's future, and Stephen wears watches. Then: a whole stack of Q&A items. clean 5222 Subtitle: His Skin Seems to Want ItFederico is at the beach, Myke questions his iPhone's future, and Stephen wears watches. Then: a whole stack of Q&A items. This episode of Connected is sponsored by: Sentry: Mobile crash reporting and app monitoring. New users get $100 in Sentry credits with code connected26. Links and Show Notes: Get 15% off a new annual Connected Pro subscription (or reactivate an expired one!) with coupon code tinyheads15 between now and July 31. Get Connected Pro: Preshow, postshow, no ads. Submit Feedback Stephen Hackett: "Marked Safe From…" - eworld.social An Update to My Apple History Calendar - 512 Pixels iPhone 18 Pro Could Be Noticeably Thicker Than iPhone 17 Pro - MacRumors iPhone 18 Pro Max Said to Be Thicker and Heavier Than Predecessor - MacRumors ArtfulType, a Markdown Editor for 68k Macs - 512 Pixels I Made AI Write 1980's Macintosh Software - Action Retro - YouTube Mode Designs Kinda Funny Games - YouTube NPC: Next Portable Console and NPC XL - MacStories Mac Power Users - Relay The Rest Is Entertainment The Town With Matthew Belloni - The Ringer The Vergecast Waveform Acquired THIS CAR POD! - YouTube Designed in California Myke's Chart in Asana Casio GW-M5610U-1 DDC-200 Timex × DDC “Standard Issue Scout Watch – Draplin Design Co.

Thu, 09 Jul 2026 19:15:00 GMT http://relay.fm/connected/611 http://relay.fm/connected/611 Federico Viticci, Stephen Hackett, and Myke Hurley Federico is at the beach, Myke questions his iPhone's future, and Stephen wears watches. Then: a whole stack of Q&A items. Federico is at the beach, Myke questions his iPhone's future, and Stephen wears watches. Then: a whole stack of Q&A items. clean 5222 Subtitle: His Skin Seems to Want ItFederico is at the beach, Myke questions his iPhone's future, and Stephen wears watches. Then: a whole stack of Q&A items. This episode of Connected is sponsored by: Sentry: Mobile crash reporting and app monitoring. New users get $100 in Sentry credits with code connected26. Links and Show Notes: Get 15% off a new annual Connected Pro subscription (or reactivate an expired one!) with coupon code tinyheads15 between now and July 31. Get Connected Pro: Preshow, postshow, no ads. Submit Feedback Stephen Hackett: "Marked Safe From…" - eworld.social An Update to My Apple History Calendar - 512 Pixels iPhone 18 Pro Could Be Noticeably Thicker Than iPhone 17 Pro - MacRumors iPhone 18 Pro Max Said to Be Thicker and Heavier Than Predecessor - MacRumors ArtfulType, a Markdown Editor for 68k Macs - 512 Pixels I Made AI Write 1980's Macintosh Software - Action Retro - YouTube Mode Designs Kinda Funny Games - YouTube NPC: Next Portable Console and NPC XL - MacStories Mac Power Users - Relay The Rest Is Entertainment The Town With Matthew Belloni - The Ringer The Vergecast Waveform Acquired THIS CAR POD! - YouTube Designed in California Myke's Chart in Asana Casio GW-M5610U-1 DDC-200 Timex × DDC “Standard Issue Scout Watch – Draplin Design Co.

The core structural shift affecting MSPs and IT service providers is a market bifurcation, where the traditional middle-ground offering—an undifferentiated blend of hardware and support—no longer matches client buying behavior. Dave Sobel referenced research from Techisle, which underscores a split between buyers seeking high-touch, managed outcomes and those opting for low-cost, self-serve technology tools. This division is further exacerbated by increasing component costs and external pressures on hardware pricing, particularly the rapidly escalating prices for memory and storage. Supporting data comes from a recent analysis of approximately 3,000 MSP websites conducted by Business of Tech. The scan found that 68% of MSPs make no mention of AI in their public-facing materials, with only about 1 in 7 offering a defined AI service. Simultaneously, reporting from both Business Insider and E2E reveals that 90% of businesses already have employees using AI tools—primarily adopted independently rather than through formal provider channels. This disconnect highlights a lag in MSP market positioning relative to how technology is actually being acquired and implemented by clients. Additional market stresses are introduced by rising hardware costs linked directly to shortages in memory and storage components. Apple's price increases for Macs and iPads serve as a tangible example, justified by upstream cost spikes in DRAM, which CNBC reported has increased nearly 9x—from approximately $35 to $300 per module. Further, AI data center buildouts are projected to divert up to 20% of consumer memory manufacturing by 2027, suggesting ongoing and intensifying cost pressures for MSPs still reliant on hardware-centric business models. Most providers, as observed by Dave Sobel, remain silent or default to restating the value of external AI platforms like Microsoft Copilot. The practical implication for MSPs and IT service providers is a pressing need to reassess positioning and operational models. Providers embedded in the undifferentiated middle face rising cost risk, declining differentiation, and potential margin erosion. Viable paths require declaring and operationalizing a clear service model, either by transparently externalizing hardware and component pricing risk, or by committing to outcome-based, managed offerings where the provider takes on measurable accountability. Those who adapt agreements and marketing to clarify their role—particularly by documenting internal AI-driven efficiencies—will be better equipped to sustain margin and client relevance as market forces continue to widen the gap. 00:00 Two-Thirds of MSPs Are Silent  04:37 The Memory Shock Splitting the Market 07:05 No Buyer Left in the Middle 10:41 Why Do We Care?  Supported by:  CometBackup ScalePad   

The Road to Macstock wraps up with organizer Mike Potter providing last-minute updates for attendees, including his Creator Camp session on why the Mac still matters and a weekend talk about giving old Macs new life. He explains the event's themed schedule, hotel room situation, late registration options, and what to bring. Mike also teases this year's T-shirt design details, talks about Macstock swag advice, and the value of curiosity, community, and conversation as a highlight of the event.  This MacVoices is supported by our new MacVoices series, Foreshadowing Tech, that examines the effect of media depictions of tech and tech culture on real-world tech, and how real-world has affected technology depictions in the media. Check it out at http://macvoices.com/foreshadowingtech. Show Notes: Chapters: 00:00 Wrapping Up the Road to MacStock01:36 Mike Potter's Own Sessions02:27 Why the Mac Still Matters05:44 MacStock Themes and Weekend Structure07:30 New Life for Old Macs11:55 Security and Unsupported Hardware16:30 Hotel Room Block and Lodging Options18:52 Late Registration and Day-Of Signups20:17 What Attendees Should Bring23:52 T-Shirt Design and Collectibles27:53 Packing Room for Swag28:33 Final Advice for First-Time Attendees30:26 MacStock's Community Spirit32:05 Final Invitation to Crystal Lake Links: Macstock Conference:MacStockConference.com Guests: Michael Potter is the Executive Producer of For Mac Eyes Only, and the organizer of the annual Macstock Conference and Expo. Mike's love-affair for all things Apple began in his Junior High's Library playing Lemonade Stand on a pair of brand new Apple ][+ computers. His penchant for Apple gear continued to be nurtured by the public school system when, in High School, he was hired as a lab supervisor to help run the Apple ][e lab for his fellow students and their Print Shop needs. Then, further still, in college he often opted to help a friend with her Computer Graphics coursework instead of focusing on his own studies, but only because it helped get him closer to the Mac-lab. Support:      Become a MacVoices Patron on Patreon     http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:     http://macvoices.com      Twitter:     http://www.twitter.com/chuckjoiner     http://www.twitter.com/macvoices      Mastodon:     https://mastodon.cloud/@chuckjoiner      Facebook:     http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:     http://www.facebook.com/macvoices/      MacVoices Group on Facebook:     http://www.facebook.com/groups/macvoice      LinkedIn:     https://www.linkedin.com/in/chuckjoiner/      Instagram:     https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes     Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

The Road to Macstock wraps up with organizer Mike Potter providing last-minute updates for attendees, including his Creator Camp session on why the Mac still matters and a weekend talk about giving old Macs new life. He explains the event's themed schedule, hotel room situation, late registration options, and what to bring. Mike also teases this year's T-shirt design details, talks about Macstock swag advice, and the value of curiosity, community, and conversation as a highlight of the event.  This MacVoices is supported by our new MacVoices series, Foreshadowing Tech, that examines the effect of media depictions of tech and tech culture on real-world tech, and how real-world has affected technology depictions in the media. Check it out at http://macvoices.com/foreshadowingtech. Show Notes: Chapters: 00:00 Wrapping Up the Road to MacStock 01:36 Mike Potter's Own Sessions 02:27 Why the Mac Still Matters 05:44 MacStock Themes and Weekend Structure 07:30 New Life for Old Macs 11:55 Security and Unsupported Hardware 16:30 Hotel Room Block and Lodging Options 18:52 Late Registration and Day-Of Signups 20:17 What Attendees Should Bring 23:52 T-Shirt Design and Collectibles 27:53 Packing Room for Swag 28:33 Final Advice for First-Time Attendees 30:26 MacStock's Community Spirit 32:05 Final Invitation to Crystal Lake Links: Macstock Conference: MacStockConference.com Guests: Michael Potter is the Executive Producer of For Mac Eyes Only, and the organizer of the annual Macstock Conference and Expo. Mike's love-affair for all things Apple began in his Junior High's Library playing Lemonade Stand on a pair of brand new Apple ][+ computers. His penchant for Apple gear continued to be nurtured by the public school system when, in High School, he was hired as a lab supervisor to help run the Apple ][e lab for his fellow students and their Print Shop needs. Then, further still, in college he often opted to help a friend with her Computer Graphics coursework instead of focusing on his own studies, but only because it helped get him closer to the Mac-lab. Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

"Don't wait for IT issues to arise." Connect With Our SponsorsSolventum - https://go.solventum.com/clarityGreyFinch - https://greyfinch.com/jillallen/A-Dec - https://www.a-dec.com/orthodonticsSmileSuite - https://getsmilesuite.com/ Summary In this episode of Hey Docs!, Jill interviews Jack Wiley from Legend Networking about the critical role of IT in orthodontic and dental practices. They discuss the importance of maintaining updated technology, especially for established practices, and the risks associated with outdated systems. Jack emphasizes the need for younger doctors to consider technology when acquiring practices and provides insights on building a strong technology foundation for startups. The conversation also touches on the challenges of HIPAA compliance and the necessity of ongoing employee training in cybersecurity. Jill and Jack also discuss the critical aspects of cybersecurity, backup strategies, and the differences between Mac and PC usage in healthcare settings. The discussion also touches on the distinctions between managed services and static IT, highlighting the benefits of a proactive IT strategy.  Connect With Our Guest Legend Networking - https://www.legendnt.com/https://www.cyber.mil/cyber-awareness-challenge Takeaways Jack Wiley works at Legend Networking, a dental-specific IT provider.IT infrastructure is crucial for the growth of practices.Many established practices operate on outdated technology, risking patient data.Younger doctors often overlook technology when acquiring practices.Evaluating the age of computers and network equipment is essential during acquisitions.Startups should prioritize hardwired connections over wireless for reliability.Planning for future growth in technology is vital for new practices.HIPAA compliance is an ongoing challenge for orthodontic practices.Regular technology evaluations can prevent costly upgrades later. Employee training is often the cause of network vulnerabilities.Backups should be incremental and frequent for quick recovery.Macs can be secure but require specific considerations.Proactive IT management prevents costly downtime.Regular checkups on IT systems can reveal vulnerabilities.Chapters 00:00 Introduction04:55 Aging Hardware Risks08:31 Acquisition IT Checklist14:45 Startup Tech Foundation23:30 Security and HIPAA Basics26:32 Backups and Access Control28:15 Mac vs PC in Ortho31:43 WiFi Segmentation Story33:35 Managed IT Explained36:23 How to Contact Legend Networking Episode Credits:  Hosted by Jill AllenProduced by Jordann KillionAudio Engineering by Garrett LuceroAre you ready to start a practice of your own? Do you need a fresh set of eyes or some advice in your existing practice?Reach out to me- www.practiceresults.com.    If you like what we are doing here on Hey Docs! and want to hear more of this awesome content, give us a 5-star Rating on your preferred listening platform and subscribe to our show so you never miss an episode.    New episodes drop every Thursday!   

Windows 10's Extended Security Update program quietly gets extended for another year for consumers. Microsoft reportedly kills Surface Go products & is now selling 8 GB Surface Pro & Laptop models. And Xbox Series X & S prices are going to go up again. Windows Microsoft quietly extends the Windows 10 Extended Security Update program one year to October 2027 for consumers Windows Insider Windows Update is transitioning to the new Windows Insider experience by default Plus, five new builds across Beta, Experimental, Beta (26H1), Experimental (26H1), and Experimental (Future Platforms) — new Taskbar size setting is the much-needed new feature Hardware Apple raises prices on Macs, iPads, and more, easing pressure on PC makers Microsoft quietly begins selling 8 GB Surface Pro/Laptop models Microsoft reportedly kills Surface Go products right when we need them the most The ASUS Zenbook A16 is nearly perfect, but it's unclear what you get with an X2 Elite Extreme chip AI HP partners with OpenAI for its agentic makeover Anthropic seizes on the "good enough" AI movement with Sonnet 5 Proton Lumo 2.0 is here Gemini personalized image creator is available for free in the US Notion blames agentic AI for it killing Notion Mail Xbox and Gaming The 2026 Doom and Gloom Watch Xbox Series X|S prices to go up again, by $150, on August 1 - and the 2 TB X is going away Undead Labs and Arkane Lyon possible victims of pending closures Latest rumor: Microsoft to layoff 2.5 percent of workforce next week - that's 5,500 people, less than expected, but that's because of the earlier voluntary buyouts, which apparently met internal expectations Minecraft Bedrock edition gets closed captions Sony will stop selling PS physical media in 2028 Tips and Picks Tip of the week: Help or get out of the way People who just complain aren't solving problems, they're just making noise and distracting us from the real problems. App Pick of the Week: Snapdragon Control Panel If you have a Windows 11 on Arm PC on Snapdragon X or X2, you need this app to make games run as well as possible. Plus - Settings > System > Display > Graphics for Auto SR and other settings and whatever is in each game RunAs Radio this week: AI-Accelerated Supply Chain Attacks with Mackenzie Duncan Brown liquor pick of the week: Rupert's Exceptional Canadian Whisky 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: blackhat.com/us-26 and use code TWIT zscaler.com/security cohesity.com/Resilience

Windows 10's Extended Security Update program quietly gets extended for another year for consumers. Microsoft reportedly kills Surface Go products & is now selling 8 GB Surface Pro & Laptop models. And Xbox Series X & S prices are going to go up again. Windows Microsoft quietly extends the Windows 10 Extended Security Update program one year to October 2027 for consumers Windows Insider Windows Update is transitioning to the new Windows Insider experience by default Plus, five new builds across Beta, Experimental, Beta (26H1), Experimental (26H1), and Experimental (Future Platforms) — new Taskbar size setting is the much-needed new feature Hardware Apple raises prices on Macs, iPads, and more, easing pressure on PC makers Microsoft quietly begins selling 8 GB Surface Pro/Laptop models Microsoft reportedly kills Surface Go products right when we need them the most The ASUS Zenbook A16 is nearly perfect, but it's unclear what you get with an X2 Elite Extreme chip AI HP partners with OpenAI for its agentic makeover Anthropic seizes on the "good enough" AI movement with Sonnet 5 Proton Lumo 2.0 is here Gemini personalized image creator is available for free in the US Notion blames agentic AI for it killing Notion Mail Xbox and Gaming The 2026 Doom and Gloom Watch Xbox Series X|S prices to go up again, by $150, on August 1 - and the 2 TB X is going away Undead Labs and Arkane Lyon possible victims of pending closures Latest rumor: Microsoft to layoff 2.5 percent of workforce next week - that's 5,500 people, less than expected, but that's because of the earlier voluntary buyouts, which apparently met internal expectations Minecraft Bedrock edition gets closed captions Sony will stop selling PS physical media in 2028 Tips and Picks Tip of the week: Help or get out of the way People who just complain aren't solving problems, they're just making noise and distracting us from the real problems. App Pick of the Week: Snapdragon Control Panel If you have a Windows 11 on Arm PC on Snapdragon X or X2, you need this app to make games run as well as possible. Plus - Settings > System > Display > Graphics for Auto SR and other settings and whatever is in each game RunAs Radio this week: AI-Accelerated Supply Chain Attacks with Mackenzie Duncan Brown liquor pick of the week: Rupert's Exceptional Canadian Whisky 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: blackhat.com/us-26 and use code TWIT zscaler.com/security cohesity.com/Resilience

Windows 10's Extended Security Update program quietly gets extended for another year for consumers. Microsoft reportedly kills Surface Go products & is now selling 8 GB Surface Pro & Laptop models. And Xbox Series X & S prices are going to go up again. Windows Microsoft quietly extends the Windows 10 Extended Security Update program one year to October 2027 for consumers Windows Insider Windows Update is transitioning to the new Windows Insider experience by default Plus, five new builds across Beta, Experimental, Beta (26H1), Experimental (26H1), and Experimental (Future Platforms) — new Taskbar size setting is the much-needed new feature Hardware Apple raises prices on Macs, iPads, and more, easing pressure on PC makers Microsoft quietly begins selling 8 GB Surface Pro/Laptop models Microsoft reportedly kills Surface Go products right when we need them the most The ASUS Zenbook A16 is nearly perfect, but it's unclear what you get with an X2 Elite Extreme chip AI HP partners with OpenAI for its agentic makeover Anthropic seizes on the "good enough" AI movement with Sonnet 5 Proton Lumo 2.0 is here Gemini personalized image creator is available for free in the US Notion blames agentic AI for it killing Notion Mail Xbox and Gaming The 2026 Doom and Gloom Watch Xbox Series X|S prices to go up again, by $150, on August 1 - and the 2 TB X is going away Undead Labs and Arkane Lyon possible victims of pending closures Latest rumor: Microsoft to layoff 2.5 percent of workforce next week - that's 5,500 people, less than expected, but that's because of the earlier voluntary buyouts, which apparently met internal expectations Minecraft Bedrock edition gets closed captions Sony will stop selling PS physical media in 2028 Tips and Picks Tip of the week: Help or get out of the way People who just complain aren't solving problems, they're just making noise and distracting us from the real problems. App Pick of the Week: Snapdragon Control Panel If you have a Windows 11 on Arm PC on Snapdragon X or X2, you need this app to make games run as well as possible. Plus - Settings > System > Display > Graphics for Auto SR and other settings and whatever is in each game RunAs Radio this week: AI-Accelerated Supply Chain Attacks with Mackenzie Duncan Brown liquor pick of the week: Rupert's Exceptional Canadian Whisky 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: blackhat.com/us-26 and use code TWIT zscaler.com/security cohesity.com/Resilience

Windows 10's Extended Security Update program quietly gets extended for another year for consumers. Microsoft reportedly kills Surface Go products & is now selling 8 GB Surface Pro & Laptop models. And Xbox Series X & S prices are going to go up again. Windows Microsoft quietly extends the Windows 10 Extended Security Update program one year to October 2027 for consumers Windows Insider Windows Update is transitioning to the new Windows Insider experience by default Plus, five new builds across Beta, Experimental, Beta (26H1), Experimental (26H1), and Experimental (Future Platforms) — new Taskbar size setting is the much-needed new feature Hardware Apple raises prices on Macs, iPads, and more, easing pressure on PC makers Microsoft quietly begins selling 8 GB Surface Pro/Laptop models Microsoft reportedly kills Surface Go products right when we need them the most The ASUS Zenbook A16 is nearly perfect, but it's unclear what you get with an X2 Elite Extreme chip AI HP partners with OpenAI for its agentic makeover Anthropic seizes on the "good enough" AI movement with Sonnet 5 Proton Lumo 2.0 is here Gemini personalized image creator is available for free in the US Notion blames agentic AI for it killing Notion Mail Xbox and Gaming The 2026 Doom and Gloom Watch Xbox Series X|S prices to go up again, by $150, on August 1 - and the 2 TB X is going away Undead Labs and Arkane Lyon possible victims of pending closures Latest rumor: Microsoft to layoff 2.5 percent of workforce next week - that's 5,500 people, less than expected, but that's because of the earlier voluntary buyouts, which apparently met internal expectations Minecraft Bedrock edition gets closed captions Sony will stop selling PS physical media in 2028 Tips and Picks Tip of the week: Help or get out of the way People who just complain aren't solving problems, they're just making noise and distracting us from the real problems. App Pick of the Week: Snapdragon Control Panel If you have a Windows 11 on Arm PC on Snapdragon X or X2, you need this app to make games run as well as possible. Plus - Settings > System > Display > Graphics for Auto SR and other settings and whatever is in each game RunAs Radio this week: AI-Accelerated Supply Chain Attacks with Mackenzie Duncan Brown liquor pick of the week: Rupert's Exceptional Canadian Whisky 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: blackhat.com/us-26 and use code TWIT zscaler.com/security cohesity.com/Resilience

In This Episode: This week the TEH Podcast is hosted by Leo Notenboom, the “Chief Question Answerer” at Ask Leo!, and Gary Rosenzweig, the host and producer of MacMost, and mobile game developer at Clever Media. (You’ll find longer Bios on the Hosts page.) Top Stories 0:00 LN: Windows Quickies: Windows 10 ESU 1:46 Windows 11 26H2 3:03 GR: Apple price increases 4:11 RAM shortages 12:30 GR: ClickFix attacks (https://hackmag.com/news/gizmodo-clickfix) Macs targeted too Apple fixed some parts, so switching tactics (Script Editor) https://www.bitdefender.com/en-us/blog/hotforsecurity/the-clickfix-scam-infect-your-own-mac  16:00 Why get more complex when simple keeps working? 17:00 LN: https://askleo.com/tip-of-the-day-captchas-never-use-start-run/ (For my Tip of the Day subscribers) 21:00 LN: Today in AI: flooded with ads for a back relief product, I asked Claude: “is this real?” – the answer was genuinely helpful. – https://claude.ai/share/be7e88cc-ccca-4ebd-b0b4-6187eb510584  25:00 GR: Mark Twain poison medicine. 27:40 Bonus for fun, what's the English for 14,016,833,949,999,999,673,980,362,756,575,985,664 (38 digits)? – 20 character password made up of letters, numbers, and 10 different special characters. 20^72 14 undecillion, 016 decillion, 833 nonillion, 949 octillion, 999 septillion, 999 sextillion, 673 quintillion, 980 quadrillion, 362 trillion, 756 billion, 575 million, 985 thousand, 664. 31:40 GR: The lucrative market for fake dashboards (https://www.404media.co/how-i-bought-a-private-jet-by-selling-10-subscriptions-to-404-media/?ref=daily-stories-newsletter)  Ain’t it Cool 35:00 LN: Dungeon Crawler Carl – with caveats 39:20 GR: Widow's Bay BSP: Blatant Self-Promotion 41:39 LN: How Long Should a Password Be? – https://askleo.com/4844  42:19 GR: https://macmost.com/helping-others-with-their-macs-using-screen-sharing-through-messages.html Transcript teh_271 Video

Tech giants and chipmakers are facing off as AI-fueled memory shortages trigger sweeping price hikes on everything from Macs to game consoles. Hear why global supply chain standoffs, long-term contracts, and old-school market forces are quietly reshaping your daily technology. • Apple and Microsoft hike prices on devices amid global memory shortages • Surge in AI data centers drives RAM and storage crisis • Intel's comeback: Core Ultra chips compete with AMD in handheld gaming • Microsoft's pivot to ARM, Qualcomm-NVIDIA alliance, and x86 rivalry • AI fear and backlash; organic concern amplified by international actors • White House abruptly pulls Anthropic's Fable model, sparking industry uproar • US government U-turns on AI regulation, restricts top models to select partners • Tension over AI innovation vs. regulatory "rug pull" and global competition • Smart home chaos: Matter 1.6 standard, Samsung and Level Lock shake-up • Debate over local vs. cloud smart home control and API access fees • Ring and Flock cameras ignite privacy and surveillance state concerns • Social media bans for under-16s fail in Australia, UK, and Norway plan similar rules • BBC Radio 4 long wave broadcast ends after a century • Meta gets caught tracking employees for AI; PlayStation deletes owned movies • US regulators propose removing brake pedals from Robotaxis • Ford's automated systems flop, company rehiring engineers • Farewell to tech journalist and GigaOm founder Om Malik Host: Leo Laporte Guests: Jennifer Pattison Tuohy, Dan Patterson, and Daniel Rubino Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech 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: Simply CX box.com/AI meter.com/twit ZipRecruiter.com/twit superhuman.com

- Gurman: Apple to Forego Pro and Max Versions of Anticipated M6 Processor - Gurman: High-End M5 Processors to Power Touchscreen MacBooks - Ming-Chi Kuo: iPhone 18 Sport 9GB Unified Memory - IDC Adjusts iPhone Pricing Expectations - New MacBook Neo Units Still Over a Week for Delivery - Apple Adds MacBook Neo to Refurbished Store - More 2026 Macs and Displays Hit Apple Refurbished Store - Apple CEO Pledges Donation for Venezuela Earthquake Relief - Report: Apple Vision Pro/Smart Glasses Guy Leaves for OpenAI - Joanna Stern Takes Apple Books to Task Over A.I.-Generated Fakes - Apple Sets One-Week Theatrical Run for "Tenzing" - Apple TV Opens "Camp Snoopy" for Second Season - Sponsored by NordLayer: Get an exclusive offer - up to 22% off NordLayer yearly plans plus 10% on top with coupon code: macosken-10-NORDLAYER at nordlayer.com/macosken - Sponsored by Copilot Money: Get a two month free trial with Offer Code MACOSKEN at copilot.money/macosken - Catch Ken on Mastodon - @macosken@mastodon.social - Send Ken an email: info@macosken.com - Chat with us on Patreon for as little as $1 a month. Support the show at Patreon.com/macosken

Tech giants and chipmakers are facing off as AI-fueled memory shortages trigger sweeping price hikes on everything from Macs to game consoles. Hear why global supply chain standoffs, long-term contracts, and old-school market forces are quietly reshaping your daily technology. • Apple and Microsoft hike prices on devices amid global memory shortages • Surge in AI data centers drives RAM and storage crisis • Intel's comeback: Core Ultra chips compete with AMD in handheld gaming • Microsoft's pivot to ARM, Qualcomm-NVIDIA alliance, and x86 rivalry • AI fear and backlash; organic concern amplified by international actors • White House abruptly pulls Anthropic's Fable model, sparking industry uproar • US government U-turns on AI regulation, restricts top models to select partners • Tension over AI innovation vs. regulatory "rug pull" and global competition • Smart home chaos: Matter 1.6 standard, Samsung and Level Lock shake-up • Debate over local vs. cloud smart home control and API access fees • Ring and Flock cameras ignite privacy and surveillance state concerns • Social media bans for under-16s fail in Australia, UK, and Norway plan similar rules • BBC Radio 4 long wave broadcast ends after a century • Meta gets caught tracking employees for AI; PlayStation deletes owned movies • US regulators propose removing brake pedals from Robotaxis • Ford's automated systems flop, company rehiring engineers • Farewell to tech journalist and GigaOm founder Om Malik Host: Leo Laporte Guests: Jennifer Pattison Tuohy, Dan Patterson, and Daniel Rubino Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech 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: Simply CX box.com/AI meter.com/twit ZipRecruiter.com/twit superhuman.com

Tech giants and chipmakers are facing off as AI-fueled memory shortages trigger sweeping price hikes on everything from Macs to game consoles. Hear why global supply chain standoffs, long-term contracts, and old-school market forces are quietly reshaping your daily technology. • Apple and Microsoft hike prices on devices amid global memory shortages • Surge in AI data centers drives RAM and storage crisis • Intel's comeback: Core Ultra chips compete with AMD in handheld gaming • Microsoft's pivot to ARM, Qualcomm-NVIDIA alliance, and x86 rivalry • AI fear and backlash; organic concern amplified by international actors • White House abruptly pulls Anthropic's Fable model, sparking industry uproar • US government U-turns on AI regulation, restricts top models to select partners • Tension over AI innovation vs. regulatory "rug pull" and global competition • Smart home chaos: Matter 1.6 standard, Samsung and Level Lock shake-up • Debate over local vs. cloud smart home control and API access fees • Ring and Flock cameras ignite privacy and surveillance state concerns • Social media bans for under-16s fail in Australia, UK, and Norway plan similar rules • BBC Radio 4 long wave broadcast ends after a century • Meta gets caught tracking employees for AI; PlayStation deletes owned movies • US regulators propose removing brake pedals from Robotaxis • Ford's automated systems flop, company rehiring engineers • Farewell to tech journalist and GigaOm founder Om Malik Host: Leo Laporte Guests: Jennifer Pattison Tuohy, Dan Patterson, and Daniel Rubino Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech 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: Simply CX box.com/AI meter.com/twit ZipRecruiter.com/twit superhuman.com

Step into the wonderfully weird world of late-night talk radio with The Other Side of Midnight, hosted by the delightfully unsupervised Walter Sterling. In this hour, Walter rants about the misery of PCs versus Macs, gets the latest Gen-Z trends from his teenage correspondent Wyatt Sharpe, and debates the movie Supergirl ) with Hollywood critic Matias Bombal. Learn more about your ad choices. Visit megaphone.fm/adchoices

Tech giants and chipmakers are facing off as AI-fueled memory shortages trigger sweeping price hikes on everything from Macs to game consoles. Hear why global supply chain standoffs, long-term contracts, and old-school market forces are quietly reshaping your daily technology. • Apple and Microsoft hike prices on devices amid global memory shortages • Surge in AI data centers drives RAM and storage crisis • Intel's comeback: Core Ultra chips compete with AMD in handheld gaming • Microsoft's pivot to ARM, Qualcomm-NVIDIA alliance, and x86 rivalry • AI fear and backlash; organic concern amplified by international actors • White House abruptly pulls Anthropic's Fable model, sparking industry uproar • US government U-turns on AI regulation, restricts top models to select partners • Tension over AI innovation vs. regulatory "rug pull" and global competition • Smart home chaos: Matter 1.6 standard, Samsung and Level Lock shake-up • Debate over local vs. cloud smart home control and API access fees • Ring and Flock cameras ignite privacy and surveillance state concerns • Social media bans for under-16s fail in Australia, UK, and Norway plan similar rules • BBC Radio 4 long wave broadcast ends after a century • Meta gets caught tracking employees for AI; PlayStation deletes owned movies • US regulators propose removing brake pedals from Robotaxis • Ford's automated systems flop, company rehiring engineers • Farewell to tech journalist and GigaOm founder Om Malik Host: Leo Laporte Guests: Jennifer Pattison Tuohy, Dan Patterson, and Daniel Rubino Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech 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: Simply CX box.com/AI meter.com/twit ZipRecruiter.com/twit superhuman.com

Pre-show: Casey has some fantastic news about nugs.net for Marco Marco’s trip report Gucci Osteria Follow-up: John guested on a preview of Designed in California, published as part of Upgrade #624 (also on YouTube) Apple Intelligence

- Om Malik, 1966-2026 - Apple Raises Prices on Macs, iPads, and Home Devices - Apple Issues Statement on Price Increases - No Price Increases for iPhone Nor Apple Watch (Yet) - Micron Seems to Blame Apple for RAMnarök - Apple Raises Prices on Refurbished Macs and iPads - AAPL Down 6% After Price Increases - Reactions from Financial Folk - Sponsored by CleanMyMac: Use code MACOSKEN20 for 20% off at clnmy.com/MACOSKEN - Sponsored by OneSkin: Get 15% off OneSkin with the code MACOSKEN at oneskin.co/MACOSKEN #oneskinpod #sponsored - Catch Ken on Mastodon - @macosken@mastodon.social - Send Ken an email: info@macosken.com - Chat with us on Patreon for as little as $1 a month. Support the show at Patreon.com/macosken

Tim Cook warned us it was coming, but still the price rises on most, although not all, Apple products is a blow if you were just about to order. Here's what's changed, and when it might change back. Plus we're now on developer beta 2 of the new OSes, and of course there's more news about the iPhone Fold.Contact your hosts:@williamgallagher_ on Threads@WGallagher on TwitterWilliam's 58keys on YouTubeWilliam Gallagher on emailWes on BlueskyWes Hilliard on emailWes's blog HillitechSponsored by:MasterClass: Get 15% off annual memberships at MasterClass.comNordStellar: Unlock your 10% discount at nordstellar.com/appleinsider with the coupon code nordappleinsider-10-NORDSTELLARLinks from the Show:Apple confirms big price hikes across Macs, iPads, and moreThe Apple Store is back online after being taken down briefly, and its return has brought with it some significant price increases across some of Apple's most popular products, including the MacBook Neo.Apple's home automation updates & new product releases will stretch into 2028Apple left Apple TV & HomePod out of WWDC and Siri AI is the culprittvOS 27 beta code backs up HomePod and Apple TV Siri AI rumorsNew supply chain source is sure iPhone Fold will launch in September 2026Folding iPhone hinge issues may have been worked out, rumored to ship in fallLeak claims iPhone Ultra 2 is already greenlit, but maybe not iPhone Air 3Second developer betas of iOS 27, macOS 27 are outNew in iOS 27 beta 2: Update an Apple TV in the Home app, Wallet InsightsCannes Lions 2026 Entertainment Person of the Year is Apple TV chiefApple Ring would dominate the fitness market, which is why it can't existGoogle's new payment policies are a preview of what could come to Apple platformsSupport the show:Support the show on Patreon or Apple Podcasts to get ad-free episodes every week, access to our private Discord channel, and early release of the show! We would also appreciate a 5-star rating and review in Apple PodcastsMore AppleInsider podcastsTune in to our HomeKit Insider podcast covering the latest news, products, apps and everything HomeKit related. Subscribe in Apple Podcasts, Overcast, or just search for HomeKit Insider wherever you get your podcasts.Subscribe and listen to our AppleInsider Daily podcast for the latest Apple news Monday through Friday. You can find it on Apple Podcasts, Overcast, or anywhere you listen to podcasts.Those interested in sponsoring the show can reach out to us at: advertising@appleinsider.com (00:00) - Intro (01:18) - Price increases (10:16) - Apple Home (13:18) - iPhone Fold (27:14) - Beta 2 (55:36) - Eddy Cue (01:04:45) - Google Play Commissions ★ Support this podcast on Patreon ★

Ben and Tom discuss OpenAI delaying its $1 trillion IPO to 2027 as bankers cite tech volatility and the post-SpaceX retail enthusiasm hangover, the resulting sell-off in AI infrastructure names with Softbank down 12% and Micron, NVDA, AVGO, and SNDK all lower, Apple and Microsoft's 15-20% price hikes on iPads, Macs, and Xbox tied to memory chip prices and the likelihood that big tech vertically integrates in response, ON Semi's $6 billion all-stock acquisition of Synaptics to expand into physical AI and robotics with a $100 billion TAM by 2030, and FedEx Freight's commentary that demand may finally be stabilizing.Join our live YouTube stream Monday through Friday at 8:30 AM EST:http://www.youtube.com/@TheMorningMarketBriefingPlease see disclosures:https://www.narwhal.com/disclosure

Training data is the raw material of the AI industry. Claude, ChatGPT, Gemini, and the rest are built on top of oceans of stuff. What is that stuff? Books. Blog posts. YouTube videos. Reddit comments. All of it and more, in virtually incomprehensible quantities. Alex Reisner, a staff writer at The Atlantic who has been investigating training data, explains how AI companies get all this data, why they'd really prefer you not know what's in it, and whether training data could ever be a fair trade. Further reading: Apple raises prices on Macs, iPads, and more by hundreds of dollars | The Verge⁠ ⁠Disney agrees to pay $50 million to YouTube TV and DirecTV subscribers | The Verge⁠ Two handlebars are better than one, right? | The Verge⁠ At Least 15 Million YouTube Videos Have Been Snatched by AI Companies⁠⁠ ⁠⁠The Hypocrisy at the Heart of the AI Industry ⁠⁠ ⁠⁠The Millions of Songs Mashed Into AI-Generated Music⁠⁠ ⁠⁠Common Crawl Is Doing the AI Industry's Dirty Work⁠⁠ Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed. We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Plus: The European Union says Amazon and Microsoft's cloud services should be regulated under its Digital Markets Act. And BlackBerry boosted its 2027 fiscal outlook, thanks to expanding opportunities in AI. Danny Lewis hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

Oil prices rise as Iran attacks a cargo ship in the Strait of Hormuz. Plus: Apple stock slides after raising prices on its Macs and iPads. Alexis Green hosts. Sign up for WSJ's free What's News newsletter. An artificial-intelligence tool assisted in the making of this episode by creating summaries that were based on Wall Street Journal reporting and reviewed and adapted by an editor. Learn more about your ad choices. Visit megaphone.fm/adchoices

Apple acaba de subir los precios de varios modelos de Mac y iPad y el mercado ha reaccionado castigando sus acciones con caídas cercanas al 5%. Detrás de este movimiento está la brutal subida de precios de la memoria y el almacenamiento, impulsada por el boom de la inteligencia artificial y los centros de datos que acaparan la producción de chips. Por último, pondremos en contexto estas subidas con los impresionantes márgenes de hardware que Apple sigue reportando y lo que esto significa para los inversores. Si te interesa la tecnología, la IA y el mundo Apple, este episodio te va a ayudar a entender qué está pasando detrás de bastidores. Quédate hasta el final porque te cuento qué podría ocurrir con los precios de futuros dispositivos si la fiebre de la IA continúa. ---------------------------------- 00:00 – 05:00 Presentación, contexto general y por qué la subida de precios de Apple importa a usuarios e inversores. 05:00 – 15:00 Detalle de los nuevos precios de Mac y iPad: qué modelos suben, cuánto suben y comparación con generaciones anteriores. 15:00 – 30:00 Qué está pasando con la memoria y el almacenamiento: explicación de DRAM, NAND, HBM, y cómo la IA está acaparando la producción. 30:00 – 45:00 El papel de los centros de datos de IA y gigantes como Nvidia: por qué sus pedidos tienen prioridad frente a la electrónica de consumo. 45:00 – 60:00 “Chipflation”: qué dicen Morgan Stanley, JPMorgan y otros analistas sobre la subida extrema de precios de memoria y cuánto puede durar. 60:00 – 75:00 Impacto directo en Apple: márgenes de hardware, beneficios recientes y cómo intentan proteger rentabilidad sin matar la demanda. 75:00 – 90:00 Proveedores y geopolítica: Micron, SK Hynix, Samsung, intento de Apple de usar memoria china (YMTC, CXMT) y bloqueos desde EE. UU. 90:00 – 105:00 Efecto en el usuario final: ¿vale la pena comprar ahora?, posibles estrategias de compra, impacto en estudiantes, creadores y profesionales. 105:00 – 115:00 Escenarios futuros: qué puede pasar con los precios de Macs, iPads e incluso iPhones si la fiebre de la IA no se frena. 115:00 – 120:00 Cierre, resumen, opinión personal y llamada a la acción (comentarios, suscripción, siguiente episodio, etc.) ---------------------------------- #Apple #Mac #iPad #Tecnología #Bolsa #AccionesApple #InteligenciaArtificial #Chipflation #MemoriaRAM #PodcastTech ---------------------------------- https://seoxan.es/crear_pedido_hosting Codigo Cupon "APPLE" ---------------------------------- PATROCINADO POR SEOXAN Optimización SEO profesional para tu negocio https://seoxan.es https://uptime.urtix.es ---------------------------------- PARTICIPA EN DIRECTO Deja tu opinión en los comentario ---------------------------------- ¿TE GUSTÓ EL EPISODIO? ✨ Dale LIKE SUSCRÍBETE y activa la campanita para no perderte nada COMENTA COMPARTE con tus amigos Applelianos ---------------------------------- SÍGUENOS EN TODAS NUESTRAS PLATAFORMAS: YouTube: https://www.youtube.com/@Applelianos Telegram: https://t.me/+Jm8IE4n3xtI2Zjdk X (Twitter): https://x.com/ApplelianosPod Facebook: https://www.facebook.com/applelianos Apple Podcasts: https://apple.co/39QoPbO ----------------------------------

Emergency talks fail to free Anthropic's Fable 5. Trump moves to strengthen national security systems. Microsoft patches a critical Copilot flaw. ShinyHunters weaponize a PeopleSoft zero-day. DragonForce hides in Microsoft Teams for months. Plus, Amos Stealer targets Macs, CISA issues a three-day patch deadline, Delta avoids penalties, and researchers show just how easy it is to manipulate AI search. Our guest is Mike Fey, Co-Founder & CEO at Island, discussing the architectural differences between network and modern SASE. Consulting meets confabulation. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest On today's Industry Voices, we are joined by Mike Fey, Co-Founder & CEO at Island, discussing the architectural differences between network and modern SASE. If you enjoyed this conversation, check out the full interview here.  Selected Reading Anthropic Is Still at Odds With the White House Over Claude Fable 5 (WIRED) Feds freaked over Fable 5 after simple 'fix this code' prompt, not jailbreak, says researcher (The Register) White House Issues Memo to Bolster NSS Cybersecurity (SecurityWeek) Microsoft Patches Critical SearchLeak Vulnerability in Copilot Enterprise (Beyond Machines) ShinyHunters Hits Universities Via Oracle Zero-Day (GovInfo Security) DragonForce Ransomware Exploited Microsoft Teams to Hide Attack (Infosecurity Magazine) Inside Amos Stealer: How This Threat Targets macOS Credentials and Keychains (CyberProof) CISA warns of another cPanel plugin flaw exploited in attacks (Bleeping Computer) US closes probe into 2024 Delta Air Lines meltdown sparked by CrowdStrike outage (Reuters) It Is Trivially Easy to Use Reddit to Manipulate AI Search, Research Suggests (404 Media) KPMG pulls report on AI usage due to apparent hallucinations (TechCrunch) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Today's episode starts exactly how you'd expect from a group of professional broadcasters... by arguing over cartoon dwarves and immediately proving why the game is called Matchup With The Morons.The crew jumps into a surprisingly intense round of trivia featuring Moon, King Scott, Rafe, and Learn, where confidence levels are high and actual knowledge levels vary dramatically. One wrong dwarf answer sparks a chain reaction of chaos that somehow leads to discussions about Indiana Jones, giant lizards, world rivers, and whether anyone actually knows where French fries came from.Things get even stranger when the gang learns about a man who has eaten more than 34,000 Big Macs in his lifetime. That's not a typo. That's a lifestyle choice. The crew tries to guess the Guinness World Record total and discovers that some people collect baseball cards while others collect burger receipts for five decades.Meanwhile, Rafe and Learn square off in a battle that becomes unexpectedly competitive thanks to classic rock knowledge, superhero trivia, and one question about collective nouns that nearly sends everyone into a full-scale grammatical civil war. Is it a knot of toads? An army of toads? A conference of toads? Nobody leaves this episode feeling smarter.The music trivia alone is worth the ride. The crew debates Led Zeppelin, The Yardbirds, Paul McCartney, and enough rock history to make your dad text the family group chat. Add in random movie facts, Titanic budget discussions, and the usual barrage of sarcastic commentary, and you've got another perfectly ridiculous day with The Rizzuto Show.This comedy podcast proves once again that a room full of adults can spend half an hour debating topics that absolutely should not require debate. Somehow that turns into entertainment.If you love a comedy podcast packed with weird facts, hilarious fails, pop culture randomness, competitive nonsense, and the kind of arguments that only happen on live radio, this episode delivers all of it.Thanks for listening to another comedy podcast from The Rizzuto Show, where the facts are questionable, the confidence is unlimited, and the Big Mac math is somehow the most accurate thing discussed all day.Follow The Rizzuto Show → https://linktr.ee/rizzshow for more from your favorite daily comedy show.Connect with The Rizzuto Show Comedy Podcast online → https://1057thepoint.com/RizzShow.Hear The Rizz Show daily on the radio at 105.7 The Point | Hubbard Radio in St. Louis, MO.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.