Podcasts about Gemini

AI is the new Google? Sort of! In this video, I break down how AI search tools like ChatGPT, Gemini, and Perplexity are changing the way people find content. I'll show you how to optimize your content for both traditional SEO and the new AI-driven search. Learn why long form content, schema, and your unique expertise matter more than ever for getting found in AI chatbots.

In this episode, Renee Sills is joined by Chinese astrologer and researcher, Berna Lee. Berna shares about her background and family practice, as a 45th generation astrologer, and the two discuss the intersections of astrology, cosmology, and spiritual practice.Berna also speaks to the Year of the Fire Horse, beginning in February 2026, and draws some fascinating parallels with Tropical Astrology's marking of Saturn and Neptune's entry into Aries, with the coordination with Uranus in Gemini, and Pluto in Aquarius. About BernaBerna Lee is a Chinese astrologer and researcher, founder of Astromergency, where ancestral wisdom meets contemporary life. Rooted in longstanding Chinese cultural traditions, her work draws on Bazi, Feng Shui, auspicious timing, and Eastern philosophy. Currently pursuing a PhD in Chinese cultural astronomy and theology, she brings academic depth alongside lived lineage, and has been invited to teach internationally, including at the Oxford Summer School through the Faculty of Astrological Studies in 2026.Visit Berna's websiteVisit Berna's InstagramRead Berna's MA researchUpcoming at Embodied Astrology

The pace of AI model releases is becoming almost impossible to follow. In just two weeks we saw GPT-5.3-Codex, GPT-5.2 updates, Gemini 3 Deep Think upgrades, Claude Opus 4.6 with a 1M context window in beta, Qwen3-Coder-Next, GLM-5, MiniMax M2.5, Cursor Composer 1.5, and even Kimi 2.5 just outside the window. This isn't a quarterly product cycle anymore - it's a daily arms race. In this episode Matt and Mike break down what this acceleration means for developers, open source, frontier labs, and the broader industry. Are we witnessing healthy innovation, or unsustainable velocity? At what point does this stabilize - if it ever does? If you're trying to build, learn, or compete in AI right now… this conversation is for you. ‍Show Notes: https://www.htmlallthethings.com/podcast/ai-competition-is-out-of-control

Discover what the stars have aligned for you today. Whether you are looking for clarity in your love life, seeking direction in your career, or simply need a moment of mindfulness to start your morning, this reading offers the cosmic guidance you need to navigate today with confidence. In today's episode, we cover: Love and Relationships: Insight into how today's planetary alignment affects your romantic connections, family dynamics, and social life. Find out if it's a day for communication or a day for reflection. Career and Finance: Practical astrological advice for your professional life. We explore opportunities for growth, financial caution, and productivity tips tailored to the unique energy of your Zodiac Sign. Personal Growth and Wellness: Daily affirmations and spiritual guidance to help you stay grounded. Learn how to harness the energy of the moon and the planets to improve your mental and emotional well-being. Why Listen to Your Daily Horoscope? Astrology is more than just prediction; it is a tool for self-discovery and mindfulness. By tuning into the cosmic climate, you can align your actions with the universe's energy. Our daily episodes are short, actionable, and designed to help you live your best life, every single day. Connect with the Cosmos: If this episode resonated with you, please subscribe and leave a review! Your support helps us bring daily celestial wisdom to listeners around the world. Disclaimer: The information and astrological interpretations in this podcast are for entertainment purposes only. Listeners are encouraged to use their own discretion and should not replace professional medical, legal, or financial advice with the content of this show.

GEMINI DAILY HOROSCOPE  (Sunday February 15 2026)

GEMINI DAILY HOROSCOPE  (Saturday February 14 2026)

Braucht man im Zeitalter von ChatGPT & Co. überhaupt noch eine Website? In dieser Folge geht es darum, wie KI-Antwortmaschinen die Online-Suche verändern, warum Traffic neu verteilt wird und welche Rolle die eigene Website 2026 noch für Sichtbarkeit, Vertrauen und Conversion spielt. Das ist eine Zusammenfassung des Google-SEO-Podcasts „Search Off the Record“ mit Martin Splitt und Gary Illyes. Quelle: www.youtube.com/watch?v=IM3UAX3MhnI

Dan Ives is the Global Head of Technology Research at Wedbush Securities and one of the most widely followed analysts covering AI and U.S. tech. This conversation was recorded live at Bitcoin Investor Week in New York. In this discussion, Dan explains why the recent selloff in software is disconnected from fundamentals, how AI CapEx is driving a fourth industrial revolution, and why U.S. tech remains structurally ahead. We also discuss the relationship between AI and bitcoin, the current risk-off environment, and why Dan believes we're still early in a multi-year tech bull cycle.======================Sign up for the Gemini Credit Card: https://gemini.com/pomp #GeminiCreditCard #CryptoRewards This video is sponsored by Gemini. All opinions expressed are my own and not influenced or endorsed by Gemini. Gemini-branded credit products are issued by WebBank. For more information regarding fees, interest, and other cost information, see Rates & Fees: https://gemini.com/legal/cardholder-agreement Some exclusions apply to instant rewards; these are deposited when the transaction posts. 4% back is available on up to $300 in spend per month for a year (then 1% on all other Gas, EV charging, and transit purchases that month). Spend cycle will refresh on the 1st of each calendar month. See Rewards Program Terms for details: https://gemini.com/legal/credit-card-rewards-agreement Checking if you're eligible will not impact your credit score. If you're eligible and choose to proceed, a hard credit inquiry will be conducted that can impact your credit score. Eligibility does not guarantee approval.======================Arch Public is an agentic trading platform that automates the buying and selling of your preferred crypto strategies. Sign up today at https://www.archpublic.com and start your automated trading strategy for free. No catch. No hidden fees. Just smarter trading.======================0:00 - Intro0:22 - Is the tech bull market over?1:55 - Why do people want U.S. software companies to fail?4:30 - If tech is up 20%, what happens to the S&P 500?6:16 - Are AI & Bitcoin actually connected?9:02 - Do power and data center constraints limit AI's upside?13:22 - How robotics will change labor & profits15:45 - Where does bitcoin go over the next few years?17:06 - Which tech companies are misunderstood or undervalued?19:30 - Are we near a market bottom?

Discover why inflation is lower than expected.  Are you on track for financial freedom...or not? Financial freedom is a combination of money, compounding and time (my McT Formula). How well you invest can make the biggest difference to your financial freedom and lifestyle. If you invested well for the long-term, what a difference it would make because the difference between investing $100k and earning 5 percent or 10 percent on your money over 30 years, is the difference between it growing to $432,194 or $1,744,940, an increase of over $1.3 million dollars. Your compounding rate, and how well you invest, matters!  INVESTING IS WHAT THE BE WEALTHY & SMART VIP EXPERIENCE IS ALL ABOUT - Invest in digital assets and stock ETFs for potential high compounding rates - Receive an Asset Allocation model with ticker symbols and what % to invest -Monthly LIVE investment webinars with Linda 10 months per year, with Q & A -Private VIP Facebook group with daily community interaction -Weekly investment commentary -Extra educational wealth classes available -Pay once, have lifetime access! NO recurring membership fees. -US and foreign investors are welcome -No minimum $ amount to invest -Tech Team available for digital assets (for hire per hour) For a limited time, enjoy a 50% savings on my private investing group, the Be Wealthy & Smart VIP Experience. Pay once and enjoy lifetime access without any recurring fees. Enter "SAVE50" to save 50%here: http://tinyurl.com/InvestingVIP Or set up a complimentary conversation to answer your questions about the Be Wealthy & Smart VIP Experience. Request an appointment to talk with Linda here: https://tinyurl.com/TalkWithLinda (yes, you talk to Linda!). SUBSCRIBE TO BE WEALTHY & SMART Click Here to Subscribe Via iTunes Click Here to Subscribe Via Stitcher on an Android Device Click Here to Subscribe Via RSS Feed LINDA'S WEALTH BOOKS 1. Get my book, "3 Steps to Quantum Wealth: The Wealth Heiress' Guide to Financial Freedom by Investing in Cryptocurrencies". 2. Get my book, "You're Already a Wealth Heiress, Now Think and Act Like One: 6 Practical Steps to Make It a Reality Now!" Men love it too! After all, you are Wealth Heirs. :) International buyers (if you live outside of the US) get my book here. WANT MORE FROM LINDA? Check out her programs. Join her on Instagram. WEALTH LIBRARY OF PODCASTS Listen to the full wealth library of podcasts from the beginning.  SPECIAL DEALS #Ad Apply for a Gemini credit card and get FREE XRP back (or any crypto you choose) when you use the card. Charge $3000 in first 90 days and earn $200 in crypto rewards when you use this link to apply and are approved: https://tinyurl.com/geminixrp This is a credit card, NOT a debit card. There are great rewards. Set your choice to EARN FREE XRP! #Ad Protect yourself online with a Virtual Private Network (VPN). Get 3 MONTHS FREE when you sign up for a NORD VPN plan here.  #Ad To safely and securely store crypto, I recommend using a Tangem wallet. Get a 10% discount when you purchase here. #Ad If you are looking to simplify your crypto tax reporting, use Koinly. It is highly recommended and so easy for tax reporting. You can save $20, click here. Be Wealthy & Smart,™ is a personal finance show with self-made millionaire Linda P. Jones, America's Wealth Mentor.™ Learn simple steps that make a big difference to your financial freedom.  (This post contains affiliate links. If you click on a link and make a purchase, I may receive a commission. There is no additional cost to you.)  

Markets closed out the week balancing cooler inflation against renewed volatility in tech and AI. U.S. CPI rose 2.4% year-over-year in January, with core inflation falling to 2.5% — the lowest level since March 2021. While the report strengthens the case for potential Fed rate cuts, it follows a robust labor market update earlier in the week, keeping policy expectations finely balanced. Equities struggled, with the Nasdaq dropping 2% amid fresh AI disruption fears despite Anthropic raising $30 billion at a $380 billion valuation. Meanwhile, China posted a record $242 billion current account surplus in Q4 2025, highlighting export resilience despite weak domestic demand. Oil slipped on reports that OPEC+ may resume production increases in April. Gold rebounded after briefly falling below $5,000 per ounce. The yen is on track for its strongest week in a year versus the dollar. In crypto, Bitcoin remains stable week-over-week. Coinbase shares rose despite a Q4 earnings miss, even as reports surfaced that CEO Brian Armstrong has sold roughly $500 million in stock over the past nine months. Several crypto CEOs, including leaders from Ripple, Gemini, Uniswap, and Chainlink, have joined the CFTC advisory group. A volatile week wraps with inflation cooling — but crosscurrents in AI, geopolitics, and liquidity remain firmly in play.

Welcome to the Watson Weekly Weekend Edition. Hosts Rick Watson and Jessica Lesesky dive into the post-Super Bowl landscape to break down the biggest moves in retail, tech, and AI. From the high-stakes world of $7 million for 30 second ads at the big game to the massive earnings reports from industry titans (hello Amazon), we're unpacking what these shifts mean for the future of commerce.In This Episode:The AI Ad Wars: We review the ads from the big game spots from Claude (Anthropic), OpenAI, and Gemini. Is Anthropic just throwing shade at competitors, or is there a method to the "negative ad" madness?Target's Executive Carousel: Target is shuffling the deck chairs again. With a new CEO and a history of moving merchants into marketing roles, we ask: why does Target seem "allergic" to a real CMO?Spotify vs. Amazon (The Book Edition): In a surprising move, Spotify is partnering with Bookshop.org to sell physical books. We explore why they're helping independent bookstores while indie artists feel left behind.Amazon Earnings Deep Dive: AWS is back on "high-speed rail" growth, and the new AI assistant Rufus is already driving billions in sales. Plus, we discuss the genius of the "add to delivery" feature.Shopify's AI Strategy: Shopify is growing at nearly 30% a year, but investors have one question: What is the AI strategy? Rick explains why Shopify's "one trick"—the checkout—is still their greatest strength.The Watson Weekly Weekend Edition is sponsored by Mirakl: Powering the next era of retail.Video Timestamps0:00 - Welcome to Watson Weekend0:54 - The Big Game Ad Economics: $233,000 Per Second2:18 - The AI Ad Wars: Anthropic (Claude) vs. OpenAI3:56 - Google Gemini's "Heartstrings" Ad Campaign5:14 - Target's Leadership Shuffle: Why the "Roach Motel" Strategy?8:17 - Spotify's Strange Pivot into Physical Books9:54 - The Indie Artist Royalty Gap which Should Make Publishers Worried11:13 - Amazon Earnings: AWS High-Speed Growth & Rufus+213:06 - Amazon's New "Add to Delivery" Feature14:26 - The Future of Amazon Grocery & Whole Foods15:05 - Shopify Earnings: B2B and International Growth16:51 - Shopify's AI Narrative: "It's the Checkout, Stupid"19:54 - Final ThoughtsStay Bold. Stay ClassySubscribe to our Newsletter: watsonweekly.com and YouTube channel.

Al and Gemini are at Ellis Island, the longtime locally-owned value-forward casino, hotel and restaurant complex just east of The Strip. Owner Christina Ellis shows off the new rooftop deck - known for a few more days as "Cupid's Deck." We also talk to Barbara Valle about the new tiki bar at Excalibur, get the lowdown on the new burgers offered by Naked City Pizza, and preview two big Lunar New Year events at Hakkasan and China Poblano. And, as usual, The Happy Report and lots of restaurant visits.     

We discuss the upcoming iPhone 17e and iPad models, as well as Apple's apparent issues finalizing the revamped version of Siri, on this week's episode of The MacRumors Show.The announcement of the ‌iPhone‌ 17e is said to be “imminent," with stock of the iPhone 16e now dwindling. The new device is rumored to come with four main new features, including the A19 chip from the iPhone 17, MagSafe connectivity, the C1X cellular modem, and the N1 chip for Bluetooth, Wi-Fi, and Thread connectivity.New iPads are also on the horizon for the near future. The eighth-generation iPad Air is expected to move to the M4 chip, while the 12th-generation ‌iPad‌ is expected to jump a chip generation up to the A18, which will also enable Apple Intelligence support for the first time on the device.This week's biggest story was the news that Apple has again “run into snags" testing the personalized, smarter version of ‌Siri‌ originally planned for iOS 26.4. Due to the issues, the upcoming ‌Siri‌ features will likely be partially delayed and spread across several upcoming iOS releases. Apple could postpone some or all of the new ‌Siri‌ features until iOS 26.5, an update planned for May, and iOS 27, which will launch this September.Apple announced a significantly upgraded version of ‌Siri‌ powered by ‌Apple Intelligence‌ at its 2024 Worldwide Developers Conference, and they were originally supposed to be part of iOS 18. The following spring, Apple announced that the new ‌Siri‌ would take longer than expected, with the functionality delayed for a year.Since then, Apple has ostensibly been targeting iOS 26.4, which the company will begin beta testing later this month, but there have apparently been unforeseen problems: ‌Siri‌ sometimes doesn't properly process queries and can take too long to respond to requests.Apple engineers have been told to use iOS 26.5 for further internal testing, suggesting the new ‌Siri‌ features will be delayed until that update. Employees that are testing iOS 26.5 say the update includes all of the features Apple promised, including personalization, onscreen awareness, and the ability for ‌Siri‌ to do more in and between apps, but not all of the features are working reliably and there are problems with accuracy.‌Siri‌ also apparently sometimes falls back on using ChatGPT for information instead of relying on the Gemini-powered technology that Apple has partnered with Google to use, even when the new version of ‌Siri‌ is capable of handling a user's request.Apple also planned to include features that haven't yet been announced, such as options to generate images with Image Playground or search the web. Image generation and web search were tested as part of iOS 26.4, and it's possible they will still be included in the update, so Apple might still be able to release some of the new ‌Siri‌ functionality. Bloomberg says the situation is "fluid," though, so Apple's plans could change, and executives are reluctant to further delay the ‌Siri‌ functionality beyond spring 2026.There are still major changes planned for ‌Siri‌ as part of iOS 27, with Apple aiming to add chatbot functionality to better compete with the likes of like Gemini and ChatGPT. This new version of ‌Siri‌ will also reportedly have deeper integration with apps and Apple's operating systems.Start your business with Shopify and get everything you need to sell online and in person. Start today at https://www.shopify.com/mac

This is a recap of the top 10 posts on Hacker News on February 12, 2026. This podcast was generated by wondercraft.ai (00:30): An AI agent published a hit piece on meOriginal post: https://news.ycombinator.com/item?id=46990729&utm_source=wondercraft_ai(01:59): Warcraft III Peon Voice Notifications for Claude CodeOriginal post: https://news.ycombinator.com/item?id=46985151&utm_source=wondercraft_ai(03:28): AI agent opens a PR write a blogpost to shames the maintainer who closes itOriginal post: https://news.ycombinator.com/item?id=46987559&utm_source=wondercraft_ai(04:57): Gemini 3 Deep ThinkOriginal post: https://news.ycombinator.com/item?id=46991240&utm_source=wondercraft_ai(06:26): GPT‑5.3‑Codex‑SparkOriginal post: https://news.ycombinator.com/item?id=46992553&utm_source=wondercraft_ai(07:55): ai;drOriginal post: https://news.ycombinator.com/item?id=46991394&utm_source=wondercraft_ai(09:25): Improving 15 LLMs at Coding in One Afternoon. Only the Harness ChangedOriginal post: https://news.ycombinator.com/item?id=46988596&utm_source=wondercraft_ai(10:54): Major European payment processor can't send email to Google Workspace usersOriginal post: https://news.ycombinator.com/item?id=46989217&utm_source=wondercraft_ai(12:23): US businesses and consumers pay 90% of tariff costs, New York Fed saysOriginal post: https://news.ycombinator.com/item?id=46990056&utm_source=wondercraft_ai(13:52): Anthropic raises $30B in Series G funding at $380B post-money valuationOriginal post: https://news.ycombinator.com/item?id=46993345&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Hackers abuse Gemini AI for all attack stages, says Google Apple patches decade-old possibly exploited iOS zero-day Acting CISA chief critiques potential DHS funding lapse Get the show notes here: https://cisoseries.com/cybersecurity-news-hackers-abuse-gemini-apple-patches-ancient-bug-cisa-criticizes-shutdown/ Huge thanks to our episode sponsor, ThreatLocker Want real Zero Trust training? Zero Trust World 2026 delivers hands-on labs and workshops that show CISOs exactly how to implement and maintain Zero Trust in real environments. Join us March 4–6 in Orlando, plus a live CISO Series episode on March 6. Get $200 off with ZTWCISO26 at ztw.com.

 Ryan Strachan is back!

Discover what the stars have aligned for you today. Whether you are looking for clarity in your love life, seeking direction in your career, or simply need a moment of mindfulness to start your morning, this reading offers the cosmic guidance you need to navigate today with confidence. In today's episode, we cover: Love and Relationships: Insight into how today's planetary alignment affects your romantic connections, family dynamics, and social life. Find out if it's a day for communication or a day for reflection. Career and Finance: Practical astrological advice for your professional life. We explore opportunities for growth, financial caution, and productivity tips tailored to the unique energy of your Zodiac Sign. Personal Growth and Wellness: Daily affirmations and spiritual guidance to help you stay grounded. Learn how to harness the energy of the moon and the planets to improve your mental and emotional well-being. Why Listen to Your Daily Horoscope? Astrology is more than just prediction; it is a tool for self-discovery and mindfulness. By tuning into the cosmic climate, you can align your actions with the universe's energy. Our daily episodes are short, actionable, and designed to help you live your best life, every single day. Connect with the Cosmos: If this episode resonated with you, please subscribe and leave a review! Your support helps us bring daily celestial wisdom to listeners around the world. Disclaimer: The information and astrological interpretations in this podcast are for entertainment purposes only. Listeners are encouraged to use their own discretion and should not replace professional medical, legal, or financial advice with the content of this show.

Dive into Live Events, a powerhouse bringing together top brands like Pyrotecnico, Gemini, Zenith Lighting, Delicate Productions, Active Lighting and GearCo under one roof. Discover how this integration transforms logistics, creativity, and production across multi-city tours. Plus, hear firsthand insights into the exciting synergies shaping the future of entertainment technology.

Waymo hat doch Menschen im Hintergrund: Remote-Operatoren auf den Philippinen übernehmen, wenn ein Fahrzeug nicht weiterkommt – inklusive Infinite-Money-Glitch über DoorDash. Anthropic sammelt weitere $30 Mrd. ein bei $380 Mrd. Bewertung – praktisch jeder große Investor ist dabei. Bloomberg berichtet vom Tabubruch, in OpenAI und Anthropic gleichzeitig zu investieren. X erreicht $1 Mrd. Subscription-ARR, lag als Twitter aber mal bei $5 Mrd. Werbeumsatz. Spotify behauptet, die besten Entwickler hätten seit Dezember keine Zeile Code geschrieben – die R&D-Kosten sind aber tatsächlich um fast 40% gesunken. OpenAI und Google warnen US-Abgeordnete vor chinesischer Modell-Destillation. Die EU eröffnet ein neues Antitrust-Verfahren gegen Googles Werbeauktionen, während AI Overviews das offene Web weiter austrocknen. Die FTC attackiert Apple News wegen angeblichem Links-Bias. Robinhood enttäuscht mit schwachem Krypto-Geschäft, Cloudflare glänzt mit 34% Umsatzwachstum und 40% Kundenwachstum. Die EPA streicht unter Trump die wissenschaftliche Basis für die Schädlichkeit von Treibhausgasen. Eine Juniper-Research-Studie zeigt: Jede 10. Social-Media-Anzeige in Europa ist ein Scam. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Waymo (00:08:59) Anthropic $30 Mrd. Funding (00:15:11) X erreicht $1 Mrd. Subscription ARR (00:18:02) Spotify: Beste Entwickler schreiben keinen Code mehr (00:21:07) Jonas Andrulis und Roland Berger Joint Venture (00:26:55) ai.com Domain für $70 Mio. verkauft (00:30:01) China destilliert OpenAI und Google Modelle (00:40:24) Distillation Attacks: Die Debatte um Content-Klau (00:41:59) Google Antitrust: EU untersucht Werbeauktionen (00:46:07) AI Overviews und das Sterben des Open Web (00:51:49) FTC vs. Apple News (01:07:34) Robinhood und Coinbase Earnings (01:13:12) Cloudflare Earnings (01:19:55) Verbraucherschutz: Elster Phishing und Scam Ads Studie (01:25:41) EPA streicht Klimaschutz-Grundlage Shownotes Waymo setzt menschliche Agenten im Ausland ein - cybernews.com Waymo stellt DoorDash-Fahrer ein, um Autotüren zu schließen. - x.com Anthropic schließt $30 Milliarden Finanzierungsrunde für KI-Startups ab. - cnbc.com Anthropic erhält $30 Milliarden in Serie-G-Finanzierung. - anthropic.com VCs brechen Tabu: Unterstützung für Anthropic und OpenAI. - bloomberg.com 1. X Subscriptions - theinformation.com Elon Musks xAI verliert zweiten Mitgründer in 48 Stunden. - businessinsider.com Spotify: Beste Entwickler schreiben seit Dezember keinen Code dank KI - techcrunch.com Roland Berger and Jonas Andrulis start start-up - handelsblatt.com AI domain - x.com OpenAI beschuldigt DeepSeek, US-Modelle zur Vorteilsgewinnung zu destillieren. - bloomberg.com Google says attackers used 100,000+ prompts to try to clone AI chatbot Gemini - nbcnews.com EU untersucht Google wegen Suchanzeigen-Preisen erneut auf Kartellverstöße - bloomberg.com Apple steht vor neuen Spannungen mit Trump-Regierung - ft.com FTC Apple - x.com Apple News bevorzugt linke Medien, schließt konservative aus: Studie - nypost.com Tech companies pressured to share data on Trump critics, according to reports - msn.com ‘What Oligarchy Looks Like' - commondreams.org Google übermittelte persönliche und finanzielle Daten eines Studentenjournalisten an ICE - techcrunch.com EPA - nbcnews.com Scam Ads - juniperresearch.com

Marketing is being rebuilt from the infrastructure up. Search is changing. Commerce is becoming agent-driven. Measurement is being redefined in real time. And the line between engineering and marketing is disappearing. In this episode of Frontier CMO, host Josh Spanier sits down with Vidhya Srinivasan, Head of Ads and Commerce at Google. As the leader responsible for Google Ads, YouTube Ads, Shopping, Merchant Center, Gemini integrations, and payments, Vidhya is helping architect how the modern marketplace actually works. The conversation explores what “agentic commerce” really means, why the Universal Commerce Protocol could reshape how brands interface with AI systems, and how Gemini is already rewiring performance, creative, and intent matching across the ad stack. Vidhya explains why CMOs don't need to code — but must become technologically fluent — and outlines a five-part leadership blueprint for navigating AI transformation with optimism, speed, and accountability. 00:00 – The Vision: Reducing the “Commute Cost” from Desire to Purchase 00:28 – Engineering Meets Marketing: Why the Worlds Are Merging 01:31 – Inside Google Ads & Commerce: The Scale of the Role 03:13 – Agentic Commerce & the Universal Commerce Protocol Explained 04:29 – AI Search, Longer Queries & Reimagining Ads 05:05 – YouTube Creators, Culture & the Creator Partnership Hub 06:18 – How Gemini Powers Google's Ad Systems 07:06 – Why Trust Is the Foundation of AI Advertising 07:51 – What CMOs Must Understand in the AI Era 14:19 – Measurement, First-Party Data & Cracking Attribution 21:38 – Leading AI Transformation: A 5-Point Playbook 25:32 – The Holy Grail: The Right Ad, Right Person, Right Moment

Google Docs ganha resumo de texto em áudio com o Gemini; saiba ativar. Governo usa WhatsApp para avisar sobre isenção de IR para quem ganha até R$ 5 mil. Mundo em perigo': ex-pesquisador da Anthropic se demite e alerta sobre a IA. Sony anuncia God of War Remake Trilogy e lança novo jogo da franquia de surpresa no PS5.

In this session of the Way of the Mystic Weekend Online Revival, the community gathers to celebrate the miracle of collaboration and the profound release of personal responsibility. Centered around the "Big Yes," a core team—including Jeffrey, Nicholas, Eric, Dini, and Natalie—shares their journey of transitioning the ministry into a unified digital ecosystem. What began as an exploration of tools became a mystical experience of mind-merging, where technical details like emails and files served as a backdrop for deep spiritual healing.Through the lens of A Course in Miracles, the group explores how AI and automation can be repurposed by the Holy Spirit to foster a state of rest. By surrendering the "how" and "why" to the Spirit, the community experienced a collapse of time and an expansion of trust. The transition symbolized leaving behind separate "apartments" of isolation for a shared "mansion" of oneness, illustrating that when we join in purpose, every perceived problem is already solved.Highlights of the Session:The Power of the "Yes": Nicholas shares the vulnerability of leading a transformative project without knowing the way, demonstrating that willingness is the only requirement for miracles.Repurposing Form: The community discusses how Google Workspace and Gemini have streamlined mundane tasks, allowing more space for spiritual vigilance and joy.Humor as a Healing Tool: The team presents a series of comedic sketches that use a metaphor for global oneness to gently dissolve resistance to change.A New State of Mind: Participants share "miracle sharings"—from automating menu plans to feeling supported by "spa" sessions for spiritual tech support—showing that healing is indeed a collaborative venture."Healing is the way in which separation is overcome. It is just showing that you are not alone; you don't need to figure it out yourself. You are so loved." — A Participant quoting Jesus from A Course in Miracles.Recorded Saturday Evening, February 7, 2026Follow us on:YouTube: https://www.youtube.com/DavidHoffmeister Facebook: https://www.facebook.com/ACIM.ACourseInMiracles Learn more about David & Living Miracles: https://livingmiraclescenter.orgLearn more about A Course in Miracles: https://ACIM.bizDavid's Spanish Youtube Channel is: https://www.youtube.com/channel/UCP9Gw00CldPUmiu43y7fdWw

Google reports that its Gemini AI chatbot has been targeted by actors using model extraction techniques, prompting it over 100,000 times to train cheaper imitation models. This practice, called distillation, involves using the outputs of existing AI models to create smaller, cost-effective versions. Google has enhanced Gemini's defenses but has not detailed specific countermeasures. The issue highlights broader industry concerns about intellectual property and ethical boundaries in AI development.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

This New Moon in Aquarius falls in Dhaniṣṭhā and takes the form of an annular solar eclipse. This is a highly charged reset. The eclipse is hemmed in by powerful planetary forces: Mars and Pluto on one side, Saturn and Neptune on the other, with Venus exactly conjunct Rahu. Mercury is drawn into the mix, while Jupiter aspects from Gemini and is itself aspected by Rahu. Everything is activated.Even without the eclipse, the Saturn–Neptune conjunction signals profound shifts in global structures. Although the eclipse path is largely over Antarctica, symbolism matters: previously unseen landscapes are being revealed even here, mirroring how hidden realities are coming to light elsewhere.Dhaniṣṭhā is ruled by the Vasudevas, the eight elemental deities that together provide everything needed to accomplish a goal. For this reason, it is closely tied to alliances, coordination, and collective purpose - an Aquarius theme. Under an eclipse, however, some alliances are breaking while others are forged. These changes rarely occur on the exact date; the eclipse serves as a pivot point for developments already underway and those yet to unfold.Globally, this points to major realignments in power blocs and partnerships, with organisations such as NATO being one visible expression. Seismic activity may also accompany this period, but it may also simply be figurative. Personally, this eclipse asks you to observe how all the elements in this area of your life are coming together - or falling apart - so that something can be reconfigured. Eclipses mark changes that must happen, regardless of preference, and emotions tend to run high around them.By the lunar eclipse on March 3, you're more likely to reach clarity or resolution around what this reset has set in motion.The night before the New Moon and eclipse, the 14th lunar day of the dark fortnight, is called Mahā-Śivarātri, the ‘great night of Śiva'. Śiva wears the crescent Moon in his hair, a symbol of the Moon's resetting of time. This year, this is an even more powerful reset because it's an eclipse; a vigil at the edge of dissolution, where everything falls silent for a time.

Malicious Chrome extensions pose as AI tools. Google says nation-states are increasingly abusing its Gemini artificial intelligence tool.  Data extortion group World Leaks deploys a new malware tool called RustyRocket. An Atlanta healthcare provider data breach affects over 625,000. Apple patches an iOS zero-day that's been around since version 1.0. A government shutdown would furlough more than half of CISA's staff. Dutch police arrest the alleged seller of the JokerOTP phishing automation service. Our guest is Simon Horswell, Senior Fraud Specialist at Entrust, discussing evolving romance scams for Valentine's Day. Fun with filters provides fuel for phishers.  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 Today we are joined by Simon Horswell, Senior Fraud Specialist at Entrust, discussing evolving romance scams for Valentine's Day. If you enjoyed this conversation, tune into Hacking Humans to hear the full interview. Selected Reading Fake AI Chrome extensions with 300K users steal credentials, emails (Bleeping Computer) Nation-state hackers ramping up use of Gemini for target reconnaissance, malware coding, Google says (The Record) World Leaks Ransomware Adds Custom Malware ‘RustyRocket' to Attacks (Infosecurity Magazine) ApolloMD Data Breach Impacts 626,000 Individuals (SecurityWeek) Apple patches decade-old iOS zero-day exploited in the wild (The Register) CISA: DHS Funding Lapse Would Sideline Federal Cyber Staff (Gov Infosecurity) CISA Shares Lessons Learned from an Incident Response Engagement (CISA.gov) Police arrest seller of JokerOTP MFA passcode capturing tool (Bleeping Computer) What Can the AI Work Caricature Trend Teach Us About the Risks of Shadow AI? (Fortra) 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

After years of ignoring and maligning Windows, Microsoft has finally woken up and is making some happy noises. Last week, we discussed how Microsoft plans to improve the quality of Windows and that there are already many signs of that work in various security features and new OneDrive Folder Backup changes - plus those two new direct reports to Nadella. Then, Microsoft announced its Windows Baseline Security Mode and User Transparency and Consent initiatives with questions about the timing. And now, Microsoft just explained Windows 11 version 26H1, and it's not like 24H2 at all despite being tied to Snapdragon X2 silicon.Something happened ... and that something is tied to 26H1 26H1: Only for Snapdragon X2, a "scoped release," based on a "different core" from 24H2 and 25H2 You cannot upgrade 24H2 or 25H2 to 26H1 You cannot upgrade 26H1 to 26H2 (!) - instead, those on 26H1 "will have a path to update in a future Windows release." - Is that future Windows release Windows 12? Probably 24H2, 25H2, and 26H1 will all have the same user-facing features, this has been the case with all support Windows (11) versions for 2+ years (Remember, this is not what happened with 24H2. Shipped early on Snapdragon X1, but was made available to all Windows 11 PCs later that year) So why is this happening now? Fortune 500/corporate customer pushback on AI is one guess This is GOOD news, however it all unfolds More Windows 11 Yesterday was Patch Tuesday, so get to work. Updates this month include: Agent in Settings (Copilot+ PCs only) improvements. Settings improvements, cross-device Resume improvements, Windows MIDI Services improvements, Narrator improvements, Smart App Control improvements, Windows Hello New ESS improvements, and File Explorer improvements Somewhat related to the quality/security push noted above, Microsoft is rolling out new Secure Boot certificates this year for older (pre-2024/25) PCs Microsoft announces a Store CLI that does (almost) nothing new compared to winget New Dev and Beta builds with minor changes: Emoji 16.0, camera improvements, various fixes More earnings Amazon hits $213.4 billion in revenues, will spend $200 billion CAPEX/AI infrastructure this fiscal year, more than Google ($175/$185 billion) or Microsoft (estimated $150+ billion) Qualcomm $12.25 billion in revenues, up 5 percent Alphabet/Google - Up 18 percent (!) to $113.8 billion - 750 million MAUs on Gemini, 74 percent of revenues come from advertising Spotify - somehow has over 750 million MAUs now AI and dev OpenAI and Anthropic release dueling agentic AI coding models that do more than agentic AI coding within minutes of each other Ads appear in ChatGPT Free and Go as threatened Duck.ai adds private, anonymous real-time AI voice chat NET 11 Preview 1 arrives, but there's nothing major here Xbox & games Microsoft announces the 2025 Xbox Excellence Awards Celebrate 35 years of Id Software - Castle Wolfenstein 3D was a wake-up call for PC gaming, but DOOM was a miracle, and Quake was a real WTF moment Sony sold 8 million PlayStation 5s (down 16 percent YOY) in the holiday quarter, 92 million (!) overall Valve predictably delays the vaporware Steam Machine Epic Games is having a winter sale - for example, Silent Hill 2, GTA V Enhanced are 50 percentR These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/970 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: threatlocker.com/twit helixsleep.com/windows trustedtech.team/windowsweekly365 cachefly.com/twit

Benjamin and Chance react to the disappointing news shared by Bloomberg's Mark Gurman that the new Siri features are facing even more delays, but in happier news, a bunch of iPhones, iPads and Macs are due for an imminent refresh. Meanwhile, Tim Cook reminisces ahead of Apple's 50th birthday.   And in Happy Hour Plus, thoughts on the design of the Ferrari Luce and Jony Ive's sniping comments about his former employer. Sponsored by Shopify: See less carts go abandoned and more sales. Sign up for a $1 per month trial at shopify.com/happyhour. Sponsored by Square: Get up to $200 off Square hardware when you sign up at square.com/go/happyhour. Sponsored by 1Password: Take the first step to better security by securing your team's credentials. Find out more at 1password.com/happyhour and start securing every login. Hosts Chance Miller @ChanceHMiller on Twitter @ChanceHMiller on Instagram @ChanceHMiller on Threads Benjamin Mayo @bzamayo on Twitter @bzamayo@mastodon.social @bzamayo on Threads Subscribe, Rate, and Review Apple Podcasts Overcast Spotify 9to5Mac Happy Hour Plus Subscribe to 9to5Mac Happy Hour Plus! Support Benjamin and Chance directly with Happy Hour Plus! 9to5Mac Happy Hour Plus includes:  Ad-free versions of every episode  Pre- and post-show content Bonus episodes Join for $5 per month or $50 a year at 9to5mac.com/join.  Feedback Submit #Ask9to5Mac questions on Twitter, Mastodon, or Threads Email us feedback and questions to happyhour@9to5mac.com Links iOS 26.3: Here's what's new for your iPhone Apple releases iOS 26.3 for iPhone, here's what's new iOS 26.4: Here's when Apple will release the first beta Report: M5 Pro and M5 Max MacBook Pro could launch 'as early as' March 2nd New iPhone launching this month with four key changes: report iPhone 17e 'due imminently' with three key upgrades, no price change: report New MacBook Air coming soon: Here's what we know Apple's cheapest iPad to get Apple Intelligence support at just the right time Apple reportedly pushing back Gemini-powered Siri features beyond iOS 26.4 Apple's iOS 26.4 Siri Update Runs Into Snags in Internal Testing; iOS 26.5, 27 Tim Cook promises Apple will celebrate its upcoming 50th anniversary Latest macOS 26.3 beta adds to signs that new Macs are imminent Leak suggests Apple's M5 Pro and M5 Max may be the same chip Apple reportedly bringing third-party AI chatbots to CarPlay Apple Plans to Allow Outside Voice-Controlled AI Chatbots in CarPlay Apple removing 'iTunes Wish List' feature, here's how to migrate selections New iPad and iPad Air models should be launching soon, but don't get too excited iTunes might be more popular than you think, per report Ferrari reveals name and interior of its first electric car | Electrek Jony Ive Ferrari interior might be a glimpse of the Apple Car Wired Interview with Jony Ive

Leo Laporte and Paris Martineau go head-to-head over whether today's AI breakthroughs are truly unprecedented or history repeating itself. Hear what happens when the show's hosts use cutting-edge tools to challenge each other's optimism, skepticism, and predictions for the future of work. Something Big Is Happening Building a C compiler with a team of parallel Claudes Amazon's $8 billion Anthropic investment balloons to $61 billion Google is going for the jugular — by doubling capex and outspending the rest of Big Tech Google's Gemini app has surpassed 750M monthly active users OpenAI's Meta makeover ChatGPT's deep research tool adds a built-in document viewer so you can read its reports Alexa+, Amazon's AI assistant, is now available to everyone in the U.S. Amazon Plans To Use AI To Speed Up TV and Film Production AI didn't kill customer support. It's rebuilding it Worried about AI taking jobs? Ex-Microsoft exec tells parents what kind of education matters most for their kids. A new bill in New York would require disclaimers on AI-generated news content AI Bots Are Now a Signifigant Source of Web Traffic Crypto.com places $70M bet on AI.com domain ahead of Super Bowl Frontier AI agents violate ethical constraints 30–50% of time, pressured by KPIs How To Think About AI: Is It The Tool, Or Are You? LEO! Reliability of LLMs as medical assistants for the general public: a randomized preregistered study HBR: AI Doesn't Reduce Work—It Intensifies It As AI enters the operating room, reports arise of botched surgeries and misidentified body parts Waymo Exec Admits Remote Operators in Philippines Help Guide US Robotaxis Medicare's new pilot program taps AI to review claims. Here's why it's risky Section 230 Turns 30; Both Parties Want It Gone—For Contradictory Reasons Meet Gizmo: A TikTok for interactive, vibe-coded mini apps The Evolution of Bengt Betjänt Uber Eats adds AI assistant to help with grocery shopping Is having AI ghostwrite your Valentine's Day messages a good idea? As Saudi Arabia's 100-Mile Skyscraper Crumbles, They're Replacing It With the Most Desperate Thing Imaginable YouTube Argues It Isn't Social Media in Landmark Tech Addiction Trial 'Man down:' Watch Amazon delivery drone crash in North Texas Understanding Neural Network, Visually Leo's AI Journey The TIMELINE TWiT x 2 in Super Bowl commercials Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. 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: preview.modulate.ai Melissa.com/twit spaceship.com/twit

Listen to a recap of the top stories of the day from 9to5Mac. 9to5Mac Daily is available on iTunes and Apple's Podcasts app, Stitcher, TuneIn, Google Play, or through our dedicated RSS feed for Overcast and other podcast players. Sponsored by Stuff: Stuff helps you get everything out of your head and into a simple, elegant system—closing open loops and reducing mental stress. Use code 9TO5 at checkout for 50% off your first year. New episodes of 9to5Mac Daily are recorded every weekday. Subscribe to our podcast in Apple Podcast or your favorite podcast player to guarantee new episodes are delivered as soon as they're available. Stories discussed in this episode: Apple reportedly pushing back Gemini-powered Siri features beyond iOS 26.4 Apple releases iOS 26.3 for iPhone, here's what's new Hands-on: iOS 26.3 changes and features [Video] Apple Music doubles the penalty for fraudulent streaming Listen & Subscribe: Apple Podcasts Overcast RSS Spotify TuneIn Google Podcasts Subscribe to support Chance directly with 9to5Mac Daily Plus and unlock: Ad-free versions of every episode Bonus content Catch up on 9to5Mac Daily episodes! Don't miss out on our other daily podcasts: Quick Charge 9to5Toys Daily Share your thoughts! Drop us a line at happyhour@9to5mac.com. You can also rate us in Apple Podcasts or recommend us in Overcast to help more people discover the show.

Bitcoin is no longer just a savings technology. It's collateral for the real economy. Andrew Hohns, Founder & CEO of Newmarket Capital and Battery Finance, explains how his firm structured one of the first Bitcoin-backed real estate loans, why credit markets desperately need Bitcoin, and how BTC could reshape mortgages, commercial buildings, and even small-business lending. We discuss: Why Bitcoin is pristine collateral for credit instruments The first Bitcoin-backed apartment building Bitcoin mortgages vs traditional financing Will institutions change or be changed by Bitcoin ---- Order Natalie's new book "Bitcoin is For Everyone," a simple introduction to Bitcoin and what's broken in our current financial system: https://amzn.to/3WzFzfU  --- Coin Stories is powered by Gemini. Invest as you spend with the Gemini Credit Card. Sign up today to earn a $200 intro Bitcoin bonus. The Gemini Credit Card is issued by WebBank. See website for rates & fees. Learn more at https://www.gemini.com/natalie  ---- Ledn is the global leader in Bitcoin-backed loans, issuing over $9 billion in loans since 2018, and they were the first to offer proof of reserves. With Ledn, you get custody loans, no credit checks, no monthly payments, and more. Get .25% off your first loan, learn more at https://www.Ledn.io/natalie  ---- Earn passive Bitcoin income with industry-leading uptime, renewable energy, ideal climate, expert support, and one month of free hosting when you join Abundant Mines at https://www.abundantmines.com/natalie  ---- Natalie's Bitcoin Product Partners: For easy, low-cost, instant Bitcoin payments, I use Speed Lightning Wallet. Play Bitcoin trivia and win up to 1 million sats! Download and use promo code COINSTORIES10 for 5,000 free sats: https://www.speed.app/coinstories  Block's Bitkey Cold Storage Wallet was named to TIME's prestigious Best Inventions of 2024 in the category of Privacy & Security. Get 20% off using code STORIES at https://bitkey.world   Master your Bitcoin self-custody with 1-on-1 help and gain peace of mind with the help of The Bitcoin Way: https://www.thebitcoinway.com/natalie  With BitcoinIRA, you can invest in bitcoin 24/7 inside a tax-advantaged IRA. Choose a Traditional IRA to defer taxes, or a Roth IRA for tax-free withdrawals later. Take control of your future with BitcoinIRA: https://www.bitcoinira.com/natalie  Natalie's Upcoming Events: Bitcoin 2026 will be here before you know it. Get 10% off Early Bird passes using the code HODL: https://tickets.b.tc/event/bitcoin-2026?promoCodeTask=apply&promoCodeInput=  Strategy World 2026 in Las Vegas on February 23-26th - Use code HODL for discounted tickets: https://www.strategysoftware.com/world26    Extra Services to Consider: Protect yourself from SIM Swaps that can hack your accounts and steal your Bitcoin. Join America's most secure mobile service, trusted by CEOs, VIPs and top corporations: https://www.efani.com/natalie   Ditch your fiat health insurance like I did four years ago! Join me at CrowdHealth: www.joincrowdhealth.com/natalie  ---- This podcast is for educational purposes and should not be construed as official investment advice. ---- VALUE FOR VALUE — SUPPORT NATALIE'S SHOWS Strike ID https://strike.me/coinstoriesnat/ Cash App $CoinStories #money #Bitcoin #investing

Leo Laporte and Paris Martineau go head-to-head over whether today's AI breakthroughs are truly unprecedented or history repeating itself. Hear what happens when the show's hosts use cutting-edge tools to challenge each other's optimism, skepticism, and predictions for the future of work. Something Big Is Happening Building a C compiler with a team of parallel Claudes Amazon's $8 billion Anthropic investment balloons to $61 billion Google is going for the jugular — by doubling capex and outspending the rest of Big Tech Google's Gemini app has surpassed 750M monthly active users OpenAI's Meta makeover ChatGPT's deep research tool adds a built-in document viewer so you can read its reports Alexa+, Amazon's AI assistant, is now available to everyone in the U.S. Amazon Plans To Use AI To Speed Up TV and Film Production AI didn't kill customer support. It's rebuilding it Worried about AI taking jobs? Ex-Microsoft exec tells parents what kind of education matters most for their kids. A new bill in New York would require disclaimers on AI-generated news content AI Bots Are Now a Signifigant Source of Web Traffic Crypto.com places $70M bet on AI.com domain ahead of Super Bowl Frontier AI agents violate ethical constraints 30–50% of time, pressured by KPIs How To Think About AI: Is It The Tool, Or Are You? LEO! Reliability of LLMs as medical assistants for the general public: a randomized preregistered study HBR: AI Doesn't Reduce Work—It Intensifies It As AI enters the operating room, reports arise of botched surgeries and misidentified body parts Waymo Exec Admits Remote Operators in Philippines Help Guide US Robotaxis Medicare's new pilot program taps AI to review claims. Here's why it's risky Section 230 Turns 30; Both Parties Want It Gone—For Contradictory Reasons Meet Gizmo: A TikTok for interactive, vibe-coded mini apps The Evolution of Bengt Betjänt Uber Eats adds AI assistant to help with grocery shopping Is having AI ghostwrite your Valentine's Day messages a good idea? As Saudi Arabia's 100-Mile Skyscraper Crumbles, They're Replacing It With the Most Desperate Thing Imaginable YouTube Argues It Isn't Social Media in Landmark Tech Addiction Trial 'Man down:' Watch Amazon delivery drone crash in North Texas Understanding Neural Network, Visually Leo's AI Journey The TIMELINE TWiT x 2 in Super Bowl commercials Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. 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: preview.modulate.ai Melissa.com/twit spaceship.com/twit

After years of ignoring and maligning Windows, Microsoft has finally woken up and is making some happy noises. Last week, we discussed how Microsoft plans to improve the quality of Windows and that there are already many signs of that work in various security features and new OneDrive Folder Backup changes - plus those two new direct reports to Nadella. Then, Microsoft announced its Windows Baseline Security Mode and User Transparency and Consent initiatives with questions about the timing. And now, Microsoft just explained Windows 11 version 26H1, and it's not like 24H2 at all despite being tied to Snapdragon X2 silicon.Something happened ... and that something is tied to 26H1 26H1: Only for Snapdragon X2, a "scoped release," based on a "different core" from 24H2 and 25H2 You cannot upgrade 24H2 or 25H2 to 26H1 You cannot upgrade 26H1 to 26H2 (!) - instead, those on 26H1 "will have a path to update in a future Windows release." - Is that future Windows release Windows 12? Probably 24H2, 25H2, and 26H1 will all have the same user-facing features, this has been the case with all support Windows (11) versions for 2+ years (Remember, this is not what happened with 24H2. Shipped early on Snapdragon X1, but was made available to all Windows 11 PCs later that year) So why is this happening now? Fortune 500/corporate customer pushback on AI is one guess This is GOOD news, however it all unfolds More Windows 11 Yesterday was Patch Tuesday, so get to work. Updates this month include: Agent in Settings (Copilot+ PCs only) improvements. Settings improvements, cross-device Resume improvements, Windows MIDI Services improvements, Narrator improvements, Smart App Control improvements, Windows Hello New ESS improvements, and File Explorer improvements Somewhat related to the quality/security push noted above, Microsoft is rolling out new Secure Boot certificates this year for older (pre-2024/25) PCs Microsoft announces a Store CLI that does (almost) nothing new compared to winget New Dev and Beta builds with minor changes: Emoji 16.0, camera improvements, various fixes More earnings Amazon hits $213.4 billion in revenues, will spend $200 billion CAPEX/AI infrastructure this fiscal year, more than Google ($175/$185 billion) or Microsoft (estimated $150+ billion) Qualcomm $12.25 billion in revenues, up 5 percent Alphabet/Google - Up 18 percent (!) to $113.8 billion - 750 million MAUs on Gemini, 74 percent of revenues come from advertising Spotify - somehow has over 750 million MAUs now AI and dev OpenAI and Anthropic release dueling agentic AI coding models that do more than agentic AI coding within minutes of each other Ads appear in ChatGPT Free and Go as threatened Duck.ai adds private, anonymous real-time AI voice chat NET 11 Preview 1 arrives, but there's nothing major here Xbox & games Microsoft announces the 2025 Xbox Excellence Awards Celebrate 35 years of Id Software - Castle Wolfenstein 3D was a wake-up call for PC gaming, but DOOM was a miracle, and Quake was a real WTF moment Sony sold 8 million PlayStation 5s (down 16 percent YOY) in the holiday quarter, 92 million (!) overall Valve predictably delays the vaporware Steam Machine Epic Games is having a winter sale - for example, Silent Hill 2, GTA V Enhanced are 50 percentR These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/970 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: threatlocker.com/twit helixsleep.com/windows trustedtech.team/windowsweekly365 cachefly.com/twit

After years of ignoring and maligning Windows, Microsoft has finally woken up and is making some happy noises. Last week, we discussed how Microsoft plans to improve the quality of Windows and that there are already many signs of that work in various security features and new OneDrive Folder Backup changes - plus those two new direct reports to Nadella. Then, Microsoft announced its Windows Baseline Security Mode and User Transparency and Consent initiatives with questions about the timing. And now, Microsoft just explained Windows 11 version 26H1, and it's not like 24H2 at all despite being tied to Snapdragon X2 silicon.Something happened ... and that something is tied to 26H1 26H1: Only for Snapdragon X2, a "scoped release," based on a "different core" from 24H2 and 25H2 You cannot upgrade 24H2 or 25H2 to 26H1 You cannot upgrade 26H1 to 26H2 (!) - instead, those on 26H1 "will have a path to update in a future Windows release." - Is that future Windows release Windows 12? Probably 24H2, 25H2, and 26H1 will all have the same user-facing features, this has been the case with all support Windows (11) versions for 2+ years (Remember, this is not what happened with 24H2. Shipped early on Snapdragon X1, but was made available to all Windows 11 PCs later that year) So why is this happening now? Fortune 500/corporate customer pushback on AI is one guess This is GOOD news, however it all unfolds More Windows 11 Yesterday was Patch Tuesday, so get to work. Updates this month include: Agent in Settings (Copilot+ PCs only) improvements. Settings improvements, cross-device Resume improvements, Windows MIDI Services improvements, Narrator improvements, Smart App Control improvements, Windows Hello New ESS improvements, and File Explorer improvements Somewhat related to the quality/security push noted above, Microsoft is rolling out new Secure Boot certificates this year for older (pre-2024/25) PCs Microsoft announces a Store CLI that does (almost) nothing new compared to winget New Dev and Beta builds with minor changes: Emoji 16.0, camera improvements, various fixes More earnings Amazon hits $213.4 billion in revenues, will spend $200 billion CAPEX/AI infrastructure this fiscal year, more than Google ($175/$185 billion) or Microsoft (estimated $150+ billion) Qualcomm $12.25 billion in revenues, up 5 percent Alphabet/Google - Up 18 percent (!) to $113.8 billion - 750 million MAUs on Gemini, 74 percent of revenues come from advertising Spotify - somehow has over 750 million MAUs now AI and dev OpenAI and Anthropic release dueling agentic AI coding models that do more than agentic AI coding within minutes of each other Ads appear in ChatGPT Free and Go as threatened Duck.ai adds private, anonymous real-time AI voice chat NET 11 Preview 1 arrives, but there's nothing major here Xbox & games Microsoft announces the 2025 Xbox Excellence Awards Celebrate 35 years of Id Software - Castle Wolfenstein 3D was a wake-up call for PC gaming, but DOOM was a miracle, and Quake was a real WTF moment Sony sold 8 million PlayStation 5s (down 16 percent YOY) in the holiday quarter, 92 million (!) overall Valve predictably delays the vaporware Steam Machine Epic Games is having a winter sale - for example, Silent Hill 2, GTA V Enhanced are 50 percentR These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/970 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: threatlocker.com/twit helixsleep.com/windows trustedtech.team/windowsweekly365 cachefly.com/twit

Leo Laporte and Paris Martineau go head-to-head over whether today's AI breakthroughs are truly unprecedented or history repeating itself. Hear what happens when the show's hosts use cutting-edge tools to challenge each other's optimism, skepticism, and predictions for the future of work. Something Big Is Happening Building a C compiler with a team of parallel Claudes Amazon's $8 billion Anthropic investment balloons to $61 billion Google is going for the jugular — by doubling capex and outspending the rest of Big Tech Google's Gemini app has surpassed 750M monthly active users OpenAI's Meta makeover ChatGPT's deep research tool adds a built-in document viewer so you can read its reports Alexa+, Amazon's AI assistant, is now available to everyone in the U.S. Amazon Plans To Use AI To Speed Up TV and Film Production AI didn't kill customer support. It's rebuilding it Worried about AI taking jobs? Ex-Microsoft exec tells parents what kind of education matters most for their kids. A new bill in New York would require disclaimers on AI-generated news content AI Bots Are Now a Signifigant Source of Web Traffic Crypto.com places $70M bet on AI.com domain ahead of Super Bowl Frontier AI agents violate ethical constraints 30–50% of time, pressured by KPIs How To Think About AI: Is It The Tool, Or Are You? LEO! Reliability of LLMs as medical assistants for the general public: a randomized preregistered study HBR: AI Doesn't Reduce Work—It Intensifies It As AI enters the operating room, reports arise of botched surgeries and misidentified body parts Waymo Exec Admits Remote Operators in Philippines Help Guide US Robotaxis Medicare's new pilot program taps AI to review claims. Here's why it's risky Section 230 Turns 30; Both Parties Want It Gone—For Contradictory Reasons Meet Gizmo: A TikTok for interactive, vibe-coded mini apps The Evolution of Bengt Betjänt Uber Eats adds AI assistant to help with grocery shopping Is having AI ghostwrite your Valentine's Day messages a good idea? As Saudi Arabia's 100-Mile Skyscraper Crumbles, They're Replacing It With the Most Desperate Thing Imaginable YouTube Argues It Isn't Social Media in Landmark Tech Addiction Trial 'Man down:' Watch Amazon delivery drone crash in North Texas Understanding Neural Network, Visually Leo's AI Journey The TIMELINE TWiT x 2 in Super Bowl commercials Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. 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: preview.modulate.ai Melissa.com/twit spaceship.com/twit

Super Bowl AI ads are probably why RAM is so expensive, ChatGPT ads are here, Apple's Siri update with Gemini is delayed “again,” Ferrari's first EV with Jony Ive-designed interior, and a wild  Vision Pro experiment.Ad-Free + Bonus EpisodesShow Notes via EmailCreative Effort - Jason's PodcastWatch on YouTube!Join the CommunityEmail Us: podcast@primarytech.fm@stephenrobles on Threads@jasonaten on Threads------------------------------Sponsors:Claude AI - Ready to tackle bigger problems? Sign up for Claude today and get 50% off Claude Pro, which includes access to Claude Code at: claude.ai/primaryCleanMyMac - Get Tidy Today! Try 7 days free and use my code PRIMARYTECH for 20% off at clnmy.com/PRIMARYTECH------------------------------Links from the showStephen's Vision Pro Experiment - YouTubeAn app developer is suing Apple for Sherlocking it with Continuity Camera | The VergeBen Affleck & Jennifer Aniston Star In 'Good Will Dunkin' Super Bowl Ad - YouTubeJurassic Park... Works | Big Game Commercial 2026 | Xfinity - YouTubeArtlist's Official Big Game Commercial 2026 - YouTubeYouTube TV Gets Cheaper Sports, News, and Entertainment Bundles - MacRumorsChatGPT's cheapest options now show you ads | The VergeHere are the brands bringing ads to ChatGPT | The VergeiOS 26.3 has fixes for 35+ security issues on iPhone, details here - 9to5MacApple's iOS 26.4 Siri Update Runs Into Snags in Internal Testing; iOS 26.5, 27 - BloombergDaring Fireball: Apple Is Delaying the ‘More Personalized Siri' Apple Intelligence FeaturesApple picks Google's Gemini to run AI-powered Siri coming this yearBlastDoor for Messages and IDS - Apple SupportApple Acquires 'Severance', Eyes Season 3 Start and Season 4 (Exclusive)Ferrari's first EV will have an interior designed by Jony Ive | The VergeGoogle Photos brings 'Create with AI' templates to iPhoneOpenAI's Jony Ive-Designed Device Delayed to 2027 - MacRumorsAirDrop-Quick Share Interoperability Expanding to More Android Phones - MacRumorsMeta launches AI algorithm personalization feature for ThreadsTikTok launches an opt-in Local Feed in the US leveraging users' precise location | TechCrunchCoinbase rolls out AI tool to 'give any agent a wallet' | The Block ★ Support this podcast ★

Episode 161: In this episode of Critical Thinking - Bug Bounty Podcast Justin Gives us some quick hits regarding CSRF and Cross Consumer Attacks, and also touches on some breaking questions surrounding HackerOneFollow us on twitter at: https://x.com/ctbbpodcastGot any ideas and suggestions? Feel free to send us any feedback here: info@criticalthinkingpodcast.ioShoutout to YTCracker for the awesome intro music!====== Links ======Follow your hosts Rhynorater, rez0 and gr3pme on X: https://x.com/Rhynoraterhttps://x.com/rez0__https://x.com/gr3pmeCritical Research Lab:https://lab.ctbb.show/ ====== Ways to Support CTBBPodcast ======Hop on the CTBB Discord at https://ctbb.show/discord!We also do Discord subs at $25, $10, and $5 - premium subscribers get access to private masterclasses, exploits, tools, scripts, un-redacted bug reports, etc.You can also find some hacker swag at https://ctbb.show/merch!Today's Sponsor: Join Justin at Zero Trust World in March and get $200 off registration with Code ZTWCTBB26https://ztw.com/====== This Week in Bug Bounty ======AS Watsonhttps://app.intigriti.com/programs/aswatson/watsons/detailYesWeHack 2026 Reporthttps://choose.yeswehack.com/bug-bounty-report-2026-trends-and-key-insights-yeswehack?utm_source=youtube&utm_medium=sponsor-critical-thinking&utm_campaign=yeswehack-report-2026 ====== Resources ======PhoneLeak: Data Exfiltration in Gemini via Phone Callhttps://blog.starstrike.ai/posts/phoneleak-data-exfiltration-in-gemini-via-phone-call/Max's Tweet about decreasing bountieshttps://x.com/0xw2w/status/2020788164378427483HackerOne General Terms and Conditionshttps://www.hackerone.com/terms/generalResearch Review #-2: RCE in Google's AI code editor Antigravity (sudi)https://www.youtube.com/watch?v=JqvJSF2UMyY====== Timestamps ======(00:00:00) Introduction(00:03:26) YesWeHack 2026 Report(00:09:12) CSRF Realizations & Data Exfiltration in Gemini via Phone Call(00:14:38) 7urb0's Youtube, HackerOne decreasing bounties and Section 3.1 controversy.(00:19:06) Cross Consumer Attacks

Leo Laporte and Paris Martineau go head-to-head over whether today's AI breakthroughs are truly unprecedented or history repeating itself. Hear what happens when the show's hosts use cutting-edge tools to challenge each other's optimism, skepticism, and predictions for the future of work. Something Big Is Happening Building a C compiler with a team of parallel Claudes Amazon's $8 billion Anthropic investment balloons to $61 billion Google is going for the jugular — by doubling capex and outspending the rest of Big Tech Google's Gemini app has surpassed 750M monthly active users OpenAI's Meta makeover ChatGPT's deep research tool adds a built-in document viewer so you can read its reports Alexa+, Amazon's AI assistant, is now available to everyone in the U.S. Amazon Plans To Use AI To Speed Up TV and Film Production AI didn't kill customer support. It's rebuilding it Worried about AI taking jobs? Ex-Microsoft exec tells parents what kind of education matters most for their kids. A new bill in New York would require disclaimers on AI-generated news content AI Bots Are Now a Signifigant Source of Web Traffic Crypto.com places $70M bet on AI.com domain ahead of Super Bowl Frontier AI agents violate ethical constraints 30–50% of time, pressured by KPIs How To Think About AI: Is It The Tool, Or Are You? LEO! Reliability of LLMs as medical assistants for the general public: a randomized preregistered study HBR: AI Doesn't Reduce Work—It Intensifies It As AI enters the operating room, reports arise of botched surgeries and misidentified body parts Waymo Exec Admits Remote Operators in Philippines Help Guide US Robotaxis Medicare's new pilot program taps AI to review claims. Here's why it's risky Section 230 Turns 30; Both Parties Want It Gone—For Contradictory Reasons Meet Gizmo: A TikTok for interactive, vibe-coded mini apps The Evolution of Bengt Betjänt Uber Eats adds AI assistant to help with grocery shopping Is having AI ghostwrite your Valentine's Day messages a good idea? As Saudi Arabia's 100-Mile Skyscraper Crumbles, They're Replacing It With the Most Desperate Thing Imaginable YouTube Argues It Isn't Social Media in Landmark Tech Addiction Trial 'Man down:' Watch Amazon delivery drone crash in North Texas Understanding Neural Network, Visually Leo's AI Journey The TIMELINE TWiT x 2 in Super Bowl commercials Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. 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: preview.modulate.ai Melissa.com/twit spaceship.com/twit

Jaeden and Connor discuss the latest developments from Anthropic, particularly the release of Opus 4.6. They explore the company's innovative features, including Agent Teams and a significant increase in context window size, and analyze Anthropic's growing impact on the AI market, especially in comparison to competitors like OpenAI and Gemini. The conversation highlights the unique approach Anthropic takes towards AI safety and functionality, as well as its potential future in the industry.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiConor's AI Course: https://www.ai-mindset.ai/coursesConor's AI Newsletter: https://www.ai-mindset.ai/Jaeden's AI Hustle Community: https://www.skool.com/aihustleWatch on YouTube: https://youtu.be/P01MU1AlIlUChapters00:00 Introduction to Anthropic and Opus 4.601:25 Anthropic's Rise and Unique Approach05:25 Innovations in Opus 4.6: Agent Teams and Context Windows09:14 Anthropic's Market Impact and Future Prospects See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

After years of ignoring and maligning Windows, Microsoft has finally woken up and is making some happy noises. Last week, we discussed how Microsoft plans to improve the quality of Windows and that there are already many signs of that work in various security features and new OneDrive Folder Backup changes - plus those two new direct reports to Nadella. Then, Microsoft announced its Windows Baseline Security Mode and User Transparency and Consent initiatives with questions about the timing. And now, Microsoft just explained Windows 11 version 26H1, and it's not like 24H2 at all despite being tied to Snapdragon X2 silicon.Something happened ... and that something is tied to 26H1 26H1: Only for Snapdragon X2, a "scoped release," based on a "different core" from 24H2 and 25H2 You cannot upgrade 24H2 or 25H2 to 26H1 You cannot upgrade 26H1 to 26H2 (!) - instead, those on 26H1 "will have a path to update in a future Windows release." - Is that future Windows release Windows 12? Probably 24H2, 25H2, and 26H1 will all have the same user-facing features, this has been the case with all support Windows (11) versions for 2+ years (Remember, this is not what happened with 24H2. Shipped early on Snapdragon X1, but was made available to all Windows 11 PCs later that year) So why is this happening now? Fortune 500/corporate customer pushback on AI is one guess This is GOOD news, however it all unfolds More Windows 11 Yesterday was Patch Tuesday, so get to work. Updates this month include: Agent in Settings (Copilot+ PCs only) improvements. Settings improvements, cross-device Resume improvements, Windows MIDI Services improvements, Narrator improvements, Smart App Control improvements, Windows Hello New ESS improvements, and File Explorer improvements Somewhat related to the quality/security push noted above, Microsoft is rolling out new Secure Boot certificates this year for older (pre-2024/25) PCs Microsoft announces a Store CLI that does (almost) nothing new compared to winget New Dev and Beta builds with minor changes: Emoji 16.0, camera improvements, various fixes More earnings Amazon hits $213.4 billion in revenues, will spend $200 billion CAPEX/AI infrastructure this fiscal year, more than Google ($175/$185 billion) or Microsoft (estimated $150+ billion) Qualcomm $12.25 billion in revenues, up 5 percent Alphabet/Google - Up 18 percent (!) to $113.8 billion - 750 million MAUs on Gemini, 74 percent of revenues come from advertising Spotify - somehow has over 750 million MAUs now AI and dev OpenAI and Anthropic release dueling agentic AI coding models that do more than agentic AI coding within minutes of each other Ads appear in ChatGPT Free and Go as threatened Duck.ai adds private, anonymous real-time AI voice chat NET 11 Preview 1 arrives, but there's nothing major here Xbox & games Microsoft announces the 2025 Xbox Excellence Awards Celebrate 35 years of Id Software - Castle Wolfenstein 3D was a wake-up call for PC gaming, but DOOM was a miracle, and Quake was a real WTF moment Sony sold 8 million PlayStation 5s (down 16 percent YOY) in the holiday quarter, 92 million (!) overall Valve predictably delays the vaporware Steam Machine Epic Games is having a winter sale - for example, Silent Hill 2, GTA V Enhanced are 50 percentR These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/970 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: threatlocker.com/twit helixsleep.com/windows trustedtech.team/windowsweekly365 cachefly.com/twit

The story of The Cure: a tall tale of a truly unique British band. The Cure's story is a fantastical pop fable, but their trajectory has not been one of unbroken success. Along the way, their uneven, uneasy pop odyssey has taken in fierce intra-band tensions and fall-outs, numerous line-up changes and even a bitter court case that saw original group members feuding over payments and ownership of the band's name. There has been alcoholism, substance abuse and countless long, dark nights of the soul, many of which have been translated into luscious dark-rock symphonies. From gawky teenage art-punks in Crawley to gnomic, venerable rock royalty with 30 million record sales to their name, their journey has been a scarcely believable, vivid pop hallucination. The Cure: A Perfect Dream (Gemini Books, 2025) is the tall tale of a truly unique British band. It's the story of The Cure. This fully updated edition includes a deep dive into the band's long-awaited 14th studio album released in 2024: Songs of a Lost World. Ian Gittins has interviewed and reviewed The Cure during a 30-year career as a music writer on titles such as Melody Maker, Time Out, Q and the Guardian. He is the co-author with Motley Crew's Nikki Sixx of the 2007 New York Times best-seller The Heroin Diaries: A Year in the Life of a Shattered Rock Star. He lives in London. Ian Gittin's website. Bradley Morgan is a media arts professional in Chicago and author of U2's The Joshua Tree: Planting Roots in Mythic America (Backbeat Books, 2021), Frank Zappa's America (LSU Press, 2025), and U2: Until the End of the World (Gemini Books, 2025). He manages partnerships on behalf of CHIRP Radio 107.1 FM and is the director of its music film festival. Bradley on Facebook and Bluesky. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

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

After years of ignoring and maligning Windows, Microsoft has finally woken up and is making some happy noises. Last week, we discussed how Microsoft plans to improve the quality of Windows and that there are already many signs of that work in various security features and new OneDrive Folder Backup changes - plus those two new direct reports to Nadella. Then, Microsoft announced its Windows Baseline Security Mode and User Transparency and Consent initiatives with questions about the timing. And now, Microsoft just explained Windows 11 version 26H1, and it's not like 24H2 at all despite being tied to Snapdragon X2 silicon.Something happened ... and that something is tied to 26H1 26H1: Only for Snapdragon X2, a "scoped release," based on a "different core" from 24H2 and 25H2 You cannot upgrade 24H2 or 25H2 to 26H1 You cannot upgrade 26H1 to 26H2 (!) - instead, those on 26H1 "will have a path to update in a future Windows release." - Is that future Windows release Windows 12? Probably 24H2, 25H2, and 26H1 will all have the same user-facing features, this has been the case with all support Windows (11) versions for 2+ years (Remember, this is not what happened with 24H2. Shipped early on Snapdragon X1, but was made available to all Windows 11 PCs later that year) So why is this happening now? Fortune 500/corporate customer pushback on AI is one guess This is GOOD news, however it all unfolds More Windows 11 Yesterday was Patch Tuesday, so get to work. Updates this month include: Agent in Settings (Copilot+ PCs only) improvements. Settings improvements, cross-device Resume improvements, Windows MIDI Services improvements, Narrator improvements, Smart App Control improvements, Windows Hello New ESS improvements, and File Explorer improvements Somewhat related to the quality/security push noted above, Microsoft is rolling out new Secure Boot certificates this year for older (pre-2024/25) PCs Microsoft announces a Store CLI that does (almost) nothing new compared to winget New Dev and Beta builds with minor changes: Emoji 16.0, camera improvements, various fixes More earnings Amazon hits $213.4 billion in revenues, will spend $200 billion CAPEX/AI infrastructure this fiscal year, more than Google ($175/$185 billion) or Microsoft (estimated $150+ billion) Qualcomm $12.25 billion in revenues, up 5 percent Alphabet/Google - Up 18 percent (!) to $113.8 billion - 750 million MAUs on Gemini, 74 percent of revenues come from advertising Spotify - somehow has over 750 million MAUs now AI and dev OpenAI and Anthropic release dueling agentic AI coding models that do more than agentic AI coding within minutes of each other Ads appear in ChatGPT Free and Go as threatened Duck.ai adds private, anonymous real-time AI voice chat NET 11 Preview 1 arrives, but there's nothing major here Xbox & games Microsoft announces the 2025 Xbox Excellence Awards Celebrate 35 years of Id Software - Castle Wolfenstein 3D was a wake-up call for PC gaming, but DOOM was a miracle, and Quake was a real WTF moment Sony sold 8 million PlayStation 5s (down 16 percent YOY) in the holiday quarter, 92 million (!) overall Valve predictably delays the vaporware Steam Machine Epic Games is having a winter sale - for example, Silent Hill 2, GTA V Enhanced are 50 percentR These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/970 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: threatlocker.com/twit helixsleep.com/windows trustedtech.team/windowsweekly365 cachefly.com/twit

Leo Laporte and Paris Martineau go head-to-head over whether today's AI breakthroughs are truly unprecedented or history repeating itself. Hear what happens when the show's hosts use cutting-edge tools to challenge each other's optimism, skepticism, and predictions for the future of work. Something Big Is Happening Building a C compiler with a team of parallel Claudes Amazon's $8 billion Anthropic investment balloons to $61 billion Google is going for the jugular — by doubling capex and outspending the rest of Big Tech Google's Gemini app has surpassed 750M monthly active users OpenAI's Meta makeover ChatGPT's deep research tool adds a built-in document viewer so you can read its reports Alexa+, Amazon's AI assistant, is now available to everyone in the U.S. Amazon Plans To Use AI To Speed Up TV and Film Production AI didn't kill customer support. It's rebuilding it Worried about AI taking jobs? Ex-Microsoft exec tells parents what kind of education matters most for their kids. A new bill in New York would require disclaimers on AI-generated news content AI Bots Are Now a Signifigant Source of Web Traffic Crypto.com places $70M bet on AI.com domain ahead of Super Bowl Frontier AI agents violate ethical constraints 30–50% of time, pressured by KPIs How To Think About AI: Is It The Tool, Or Are You? LEO! Reliability of LLMs as medical assistants for the general public: a randomized preregistered study HBR: AI Doesn't Reduce Work—It Intensifies It As AI enters the operating room, reports arise of botched surgeries and misidentified body parts Waymo Exec Admits Remote Operators in Philippines Help Guide US Robotaxis Medicare's new pilot program taps AI to review claims. Here's why it's risky Section 230 Turns 30; Both Parties Want It Gone—For Contradictory Reasons Meet Gizmo: A TikTok for interactive, vibe-coded mini apps The Evolution of Bengt Betjänt Uber Eats adds AI assistant to help with grocery shopping Is having AI ghostwrite your Valentine's Day messages a good idea? As Saudi Arabia's 100-Mile Skyscraper Crumbles, They're Replacing It With the Most Desperate Thing Imaginable YouTube Argues It Isn't Social Media in Landmark Tech Addiction Trial 'Man down:' Watch Amazon delivery drone crash in North Texas Understanding Neural Network, Visually Leo's AI Journey The TIMELINE TWiT x 2 in Super Bowl commercials Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. 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: preview.modulate.ai Melissa.com/twit spaceship.com/twit

AI có thể tạo slide trong 5 phút. Tuy nhiên, vấn đề là: thuyết trình không phải là chuyện có bao nhiêu slide, hay tạo slide trong bao lâu.

The story of The Cure: a tall tale of a truly unique British band. The Cure's story is a fantastical pop fable, but their trajectory has not been one of unbroken success. Along the way, their uneven, uneasy pop odyssey has taken in fierce intra-band tensions and fall-outs, numerous line-up changes and even a bitter court case that saw original group members feuding over payments and ownership of the band's name. There has been alcoholism, substance abuse and countless long, dark nights of the soul, many of which have been translated into luscious dark-rock symphonies. From gawky teenage art-punks in Crawley to gnomic, venerable rock royalty with 30 million record sales to their name, their journey has been a scarcely believable, vivid pop hallucination. The Cure: A Perfect Dream (Gemini Books, 2025) is the tall tale of a truly unique British band. It's the story of The Cure. This fully updated edition includes a deep dive into the band's long-awaited 14th studio album released in 2024: Songs of a Lost World. Ian Gittins has interviewed and reviewed The Cure during a 30-year career as a music writer on titles such as Melody Maker, Time Out, Q and the Guardian. He is the co-author with Motley Crew's Nikki Sixx of the 2007 New York Times best-seller The Heroin Diaries: A Year in the Life of a Shattered Rock Star. He lives in London. Ian Gittin's website. Bradley Morgan is a media arts professional in Chicago and author of U2's The Joshua Tree: Planting Roots in Mythic America (Backbeat Books, 2021), Frank Zappa's America (LSU Press, 2025), and U2: Until the End of the World (Gemini Books, 2025). He manages partnerships on behalf of CHIRP Radio 107.1 FM and is the director of its music film festival. Bradley on Facebook and Bluesky. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/biography

Discover why and where the wealthy are moving. Are you on track for financial freedom...or not? Financial freedom is a combination of money, compounding and time (my McT Formula). How well you invest can make the biggest difference to your financial freedom and lifestyle. If you invested well for the long-term, what a difference it would make because the difference between investing $100k and earning 5 percent or 10 percent on your money over 30 years, is the difference between it growing to $432,194 or $1,744,940, an increase of over $1.3 million dollars. Your compounding rate, and how well you invest, matters!  INVESTING IS WHAT THE BE WEALTHY & SMART VIP EXPERIENCE IS ALL ABOUT - Invest in digital assets and stock ETFs for potential high compounding rates - Receive an Asset Allocation model with ticker symbols and what % to invest -Monthly LIVE investment webinars with Linda 10 months per year, with Q & A -Private VIP Facebook group with daily community interaction -Weekly investment commentary -Extra educational wealth classes available -Pay once, have lifetime access! NO recurring membership fees. -US and foreign investors are welcome -No minimum $ amount to invest -Tech Team available for digital assets (for hire per hour) For a limited time, enjoy a 50% savings on my private investing group, the Be Wealthy & Smart VIP Experience. Pay once and enjoy lifetime access without any recurring fees. Enter "SAVE50" to save 50%here: http://tinyurl.com/InvestingVIP Or set up a complimentary conversation to answer your questions about the Be Wealthy & Smart VIP Experience. Request an appointment to talk with Linda here: https://tinyurl.com/TalkWithLinda (yes, you talk to Linda!). SUBSCRIBE TO BE WEALTHY & SMART Click Here to Subscribe Via iTunes Click Here to Subscribe Via Stitcher on an Android Device Click Here to Subscribe Via RSS Feed LINDA'S WEALTH BOOKS 1. Get my book, "3 Steps to Quantum Wealth: The Wealth Heiress' Guide to Financial Freedom by Investing in Cryptocurrencies". 2. Get my book, "You're Already a Wealth Heiress, Now Think and Act Like One: 6 Practical Steps to Make It a Reality Now!" Men love it too! After all, you are Wealth Heirs. :) International buyers (if you live outside of the US) get my book here. WANT MORE FROM LINDA? Check out her programs. Join her on Instagram. WEALTH LIBRARY OF PODCASTS Listen to the full wealth library of podcasts from the beginning.  SPECIAL DEALS #Ad Apply for a Gemini credit card and get FREE XRP back (or any crypto you choose) when you use the card. Charge $3000 in first 90 days and earn $200 in crypto rewards when you use this link to apply and are approved: https://tinyurl.com/geminixrp This is a credit card, NOT a debit card. There are great rewards. Set your choice to EARN FREE XRP! #Ad Protect yourself online with a Virtual Private Network (VPN). Get 3 MONTHS FREE when you sign up for a NORD VPN plan here.  #Ad To safely and securely store crypto, I recommend using a Tangem wallet. Get a 10% discount when you purchase here. #Ad If you are looking to simplify your crypto tax reporting, use Koinly. It is highly recommended and so easy for tax reporting. You can save $20, click here. Be Wealthy & Smart,™ is a personal finance show with self-made millionaire Linda P. Jones, America's Wealth Mentor.™ Learn simple steps that make a big difference to your financial freedom.  (This post contains affiliate links. If you click on a link and make a purchase, I may receive a commission. There is no additional cost to you.)  

Patrick opens the hour reflecting on family, technology, and the way a simple promise to put down our phones reveals deeper truths about connection. He considers Elon Musk’s warnings about artificial intelligence, questioning not just the coming wave of change but what meaning and purpose look like in a society where work could disappear. Calls pour in, some recounting firsthand experience with isolation and the value of work, others applauding how AI transforms accessibility, while tough conversations around political controversies and race keep the hour urgent, restless, and real. Audio: Aaron Paul (from Breaking Bad) We owe it to our kids to put our phones down - https://x.com/newstart_2024/status/2021281366608924854?s=20 (00:19) Audio: Musk – Most important thing for AI safety is to keep AI “truth seeking” - https://x.com/r0ck3t23/status/2021034543243788467?s=46&t=m_l2itwnFvka2DG8_72nHQ (03:18) Audio: Musk, talking with Katie Miller - in the future, no once will need to worry about money – https://x.com/katiemillerpod/status/1998528084631249096?t=476 (08:09) Paul (email) - It was a surprise that you would give such a false witness to your listeners (21:45) Audio: Father speaks out against critical race theory at his children’s middle school - https://x.com/ThomasSowell/status/2021350967573348539?s=20 (30:33) Magdalena - I used to work in a forensic hospital, and the greatest desire of all of my patients was to work. I think without work, life is boring. (33:23) Mark - Drawing someone as an ape is not racist, it's just a caricature. (35:32) Maria - I think for a blind person using Alexa and Gemini is very helpful. I have been blind for 8 years and found AI has helped me. (41:38) Dave - I think you are leaving out an important fact about the ape video (45:10)

Silver, Gold and Crypto (oh my) Hang on – Wild ride here Superbowl, Olympics- Wait until you hear about the CAPex spending! Shakeup in Dietville PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter Interactive Brokers  Warm-Up - Silver, Gold and Crypto (oh my) - Need a stock for CTP - Hang on - Wild ride here - Superbowl, Olympics- Wait until you hear about the CAPex spending! - Shakeup in Dietville Markets - Massive moved during the week - - Bitcoin clipped $60k before rebounding - DJIA tops 50,000 for the first time - Wait until you hear about the CAPex spending! - CAT == 1,100 points on the DJIA in 2026 Superbowl and Superbowl ads - Game review - Any ad stick out? - $10M per ad this year - Half Time with Bad Bunny? - Anthropic busting on OpenAi Last Week! - Massive moved - quick calc showed that about $1T was wiped from market caps in the sell-off, particularly in tech names. - HOWEVER - Friday alone is estimated to have added $1.5T to market cap AI Ripping Through - Plenty of names getting cooked over AI announcements - First it was the software companies - Now there are names in legal and finance that got clocked - Today - Altruist.ai can do tax planning and that hurt companies in financial space Earnings Season Update - Reporting so far: 59% of S&P 500 companies have reported Q4 2025 results. - Beat rate: 76% have topped EPS estimates (vs. 5-yr average: 78% (slightly lower) vs. 10-yr average: 76% (in line) - Magnitude of beats (aggregate): earnings are 7.6% above estimates vs. 5-yr average: 7.7% (about the same) vs. 10-yr average: 7.0% (a bit better) - Nothing great,  like Goldilocks Earnings Highlights - Palantir (PLTR): Reported strong Q4 results early in the week , beating estimates with revenue ~$1.41B (vs. ~$1.33B expected) and EPS $0.25 (vs. $0.23). Guidance for 2026 was upbeat (~61% revenue growth). Shares rallied sharply initially (~7–11% post-earnings), but gave back some gains amid broader tech volatility (e.g., down ~11–22% in parts of the week from peaks). - AMD: Reported mid-week, beating EPS (~$1.53 vs. lower expectations) with solid data center growth (~39%). However, Q1 guidance disappointed relative to high expectations in the AI chip space. Shares sank dramatically — down ~15–17% the next day, with some reports noting up to 20%+ drops at points, contributing to broader chip sector pressure. - Alphabet (GOOGL/GOOG): Reported beating on revenue (~$113.8B) and EPS (~$2.82), with strong core performance. But capex guidance for 2026 ($175–$185B, roughly double prior levels) sparked AI spending worries. Shares dipped post-earnings (down ~0.5–5% initially, flat to lower the next day, with some volatility pulling it below key moving averages). - Amazon (AMZN): Reported after hours on February 5, with mixed results — EPS ~$1.95 (narrow miss vs. ~$1.97 expected), but solid overall. The big negative was a surprise $200B capex forecast for 2026 (well above expectations), tied to AI/cloud buildout. Shares plunged sharply — down ~7–10% in after-hours/extended trading, with Friday moves around -5–8% in some sessions. Recent Tech CAPEX announcements - Amazon (AMZN) — Guided to approximately $200 billion in capex for 2026 (a massive jump from ~$125–131 billion in 2025, with ~80% likely AI-related per analyst commentary). This was the largest single-company figure and a major surprise, contributing heavily to the week's "wild" reactions. - Alphabet (GOOGL/GOOG) — Guided to $175–185 billion in capex for 2026 (roughly double the $91 billion spent in 2025, far above analyst expectations of ~$115–119 billion). Emphasis was on AI compute capacity, servers, data centers, and networking to meet demand for Gemini and cloud services. - Meta Platforms (META) — Guidance from late January (but heavily discussed last week): $115–135 billion for 2026 (up significantly from ~$70–72 billion in 2025, potentially an ~87% increase). - Microsoft (MSFT) — No new full explicit 2026 guidance in early February (fiscal year runs July–June), but recent quarterly run-rate and analyst projections put it around $97–145 billion (with some sources citing ~$105 billion or higher based on Q2 spending trends and signals of continued growth from prior levels of ~$88 billion in FY2025). ------!!!!Combined 2026 capex projected at $635–665 billion (low/high ends) or up to $650–700 billion in some reports — a ~60–74% increase from their collective ~$381 billion in 2025. Market Reaction from all of this.... - Markets were a bit spooked on the Anthropic announcement earlier in the week - software sold off and set a sour mood - Microsoft dumped pretty hard as the amount of spend was higher than anticipated, especially with some slower growth in Azure. - Amazon took a beating on the increased spend they anticipate *(extra by $50B) - BUT: Friday markets rallied as there was realization that the $200B spend by Amazon would seep into the economy and fuel infrastructure spending along with chips, tech etc. Other Earnings of Interest -  Reddit reported fourth-quarter earnings on Thursday in which the social media company beat on the top and bottom lines. - The company said it expects first-quarter sales to come in the range of $595 million to $605 million, which is higher than Wall Street expectations of $577 million. - Reddit also announced a $1 billion share repurchase program. - Reddit gets about $250 million a year from OpenAi and Google to have your data for training their LLMs While we are on the subject - Friday, DJIA hit 50,000 - first time ever! - Up 1,200 point of which approx 350 was from caterpillar and 280 was from Goldman Sachs Hats off to WalMart - Walmart Inc. shares pushed its market capitalization past $1 trillion on Tuesday for the first time ever| - Big transformation over the pst year - Walmart has maintained its appeal to households looking for value, its online offerings are drawing new, wealthier shoppers seeking convenience. Google Bond Offering - Issuing several tranches of bonds, denominated in Stirling - one as long as 100 years - Would you buy that? - The Google parent is set to raise $20 billion from a US dollar bond offering on Monday — more than the $15 billion initially expected — and is also pitching investors on what would be its first ever offerings in Switzerland and the UK. - The latter would include a rare sale of 100-year bonds, the first time a tech company has tried such an offering since the dotcom frenzy of the late 1990s Fat Profits in Dietville - Really interesting sequence of events happening... - Hims launches compounded pill at prices as low as $49 per month - Analysts cite questions on efficacy, legality of pill - Hims' move shifts focus from Novo's strong Wegovy pill launch - Broader obesity market whipsawed as pricing pressure rises THEN.. - Hims and Hers Health shares dive 14% after hours on Friday (Down 25% on Monday) - FDA cites concerns over quality, safety, federal law - The U.S. Food and Drug Administration said on Friday it would take action against telehealth provider Hims & Hers, for its $49 weight-loss pill, including restricting access to the drug's ingredients and referring the company to the Department of Justice for potential violations of federal law. AND.... - Eli Lilly last Wednesday posted fourth-quarter earnings and revenue and 2026 guidance that blew past estimates, as demand for its blockbuster weight loss drug Zepbound and diabetes treatment Mounjaro soars. - The pharmaceutical giant anticipates its 2026 revenue will come in between $80 billion and $83 billion. Analysts expected revenue of $77.62 billion, according to LSEG. - Meanwhile, NOVO had a really bad outlook that took the shares down 13% after the report. Japan Markets Soar - Japanese stocks jumped to a record high Monday, leading gains in the region after Prime Minister Sanae Takaichi won a landmark election victory. - The ruling Liberal Democratic Party captured a two-thirds supermajority in the 465-seat lower house, public broadcaster NHK reported. - Japan's Nikkei 225 jumped past 57,000 for the first time before paring gains to close 3.9% higher at 56,363.94, while the Topix also notched a record high, closing at 3,783.94, up 2.3%. Employment Report? - Government shutdown is forcing them to postpone again (Which is dumb) - Number due this Wednesday - Maybe because of this:U.S. employers announced 108,435 layoffs for the month, up 118% from the same period a year ago and 205% from December 2025. The total marked the highest for any January since 2009. - At the same time, companies announced just 5,306 new hires, also the lowest January since 2009, which is when Challenger, Gray & Christmas began tracking such data. - Also, job openings fell sharply in December to 6.54 million, to their lowest since September 2020. - Available jobs are down by more than 900,000 just since October. - NO! Ai and advancements in tech have noting to do with this! NO NO NO M&A - Texas Instruments Inc. has reached an agreement to buy Silicon Laboratories Inc. for about $7.5 billion, deepening its exposure to several markets for chips. - Silicon Labs investors will receive $231 in cash for each share of the company's common stock and the transaction is expected to close in the first half of 2027. - The transaction still needs to win approval by investors in Silicon Labs and shares of Silicon Labs surged by 51% to $206.48 after the announcement. Inflation - This helps - PepsiCo (PEP.O), opens new tab will cut prices on core brands such as Lay's and Doritos by up to 15% following a consumer backlash against several previous price hikes, the snacks and beverage maker said on Tuesday after it topped fourth-quarter results. Miran - Moving - Federal Reserve Governor Stephen Miran is leaving his post as chair of the Council of Economic Advisers, CNBC has confirmed. - He joined the CEA in January 2025, but had been on leave from that post since last September when he filled the unexpired term of former Fed Governor Adriana Kugler.- He reamins on Fed board No Biggie???? - There are some astonishing cased being reported of Bad AI in the operating room - JNJ's TruDi Navigation System - Since AI was added to the device, the FDA has received unconfirmed reports of at least 100 malfunctions and adverse events. - At least 10 people were injured between late 2021 and November 2025, according to the reports. Most allegedly involved errors in which the TruDi Navigation System misinformed surgeons about the location of their instruments while they were using them inside patients' heads during operations. - Cerebrospinal fluid reportedly leaked from one patient's nose. In another reported case, a surgeon mistakenly punctured the base of a patient's skull. In two other cases, patients each allegedly suffered strokes after a major artery was accidentally injured. Cuba - The main airport has putt out a bulletin that they are out of Jet Fuel - Blackouts and lack of other fuels are creating big problems - No airlines have stopped running at this point, but many will as they cannot refuel - This is a bigger problem for cargo planes (supplies) that may not be able to risk flying to Cuba as they will not be able to get out. Love the Show? Then how about a Donation? ANNOUNCING THE WINNER OF THE THE CLOSEST TO THE PIN CUP 2025 Winners will be getting great stuff like the new "OFFICIAL" DHUnplugged Shirt!     FED AND CRYPTO LIMERICKS   See this week's stock picks HERE Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter