Podcasts about gtc

The PPI came in hot: What it means for interest rates… Why the market NEEDS a rate cut… Key takeaways from Nvidia's (NVDA) GTC conference… Is NVDA a buy at current levels? … And the latest massive tech layoffs. In this episode: Happy late St. Patrick's Day! (And my Irish car bomb story) [0:15] The PPI came in hot: What it means for interest rates [5:30] Why the market NEEDS a rate cut [7:57] Key takeaways from Nvidia's GTC conference [22:02] Is NVDA a buy at current levels? [24:47] Is AI to blame for the massive tech layoffs? [38:14] My pick for the March Madness champion [48:56] Did you like this episode? Get more Wall Street Unplugged FREE each week in your inbox. Sign up here: https://curzio.me/syn_wsu Find Wall Street Unplugged podcast… --Curzio Research App: https://curzio.me/syn_app --iTunes: https://curzio.me/syn_wsu_i --Stitcher: https://curzio.me/syn_wsu_s --Website: https://curzio.me/syn_wsu_cat Follow Frank… X: https://curzio.me/syn_twt Facebook: https://curzio.me/syn_fb LinkedIn: https://curzio.me/syn_li

NVIDIAs DLSS 5 Ankündigung auf der GTC spaltet das Netz wie lange nichts mehr! Während CEO Jensen Huang vom revolutionären „GPT-Moment für Gaming-Grafik“ schwärmt, läuft die Community Sturm. Überall fällt das Wort „AI Slop“: Gamer fürchten zerstörte Entwickler-Visionen und generischen KI-Matsch statt echter Grafik-Meilensteine. In diesem Talk dröseln wir den kompletten Hype und die massive Kritik objektiv auf: Steht uns wirklich eine technische Revolution bevor oder geht die Industrie gerade einen völlig falschen Weg? Schreibt uns eure Meinung zu DLSS 5 direkt in die Kommentare! Alle Links zum GameStar Podcast und unseren Werbepartnern: https://linktr.ee/gamestarpodcast

Time Stamps ⏰00:07—Investing Fact Of The Week19:00—Nvidia's GTC Outlook28:00—Time To Sell Nvidia?36:00—Is The Crash Coming?46:00—Top Assets For The Next 12 Months?52:00—Commodities54:00—Robinhood Stock Outlook59:00—Invest Fest Sneak Peek1:05:00—4 Stocks You Hate1:16:00—Target Boycott Fiasco1:30:00—Will Oil Go Above $100?1:38:00—FICO1:40:00—2026 Market CycleIn this episode of Market Mondays, we break down the biggest stories shaping the market right now. From Nvidia's GTC conference and the future of AI, to the possibility of a market crash, oil potentially hitting $100, and the assets investors should be watching over the next 12 months.We also discuss the outlook for Robinhood, the growing conversation around commodities, and four stocks we are not fans of right now. Plus, we address the Target boycott controversy, take a look at the upcoming 2026 market cycle, and give a sneak peek at what's coming for Invest Fest.If you want to stay ahead of the market and understand where the opportunities are in the current economic environment, this episode is packed with insights for investors and entrepreneurs alike.#MarketMondays #StockMarket #Investing #Nvidia #AIStocks #StockMarketNews #FinancialEducation #InvestingTips #Robinhood #Commodities #OilPrices #InvestFest #EarnYourLeisure #WealthBuildingSupport this podcast at — https://redcircle.com/marketmondays/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Jensen Huang just stood on stage and said $1 trillion. He wasn't joking. NVIDIA's GTC 2026 keynote was a masterclass in flexing, and we're breaking down every layer of the cake. We walk through Jensen Huang's massive GTC 2026 keynote, from NVIDIA's $1 trillion business projection to the inference inflection point that's reshaping the entire AI industry. We dig into DLSS 5 and why AI-powered neural rendering is about to change gaming forever (sorry, gamers), NVIDIA's deep integration with OpenClaw and the launch of NemoClaw for enterprise agents, chips in space, and what it all means when every company becomes an agentic-as-a-service company. Plus the Dwarkesh podcast with Dylan Patel on the real bottlenecks in compute that nobody's talking about. JENSEN HUANG SAID ONE TRILLION DOLLARS AND DIDN'T BLINK. WE BLINKED. PS, we're now coming to you TWICE a week (both a little shorter). Come to our Discord: https://discord.gg/muD2TYgC8f Join our Patreon: https://www.patreon.com/AIForHumansShow AI For Humans Newsletter: https://aiforhumans.beehiiv.com/ Follow us for more on X @AIForHumansShow Join our TikTok @aiforhumansshow To book us for speaking, please visit our website: https://www.aiforhumans.show/   // Show Links // NVIDIA GTC 2026 Full Keynote with Jensen Huang https://www.youtube.com/live/jw_o0xr8MWU?si=VZAIG3E7vuUCwz6N DLSS 5: Breakthrough in Visual Fidelity for Games https://www.nvidia.com/en-us/geforce/news/dlss5-breakthrough-in-visual-fidelityfor-games/ DLSS 5 Official Trailer https://youtu.be/dJACkKbN-Eo?si=fIJvsV52---bOyTr Digital Foundry Deep Dive on DLSS 5 https://youtu.be/4ZlwTtgbgVA?si=g8TMgNlOWknKnqHo Good Til' Cancelled: The GTC Game https://x.com/SAlexashenko/status/2033585849586331985?s=20 Dwarkesh Podcast: Dylan Patel on Compute Bottlenecks and Chips https://youtu.be/mDG_Hx3BSUE?si=YnLEIVhsaCpdVQgi  

The S&P 500 needs to show defining strength Tuesday after a technical rebound Monday, says Kevin Green. He offers insight into the index futures as crude oil prices move higher. That's not the only geopolitical mover, with KG touching on President Trump delaying his meeting with China's president "by a month or so." He later offers takeaways from Nvidia (NVDA) CEO Jensen Huang's keynote speech during the GTC 2026 keynote speech, including expectations of $1 trillion in revenue through 2027 and a partnership with Uber Technologies (UBER).======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – / schwabnetwork Follow us on Facebook – / schwabnetwork Follow us on LinkedIn - / schwab-network About Schwab Network - https://schwabnetwork.com/about

Energy infrastructure continues to take a hit even as ships move through the Strait of Hormuz, causing crude oil prices to fluctuate. Kevin Green says the futures curve points to the U.S.-Iran War being a short-lived conflict. Both $60 and $120 "are in the cards" for oil, says KG, highlighting scenarios where both will manifest. On stock movers, he explains the "buy the rumor, sell the news" price reaction to Nvidia (NVDA) CEO Jensen Huang's GTC 2026 keynote speech. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Charles Schwab's Joe Mazzola makes the case that Nvidia (NVDA) is "cheap" as the company's GTC 2026 event hits its second day, despite investors staying wary on the AI trade. Tom White adds to the discussion by calling the Mag 7 giant's price action "puzzling" with a lack of bullish conviction despite partnership and tech advancements. Both offer example options trades for Nvidia as shares stay in tight consolidation. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

This week: OpenAI's Pentagon deal sparked the #QuitGPT movement with 2.5 million supporters, Anthropic got labeled a supply-chain risk by the DOD, AI-driven layoffs hit Oracle and Block hard, NVIDIA teased its biggest GTC yet, and Apple revealed a $599 AI laptop.Key Topics CoveredOpenAI's classified Pentagon deal sparks #QuitGPT revolt with 2.5M supporters and 295% surge in ChatGPT uninstallsPentagon labels Anthropic a supply-chain risk; OpenAI and Google employees rally behind Anthropic in courtOracle eyes 30,000 layoffs and Block cuts 40% of workforce as AI replaces jobs at scaleNVIDIA GTC 2026 preview: $26B open-source investment, new inference chip, and enterprise AI platform expectedApple announces rebuilt Siri with Google Gemini and the $599 MacBook Neo AI laptopEpisode Timestamps00:00 — OpenAI's Pentagon Deal and the #QuitGPT Revolt01:00 — Pentagon vs. Anthropic: The Supply-Chain Risk Showdown02:00 — AI Layoffs Hit Oracle, Block, and Atlassian03:00 — NVIDIA GTC 2026: The Super Bowl of AI04:00 — Apple's Mass-Market AI PlayAbout The AI WhyThe AI Why with Liam Lawson covers enterprise AI — how it's being implemented at scale, and why the people building it do what they do. New episodes every Tuesday (weekly news in 5 minutes) and Thursday (hour-long interviews with founders and C-suite execs).Our LinksFree Newsletter — https://newsletter.theaireport.ai/subscribeWebsite — https://www.theaireport.aiLiam's LinkedIn — https://www.linkedin.com/in/not-the-f1-driver-liam-lawson/Book Enterprise Training — https://www.upscaile.com/

SUMMARY DEL SHOW Futuros en rojo mientras arranca la reunión de la Fed, con el mercado esperando tasas sin cambios y atento al tono sobre inflación. Petróleo arriba de $100 con riesgo en Ormuz y aliados sin apoyar a EE. UU. para reabrir la ruta, manteniendo el miedo a disrupción e inflación. $NVDA en GTC habla de oportunidad de $1 Trillón hasta 2027, $TSLA firma $4.3 Billones con LGES para baterías LFP en Michigan y $AMZN lanza entregas de 1 y 3 horas con fee extra

Mardi 17 mars, François Sorel a reçu Claudia Cohen, journaliste chez Bloomberg, Frédéric Krebs, président de Krebs & Partners, et Frédéric Simottel, journaliste BFM Business. Ils se sont penchés sur les annonces de Nvidia sur les nouvelles puces et les avancées en IA lors du GTC, et la collaboration de Mistral avec Nvidia pour accélérer son modèle, dans l'émission Tech & Co, la quotidienne, sur BFM Business. Retrouvez l'émission du lundi au jeudi et réécoutez la en podcast.

【謝晨彥分析師Line官方帳號】 https://lin.ee/se5Bh8n 2026.03.18【記憶體非買不可？輝達GTC再釋利多，記憶體漲到2028年！】#華爾街見聞 謝晨彥分析師 ☆ 黃仁勳GTC演講重點分析 ☆ 台廠供應鏈GTC背板股 ☆ 輝達佈局AI Agent 這產業成關鍵！ 馬上加入Line帳號! 獲取更多股票訊息! LINE搜尋ID：@gp520 https://lin.ee/se5Bh8n 也可來電免付費專線洽詢任何疑問! 0800-66-8085 獲取更多股票訊息 #摩爾投顧 #謝晨彥 #分析師 #股怪教授 #股票 #台股 #飆股 #三大法人 #漲停 #選股 #技術分析 #波段 #獲利 #飆股啟航 #大賺 #美債 #華爾街見聞 -- Hosting provided by SoundOn

A senior Iranian official said the new Supreme Leader rejected proposals that were sent to Iran's Foreign Ministry by two intermediary countries, with the Leader stating it is not the right time for peace and that the US and Israel must be defeated.Israeli Defence Minister Katz said Iran's Top Security Chief Larijani was killed in the airstrike; Iran has not confirmed the status of Larijani.Crude gains in choppy trade as the Iranian war continues; metals move with the dollar.European bourses are mixed, Neste lifts the Utilities sector; US equity futures fall following Nvidia's GTC event.DXY muted, G10s mixed while AUD outperforms after the RBA hiked rates.Fixed income continues to be driven by energy and geopolitics.Looking ahead, highlights include US ADP Employment Weekly, Japanese Trade Balance (Feb), Comments from ECB's Nagel, and Supply from the US.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

David Faber and the Investment Committee debate how to trade oil and the market as turmoil in the energy sector grows. CNBC's Brian Sullivan joins us with the latest comments from Treasury Secretary Scott Bessent. Plus, CNBC's Kristina Partsinevelos joins us to discuss the latest news out of San Jose, California, where Nvidia is set to kick off its annual GTC event. The Committee debate how to trade the company ahead of the Jensen Huang's keynote speech. And later, the desk debate retail investors abandoning private credit and what it means for the sector.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Stocks rose while oil prices pulled back, Meta reports that it is planning to lay off more than 20 percent of its workforce and Nvidia shares rose ahead of its GTC conference, Next Rob Black event is Pints and Portfolios in Sunnyvale on Saturday April 18th 11:30am to 1:30pm sign up for exact location

Artificial Intelligence isn't coming… it's already here, and the pace of change is accelerating faster than most investors realize. In today's episode, we explore how AI is reshaping global markets, from technology and semiconductors to productivity, capital flows, and the next generation of innovation-driven companies. The implications go far beyond just tech stocks—AI is beginning to influence everything from corporate earnings to economic growth expectations. We'll also dive into highlights from NVIDIA GTC, where NVIDIA showcased the next wave of breakthroughs in artificial intelligence hardware and software. The conference made one thing clear: the AI arms race is accelerating, and the companies leading it could shape the future of the global economy. If you want to understand how this technological wave could influence markets, investment opportunities, and the pace of innovation, this episode connects the dots. Listen now:

Rick Ducat returns with Options Corner to highlight unusual options activity among stocks, ETFs and commodities. He points to headlines surrounding Nvidia (NVDA) as CEO Jensen Huang kicks off GTC 2026 — and a bearish trade worth millions of dollars. Rick sticks with the AI industry by highlighting a bullish Micron (MU) position and another odd trade in Carrier Global (CARR). ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – / schwabnetwork Follow us on Facebook – / schwabnetwork Follow us on LinkedIn - / schwab-network About Schwab Network - https://schwabnetwork.com/about

@ProsperTradingAcademy's Charles Moon offers an example options trade for Nvidia (NVDA) as its GTC conference kicks off later today, also pointing out areas to watch on the Mag 7 giant's stock chart. Sam Vadas runs through what investors are watching for, including robotics, inference, and other A.I.-related announcements. ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – / schwabnetwork Follow us on Facebook – / schwabnetwork Follow us on LinkedIn - / schwab-network About Schwab Network - https://schwabnetwork.com/about

The Information's Wayne Ma talks with TITV Host Akash Pasricha about Nvidia's GTC keynote and the company's new inference chip technology. We also talk with Khosla Ventures' Ethan Choi about the U.S.-China AI race and the rise of AI agents in super apps, AI Reporter Laura Bratton about why SaaS companies are quietly flagging AI as a major business risk in regulatory filings, and we get into industrial-scale data center hacks with Columnist Ann Davis Vaughan.Articles discussed on this episode: https://www.theinformation.com/newsletters/the-briefing/expect-gtc-nvidias-groq-chiphttps://www.theinformation.com/articles/figma-hubspot-ceos-say-fazed-risks-ai-agents-disclosures-say-otherwisehttps://www.theinformation.com/newsletters/ai-infrastructure/5-ingenious-hacks-boosting-ai-data-centersSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

Stocks rose while oil prices pulled back, Meta reports that it is planning to lay off more than 20 percent of its workforce and Nvidia shares rose ahead of its GTC conference, Next Rob Black event is Pints and Portfolios in Sunnyvale on Saturday April 18th 11:30am to 1:30pm sign up for exact locationSee omnystudio.com/listener for privacy information.

【謝晨彥分析師Line官方帳號】 https://lin.ee/se5Bh8n 2026.03.17【GTC登場 投信竟重壓這一檔!?】#華爾街見聞 謝晨彥分析師 ☆ 輝達GTC核心台廠 ☆ 技術升級如何牽動供應鏈？ ☆ 投信佈局哪些重點股？ 馬上加入Line帳號! 獲取更多股票訊息! LINE搜尋ID：@gp520 https://lin.ee/se5Bh8n 也可來電免付費專線洽詢任何疑問! 0800-66-8085 獲取更多股票訊息 #摩爾投顧 #謝晨彥 #分析師 #股怪教授 #股票 #台股 #飆股 #三大法人 #漲停 #選股 #技術分析 #波段 #獲利 #飆股啟航 #大賺 #美債 #華爾街見聞 -- Hosting provided by SoundOn

A fresh wave of semi catalysts lining up for next — from Nvidia's GTC event to Micron earnings and an AWS–Cerebras tie-up. What Fast Money Friend Gene Munster is watching, and what to expect from Nvidia's CEO Jensen Huang when he takes the stage. Plus Jefferies' David Zervos joins us with a simple message for investors: “Don't panic,” as traders weigh inflation risks, Meta's reported AI delay, and Boeing's push to fix wiring issues. Fast Money Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, Chris Cochrane dives into Apple’s $599 MacBook Neo – the cheapest Mac laptop ever made – and whether it spells trouble for Chromebook makers. He also covers Samsung’s CEO blaming AI for rising phone prices, Framework raising RAM prices for the third time in three months, Meta unveiling four custom AI chips, NVIDIA’s GTC 2026 conference preview, a billion-dollar bet against large language models, Microsoft’s game-changing Project Helix Xbox with native Steam support, Windows 11’s new Xbox Mode, and SpaceX gearing up for a critical Starship Flight 12 test. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Chris if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Apple MacBook Neo The lead story covers Apple’s MacBook Neo. It launched at $599 and marks the cheapest Mac laptop ever made. The device runs on the A18 Pro chip from the iPhone 16 Pro. Cochrane notes a solid market for students, casual users, and anyone who needs a reliable home laptop. However, he advises photographers and videographers to invest in a MacBook Air or Pro instead. The real question remains whether this kills Chromebook sales in education. Samsung CEO Blames AI for Price Hikes Cochrane tackles Samsung’s Galaxy S26 price increases. CEO TM Roh blamed AI infrastructure demand for the hikes. Meanwhile, DDR4 DRAM prices surged sevenfold in a single year. Cochrane points out the irony. Samsung manufactures memory chips, shifted production toward AI data centers, and now cites that same shortage to justify higher consumer prices. He calls the situation “a little shady” but appreciates the transparency. Framework RAM Prices Up Again The RAM crisis extends beyond phones. Framework raised RAM prices for the third consecutive time in three months. Cochrane reinforces advice from a recent episode. He urges listeners to buy now before prices climb further. Analysts project peak prices by mid-2026. The shortage could last through late 2027. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Meta Unveils Four Custom AI Chips Cochrane reports on Meta’s four new MTIA chip generations. The company aims to reduce its dependence on NVIDIA by building custom silicon. The MTIA 300 is already in production. New generations will ship every six months through 2027. The chips are built on open-source RISC-V architecture and manufactured by TSMC. NVIDIA GTC 2026 Preview NVIDIA’s GTC conference starts Monday in San Jose. Jensen Huang promises “chips the world has never seen.” Rumored architectures include Rubin Ultra and Feynman. The keynote streams free at nvidia.com on Monday at 11am Pacific. Cochrane notes that while companies like Meta are building chips to escape NVIDIA, competition will eventually catch up. Yann LeCun’s AMI Labs Raises $1.03 Billion Former Meta AI chief Yann LeCun raised $1.03 billion for AMI Labs at a $3.5 billion valuation. It marks the largest European seed round in history for a company just four months old. LeCun is building “world models” that learn from physical reality rather than text. Backers include Jeff Bezos, NVIDIA, and Samsung. Cochrane notes both approaches to AI can coexist. Microsoft Project Helix Microsoft revealed Project Helix at GDC 2026. For the first time, an Xbox will natively support Steam and GOG. Cochrane sees it as both desperate and inevitable. The only reason to buy from the Xbox store would be exclusives. He notes this is a breath of fresh air after months of talk that the Xbox era was ending. Dev kits ship in 2027 with a consumer launch likely late 2027 or 2028. Windows 11 Xbox Mode Microsoft is rolling out Xbox Mode to all Windows 11 PCs in April. The full-screen controller-optimized interface works with Steam, Epic, and Battle.net. Cochrane sees it as the first half of Microsoft’s two-phase gaming strategy. Xbox Mode trains users now. Project Helix delivers dedicated hardware later. He asks whether Sony and Nintendo will follow in Xbox’s footsteps. SpaceX Starship Flight 12 SpaceX announced stacking complete for the next Super Heavy booster at Starbase. Flight 12 targets April and debuts V3 hardware with Raptor 3 engines. Orbital refueling remains the critical unknown for NASA’s Artemis III moon landing. SpaceX has a track record of delivering eventually, just never on Elon’s original timeline. The post Is the MacBook Neo a Chromebook Killer? #1860 appeared first on Geek News Central.

Robert Lange, founding researcher at Sakana AI, joins Tim to discuss *Shinka Evolve* — a framework that combines LLMs with evolutionary algorithms to do open-ended program search. The core claim: systems like AlphaEvolve can optimize solutions to fixed problems, but real scientific progress requires co-evolving the problems themselves.GTC is coming, the premier AI conference, great opportunity to learn about AI. NVIDIA and partners will showcase breakthroughs in physical AI, AI factories, agentic AI, and inference, exploring the next wave of AI innovation for developers and researchers. Register for virtual GTC for free, using my link and win NVIDIA DGX Spark (https://nvda.ws/4qQ0LMg)• Why AlphaEvolve gets stuck — it needs a human to hand it the right problem. Shinka tries to invent new problems automatically, drawing on ideas from POET, PowerPlay, and MAP-Elites quality-diversity search.• The *architecture* of Shinka: an archive of programs organized as islands, LLMs used as mutation operators, and a UCB bandit that adaptively selects between frontier models (GPT-5, Sonnet 4.5, Gemini) mid-run. The credit-assignment problem across models turns out to be genuinely hard.• Concrete results — state-of-the-art circle packing with dramatically fewer evaluations, second place in an AtCoder competitive programming challenge, evolved load-balancing loss functions for mixture-of-experts models, and agent scaffolds for AIME math benchmarks.• Are these systems actually thinking outside the box, or are they parasitic on their starting conditions? When LLMs run autonomously, "nothing interesting happens." Robert pushes back with the stepping-stone argument — evolution doesn't need to extrapolate, just recombine usefully.• The AI Scientist question: can automated research pipelines produce real science, or just workshop-level slop that passes surface-level review? Robert is honest that the current version is more co-pilot than autonomous researcher.• Where this lands in 5-20 years — Robert's prediction that scientific research will be fundamentally transformed, and Tim's thought experiment about alien mathematical artifacts that no human could have conceived.Robert Lange: https://roberttlange.com/---TIMESTAMPS:00:00:00 Introduction: Robert Lange, Sakana AI and Shinka Evolve00:04:15 AlphaEvolve's Blind Spot: Co-Evolving Problems with Solutions00:09:05 Unknown Unknowns, POET, and Auto-Curricula for AI Science00:14:20 MAP-Elites and Quality-Diversity: Shinka's Evolutionary Architecture00:28:00 UCB Bandits, Mutations and the Vibe Research Vision00:40:00 Scaling Shinka: Meta-Evolution, Democratisation and the Three-Axis Model00:47:10 Applications, ARC-AGI and the Future of Work00:57:00 The AI Scientist and the Human Co-Pilot: Who Steers the Search?01:06:00 AI Scientist v2, Slop Critique and the Future of Scientific Publishing---REFERENCES:paper:[00:03:30] ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolutionhttps://arxiv.org/abs/2509.19349[00:04:15] AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discoveryhttps://arxiv.org/abs/2506.13131[00:06:30] Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agentshttps://arxiv.org/abs/2505.22954[00:09:05] Paired Open-Ended Trailblazer (POET)https://arxiv.org/abs/1901.01753[00:10:00] PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problemhttps://arxiv.org/abs/1112.5309[00:10:40] Automated Capability Discovery via Foundation Model Self-Explorationhttps://arxiv.org/abs/2502.07577[00:15:30] Illuminating Search Spaces by Mapping Elites (MAP-Elites)https://arxiv.org/abs/1504.04909[00:47:10] Automated Design of Agentic Systems (ADAS)https://arxiv.org/abs/2408.08435PDF : https://app.rescript.info/api/sessions/b8a9dcf60623657c/pdf/downloadTranscript: https://app.rescript.info/public/share/SDOD_3oXOcli3zTqcAtR8eibT5U3gam84oo4KRtI-Vk

Sam Vadas wraps up the trading week and previews the major events to keep on the calendar starting on Monday. She starts with a glimpse at Nvidia (NVDA) as investors and traders await CEO Jensen Huang's keynote address at their annual GTC event. On the earnings front, she highlights notable prints ahead for FedEx (FDX), Oklo (OKLO) and Macy's to name a few domestic names. But, Sam adds investors can't overlook Chinese equities Alibaba (BABA) and Tencent (TCEHY) as well as global central bank meetings taking place. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Jensen Huang's keynote GTC address is scheduled for Monday afternoon. Charles Schwab's Kevin Horner joins Morning Movers to discuss Nvidia's (NVDA) chart patterns in the short-term and long-term. Over the past 30 trading days, Kevin points to a "banded" range between $173-$193. Zooming out to the 1-year timeframe, he reiterates the focus on that $193 level but adds the 200-day moving average as a key technical level to track into the weekend.======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – / schwabnetwork Follow us on Facebook – / schwabnetwork Follow us on LinkedIn - / schwab-network About Schwab Network - https://schwabnetwork.com/about======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – / schwabnetwork Follow us on Facebook – / schwabnetwork Follow us on LinkedIn - / schwab-network About Schwab Network - https://schwabnetwork.com/about

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Join Kyle, Nader, Vibhu, and swyx live at NVIDIA GTC next week!Now that AIE Europe tix are ~sold out, our attention turns to Miami and World's Fair!The definitive AI Accelerator chip company has more than 10xed this AI Summer:And is now a $4.4 trillion megacorp… that is somehow still moving like a startup. We are blessed to have a unique relationship with our first ever NVIDIA guests: Kyle Kranen who gave a great inference keynote at the first World's Fair and is one of the leading architects of NVIDIA Dynamo (a Datacenter scale inference framework supporting SGLang, TRT-LLM, vLLM), and Nader Khalil, a friend of swyx from our days in Celo in The Arena, who has been drawing developers at GTC since before they were even a glimmer in the eye of NVIDIA:Nader discusses how NVIDIA Brev has drastically reduced the barriers to entry for developers to get a top of the line GPU up and running, and Kyle explains NVIDIA Dynamo as a data center scale inference engine that optimizes serving by scaling out, leveraging techniques like prefill/decode disaggregation, scheduling, and Kubernetes-based orchestration, framed around cost, latency, and quality tradeoffs. We also dive into Jensen's “SOL” (Speed of Light) first-principles urgency concept, long-context limits and model/hardware co-design, internal model APIs (https://build.nvidia.com), and upcoming Dynamo and agent sessions at GTC.Full Video pod on YouTubeTimestamps00:00 Agent Security Basics00:39 Podcast Welcome and Guests07:19 Acquisition and DevEx Shift13:48 SOL Culture and Dynamo Setup27:38 Why Scale Out Wins29:02 Scale Up Limits Explained30:24 From Laptop to Multi Node33:07 Cost Quality Latency Tradeoffs38:42 Disaggregation Prefill vs Decode41:05 Kubernetes Scaling with Grove43:20 Context Length and Co Design57:34 Security Meets Agents58:01 Agent Permissions Model59:10 Build Nvidia Inference Gateway01:01:52 Hackathons And Autonomy Dreams01:10:26 Local GPUs And Scaling Inference01:15:31 Long Running Agents And SF ReflectionsTranscriptAgent Security BasicsNader: Agents can do three things. They can access your files, they can access the internet, and then now they can write custom code and execute it. You literally only let an agent do two of those three things. If you can access your files and you can write custom code, you don't want internet access because that's one to see full vulnerability, right?If you have access to internet and your file system, you should know the full scope of what that agent's capable of doing. Otherwise, now we can get injected or something that can happen. And so that's a lot of what we've been thinking about is like, you know, how do we both enable this because it's clearly the future.But then also, you know, what, what are these enforcement points that we can start to like protect?swyx: All right.Podcast Welcome and Guestsswyx: Welcome to the Lean Space podcast in the Chromo studio. Welcome to all the guests here. Uh, we are back with our guest host Viu. Welcome. Good to have you back. And our friends, uh, Netter and Kyle from Nvidia. Welcome.Kyle: Yeah, thanks for having us.swyx: Yeah, thank you. Actually, I don't even know your titles.Uh, I know you're like architect something of Dynamo.Kyle: Yeah. I, I'm one of the engineering leaders [00:01:00] and a architects of Dynamo.swyx: And you're director of something and developers, developer tech.Nader: Yeah.swyx: You're the developers, developers, developers guy at nvidia,Nader: open source agent marketing, brev,swyx: and likeNader: Devrel tools and stuff.swyx: Yeah. BeenNader: the focus.swyx: And we're, we're kind of recording this ahead of Nvidia, GTC, which is coming to town, uh, again, uh, or taking over town, uh, which, uh, which we'll all be at. Um, and we'll talk a little bit about your sessions and stuff. Yeah.Nader: We're super excited for it.GTC Booth Stunt Storiesswyx: One of my favorite memories for Nader, like you always do like marketing stunts and like while you were at Rev, you like had this surfboard that you like, went down to GTC with and like, NA Nvidia apparently, like did so much that they bought you.Like what, what was that like? What was that?Nader: Yeah. Yeah, we, we, um. Our logo was a chaka. We, we, uh, we were always just kind of like trying to keep true to who we were. I think, you know, some stuff, startups, you're like trying to pretend that you're a bigger, more mature company than you are. And it was actually Evan Conrad from SF Compute who was just like, you guys are like previousswyx: guest.Yeah.Nader: Amazing. Oh, really? Amazing. Yeah. He was just like, guys, you're two dudes in the room. Why are you [00:02:00] pretending that you're not? Uh, and so then we were like, okay, let's make the logo a shaka. We brought surfboards to our booth to GTC and the energy was great. Yeah. Some palm trees too. They,Kyle: they actually poked out over like the, the walls so you could, you could see the bread booth.Oh, that's so funny. AndNader: no one else,Kyle: just from very far away.Nader: Oh, so you remember it backKyle: then? Yeah I remember it pre-acquisition. I was like, oh, those guys look cool,Nader: dude. That makes sense. ‘cause uh, we, so we signed up really last minute, and so we had the last booth. It was all the way in the corner. And so I was, I was worried that no one was gonna come.So that's why we had like the palm trees. We really came in with the surfboards. We even had one of our investors bring her dog and then she was just like walking the dog around to try to like, bring energy towards our booth. Yeah.swyx: Steph.Kyle: Yeah. Yeah, she's the best,swyx: you know, as a conference organizer, I love that.Right? Like, it's like everyone who sponsors a conference comes, does their booth. They're like, we are changing the future of ai or something, some generic b******t and like, no, like actually try to stand out, make it fun, right? And people still remember it after three years.Nader: Yeah. Yeah. You know what's so funny?I'll, I'll send, I'll give you this clip if you wanna, if you wanna add it [00:03:00] in, but, uh, my wife was at the time fiance, she was in medical school and she came to help us. ‘cause it was like a big moment for us. And so we, we bought this cricket, it's like a vinyl, like a vinyl, uh, printer. ‘cause like, how else are we gonna label the surfboard?So, we got a surfboard, luckily was able to purchase that on the company card. We got a cricket and it was just like fine tuning for enterprises or something like that, that we put on the. On the surfboard and it's 1:00 AM the day before we go to GTC. She's helping me put these like vinyl stickers on.And she goes, you son of, she's like, if you pull this off, you son of a b***h. And so, uh, right. Pretty much after the acquisition, I stitched that with the mag music acquisition. I sent it to our family group chat. Ohswyx: Yeah. No, well, she, she made a good choice there. Was that like basically the origin story for Launchable is that we, it was, and maybe we should explain what Brev is andNader: Yeah.Yeah. Uh, I mean, brev is just, it's a developer tool that makes it really easy to get a GPU. So we connect a bunch of different GPU sources. So the basics of it is like, how quickly can we SSH you into a G, into a GPU and whenever we would talk to users, they wanted A GPU. They wanted an A 100. And if you go to like any cloud [00:04:00] provisioning page, usually it's like three pages of forms or in the forms somewhere there's a dropdown.And in the dropdown there's some weird code that you know to translate to an A 100. And I remember just thinking like. Every time someone says they want an A 100, like the piece of text that they're telling me that they want is like, stuffed away in the corner. Yeah. And so we were like, what if the biggest piece of text was what the user's asking for?And so when you go to Brev, it's just big GPU chips with the type that you want withswyx: beautiful animations that you worked on pre, like pre you can, like, now you can just prompt it. But back in the day. Yeah. Yeah. Those were handcraft, handcrafted artisanal code.Nader: Yeah. I was actually really proud of that because, uh, it was an, i I made it in Figma.Yeah. And then I found, I was like really struggling to figure out how to turn it from like Figma to react. So what it actually is, is just an SVG and I, I have all the styles and so when you change the chip, whether it's like active or not it changes the SVG code and that somehow like renders like, looks like it's animating, but it, we just had the transition slow, but it's just like the, a JavaScript function to change the like underlying SVG.Yeah. And that was how I ended up like figuring out how to move it from from Figma. But yeah, that's Art Artisan. [00:05:00]Kyle: Speaking of marketing stunts though, he actually used those SVGs. Or kind of use those SVGs to make these cards.Nader: Oh yeah. LikeKyle: a GPU gift card Yes. That he handed out everywhere. That was actually my first impression of thatNader: one.Yeah,swyx: yeah, yeah.Nader: Yeah.swyx: I think I still have one of them.Nader: They look great.Kyle: Yeah.Nader: I have a ton of them still actually in our garage, which just, they don't have labels. We should honestly like bring, bring them back. But, um, I found this old printing press here, actually just around the corner on Ven ness. And it's a third generation San Francisco shop.And so I come in an excited startup founder trying to like, and they just have this crazy old machinery and I'm in awe. ‘cause the the whole building is so physical. Like you're seeing these machines, they have like pedals to like move these saws and whatever. I don't know what this machinery is, but I saw all three generations.Like there's like the grandpa, the father and the son, and the son was like, around my age. Well,swyx: it's like a holy, holy trinity.Nader: It's funny because we, so I just took the same SVG and we just like printed it and it's foil printing, so they make a a, a mold. That's like an inverse of like the A 100 and then they put the foil on it [00:06:00] and then they press it into the paper.And I remember once we got them, he was like, Hey, don't forget about us. You know, I guess like early Apple and Cisco's first business cards were all made there. And so he was like, yeah, we, we get like the startup businesses but then as they mature, they kind of go somewhere else. And so I actually, I think we were talking with marketing about like using them for some, we should go back and make some cards.swyx: Yeah, yeah, yeah. You know, I remember, you know, as a very, very small breadth investor, I was like, why are we spending time like, doing these like stunts for GPUs? Like, you know, I think like as a, you know, typical like cloud hard hardware person, you go into an AWS you pick like T five X xl, whatever, and it's just like from a list and you look at the specs like, why animate this GP?And, and I, I do think like it just shows the level of care that goes throughout birth and Yeah. And now, and also the, and,Nader: and Nvidia. I think that's what the, the thing that struck me most when we first came in was like the amount of passion that everyone has. Like, I think, um, you know, you talk to, you talk to Kyle, you talk to, like, every VP that I've met at Nvidia goes so close to the metal.Like, I remember it was almost a year ago, and like my VP asked me, he's like, Hey, [00:07:00] what's cursor? And like, are you using it? And if so, why? Surprised at this, and he downloaded Cursor and he was asking me to help him like, use it. And I thought that was, uh, or like, just show him what he, you know, why we were using it.And so, the amount of care that I think everyone has and the passion, appreciate, passion and appreciation for the moment. Right. This is a very unique time. So it's really cool to see everyone really like, uh, appreciate that.swyx: Yeah.Acquisition and DevEx Shiftswyx: One thing I wanted to do before we move over to sort of like research topics and, uh, the, the stuff that Kyle's working on is just tell the story of the acquisition, right?Like, not many people have been, been through an acquisition with Nvidia. What's it like? Uh, what, yeah, just anything you'd like to say.Nader: It's a crazy experience. I think, uh, you know, we were the thing that was the most exciting for us was. Our goal was just to make it easier for developers.We wanted to find access to GPUs, make it easier to do that. And then all, oh, actually your question about launchable. So launchable was just make one click exper, like one click deploys for any software on top of the GPU. Mm-hmm. And so what we really liked about Nvidia was that it felt like we just got a lot more resources to do all of that.I think, uh, you [00:08:00] know, NVIDIA's goal is to make things as easy for developers as possible. So there was a really nice like synergy there. I think that, you know, when it comes to like an acquisition, I think the amount that the soul of the products align, I think is gonna be. Is going speak to the success of the acquisition.Yeah. And so it in many ways feels like we're home. This is a really great outcome for us. Like we you know, I love brev.nvidia.com. Like you should, you should use it's, it's theKyle: front page for GPUs.Nader: Yeah. Yeah. If you want GP views,Kyle: you go there, getswyx: it there, and it's like internally is growing very quickly.I, I don't remember You said some stats there.Nader: Yeah, yeah, yeah. It's, uh, I, I wish I had the exact numbers, but like internally, externally, it's been growing really quickly. We've been working with a bunch of partners with a bunch of different customers and ISVs, if you have a solution that you want someone that runs on the GPU and you want people to use it quickly, we can bundle it up, uh, in a launchable and make it a one click run.If you're doing things and you want just like a sandbox or something to run on, right. Like open claw. Huge moment. Super exciting. Our, uh, and we'll talk into it more, but. You know, internally, people wanna run this, and you, we know we have to be really careful from the security implications. Do we let this run on the corporate network?Security's guidance was, Hey, [00:09:00] run this on breath, it's in, you know, it's, it's, it's a vm, it's sitting in the cloud, it's off the corporate network. It's isolated. And so that's been our stance internally and externally about how to even run something like open call while we figure out how to run these things securely.But yeah,swyx: I think there's also like, you almost like we're the right team at the right time when Nvidia is starting to invest a lot more in developer experience or whatever you call it. Yeah. Uh, UX or I don't know what you call it, like software. Like obviously NVIDIA is always invested in software, but like, there's like, this is like a different audience.Yeah. It's aNader: widerKyle: developer base.swyx: Yeah. Right.Nader: Yeah. Yeah. You know, it's funny, it's like, it's not, uh,swyx: so like, what, what is it called internally? What, what is this that people should be aware that is going on there?Nader: Uh, what, like developer experienceswyx: or, yeah, yeah. Is it's called just developer experience or is there like a broader strategy hereNader: in Nvidia?Um, Nvidia always wants to make a good developer experience. The thing is and a lot of the technology is just really complicated. Like, it's not, it's uh, you know, I think, um. The thing that's been really growing or the AI's growing is having a huge moment, not [00:10:00] because like, let's say data scientists in 2018, were quiet then and are much louder now.The pie is com, right? There's a whole bunch of new audiences. My mom's wondering what she's doing. My sister's learned, like taught herself how to code. Like the, um, you know, I, I actually think just generally AI's a big equalizer and you're seeing a more like technologically literate society, I guess.Like everyone's, everyone's learning how to code. Uh, there isn't really an excuse for that. And so building a good UX means that you really understand who your end user is. And when your end user becomes such a wide, uh, variety of people, then you have to almost like reinvent the practice, right? Yeah. You haveKyle: to, and actually build more developer ux, right?Because the, there are tiers of developer base that were added. You know, the, the hackers that are building on top of open claw, right? For example, have never used gpu. They don't know what kuda is. They, they, they just want to run something.Nader: Yeah.Kyle: You need new UX that is not just. Hey, you know, how do you program something in Cuda and run it?And then, and then we built, you know, like when Deep Learning was getting big, we built, we built Torch and, and, but so recently the amount of like [00:11:00] layers that are added to that developer stack has just exploded because AI has become ubiquitous. Everyone's using it in different ways. Yeah. It'sNader: moving fast in every direction.Vertical, horizontal.Vibhu: Yeah. You guys, you even take it down to hardware, like the DGX Spark, you know, it's, it's basically the same system as just throwing it up on big GPU cluster.Nader: Yeah, yeah, yeah. It's amazing. Blackwell.swyx: Yeah. Uh, we saw the preview at the last year's GTC and that was one of the better performing, uh, videos so far, and video coverage so far.Awesome. This will beat it. Um,Nader: that wasswyx: actually, we have fingersNader: crossed. Yeah.DGX Spark and Remote AccessNader: Even when Grace Blackwell or when, um, uh, DGX Spark was first coming out getting to be involved in that from the beginning of the developer experience. And it just comes back to what youswyx: were involved.Nader: Yeah. St. St.swyx: Mars.Nader: Yeah. Yeah. I mean from, it was just like, I, I got an email, we just got thrown into the loop and suddenly yeah, I, it was actually really funny ‘cause I'm still pretty fresh from the acquisition and I'm, I'm getting an email from a bunch of the engineering VPs about like, the new hardware, GPU chip, like we're, or not chip, but just GPU system that we're putting out.And I'm like, okay, cool. Matters. Now involved with this for the ux, I'm like. What am I gonna do [00:12:00] here? So, I remember the first meeting, I was just like kind of quiet as I was hearing engineering VPs talk about what this box could be, what it could do, how we should use it. And I remember, uh, one of the first ideas that people were idea was like, oh, the first thing that it was like, I think a quote was like, the first thing someone's gonna wanna do with this is get two of them and run a Kubernetes cluster on top of them.And I was like, oh, I think I know why I'm here. I was like, the first thing we're doing is easy. SSH into the machine. And then, and you know, just kind of like scoping it down of like, once you can do that every, you, like the person who wants to run a Kubernetes cluster onto Sparks has a higher propensity for pain, then, then you know someone who buys it and wants to run open Claw right now, right?If you can make sure that that's as effortless as possible, then the rest becomes easy. So there's a tool called Nvidia Sync. It just makes the SSH connection really simple. So, you know, if you think about it like. If you have a Mac, uh, or a PC or whatever, if you have a laptop and you buy this GPU and you want to use it, you should be able to use it like it's A-A-G-P-U in the cloud, right?Um, but there's all this friction of like, how do you actually get into that? That's part of [00:13:00] Revs value proposition is just, you know, there's a CLI that wraps SSH and makes it simple. And so our goal is just get you into that machine really easily. And one thing we just launched at CES, it's in, it's still in like early access.We're ironing out some kinks, but it should be ready by GTC. You can register your spark on Brev. And so now if youswyx: like remote managed yeah, local hardware. Single pane of glass. Yeah. Yeah. Because Brev can already manage other clouds anyway, right?Vibhu: Yeah, yeah. And you use the spark on Brev as well, right?Nader: Yeah. But yeah, exactly. So, so you, you, so you, you set it up at home you can run the command on it, and then it gets it's essentially it'll appear in your Brev account, and then you can take your laptop to a Starbucks or to a cafe, and you'll continue to use your, you can continue use your spark just like any other cloud node on Brev.Yeah. Yeah. And it's just like a pre-provisioned centerswyx: in yourNader: home. Yeah, exactly.swyx: Yeah. Yeah.Vibhu: Tiny little data center.Nader: Tiny little, the size ofVibhu: your phone.SOL Culture and Dynamo Setupswyx: One more thing before we move on to Kyle. Just have so many Jensen stories and I just love, love mining Jensen stories. Uh, my favorite so far is SOL. Uh, what is, yeah, what is S-O-L-S-O-LNader: is actually, i, I think [00:14:00] of all the lessons I've learned, that one's definitely my favorite.Kyle: It'll always stick with you.Nader: Yeah. Yeah. I, you know, in your startup, everything's existential, right? Like we've, we've run out of money. We were like, on the risk of, of losing payroll, we've had to contract our team because we l ran outta money. And so like, um, because of that you're really always forcing yourself to I to like understand the root cause of everything.If you get a date, if you get a timeline, you know exactly why that date or timeline is there. You're, you're pushing every boundary and like, you're not just say, you're not just accepting like a, a no. Just because. And so as you start to introduce more layers, as you start to become a much larger organization, SOL is is essentially like what is the physics, right?The speed of light moves at a certain speed. So if flight's moving some slower, then you know something's in the way. So before trying to like layer reality back in of like, why can't this be delivered at some date? Let's just understand the physics. What is the theoretical limit to like, uh, how fast this can go?And then start to tell me why. ‘cause otherwise people will start telling you why something can't be done. But actually I think any great leader's goal is just to create urgency. Yeah. [00:15:00] There's an infiniteKyle: create compelling events, right?Nader: Yeah.Kyle: Yeah. So l is a term video is used to instigate a compelling event.You say this is done. How do we get there? What is the minimum? As much as necessary, as little as possible thing that it takes for us to get exactly here and. It helps you just break through a bunch of noise.swyx: Yeah.Kyle: Instantly.swyx: One thing I'm unclear about is, can only Jensen use the SOL card? Like, oh, no, no, no.Not everyone get the b******t out because obviously it's Jensen, but like, can someone else be like, no, likeKyle: frontline engineers use it.Nader: Yeah. Every, I think it's not so much about like, get the b******t out. It's like, it's like, give me the root understanding, right? Like, if you tell me something takes three weeks, it like, well, what's the first principles?Yeah, the first principles. It's like, what's the, what? Like why is it three weeks? What is the actual yeah. What's the actual limit of why this is gonna take three weeks? If you're gonna, if you, if let's say you wanted to buy a new computer and someone told you it's gonna be here in five days, what's the SOL?Well, like the SOL is like, I could walk into a Best Buy and pick it up for you. Right? So then anything that's like beyond that is, and is that practical? Is that how we're gonna, you know, let's say give everyone in the [00:16:00] company a laptop, like obviously not. So then like that's the SOL and then it's like, okay, well if we have to get more than 10, suddenly there might be some, right?And so now we can kind of piece the reality back.swyx: So, so this is the. Paul Graham do things that don't scale. Yeah. And this is also the, what people would now call behi agency. Yeah.Kyle: It's actually really interesting because there's a, there's a second hardware angle to SOL that like doesn't come up for all the org sol is used like culturally at aswyx: media for everything.I'm also mining for like, I think that can be annoying sometimes. And like someone keeps going IOO you and you're like, guys, like we have to be stable. We have to, we to f*****g plan. Yeah.Kyle: It's an interesting balance.Nader: Yeah. I encounter that with like, actually just with, with Alec, right? ‘cause we, we have a new conference so we need to launch, we have, we have goals of what we wanna launch by, uh, by the conference and like, yeah.At the end of the day, where isswyx: this GTC?Nader: Um, well this is like, so we, I mean we did it for CES, we did for GT CDC before that we're doing it for GTC San Jose. So I mean, like every, you know, we have a new moment. Um, and we want to launch something. Yeah. And we want to do so at SOL and that does mean that some, there's some level of prioritization that needs [00:17:00] to happen.And so it, it is difficult, right? I think, um, you have to be careful with what you're pushing. You know, stability is important and that should be factored into S-O-L-S-O-L isn't just like, build everything and let it break, you know, that, that's part of the conversation. So as you're laying, layering in all the details, one of them might be, Hey, we could build this, but then it's not gonna be stable for X, y, z reasons.And so that was like, one of our conversations for CES was, you know, hey, like we, we can get this into early access registering your spark with brev. But there are a lot of things that we need to do in order to feel really comfortable from a security perspective, right? There's a lot of networking involved before we deliver that to users.So it's like, okay. Let's get this to a point where we can at least let people experiment with it. We had it in a booth, we had it in Jensen's keynote, and then let's go iron out all the networking kinks. And that's not easy. And so, uh, that can come later. And so that was the way that we layered that back in.Yeah. ButKyle: It's not really about saying like, you don't have to do the, the maintenance or operational work. It's more about saying, you know, it's kind of like [00:18:00] highlights how progress is incremental, right? Like, what is the minimum thing that we can get to. And then there's SOL for like every component after that.But there's the SOL to get you, get you to the, the starting line. And that, that's usually how it's asked. Yeah. On the other side, you know, like SOL came out of like hardware at Nvidia. Right. So SOL is like literally if we ran the accelerator or the GPU with like at basically full speed with like no other constraints, like how FAST would be able to make a program go.swyx: Yeah. Yeah. Right.Kyle: Soswyx: in, in training that like, you know, then you work back to like some percentage of like MFU for example.Kyle: Yeah, that's a, that's a great example. So like, there's an, there's an S-O-L-M-F-U, and then there's like, you know, what's practically achievable.swyx: Cool. Should we move on to sort of, uh, Kyle's side?Uh, Kyle, you're coming more from the data science world. And, uh, I, I mean I always, whenever, whenever I meet someone who's done working in tabular stuff, graph neural networks, time series, these are basically when I go to new reps, I go to ICML, I walk the back halls. There's always like a small group of graph people.Yes. Absolute small group of tabular people. [00:19:00] And like, there's no one there. And like, it's very like, you know what I mean? Like, yeah, no, like it's, it's important interesting work if you care about solving the problems that they solve.Kyle: Yeah.swyx: But everyone else is just LMS all the time.Kyle: Yeah. I mean it's like, it's like the black hole, right?Has the event horizon reached this yet in nerves? Um,swyx: but like, you know, those are, those are transformers too. Yeah. And, and those are also like interesting things. Anyway, uh, I just wanted to spend a little bit of time on, on those, that background before we go into Dynamo, uh, proper.Kyle: Yeah, sure. I took a different path to Nvidia than that, or I joined six years ago, seven, if you count, when I was an intern.So I joined Nvidia, like right outta college. And the first thing I jumped into was not what I'd done in, during internship, which was like, you know, like some stuff for autonomous vehicles, like heavyweight object detection. I jumped into like, you know, something, I'm like, recommenders, this is popular. Andswyx: yeah, he did RexiKyle: as well.Yeah, Rexi. Yeah. I mean that, that was the taboo data at the time, right? You have tables of like, audience qualities and item qualities, and you're trying to figure out like which member of [00:20:00] the audience matches which item or, or more practically which item matches which member of the audience. And at the time, really it was like we were trying to enable.Uh, recommender, which had historically been like a little bit of a CP based workflow into something that like, ran really well in GPUs. And it's since been done. Like there are a bunch of libraries for Axis that run on GPUs. Uh, the common models like Deeplearning recommendation model, which came outta meta and the wide and deep model, which was used or was released by Google were very accelerated by GPUs using, you know, the fast HBM on the chips, especially to do, you know, vector lookups.But it was very interesting at the time and super, super relevant because like we were starting to get like. This explosion of feeds and things that required rec recommenders to just actively be on all the time. And sort of transitioned that a little bit towards graph neural networks when I discovered them because I was like, okay, you can actually use graphical neural networks to represent like, relationships between people, items, concepts, and that, that interested me.So I jumped into that at [00:21:00] Nvidia and, and got really involved for like two-ish years.swyx: Yeah. Uh, and something I learned from Brian Zaro Yeah. Is that you can just kind of choose your own path in Nvidia.Kyle: Oh my God. Yeah.swyx: Which is not a normal big Corp thing. Yeah. Like you, you have a lane, you stay in your lane.Nader: I think probably the reason why I enjoy being in a, a big company, the mission is the boss probably from a startup guy. Yeah. The missionswyx: is the boss.Nader: Yeah. Uh, it feels like a big game of pickup basketball. Like, you know, if you play one, if you wanna play basketball, you just go up to the court and you're like, Hey look, we're gonna play this game and we need three.Yeah. And you just like find your three. That's honestly for every new initiative that's what it feels like. Yeah.Vibhu: It also like shows, right? Like Nvidia. Just releasing state-of-the-art stuff in every domain. Yeah. Like, okay, you expect foundation models with Nemo tron voice just randomly parakeet.Call parakeet just comes out another one, uh, voice. TheKyle: video voice team has always been producing.Vibhu: Yeah. There's always just every other domain of paper that comes out, dataset that comes out. It's like, I mean, it also stems back to what Nvidia has to do, right? You have to make chips years before they're actually produced.Right? So you need to know, you need to really [00:22:00] focus. TheKyle: design process starts likeVibhu: exactlyKyle: three to five years before the chip gets to the market.Vibhu: Yeah. I, I'm curious more about what that's like, right? So like, you have specialist teams. Is it just like, you know, people find an interest, you go in, you go deep on whatever, and that kind of feeds back into, you know, okay, we, we expect predictions.Like the internals at Nvidia must be crazy. Right? You know? Yeah. Yeah. You know, you, you must. Not even without selling to people, you have your own predictions of where things are going. Yeah. And they're very based, very grounded. Right?Kyle: Yeah. It, it, it's really interesting. So there's like two things that I think that Amed does, which are quite interesting.Uh, one is like, we really index into passion. There's a big. Sort of organizational top sound push to like ensure that people are working on the things that they're passionate about. So if someone proposes something that's interesting, many times they can just email someone like way up the chain that they would find this relevant and say like, Hey, can I go work on this?Nader: It's actually like I worked at a, a big company for a couple years before, uh, starting on my startup journey and like, it felt very weird if you were to like email out of chain, if that makes [00:23:00] sense. Yeah. The emails at Nvidia are like mosh pitsswyx: shoot,Nader: and it's just like 60 people, just whatever. And like they're, there's this,swyx: they got messy like, reply all you,Nader: oh, it's in, it's insane.It's insane. They justKyle: help. You know, Maxim,Nader: the context. But, but that's actually like, I've actually, so this is a weird thing where I used to be like, why would we send emails? We have Slack. I am the entire, I'm the exact opposite. I feel so bad for anyone who's like messaging me on Slack ‘cause I'm so unresponsive.swyx: Your emailNader: Maxi, email Maxim. I'm email maxing Now email is a different, email is perfect because man, we can't work together. I'm email is great, right? Because important threads get bumped back up, right? Yeah, yeah. Um, and so Slack doesn't do that. So I just have like this casino going off on the right or on the left and like, I don't know which thread was from where or what, but like the threads get And then also just like the subject, so you can have like working threads.I think what's difficult is like when you're small, if you're just not 40,000 people I think Slack will work fine, but there's, I don't know what the inflection point is. There is gonna be a point where that becomes really messy and you'll actually prefer having email. ‘cause you can have working threads.You can cc more than nine people in a thread.Kyle: You can fork stuff.Nader: You can [00:24:00] fork stuff, which is super nice and just like y Yeah. And so, but that is part of where you can propose a plan. You can also just. Start, honestly, momentum's the only authority, right? So like, if you can just start, start to make a little bit of progress and show someone something, and then they can try it.That's, I think what's been, you know, I think the most effective way to push anything for forward. And that's both at Nvidia and I think just generally.Kyle: Yeah, there's, there's the other concept that like is explored a lot at Nvidia, which is this idea of a zero billion dollar business. Like market creation is a big thing at Nvidia.Like,swyx: oh, you want to go and start a zero billion dollar business?Kyle: Jensen says, we are completely happy investing in zero billion dollar markets. We don't care if this creates revenue. It's important for us to know about this market. We think it will be important in the future. It can be zero billion dollars for a while.I'm probably minging as words here for, but like, you know, like, I'll give an example. NVIDIA's been working on autonomous driving for a a long time,swyx: like an Nvidia car.Kyle: No, they, they'veVibhu: used the Mercedes, right? They're around the HQ and I think it finally just got licensed out. Now they're starting to be used quite a [00:25:00] bit.For 10 years you've been seeing Mercedes with Nvidia logos driving.Kyle: If you're in like the South San Santa Clara, it's, it's actually from South. Yeah. So, um. Zero billion dollar markets are, are a thing like, you know, Jensen,swyx: I mean, okay, look, cars are not a zero billion dollar market. But yeah, that's a bad example.Nader: I think, I think he's, he's messaging, uh, zero today, but, or even like internally, right? Like, like it's like, uh, an org doesn't have to ruthlessly find revenue very quickly to justify their existence. Right. Like a lot of the important research, a lot of the important technology being developed that, that's kind ofKyle: where research, research is very ide ideologically free at Nvidia.Yeah. Like they can pursue things that they wereswyx: Were you research officially?Kyle: I was never in research. Officially. I was always in engineering. Yeah. We in, I'm in an org called Deep Warning Algorithms, which is basically just how do we make things that are relevant to deep warning go fast.swyx: That sounds freaking cool.Vibhu: And I think a lot of that is underappreciated, right? Like time series. This week Google put out time. FF paper. Yeah. A new time series, paper res. Uh, Symantec, ID [00:26:00] started applying Transformers LMS to Yes. Rec system. Yes. And when you think the scale of companies deploying these right. Amazon recommendations, Google web search, it's like, it's huge scale andKyle: Yeah.Vibhu: You want fast?Kyle: Yeah. Yeah. Yeah. Actually it's, it, I, there's a fun moment that brought me like full circle. Like, uh, Amazon Ads recently gave a talk where they talked about using Dynamo for generative recommendation, which was like super, like weirdly cathartic for me. I'm like, oh my God. I've, I've supplanted what I was working on.Like, I, you're using LMS now to do what I was doing five years ago.swyx: Yeah. Amazing. And let's go right into Dynamo. Uh, maybe introduce Yeah, sure. To the top down and Yeah.Kyle: I think at this point a lot of people are familiar with the term of inference. Like funnily enough, like I went from, you know, inference being like a really niche topic to being something that's like discussed on like normal people's Twitter feeds.It's,Nader: it's on billboardsKyle: here now. Yeah. Very, very strange. Driving, driving, seeing just an inference ad on 1 0 1 inference at scale is becoming a lot more important. Uh, we have these moments like, you know, open claw where you have these [00:27:00] agents that take lots and lots of tokens, but produce, incredible results.There are many different aspects of test time scaling so that, you know, you can use more inference to generate a better result than if you were to use like a short amount of inference. There's reasoning, there's quiring, there's, adding agency to the model, allowing it to call tools and use skills.Dyno sort came about at Nvidia. Because myself and a couple others were, were sort of talking about the, these concepts that like, you know, you have inference engines like VLMS, shelan, tenor, TLM and they have like one single copy. They, they, they sort of think about like things as like one single copy, like one replica, right?Why Scale Out WinsKyle: Like one version of the model. But when you're actually serving things at scale, you can't just scale up that replica because you end up with like performance problems. There's a scaling limit to scaling up replicas. So you actually have to scale out to use a, maybe some Kubernetes type terminology.We kind of realized that there was like. A lot of potential optimization that we could do in scaling out and building systems for data [00:28:00] center scale inference. So Dynamo is this data center scale inference engine that sits on top of the frameworks like VLM Shilling and 10 T lm and just makes things go faster because you can leverage the economy of scale.The fact that you have KV cash, which we can define a little bit later, uh, in all these machines that is like unique and you wanna figure out like the ways to maximize your cash hits or you want to employ new techniques in inference like disaggregation, which Dynamo had introduced to the world in, in, in March, not introduced, it was a academic talk, but beforehand.But we are, you know, one of the first frameworks to start, supporting it. And we wanna like, sort of combine all these techniques into sort of a modular framework that allows you to. Accelerate your inference at scale.Nader: By the way, Kyle and I became friends on my first date, Nvidia, and I always loved, ‘cause like he always teaches meswyx: new things.Yeah. By the way, this is why I wanted to put two of you together. I was like, yeah, this is, this is gonna beKyle: good. It's very, it's very different, you know, like we've, we, we've, we've talked to each other a bunch [00:29:00] actually, you asked like, why, why can't we scale up?Nader: Yeah.Scale Up Limits ExplainedNader: model, you said model replicas.Kyle: Yeah. So you, so scale up means assigning moreswyx: heavier?Kyle: Yeah, heavier. Like making things heavier. Yeah, adding more GPUs. Adding more CPUs. Scale out is just like having a barrier saying, I'm gonna duplicate my representation of the model or a representation of this microservice or something, and I'm gonna like, replicate it Many times.Handle, load. And the reason that you can't scale, scale up, uh, past some points is like, you know, there, there, there are sort of hardware bounds and algorithmic bounds on, on that type of scaling. So I'll give you a good example that's like very trivial. Let's say you're on an H 100. The Maxim ENV link domain for H 100, for most Ds H one hundreds is heus, right?So if you scaled up past that, you're gonna have to figure out ways to handle the fact that now for the GPUs to communicate, you have to do it over Infin band, which is still very fast, but is not as fast as ENV link.swyx: Is it like one order of magnitude, like hundreds or,Kyle: it's about an order of magnitude?Yeah. Okay. Um, soswyx: not terrible.Kyle: [00:30:00] Yeah. I, I need to, I need to remember the, the data sheet here, like, I think it's like about 500 gigabytes. Uh, a second unidirectional for ENV link, and about 50 gigabytes a second unidirectional for Infin Band. I, it, it depends on the, the generation.swyx: I just wanna set this up for people who are not familiar with these kinds of like layers and the trash speedVibhu: and all that.Of course.From Laptop to Multi NodeVibhu: Also, maybe even just going like a few steps back before that, like most people are very familiar with. You see a, you know, you can use on your laptop, whatever these steel viol, lm you can just run inference there. All, there's all, you can, youcan run it on thatVibhu: laptop. You can run on laptop.Then you get to, okay, uh, models got pretty big, right? JLM five, they doubled the size, so mm-hmm. Uh, what do you do when you have to go from, okay, I can get 128 gigs of memory. I can run it on a spark. Then you have to go multi GPU. Yeah. Okay. Multi GPU, there's some support there. Now, if I'm a company and I don't have like.I'm not hiring the best researchers for this. Right. But I need to go [00:31:00] multi-node, right? I have a lot of servers. Okay, now there's efficiency problems, right? You can have multiple eight H 100 nodes, but, you know, is that as a, like, how do you do that efficiently?Kyle: Yeah. How do you like represent them? How do you choose how to represent the model?Yeah, exactly right. That's a, that's like a hard question. Everyone asks, how do you size oh, I wanna run GLM five, which just came out new model. There have been like four of them in the past week, by the way, like a bunch of new models.swyx: You know why? Right? Deep seek.Kyle: No comment. Oh. Yeah, but Ggl, LM five, right?We, we have this, new model. It's, it's like a large size, and you have to figure out how to both scale up and scale out, right? Because you have to find the right representation that you care about. Everyone does this differently. Let's be very clear. Everyone figures this out in their own path.Nader: I feel like a lot of AI or ML even is like, is like this. I think people think, you know, I, I was, there was some tweet a few months ago that was like, why hasn't fine tuning as a service taken off? You know, that might be me. It might have been you. Yeah. But people want it to be such an easy recipe to follow.But even like if you look at an ML model and specificKyle: to you Yeah,Nader: yeah.Kyle: And the [00:32:00] model,Nader: the situation, and there's just so much tinkering, right? Like when you see a model that has however many experts in the ME model, it's like, why that many experts? I don't, they, you know, they tried a bunch of things and that one seemed to do better.I think when it comes to how you're serving inference, you know, you have a bunch of decisions to make and there you can always argue that you can take something and make it more optimal. But I think it's this internal calibration and appetite for continued calibration.Vibhu: Yeah. And that doesn't mean like, you know, people aren't taking a shot at this, like tinker from thinking machines, you know?Yeah. RL as a service. Yeah, totally. It's, it also gets even harder when you try to do big model training, right? We're not the best at training Moes, uh, when they're pre-trained. Like we saw this with LAMA three, right? They're trained in such a sparse way that meta knows there's gonna be a bunch of inference done on these, right?They'll open source it, but it's very trained for what meta infrastructure wants, right? They wanna, they wanna inference it a lot. Now the question to basically think about is, okay, say you wanna serve a chat application, a coding copilot, right? You're doing a layer of rl, you're serving a model for X amount of people.Is it a chat model, a coding model? Dynamo, you know, back to that,Kyle: it's [00:33:00] like, yeah, sorry. So you we, we sort of like jumped off of, you know, jumped, uh, on that topic. Everyone has like, their own, own journey.Cost Quality Latency TradeoffsKyle: And I, I like to think of it as defined by like, what is the model you need? What is the accuracy you need?Actually I talked to NA about this earlier. There's three axes you care about. What is the quality that you're able to produce? So like, are you accurate enough or can you complete the task with enough, performance, high enough performance. Yeah, yeah. Uh, there's cost. Can you serve the model or serve your workflow?Because it's not just the model anymore, it's the workflow. It's the multi turn with an agent cheaply enough. And then can you serve it fast enough? And we're seeing all three of these, like, play out, like we saw, we saw new models from OpenAI that you know, are faster. You have like these new fast versions of models.You can change the amount of thinking to change the amount of quality, right? Produce more tokens, but at a higher cost in a, in a higher latency. And really like when you start this journey of like trying to figure out how you wanna host a model, you, you, you think about three things. What is the model I need to serve?How many times do I need to call it? What is the input sequence link was [00:34:00] the, what does the workflow look like on top of it? What is the SLA, what is the latency SLA that I need to achieve? Because there's usually some, this is usually like a constant, you, you know, the SLA that you need to hit and then like you try and find the lowest cost version that hits all of these constraints.Usually, you know, you, you start with those things and you say you, you kind of do like a bit of experimentation across some common configurations. You change the tensor parallel size, which is a form of parallelismVibhu: I take, it goes even deeper first. Gotta think what model.Kyle: Yes, course,ofKyle: course. It's like, it's like a multi-step design process because as you said, you can, you can choose a smaller model and then do more test time scaling and it'll equate the quality of a larger model because you're doing the test time scaling or you're adding a harness or something.So yes, it, it goes way deeper than that. But from the performance perspective, like once you get to the model you need, you need to host, you look at that and you say, Hey. I have this model, I need to serve it at the speed. What is the right configuration for that?Nader: You guys see the recent, uh, there was a paper I just saw like a few days ago that, uh, if you run [00:35:00] the same prompt twice, you're getting like double Just try itagain.Nader: Yeah, exactly.Vibhu: And you get a lot. Yeah. But the, the key thing there is you give the context of the failed try, right? Yeah. So it takes a shot. And this has been like, you know, basic guidance for quite a while. Just try again. ‘cause you know, trying, just try again. Did you try again? All adviceNader: in life.Vibhu: Just, it's a paper from Google, if I'm not mistaken, right?Yeah,Vibhu: yeah. I think it, it's like a seven bas little short paper. Yeah. Yeah. The title's very cute. And it's just like, yeah, just try again. Give it ask context,Kyle: multi-shot. You just like, say like, hey, like, you know, like take, take a little bit more, take a little bit more information, try and fail. Fail.Vibhu: And that basic concept has gone pretty deep.There's like, um, self distillation, rl where you, you do self distillation, you do rl and you have past failure and you know, that gives some signal so people take, try it again. Not strong enough.swyx: Uh, for, for listeners, uh, who listen to here, uh, vivo actually, and I, and we run a second YouTube channel for our paper club where, oh, that's awesome.Vivo just covered this. Yeah. Awesome. Self desolation and all that's, that's why he, to speed [00:36:00] on it.Nader: I'll to check it out.swyx: Yeah. It, it's just a good practice, like everyone needs, like a paper club where like you just read papers together and the social pressure just kind of forces you to just,Nader: we, we,there'sNader: like a big inference.Kyle: ReadingNader: group at a video. I feel so bad every time. I I, he put it on like, on our, he shared it.swyx: One, one ofNader: your guys,swyx: uh, is, is big in that, I forget es han Yeah, yeah,Kyle: es Han's on my team. Actually. Funny. There's a, there's a, there's a employee transfer between us. Han worked for Nater at Brev, and now he, he's on my team.He wasNader: our head of ai. And then, yeah, once we got in, andswyx: because I'm always looking for like, okay, can, can I start at another podcast that only does that thing? Yeah. And, uh, Esan was like, I was trying to like nudge Esan into like, is there something here? I mean, I don't think there's, there's new infant techniques every day.So it's like, it's likeKyle: you would, you would actually be surprised, um, the amount of blog posts you see. And ifswyx: there's a period where it was like, Medusa hydra, what Eagle, like, youKyle: know, now we have new forms of decode, uh, we have new forms of specula, of decoding or new,swyx: what,Kyle: what are youVibhu: excited? And it's exciting when you guys put out something like Tron.‘cause I remember the paper on this Tron three, [00:37:00] uh, the amount of like post train, the on tokens that the GPU rich can just train on. And it, it was a hybrid state space model, right? Yeah.Kyle: It's co-designed for the hardware.Vibhu: Yeah, go design for the hardware. And one of the things was always, you know, the state space models don't scale as well when you do a conversion or whatever the performance.And you guys are like, no, just keep draining. And Nitron shows a lot of that. Yeah.Nader: Also, something cool about Nitron it was released in layers, if you will, very similar to Dynamo. It's, it's, it's essentially it was released as you can, the pre-training, post-training data sets are released. Yeah. The recipes on how to do it are released.The model itself is released. It's full model. You just benefit from us turning on the GPUs. But there are companies like, uh, ServiceNow took the dataset and they trained their own model and we were super excited and like, you know, celebrated that work.ZoomVibhu: different. Zoom is, zoom is CGI, I think, uh, you know, also just to add like a lot of models don't put out based models and if there's that, why is fine tuning not taken off?You know, you can do your own training. Yeah,Kyle: sure.Vibhu: You guys put out based model, I think you put out everything.Nader: I believe I know [00:38:00]swyx: about base. BasicallyVibhu: without baseswyx: basic can be cancelable.Vibhu: Yeah. Base can be cancelable.swyx: Yeah.Vibhu: Safety training.swyx: Did we get a full picture of dymo? I, I don't know if we, what,Nader: what I'd love is you, you mentioned the three axes like break it down of like, you know, what's prefilled decode and like what are the optimizations that we can get with Dynamo?Kyle: Yeah. That, that's, that's, that's a great point. So to summarize on that three axis problem, right, there are three things that determine whether or not something can be done with inference, cost, quality, latency, right? Dynamo is supposed to be there to provide you like the runtime that allows you to pull levers to, you know, mix it up and move around the parade of frontier or the preto surface that determines is this actually possible with inference And AI todayNader: gives you the knobs.Kyle: Yeah, exactly. It gives you the knobs.Disaggregation Prefill vs DecodeKyle: Uh, and one thing that like we, we use a lot in contemporary inference and is, you know, starting to like pick up from, you know, in, in general knowledge is this co concept of disaggregation. So historically. Models would be hosted with a single inference engine. And that inference engine [00:39:00] would ping pong between two phases.There's prefill where you're reading the sequence generating KV cache, which is basically just a set of vectors that represent the sequence. And then using that KV cache to generate new tokens, which is called Decode. And some brilliant researchers across multiple different papers essentially made the realization that if you separate these two phases, you actually gain some benefits.Those benefits are basically a you don't have to worry about step synchronous scheduling. So the way that an inference engine works is you do one step and then you finish it, and then you schedule, you start scheduling the next step there. It's not like fully asynchronous. And the problem with that is you would have, uh, essentially pre-fill and decode are, are actually very different in terms of both their resource requirements and their sometimes their runtime.So you would have like prefill that would like block decode steps because you, you'd still be pre-filing and you couldn't schedule because you know the step has to end. So you remove that scheduling issue and then you also allow you, or you yourself, to like [00:40:00] split the work into two different ki types of pools.So pre-fill typically, and, and this changes as, as model architecture changes. Pre-fill is, right now, compute bound most of the time with the sequence is sufficiently long. It's compute bound. On the decode side because you're doing a full Passover, all the weights and the entire sequence, every time you do a decode step and you're, you don't have the quadratic computation of KV cache, it's usually memory bound because you're retrieving a linear amount of memory and you're doing a linear amount of compute as opposed to prefill where you retrieve a linear amount of memory and then use a quadratic.You know,Nader: it's funny, someone exo Labs did a really cool demo where for the DGX Spark, which has a lot more compute, you can do the pre the compute hungry prefill on a DG X spark and then do the decode on a, on a Mac. Yeah. And soVibhu: that's faster.Nader: Yeah. Yeah.Kyle: So you could, you can do that. You can do machine strat stratification.Nader: Yeah.Kyle: And like with our future generation generations of hardware, we actually announced, like with Reuben, this [00:41:00] new accelerator that is prefilled specific. It's called Reuben, CPX. SoKubernetes Scaling with GroveNader: I have a question when you do the scale out. Yeah. Is scaling out easier with Dynamo? Because when you need a new node, you can dedicate it to either the Prefill or, uh, decode.Kyle: Yeah. So Dynamo actually has like a, a Kubernetes component in it called Grove that allows you to, to do this like crazy scaling specialization. It has like this hot, it's a representation that, I don't wanna go too deep into Kubernetes here, but there was a previous way that you would like launch multi-node work.Uh, it's called Leader Worker Set. It's in the Kubernetes standard, and Leader worker set is great. It served a lot of people super well for a long period of time. But one of the things that it's struggles with is representing a set of cases where you have a multi-node replica that has a pair, right?You know, prefill and decode, or it's not paired, but it has like a second stage that has a ratio that changes over time. And prefill and decode are like two different things as your workload changes, right? The amount of prefill you'll need to do may change. [00:42:00] The amount of decode that you, you'll need to do might change, right?Like, let's say you start getting like insanely long queries, right? That probably means that your prefill scales like harder because you're hitting these, this quadratic scaling growth.swyx: Yeah.And then for listeners, like prefill will be long input. Decode would be long output, for example, right?Kyle: Yeah. So like decode, decode scale. I mean, decode is funny because the amount of tokens that you produce scales with the output length, but the amount of work that you do per step scales with the amount of tokens in the context.swyx: Yes.Kyle: So both scales with the input and the output.swyx: That's true.Kyle: But on the pre-fold view code side, like if.Suddenly, like the amount of work you're doing on the decode side stays about the same or like scales a little bit, and then the prefilled side like jumps up a lot. You actually don't want that ratio to be the same. You want it to change over time. So Dynamo has a set of components that A, tell you how to scale.It tells you how many prefilled workers and decoded workers you, it thinks you should have, and also provides a scheduling API for Kubernetes that allows you to actually represent and affect this scheduling on, on, on your actual [00:43:00] hardware, on your compute infrastructure.Nader: Not gonna lie. I feel a little embarrassed for being proud of my SVG function earlier.swyx: No, itNader: wasreallyKyle: cute. I, Iswyx: likeNader: it's all,swyx: it's all engineering. It's all engineering. Um, that's where I'mKyle: technical.swyx: One thing I'm, I'm kind of just curious about with all with you see at a systems level, everything going on here. Mm-hmm. And we, you know, we're scaling it up in, in multi, in distributed systems.Context Length and Co Designswyx: Um, I think one thing that's like kind of, of the moment right now is people are asking, is there any SOL sort of upper bounds. In terms of like, let's call, just call it context length for one for of a better word, but you can break it down however you like.Nader: Yeah.swyx: I just think like, well, yeah, I mean, like clearly you can engage in hybrid architectures and throw in some state space models in there.All, all you want, but it looks, still looks very attention heavy.Kyle: Yes. Uh, yeah. Long context is attention heavy. I mean, we have these hybrid models, um,swyx: to take and most, most models like cap out at a million contexts and that's it. Yeah. Like for the last two years has been it.Kyle: Yeah. The model hardware context co-design thing that we're seeing these days is actually super [00:44:00] interesting.It's like my, my passion, like my secret side passion. We see models like Kimmy or G-P-T-O-S-S. I'm use these because I, I know specific things about these models. So Kimmy two comes out, right? And it's an interesting model. It's like, like a deep seek style architecture is MLA. It's basically deep seek, scaled like a little bit differently, um, and obviously trained differently as well.But they, they talked about, why they made the design choices for context. Kimmy has more experts, but fewer attention heads, and I believe a slightly smaller attention, uh, like dimension. But I need to remember, I need to check that. Uh, it doesn't matter. But they discussed this actually at length in a blog post on ji, which is like our pu which is like credit puswyx: Yeah.Kyle: Um, in, in China. Chinese red.swyx: Yeah.Kyle: It's, yeah. So it, it's, it's actually an incredible blog post. Uh, like all the mls people in, in, in that, I've seen that on GPU are like very brilliant, but they, they talk about like the creators of Kimi K two [00:45:00] actually like, talked about it on, on, on there in the blog post.And they say, we, we actually did an experiment, right? Attention scales with the number of heads, obviously. Like if you have 64 heads versus 32 heads, you do half the work of attention. You still scale quadratic, but you do half the work. And they made a, a very specific like. Sort of barter in their system, in their architecture, they basically said, Hey, what if we gave it more experts, so we're gonna use more memory capacity.But we keep the amount of activated experts the same. We increase the expert sparsity, so we have fewer experts act. The ratio to of experts activated to number of experts is smaller, and we decrease the number of attention heads.Vibhu: And kind of for context, what the, what we had been seeing was you make models sparser instead.So no one was really touching heads. You're just having, uh,Kyle: well, they, they did, they implicitly made it sparser.Vibhu: Yeah, yeah. For, for Kimmy. They did,Kyle: yes.Vibhu: They also made it sparser. But basically what we were seeing was people were at the level of, okay, there's a sparsity ratio. You want more total parameters, less active, and that's sparsity.[00:46:00]But what you see from papers, like, the labs like moonshot deep seek, they go to the level of, okay, outside of just number of experts, you can also change how many attention heads and less attention layers. More attention. Layers. Layers, yeah. Yes, yes. So, and that's all basically coming back to, just tied together is like hardware model, co-design, which isKyle: hardware model, co model, context, co-design.Vibhu: Yeah.Kyle: Right. Like if you were training a, a model that was like. Really, really short context, uh, or like really is good at super short context tasks. You may like design it in a way such that like you don't care about attention scaling because it hasn't hit that, like the turning point where like the quadratic curve takes over.Nader: How do you consider attention or context as a separate part of the co-design? Like I would imagine hardware or just how I would've thought of it is like hardware model. Co-design would be hardware model context co-designKyle: because the harness and the context that is produced by the harness is a part of the model.Once it's trained in,Vibhu: like even though towards the end you'll do long context, you're not changing architecture through I see. Training. Yeah.Kyle: I mean you can try.swyx: You're saying [00:47:00] everyone's training the harness into the model.Kyle: I would say to some degree, orswyx: there's co-design for harness. I know there's a small amount, but I feel like not everyone has like gone full send on this.Kyle: I think, I think I think it's important to internalize the harness that you think the model will be running. Running into the model.swyx: Yeah. Interesting. Okay. Bash is like the universal harness,Kyle: right? Like I'll, I'll give. An example here, right? I mean, or just like a, like a, it's easy proof, right? If you can train against a harness and you're using that harness for everything, wouldn't you just train with the harness to ensure that you get the best possible quality out of,swyx: Well, the, uh, I, I can provide a counter argument.Yeah, sure. Which is what you wanna provide a generally useful model for other people to plug into their harnesses, right? So if youKyle: Yeah. Harnesses can be open, open source, right?swyx: Yeah. So I mean, that's, that's effectively what's happening with Codex.Kyle: Yeah.swyx: And, but like you may want like a different search tool and then you may have to name it differently or,Nader: I don't know how much people have pushed on this, but can you.Train a model, would it be, have you have people compared training a model for the for the harness versus [00:48:00] like post training forswyx: I think it's the same thing. It's the same thing. It's okay. Just extra post training. INader: see.swyx: And so, I mean, cognition does this course, it does this where you, you just have to like, if your tool is slightly different, um, either force your tool to be like the tool that they train for.Hmm. Or undo their training for their tool and then Oh, that's re retrain. Yeah. It's, it's really annoying and like,Kyle: I would hope that eventually we hit like a certain level of generality with respect to training newswyx: tools. This is not a GI like, it's, this is a really stupid like. Learn my tool b***h.Like, I don't know if, I don't know if I can say that, but like, you know, um, I think what my point kind of is, is that there's, like, I look at slopes of the scaling laws and like, this slope is not working, man. We, we are at a million token con

AI Reporter Stephanie Palazzolo talks with TITV Host Akash Pasricha about Anthropic's lawsuit against the Pentagon over its supply chain risk designation and how OpenAI's new GPT 5.4 model is landing with developers. We also talk with Anita Ramaswamy about OpenAI's sky‑high IPO valuation, how it compares to Anthropic, Nvidia and Palantir, and why some public investors may sit out the offering. Then we speak with Anissa Gardizy about Oracle and OpenAI's Texas data center twist, Nvidia's $150 million move to take over the site, the upcoming Groq–Nvidia chip reveal at GTC, and Anthropic's aggressive bet on Google TPUs and Fluidstack.Articles discussed on this episode: https://www.theinformation.com/briefings/anthropic-sues-defense-department-designation-supply-chain-riskhttps://www.theinformation.com/newsletters/ai-agenda/ai-agenda-anthropic-strong-legal-case-trumps-dodhttps://www.theinformation.com/articles/openais-ipo-hopes-face-skeptical-investor-communityhttps://www.theinformation.com/newsletters/ai-infrastructure/real-reason-openai-walked-away-oracle-stargate-expansion-abileneSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

What happens when Granbury Theatre Academy students head to the Junior Theatre Festival?

Dive into the realities of AI-assisted coding, the origins of modern fine-tuning, and the cognitive science behind machine learning with fast.ai founder Jeremy Howard. In this episode, we unpack why AI might be turning software engineering into a slot machine and how to maintain true technical intuition in the age of large language models.GTC is coming, the premier AI conference, great opportunity to learn about AI. NVIDIA and partners will showcase breakthroughs in physical AI, AI factories, agentic AI, and inference, exploring the next wave of AI innovation for developers and researchers. Register for virtual GTC for free, using my link and win NVIDIA DGX Spark (https://nvda.ws/4qQ0LMg)Jeremy Howard is a renowned data scientist, researcher, entrepreneur, and educator. As the co-founder of fast.ai, former President of Kaggle, and the creator of ULMFiT, Jeremy has spent decades democratizing deep learning. His pioneering work laid the foundation for modern transfer learning and the pre-training and fine-tuning paradigm that powers today's language models.Key Topics and Main Insights Discussed:- The Origins of ULMFiT and Fine-Tuning- The Vibe Coding Illusion and Software Engineering- Cognitive Science, Friction, and Learning- The Future of DevelopersRESCRIPT: https://app.rescript.info/public/share/BhX5zP3b0m63srLOQDKBTFTooSzEMh_ARwmDG_h_izkJeremy Howard:https://x.com/jeremyphowardhttps://www.answer.ai/---TIMESTAMPS (fixed):00:00:00 Introduction & GTC Sponsor00:04:30 ULMFiT & The Birth of Fine-Tuning00:12:00 Intuition & The Mechanics of Learning00:18:30 Abstraction Hierarchies & AI Creativity00:23:00 Claude Code & The Interpolation Illusion00:27:30 Coding vs. Software Engineering00:30:00 Cosplaying Intelligence: Dennett vs. Searle00:36:30 Automation, Radiology & Desirable Difficulty00:42:30 Organizational Knowledge & The Slope00:48:00 Vibe Coding as a Slot Machine00:54:00 The Erosion of Control in Software01:01:00 Interactive Programming & REPL Environments01:05:00 The Notebook Debate & Exploratory Science01:17:30 AI Existential Risk & Power Centralization01:24:20 Current Risks, Privacy & Enfeeblement---REFERENCES:Blog Post:[00:03:00] fast.ai Blog: Self-Supervised Learninghttps://www.fast.ai/posts/2020-01-13-self_supervised.html[00:13:30] DeepMind Blog: Gemini Deep Thinkhttps://deepmind.google/blog/accelerating-mathematical-and-scientific-discovery-with-gemini-deep-think/[00:19:30] Modular Blog: Claude C Compiler analysishttps://www.modular.com/blog/the-claude-c-compiler-what-it-reveals-about-the-future-of-software[00:19:45] Anthropic Engineering Blog: Building C Compilerhttps://www.anthropic.com/engineering/building-c-compiler[00:48:00] Cursor Blog: Scaling Agentshttps://cursor.com/blog/scaling-agents[01:05:15] fast.ai Blog: NB Dev Merged Driverhttps://www.fast.ai/posts/2022-08-25-jupyter-git.html[01:17:30] Jeremy Howard: Response to AI Risk Letterhttps://www.normaltech.ai/p/is-avoiding-extinction-from-ai-reallyBook:[00:08:30] M. Chirimuuta: The Brain Abstractedhttps://mitpress.mit.edu/9780262548045/the-brain-abstracted/[00:30:00] Daniel Dennett: Consciousness Explainedhttps://www.amazon.com/Consciousness-Explained-Daniel-C-Dennett/dp/0316180661[00:42:30] Cesar Hidalgo: Infinite Alphabet / Laws of Knowledgehttps://www.amazon.com/Infinite-Alphabet-Laws-Knowledge/dp/0241655676Archive Article:[00:13:45] MLST Archive: Why Creativity Cannot Be Interpolatedhttps://archive.mlst.ai/read/why-creativity-cannot-be-interpolatedResearch Study:[00:24:30] METR Study: AI OS Developmenthttps://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/Paper:[00:24:45] Fred Brooks: No Silver Bullethttps://www.cs.unc.edu/techreports/86-020.pdf[00:30:15] John Searle: Minds, Brains, and Programshttps://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/minds-brains-and-programs/DC644B47A4299C637C89772FACC2706A

- AI for military use; Anthropic, OpenAI, DoD - SambaNova: Intel partnership, new SN50 chip - GTC 2026 - Mobile World Congress 2026 [audio mp3="https://orionx.net/wp-content/uploads/2026/03/HPCNB_20260302.mp3"][/audio] The post HPC News Bytes – 20260302 appeared first on OrionX.net.

Mardi, il achetait comme si l'IA allait sauver l'humanité. Et ce soir… tout repose sur une seule entreprise. Une seule. Si Nvidia déçoit, on plonge. Si Nvidia explose les attentes, on repart en orbite. Bienvenue dans le casino le plus cher du monde. Je suis debout depuis 3h30. J'ai lu tout ce qui était lisible. Et plus je lis… moins je comprends la logique du marché. On est devenus incapables de tenir une conviction plus de 12 heures. L'IA va tous nous remplacer ? Panique. L'IA va signer des contrats ? Euphorie. Même argument. Sens inverse. 24 heures d'écart. Dans cette vidéo, on décortique : La schizophrénie actuelle des marchés Le retournement narratif express sur l'IA Le deal AMD / Meta à 100 milliards Le rôle central d'Anthropic et du “AI scare trade” La macro qui ralentit pendant que les indices battent des records Le discours de Trump et les risques liés aux droits de douane Et surtout : ce que le marché attend VRAIMENT des chiffres de Nvidia

Life is full of transitions—and many people are not prepared for them. While change is inevitable, growth is always a choice. In this message, Pastor Zuriel Jacob Fermin reminds us that seasons of transition are not meant to replace us, but to refine us.Preaching from Deuteronomy 8, this message teaches that God uses transitions to shape our hearts, deepen our dependence on Him, and remind us of His faithfulness. Discipline, testing, and even delays are not signs of God's absence but evidence of His loving work in our lives.We are reminded that:• Obedience to God is not restrictive, but life-giving• Blessings become burdens when the heart is misaligned• Refinement often comes before expansion• God humbles us, but He never abandons us• Saving grace brings us to Christ, while sustaining grace keeps us through every seasonThis message also calls believers to maintain reverence for God, humility in seasons of increase, and gratitude even in times of testing. True respect for God begins in the heart and flows outward into how we live, worship, and respond to Him.As we enter new seasons, this message invites us to do three things:1. Remember God2. Recognize His work3. Rest in His sovereigntyBe encouraged to trust the Lord in every transition—because the same God who brings you into a new season is the God who will sustain you through it.Preached on February 1, 2026, at GTC's 5:30 PM Service.#LifeTransitions#GTCNewSeason#GTC70thAnniversary#WhereLivesChange

Samsung se prepara para enviar HBM4 tras el Año Nuevo Lunar y acelerar la memoria para IAPor Félix Riaño @LocutorCoEl 9 de febrero de 2026, las fábricas de semiconductores en Corea del Sur están operando bajo un calendario especial. El Año Nuevo Lunar, conocido localmente como Seollal, se celebrará el 17 de febrero y los feriados oficiales se extenderán del 16 al 18 de febrero. Durante esos días, gran parte de la actividad industrial del país se detendrá o funcionará de forma limitada. En ese contexto, Samsung Electronics ha confirmado que retomará a pleno ritmo la producción justo después del feriado para comenzar los primeros envíos comerciales de su memoria HBM4 a Nvidia. Esta memoria está destinada a los próximos aceleradores de inteligencia artificial de Nvidia y su calendario de producción está directamente condicionado por esta pausa anual, una de las más relevantes del año para la industria tecnológica asiática.La inteligencia artificial depende de calendarios industriales muy concretosLa inteligencia artificial moderna funciona gracias a centros de datos que procesan enormes volúmenes de información de manera constante. En el núcleo de esos sistemas están los procesadores diseñados por Nvidia, una empresa estadounidense especializada en unidades de procesamiento gráfico, conocidas como GPU. Estos chips destacan por realizar muchos cálculos al mismo tiempo, pero su rendimiento depende directamente de la memoria que los alimenta. High Bandwidth Memory, o HBM, es un tipo de memoria creada para ese propósito. A diferencia de la memoria tradicional, HBM se apila en capas y se coloca muy cerca del procesador, lo que permite mover datos con mayor velocidad y menor consumo energético. La tecnología ha evolucionado por etapas: HBM, HBM2, HBM2E, HBM3, HBM3E y ahora HBM4. Cada generación responde al aumento de demanda provocado por modelos de inteligencia artificial cada vez más grandes. Samsung Electronics ha desarrollado HBM4 usando su proceso DRAM 1c, de sexta generación en la clase de diez nanómetros, junto con una base lógica fabricada con tecnología de cuatro nanómetros.La transición hacia HBM4 ocurre tras un periodo complejo para Samsung. En la generación anterior, HBM3E, la empresa no logró posicionarse con la misma rapidez que SK hynix, otra compañía surcoreana especializada en memoria. SK hynix consiguió convertirse en el principal proveedor de HBM para Nvidia y capturó la mayor parte de los contratos vinculados al auge de la inteligencia artificial. Micron Technology, fabricante estadounidense de memoria, quedó en una posición secundaria en esta categoría. Mientras la demanda de inteligencia artificial siguió creciendo, la capacidad mundial de fabricación de memoria se volvió un recurso limitado. Este problema se agrava cada año alrededor del Año Nuevo Lunar, cuando fábricas en Corea del Sur, China y otros países asiáticos reducen su actividad durante varios días. Esa pausa afecta cadenas de suministro globales y obliga a planificar con precisión qué se fabrica antes y qué se entrega después del feriado.Ante esta situación, Samsung ha organizado su calendario para que la producción y los envíos de HBM4 comiencen inmediatamente después del Seollal. En su complejo industrial de Pyeongtaek, uno de los mayores centros de fabricación de semiconductores del mundo, la empresa está ampliando la línea P4 para producir entre cien mil y ciento veinte mil obleas al mes dedicadas a HBM4. Sumadas a otras líneas, el objetivo es alcanzar alrededor de doscientas mil obleas mensuales, una parte relevante de su producción total de DRAM. Los primeros envíos a Nvidia están previstos para la tercera semana de febrero, en línea con los planes de Nvidia para presentar su nueva plataforma de aceleradores de inteligencia artificial, llamada Vera Rubin, durante la conferencia GTC 2026, programada para marzo. Aunque los analistas estiman que SK hynix mantendrá una mayor cuota de suministro, llegar temprano al mercado permite a Samsung reforzar su posición técnica y comercial.HBM4 introduce mejoras relevantes en eficiencia energética frente a la generación anterior. Esto resulta especialmente importante para centros de datos que operan de forma continua, donde el consumo eléctrico y la refrigeración representan una parte considerable de los costos. Nvidia necesita este tipo de memoria para alcanzar anchos de banda totales superiores a los veinte terabytes por segundo en sus sistemas más avanzados. Sin HBM4, ese nivel de rendimiento no sería viable. Al mismo tiempo, el énfasis de los fabricantes en producir HBM reduce la oferta de memoria convencional para computadores personales y dispositivos móviles, lo que mantiene presión sobre los precios. En este contexto, los fabricantes de memoria ya no influyen solo en componentes, sino en el ritmo general de la innovación tecnológica.)A días del Año Nuevo Lunar, Samsung se prepara para activar la producción y los envíos de HBM4 a Nvidia. Esta memoria será una pieza central de los próximos sistemas de inteligencia artificial. El calendario industrial asiático vuelve a marcar el ritmo global. Escucha más historias como esta y sigue Flash Diario en Spotify.A días del Año Nuevo Lunar, Samsung se alista para enviar HBM4 a Nvidia y acelerar la inteligencia artificial.

Simon Brooks, senior vice president of advisor success for Global Travel Collection (GTC), talks with James Shillinglaw of Insider Travel Report at last week's Internova PLUS conference at Gleneagles in Scotland. Brooks explains his role to help GTC advisors optimize all the resources that GTC and parent Internova Travel Group provide to sell more luxury travel at a high level. And since Internova and GTC advisors currently average $3.9 million a year in sales, Brooks has a major role in helping the network overall be successful. For more information, visit www.globaltravelcollection.com and www.internova.com.  All our Insider Travel Report video interviews are archived and available on our Youtube channel  (youtube.com/insidertravelreport), and as podcasts with the same title on: Spotify, Pandora, Stitcher, PlayerFM, Listen Notes, Podchaser, TuneIn + Alexa, Podbean,  iHeartRadio,  Google, Amazon Music/Audible, Deezer, Podcast Addict, and iTunes Apple Podcasts, which supports Overcast, Pocket Cast, Castro and Castbox. 

Your host, Sebastian Hassinger, talks with Alumni Ventures managing partner Chris Sklarin about how one of the most active US venture firms is building a quantum portfolio while “democratizing” access to VC as an asset class for individual investors. They dig into Alumni Ventures' co‑investor model, how the firm thinks about quantum hardware, software, and sensing, and why quantum should be viewed as a long‑term platform with near‑term pockets of commercial value. Chris also explains how accredited investors can start seeing quantum deal flow through Alumni Ventures' syndicate.Chris' background and Alumni Ventures in a nutshellChris is an MIT‑trained engineer who spent years in software startups before moving into venture more than 20 years ago.Alumni Ventures is a roughly decade‑old firm focused on “democratizing venture capital” for individual investors, with over 11,000 LPs, more than 1.5 billion dollars raised, and about 1,300 active portfolio companies.The firm has been repeatedly recognized as a highly active VC by CB Insights, PitchBook, Stanford GSB, and Time magazine.How Alumni Ventures structures access for individualsMost investors come in as individuals into LLC‑structured funds rather than traditional GP/LP funds.Alumni Ventures always co‑invests alongside a lead VC, using the lead's conviction, sector expertise, and diligence as a key signal.The platform also offers a syndicate where accredited investors can opt in to see and back individual deals, including those tagged for quantum.Quantum in the Alumni Ventures portfolioAlumni Ventures has 5–6 quantum‑related investments spanning hardware, software, and applications, including Rigetti, Atom Computing, Q‑CTRL, Classiq, and quantum‑error‑mitigation startup Qedma/Cadmus.Rigetti was one of the firm's earliest quantum investments; the team followed on across multiple rounds and was able to return capital to investors after Rigetti's SPAC and a strong period in the public markets.Chris also highlights interest in Cycle Dre (a new company from Rigetti's former CTO) and application‑layer companies like InQ and quantum sensing players.Barbell funding and the “3–5 year” viewChris responds to the now‑familiar “barbell” funding picture in quantum— a few heavily funded players and a long tail of small companies—by emphasizing near‑term revenue over pure science experiments.He sees quantum entering an era where companies must show real products, customers, and revenue, not just qubit counts.Over the next 3–5 years, he expects meaningful commercial traction first in areas like quantum sensing, navigation, and point solutions in chemistry and materials, with full‑blown fault‑tolerant systems further out.Hybrid compute and NVIDIA's signal to the marketChris points to Jensen Huang's GTC 2025 keynote slide on NVIDIA's hybrid quantum–GPU ecosystem, where Alumni Ventures portfolio companies such as Atom Computing, Classiq, and Rigetti appeared.He notes that NVIDIA will not put “science projects” on that slide—those partnerships reflect a view that quantum processors will sit tightly coupled next to GPUs to handle specific workloads.He also mentions a large commercial deal between NVIDIA and Groq (a classical AI chip company in his portfolio) as another sign of a more heterogeneous compute future that quantum will plug into.Where near‑term quantum revenue shows upChris expects early commercial wins in sensing, GPS‑denied navigation, and other narrow but valuable applications before broad “quantum advantage” in general‑purpose computing.Software and middleware players can generate revenue sooner by making today's hardware more stable, more efficient, or easier to program, and by integrating into classical and AI workflows.He stresses that investors love clear revenue paths that fit into the 10‑year life of a typical venture fund.University spin‑outs, clustering, and deal flowAlumni Ventures certainly sees clustering around strong quantum schools like MIT, Harvard, and Yale, but Chris emphasizes that the “alumni angle” is secondary to the quality of the venture deal.Mature tech‑transfer offices and standard Delaware C‑corps mean spinning out quantum IP from universities is now a well‑trodden path.Chris leans heavily on network effects—Alumni Ventures' 800,000‑person network and 1,300‑company CEO base—as a key channel for discovering the most interesting quantum startups.Managing risk in a 100‑hardware‑company worldWith dozens of hardware approaches now in play, Chris uses Alumni Ventures' co‑investor model and lead‑investor diligence as a filter rather than picking purely on physics bets.He looks for teams with credible near‑term commercial pathways and for mechanisms like sensing or middleware that can create value even if fault‑tolerant systems arrive later than hoped.He compares quantum to past enabling waves like nanotech, where the biggest impact often shows up as incremental improvements rather than a single “big bang” moment.Democratizing access to quantum ventureAlumni Ventures allows accredited investors to join its free syndicate, self‑attest accreditation, and then see deal materials—watermarked and under NDA—for individual investments, including quantum.Chris encourages people to think in terms of diversified funds (20–30 deals per fund year) rather than only picking single names in what is a power‑law asset class.He frames quantum as a long‑duration infrastructure play with near‑term pockets of usefulness, where venture can help investors participate in the upside without getting ahead of reality.

Breaking Free - Year of Acceleration - Russell Lorfing at GTC 1-11-2026 by Will Brocker

2025 has come to a close - and it's another year-end episode reflecting on the best and worst cars Jason and Derek have encountered this year. Maximum Carmudgeonation is achieved today, so hold onto your hats - and we guarantee, you've never listened to another podcast where the Vinfast VF8 and McLaren F1 are both mentioned. === Visit http://JasonSentMe.com to get a Hagerty Guaranteed Value (TM) collector-car insurance quote! === Before getting into the thick of it, Jason updates us on his MK3 Volkswagen Cabrio VR6 swap - with the 2.slow and the rest of the front + rear subframes out, we learn one other MK3 (Jetta GLX) has been sacrificed in the name of top-down VR6 burnouts. A myth is busted - Harbor Freight plastic carts don't appear to be makeshift engine stands after all. But they do explode catastrophically! Derek also goes over some highlights of another year dealing cars at OTS - with sales and consignments including the likes of the Ferrari F50, Porsche Carrera GT, and an array of modern Ferrari Challenge cars (360 Challenge Stradale, F430 Scuderia, and 458 Speciale to name a few). He also reflects on a changing market - moving away from 60s Ferraris like 250 Lusso and 330 GTC. Jason begins with his first wave highlights - including but not limited to: Lancia Stratos, Lancia Thema 8.32, Cizeta-Moroder V16T, Saab 9000 Aero, Alfa Romeo 164 Quadrifoglio, E34 BMW M5 with an S70B56 swap, the Kwiek Classics Mercedes-Benz CLK63 AMG Black Series 6-Speed, Ford Sierra Cosworth, Merkur XR4Ti, Jeep Cherokee, and of course Derek's recently acquired Mk1 Jaguar. Derek follows with the Alfa Romeo Giulia Sprint Speciale, RUF Tribute, Kimera EVO37, the Toyota 2000GT, and more recently the Porsche 911 IROC RSR (to be further explored on a future episode…) Jason remarks on many of the the other great cars he's driven for various Revelations, Ultimate Drag Race, and Ultimate Lap Battle episodes, including the Chevrolet Corvette ZR1 (C8 and C4), Porsche 992 GT3 RS, Ford Mustang GTD, Ford GT (both generations), W204 Mercedes-Benz C63 AMG (including the Anderzen manual swap), Alpine A110, Audi RS6 Avant, and the Porsche Panamera Turbo S E-Hybrid (the BMW M5 Touring was unfortunately not so good). But not to worry- plenty of Carmudgeonation goes down - with roasts of the automatic Porsche 996 Turbo, BMW i3 and i8, the ND2 Mazda Miata, and even Jason's own MK3 Cabrio (while it still had its 2.slow). All this and more, on this week's end-of-2025 finale of The Carmudgeon Show. Learn more about your ad choices. Visit megaphone.fm/adchoices

Angie Licea, president of Global Travel Collection (GTC), talks with James Shillinglaw of Insider Travel Report at this month's ILTM Cannes luxury travel show, about how GTC is evolving from a collection of luxury travel agencies into a single force in luxury travel, backed by parent Internova Travel Group. Licea tells us what the future holds for GTC and its group of luxury travel advisors and why GTC and Internova had such a big presence at this year's ILTM Cannes. For more information, visit www.globaltravelcollection.com. All our Insider Travel Report video interviews are archived and available on our Youtube channel  (youtube.com/insidertravelreport), and as podcasts with the same title on: Spotify, Pandora, Stitcher, PlayerFM, Listen Notes, Podchaser, TuneIn + Alexa, Podbean,  iHeartRadio,  Google, Amazon Music/Audible, Deezer, Podcast Addict, and iTunes Apple Podcasts, which supports Overcast, Pocket Cast, Castro and Castbox.   

Disconnect - Russell Lorfing at GTC 12-7-2025 by Will Brocker

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Send us a textTrent's back on the mic and absolutely done with anonymous complainers, lazy leadership, and generals who think PT is optional. From the “Creech gate crisis” to government shutdown panic, he's torching every excuse in sight. If you've ever thought “they should just open another gate,” buckle up—Trent's got news for you.He dives into why the Air Force burns people to the ground, how our “greatest resource” lip service is complete nonsense, and why generals need to stop hiding behind waivers and start doing pushups. It's raw, hilarious, and unfiltered commentary on the military circus we all know too well.Stick around for stories of GTC abuse, political delusion, and one final truth bomb about your toxic social media habits. Spoiler: it's not the world—it's you.This is Ones Ready at its most savage—no filters, no excuses, no PowerPoint slides.⏱️ Timestamps:00:00 – The Standards Don't Skip Generals 01:05 – Trent's Solo Mission and the “Creech Gate” Meltdown 03:31 – “Our Greatest Resource Is People”… Until It's Not 05:44 – Anonymous Complaints and Military Victim Olympics 08:05 – The Government Shutdown Reality Check 10:23 – E-1s to E-3s: The Forgotten Workforce 12:48 – When “Lethality” Becomes Bureaucratic Theater 14:49 – Generals vs. PT: The Pentagon's Soft Spot 17:12 – Too Many Chiefs, Not Enough Squadrons 19:35 – The Pete Hegseth Shake-Up and the Marine Corps Exception 21:56 – Overcorrections, Crying Generals, and Real Leadership 24:20 – The GTC Disaster Zone 26:31 – The Cure for Social Media Rage (Hint: Delete It)

 Hey everyone, it's Emma Weissmann, the host of Humans in the Hot Seat, a spinoff series from Humans of Travel. This episode features Tom Ho, luxury travel advisor at Protravel International and Global Travel Collection (GTC), who was recently cast in the newest season of "1st Look Presents: Extra Mile Club” on NBC. During this episode, you'll get a behind-the-scenes look at the audition, filming and production process of the reality television show, where GTC advisors compete to win the business of celebrity clients. Ho's episodes air on Dec. 13 (part one) and Dec. 20 (part two) following Saturday Night Live on NBC, and can be streamed via Peacock and YouTube. This episode is sponsored by Windstar Cruises. RESOURCES MENTIONED IN THIS EPISODE 1st Look Presents: Extra Mile Club Protravel International Global Travel Collection Contact Ho on Instagram: @ThomsTravel ABOUT YOUR HOST Emma Weissmann is the Executive Editor of TravelAge West, a print magazine and website for travel advisors based in the Western U.S. She is also the co-host of Trade Secrets, a podcast created with sister publication Travel Weekly, and the Editor-in-Chief of print publication AGENTatHOME.TravelAge West also produces events including Future Leaders in Travel, Global Travel Marketplace West, the WAVE Awards gala ad the Napa Valley Leadership Forum. ABOUT THE SHOW TravelAge West’s award-winning podcast, “Humans of Travel,” features conversations with exceptional people who have compelling stories to tell. Listeners will hear from the travel industry’s notable authorities, high-profile executives, travel advisors and rising stars as they share the highs and lows that make them human.See omnystudio.com/listener for privacy information.

In this episode of TechMagic, hosts Cathy Hackl and Lee Kebler explore OpenAI's Sora and how AI-driven video generation reshapes creativity, privacy, and consent. From OpenAI's massive $38B AWS deal to the ethical storm over data scraping and copyright, they unpack the week's biggest tech power plays. The duo explores Geoffrey Hinton's surprising optimism on AI's future, Meta's data mishap, and how companies are redefining roles through spatial computing. Plus, Lee shares insights from NVIDIA's GTC conference and what it reveals about the true cost and promise of AI. The episode also features Cathy's exciting interview with Melissa Tony Stires, Founding Partner and Chief Global Growth Officer, and Janna Salokangas, Co-Founder and CEO of Mia, AI. Together, they discuss strategy-first adoption of AI, the importance of AI literacy, and the mindset shifts leaders need to drive human-centred transformation in the era of intelligent tools.Come for the tech, and stay for the magic!Melissa Tony Stires BioMelissa Tony Stires is an international protocol expert and leadership innovator specialising in cross-cultural communications and women's empowerment in AI. As Founder and Head of Global Growth and Expansion at Mia AI, she bridges tradition and technology through global collaborations and billion-dollar initiatives. A certified Advanced International Protocol Officer, best-selling author, and sought-after speaker, Melissa's work has shaped dialogues from Davos to Cannes Lions, advancing inclusivity, innovation, and global understanding in the tech landscape.Melissa Tony Stires LinkedInJanna Salokangas BioJanna Salokangas is the Co-founder and CEO of Mia AI, where she's redefining how people and organisations unlock their full potential through AI-driven learning and innovation. Under her leadership, Mia has trained over 7,000 professionals across 65+ countries, partnering with leading institutions to deliver transformative AI education and solutions. A co-founder of Finnish Flow, Janna also champions Finland's business community at Davos, advocating for human-centric AI and the future of equitable, empowered innovation.Janna Salokangas LinkedInKey Discussion Topics:00:00 Intro: Welcome to Tech Magic00:28 NVIDIA GTC & Nokia's $1B AI Investment00:54 Geoffrey Hinton Shifts AI Stance on Job Displacement08:17 Sharp HealthCare's First Chief Spatial Computing Officer09:05 OpenAI's $38 Billion Amazon AWS Deal Explained15:17 Perplexity vs Reddit: Data Scraping Lawsuit Breakdown21:28 AI Augmentation Over Replacement: Secret Cinema's Approach26:19 Magic Leap's Google Partnership & New AI Glasses32:17 TEDx Atlanta: Alvin Wang Graylin & Industry Leaders35:45 AI Education Interview with Janna & Melissa from Mia AI37:18 Mia AI: Human-Centered AI Education Going Global42:35 Strategy-First AI Adoption: Define Problems Before Tools42:46 Real-World Success Stories: From Universities to Single Mothers47:28 What Differentiates Mia AI in a Crowded Market Hosted on Acast. See acast.com/privacy for more information.

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discuss NVIDIA's ever expanding autonomy ambitions and the fracturing relationship between Waymo and Uber, which may signal the end of one of the industry's most-watched partnerships. Jensen Huang's latest GTC announcements further signaled that NVIDIA is moving beyond supplying compute to potentially building their own full autonomy stack and licensing it. Grayson and Walt trace this shift back to the early days NVIDIA's automotive division and the evolution of its Hyperion platform, which is now positioned not only to power OEMs but also to compete directly with the very companies that rely on its GPUs to enable autonomous driving systems.While NVIDIA appears poised to compete with its customers, Waymo and Uber's partnership is showing signs of unraveling after Uber announced plans to deploy Lucid/Nuro autonomous vehicles in San Francisco next year, directly challenging Waymo on in their home market.Grayson likens the move to “divorce court,” raising questions about how the companies will divide the Austin and Atlanta markets, where Waymo currently operates exclusively on Uber's platform. The episode closes with updates on Aurora's strategic pivot and the Foreign Autonomy Desk, covering Baidu's expansion in Hong Kong, Uber's European ambitions, and continued progress in Tesla's FSD rollout.Episode Chapters0:00 NVIDIA's Autonomy Ambitions 7:13 Waymo & Uber's Fracturing Relationship9:35 Nuro's Upcoming Launch on Uber in San Francisco 11:51 Gemini is Coming to Waymo14:05 Boston's Autonomous Vehicle Blunder15:43 Seattle's Challenging Political Environment 17:34 Political Coalitions 19:36 Aurora's Pivot25:32 Tesla Robotaxi / FSD 14 Updates30:04 Foreign Autonomy Desk33:08 Next WeekRecorded on Thursday, October 30, 2025--------About The Road to AutonomyThe Road to Autonomy provides market intelligence and strategic advisory services to institutional investors and companies, delivering insights needed to stay ahead of emerging trends in the autonomy economy™. To learn more, say hello (at) roadtoautonomy.com.Sign up for This Week in The Autonomy Economy newsletter: https://www.roadtoautonomy.com/ae/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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