Podcasts about Bedrock

Check out my Tronic Radio on your favorite streaming platforms here: https://ssyncc.com/tronic-podcast 01.Genius Of Time - Sunswell [Oath] 02.Facundo Losardo - Readiness [Bedrock] 03.Sean Harvey - Do This [Keep Thinking] 04.Patch Park - Moca [District Records] 05.Hernan Cattaneo & Tom Pavicich - Wink [Electronic Groove] 06.Maarten van der Vleuten presents v48 - Only Human [Passiflora Records] 07.ID - ID (Nick Stoynoff Remix) [Tronic] 08.Kamilo Sanclemente - Jupiter Code [Tronic] 09.Anthony Pappa & Nick Stoynoff - 435 [Selador] 10.Zuccasam - Feel Happy [Plastic Fantastic] 11.Four Candles & Feemarx - Eudaimonia 303 [Bedrock] 12.Nick Stoynoff - The Hero's Journey (Dusty Kid Remix) [NOFF!] This show is syndicated & distributed exclusively by Syndicast. If you are a radio station interested in airing the show or would like to distribute your podcast / radio show please register here: https://syndicast.co.uk/distribution/registration

Demandbase's Vice President of Product explains why they deleted a fully-approved AI architecture two months before launch — and how the rebuild surpassed some of their customer's highest expectations.Topics Include:AWS's Achint Naveen introduces Demandbase's VP of Product, Chad HoldorfDemandbase unifies sales, marketing, and revenue data into one viewNovember's architecture used many specialized agents, all committee-approvedThat design failed constantly — only a 30% conversation pass rateOn May 11th, the team deleted the entire architectureRebuilt in May with AWS Strands: one simpler, flexible agentPass rate leapt from 30% to 94% almost overnightWeek two retention rose from the low 20s to upper 80sWeekly active users grew 45% week-over-week after launchReal customer interviews play, calling the new AI a "dream"One user cut an hour-long report down to fifteen minutesCustomers now trace ad impressions directly to closed dealsHoldorf's advice: delete and rebuild when architecture gets too complexAWS's Naveen walks through Bedrock, AgentCore, and StrandsAgentCore Memory highlighted as Demandbase's next area of explorationParticipants:Chad Holdorf – Vice President of Product Management, DemandbaseAchint Naveen – Sr Account Manager, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Season 9, Episode 1: Can Detroit become America's greatest urban comeback story? To kick off Season 9 of No Cap, we sit down with Jared Fleisher, CEO of Bedrock, to break down the future of Detroit, Cleveland, and large-scale city building. Jared shares how Bedrock, founded by Dan Gilbert, has invested billions across 140+ properties and 21M+ SF while helping reshape Detroit's urban core. Whether you're interested in public-private partnerships, downtown revitalization, GM's move to Hudson's Detroit, or the future of the Renaissance Center, this episode is a must-listen. Join us as we dive into conviction, civic commitment, riverfront development, and what it really takes to rebuild a city at scale. Shoutout to our sponsor, WareSpace — turning underused industrial, flex, office, and big-box properties into micro warehouse space for small businesses. TOPICS 00:00 - Season 9 Premiere with Jared Fleisher 05:00 - Dan Gilbert's Conviction in Detroit 10:52 - Bedrock's 21M SF Portfolio 16:49 - The Renaissance Center Challenge 22:38 - GM, Hudson's Detroit, and Civic Commitment 27:06 - Rebuilding Detroit's Riverfront 33:17 - Public-Private Partnerships and Brownfield Financing 41:48 - Innovation Districts and Talent 45:05 - Defense, Manufacturing, and Detroit's Next Chapter 48:06 - Downtown Dallas and Why Cities Need Strong Cores For more episodes of No Cap by CRE Daily visit https://www.credaily.com/podcast/ Watch this episode on YouTube: https://www.youtube.com/@NoCapCREDaily About No Cap Podcast Commercial real estate is a $20 trillion industry and a force that shapes America's economic fabric and culture. No Cap by CRE Daily is the commercial real estate podcast that gives you an unfiltered ”No Cap” look into the industry's biggest trends and the money game behind them. Each week co-hosts Jack Stone and Alex Gornik break down the latest headlines with some of the most influential and entertaining figures in commercial real estate. About CRE Daily  CRE Daily is a digital media company covering the business of commercial real estate. Our mission is to empower professionals with the knowledge they need to make smarter decisions and do more business. We do this through our flagship newsletter (CRE Daily) which is read by 65,000+ investors, developers, brokers, and business leaders across the country. Our smart brevity format combined with need-to-know trends has made us one of the fastest growing media brands in commercial real estate.

Air Date: 8/22/2026 Today we look at how the Republican Party made climate denial its policy more than twenty years ago and is now removing the government's ability to see the problem at all. The Environmental Protection Agency under Bush refused to regulate carbon in 2003, and now the Trump administration is dismantling the science that would help us both avoid worsening the climate emergency and mitigate the inevitable destruction to lives and property now baked into the system. Full Show Notes Transcript Be part of the show! Leave a voice message, message us on Signal at the handle bestoftheleft.01, or email Jay@BestOfTheLeft.com BestOfTheLeft.com/Support (Members Get Bonus Shows + No Ads!) Use our links to shop Bookshop.org and Libro.fm for a non-evil book and audiobook purchasing experience! Join our Discord community! TOP TAKES KP 1: When It Comes to Climate Change, Is There a Point When Adaptation Isn't Enough - Consider This - Air Date 7-31-26 KP 2: Fire Climate Hot, Dry, Windy Days Are Becoming Common, Making Wildfires Harder to Control - Democracy Now! - Air Date 8-6-24 KP 3: Five Lies Republicans Tell About the Climate Crisis - The Hartmann Report - Air Date 8-9-26 KP 4: Is This How We Invade Greenland - The Rachel Maddow Show - Air Date 8-11-26 KP 5: Europes Summer of Fire The New Normal Part 1 - Outrage + Optimism The Climate Podcast - Air Date 7-30-26 KP 6: Climate Wayfinding A Compass for the Climate Crisis Part 1 - Sea Change - Air Date 5-22-26 (00:52:11) NOTE FROM THE EDITOR An Update on the Show My commentaries on YouTube - Share them! DEEPER DIVES (01:00:01) SECTION A: ON THE GROUND A1: Executive Disorder Spokane Wildfires, Abdul Wins Michigan Primary, Munition Shortage Part 1 - It Could Happen Here - Air Date 8-7-26 A2: Wildfires Rage Across the Okanagan Part 1 - The Current - Air Date 8-10-26 A3: Farmers Struggle with Crops as Climate Change Makes Weather Less Predictable - PBS Newshour - Air Date 6-23-26 A4: Wildfires Rage Across the Okanagan Part 2 - The Current - Air Date 8-10-26 (01:29:50) SECTION B: EUROPE ON FIRE B1: Wildfires Are Burning Through Europe Part 1 - The Brian Lehrer Show - Air Date 8-4-26 B2: Europes Summer of Fire The New Normal Part 2 - Outrage + Optimism The Climate Podcast - Air Date 7-30-26 B3: Wildfires Are Burning Through Europe Part 2 - The Brian Lehrer Show - Air Date 8-4-26 (01:58:01) SECTION C: THE DELAY MACHINE C1: What Happens When the Bedrock of US Climate Policy Is Wiped Away Part 1 - Climate Court Voices - Air Date 5-15-26 C2: Slow Moving Disasters Climate Change, Artificial Intelligence and The Democratic Party. - UNFTR - Air Date 8-4-26 C3: What Happens When the Bedrock of US Climate Policy Is Wiped Away Part 2 - Climate Court Voices - Air Date 5-15-26 C4: Trump Administration Ends Funding for Arctic Climate Report - PBS Newshour - Air Date 8-13-26 C5: Ex Oil-Engineer Turned Climate Whistleblower_ We Face Collapse. with Kevin Anderson Part 1 - Downstream - Air Date 8-10-26 C6: Executive Disorder Spokane Wildfires, Abdul Wins Michigan Primary, Munition Shortage Part 2 - It Could Happen Here - Air Date 8-7-26 C7: Ex Oil-Engineer Turned Climate Whistleblower: We Face Collapse. with Kevin Anderson Part 2 - Downstream - Air Date 8-10-26 (02:59:45) SECTION D: WHAT COMES NEXT D1: A Biden Climate Retrospective Part 1 - Left Anchor - Air Date 8-14-26 D2: Climate Wayfinding A Compass for the Climate Crisis Part 2 - Sea Change - Air Date 5-22-26 D3: A Biden Climate Retrospective Part 2 - Left Anchor - Air Date 8-14-26   Produced by Jay! Tomlinson Visit us at BestOfTheLeft.com Listen Anywhere! BestOfTheLeft.com/Listen Follow BotL: Bluesky | Mastodon | Threads | X Like at Facebook.com/BestOfTheLeft Contact me directly at Jay@BestOfTheLeft.com

In this episode the hosts analyze a four-unit quick service restaurant franchise portfolio and debate whether buying an underperforming chicken/Mexican franchise platform is a smart acquisition or an expensive operational headache.Business Listing – https://go.franzy.com/resale/qsr-4-unit-southeast-01Welcome to Acquisitions Anonymous – the #1 podcast for small business M&A. Every week, we break down businesses for sale and talk about buying, operating, and growing them.Looking to build a professional website in minutes? Try Wix: https://wix.pxf.io/c/6898629/3115214/25616?trafcat=templateHubSpot is the backbone for how businesses scale without chaos. Try them out here: https://go.try-hubspot.com/OeG9VrSubscribe for more episodes: https://www.youtube.com/@AcquisitionsAnonymousPodcast?sub_confirmation=1Subscribe to our Newsletter: https://www.acquanon.com/newsletterSponsors:Quiet Light BrokerageThinking about selling your e-commerce or SaaS business? Quiet Light Brokerage specializes in helping founders maximize value with experienced former operators—not just brokers—and offers a free, no-obligation business valuation. Learn more at: https://quietlight.comBedrock Quality of EarningsBefore buying a business, make sure the numbers are real. Bedrock provides buyer-focused Quality of Earnings reports using experienced financial professionals and AI-powered analysis to help uncover surprises before closing. Learn more at: https://bedrockqoe.comWhat happens when you find a franchise portfolio that's growing—but still underperforming its own brand averages? In this episode, the hosts evaluate a live four-unit quick service restaurant (QSR) portfolio consisting of chicken and Mexican food franchises in the Southeast.The business generates approximately $4.2M in trailing twelve-month revenue and $676K in adjusted EBITDA, but the opportunity isn't as straightforward as it appears. The hosts dig into franchise economics, average unit volumes (AUVs), dual-brand restaurant conversions, SBA financing, franchise transfer restrictions, and whether operational improvements can realistically unlock significant upside.The discussion goes well beyond valuation. The panel debates whether these restaurants are simply poorly operated, located in weak markets, or attached to an aging franchise brand that may never reach system averages. Along the way they explore AI drive-thru ordering, franchise legal structures, pricing flexibility, restaurant labor, and why experienced multi-unit operators may view this acquisition very differently than first-time buyers.Key Highlights:- Four-unit QSR portfolio with $4.2M revenue and $676K adjusted EBITDA- One dual-brand chicken/Mexican location could potentially be converted into a standalone Mexican concept with franchisor incentives- Discussion of AUV (Average Unit Volume), franchise due diligence, and identifying operational versus location issues- SBA financing considerations, including funding acquisition costs, working capital, and restaurant conversion expenses- Deep dive into AI ordering, pricing strategy, franchise economics, and why experienced operators often outperform first-time ownersSubscribe to  weekly our Newsletter and get curated deals in your inboxAdvertise with us by clicking hereDo you love Acquanon and want to see our smiling faces? Subscribe to our Youtube channel.Do you enjoy our content? Rate our show!Follow us on Twitter @acquanon Learnings about small business acquisitions and operations.For inquiries or suggestions, email us at contact@acquanon.com

ServiceNow and AWS reveal how the DevOps Agent and MCP Server Console are turning incident response into a fast, autonomous, fully governed process. Topics Include:Govind Menon (ServiceNow) and Arun Jacob (AWS) discuss MCP and A2A strategy.ServiceNow understands workflows; partners with AWS to power them with AI.AI Control Tower governs and secures agent access to enterprise data.MCP is the industry standard for how AI agents read and act.Action Fabric spans A2A, REST APIs, and MCP for agentic work.AWS DevOps Agent, built on Bedrock, resolves incidents through sub-agents.Admin and operator access patterns integrate with Dynatrace, Datadog, Slack, GitHub.Demo: ServiceNow incident automatically triggers DevOps Agent investigation and resolution.DevOps Agent writes findings live back into the ServiceNow incident ticket.ServiceNow champions capping MCP servers at 30 tools for performance.MCP Server Console lets teams build scoped, use-case-specific tool servers.NowAssist skills, Knowledge Graph, and REST APIs become MCP tools.Live demo connects a 38-tool custom MCP server to DevOps Agent.Role-based access ensures users only see their permitted MCP tools.ServiceNow's autonomous ITOM agents point toward unsupervised future operations. Participants:Govind Menon – Head of MCP Product, ServiceNow Arunsingh Jeyasingh Jacob – Senior Solution Architect - ISV, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Coldwired Podcast (Come and say hello facebook.com/ColdwiredMusic). Live every Tuesday 8PM (UK)! www.twitch.tv/coldwired Music Vibes: Global Gathering XV (Live Stream 02/08/26). Tracklisting: [00:00] 01. Evolution Feat. Jayn Hanna - Walking On Fire (Adilian's Chrome Rework) [Bootleg] [06:20] 02. Jerome Isma-Ae - Encounter (Extended Discotheque Mix) [JEE Productions] [10:24] 03. Quivver, Dave Seaman - Starship Disco (Extended Mix) [Global Underground] [16:04] 04. Danny Howells, Lloyd Barwood - One More Sky [Mango Alley] [21:04] 05. HERMEN - Benjamin [Bandcamp] [27:10] 06. Lou8, AdamK, Loud Unity - Into the Void (Extended Mix) [Enormous Moments] [32:16] 07. Andy Rapkins - Kixka (12 Theory Remix) [Prognosis] [37:51] 08. R.E.E.V. - Summon [Affiliate] [43:41] 09. Ferry Corsten, Marsh - Attraction (Marsh's Extended Mix) [Anjunadeep] [48:16] 10. Dosem - Morning Runner (Extended Mix) [Anjunadeep] [51:59] 11. Hernan Cattaneo, Tom Pavicich - Bloom [Balance Music] [56:29] 12. Alexey Sonar, ANUQRAM - Time (Extended Mix) [Anjunabeats] [1:01:15] 13. Matty Wright - Fond Memories [Affiliate] [1:07:18] 14. Framewerk - Unfinished Sympathy (Framewerk Rewerk Part 1 and 2) [Bandcamp] [1:12:19] 15. Nick muir, Bedrock, John Digweed, Kyo - For What You Dream Of (Pyrrhus Remake) [Bandcamp] [1:13:11] 16. Space Manoeuvres - Part Three (Breaks Mix) [Lost Language] [1:21:05] 17. Kelle, Aloma Steele - Exigency [Elektroshok Records] [1:25:00] 18. Fishbone Beat - Always (Matty Wright's 'Shaken Not Stirred' Mix [Bootleg] [1:29:44] 19. Juanjo Corrales - Go Away [DistroKid] [1:35:13] 20. DRKWTR - In The End (Alt-A Remix) [Diesel Recordings] [1:40:11] 21. Sasha and Emerson - Scorchio (Pyrrhus Breaks Remix) [Bandcamp] [1:44:20] 22. Lustral - Broken (Way Out West Remix) [Lost Language] [1:48:53] 23. Barbitura - Flight Of The Navigator (Retroid Remix) [Ego Shot Recordings] [1:53:41] 24. Activa - Journey Home (Extended Mix) [Black Hole Recordings] [1:58:57] 25. Slacker - Psychout (Of Mind) [Jukebox In The Sky] [2:04:44] 26. LOUT - Utopia [Monkey League] [2:09:19] 27. Fuenka - Nitidus (Extended Mix) [FSOE] [2:14:04] 28. Hoopoe - Aracari [Forescape Digital] [2:19:20] 29. Stallings - Pursuit [Houstrike] [2:25:33] 30. Basil O'Glue - No Response (Extended) [Vandit Alternative] [2:30:56] 31. Slusnik Luna - Sun 2011 [Anjunabeats] [2:36:54] 32. Quivver - She Does (Quivver Mix) [VC Recordings] [2:44:31] 33. John 00 Fleming - Rest Now My Love [JOOF Recordings] [2:50:41] 34. Basil O'Glue, Nomas, Calantha - Ibiza 3AM [BAGRUHM] [2:56:03] 35. Mara - One (Hamel Implant Remix) [Choo Choo Records] [3:01:09] 36. Robert Nickson - Heliopause [Grotesque] [3:06:09] 37. Bicep - Water (London Hatred (Unofficial) Remix) [Bootleg] [3:10:57] 38. Thomas Datt, Magnus - Binary Complex [Borderline] [3:14:42] 39. Foley - You'll Never [Neptune Discs] [3:21:21] 40. Faithless - Tarantula (Rollo and Sister Bliss Big Mix) [Cheeky Records] [3:25:43] 41. Nomas - Residual Self [JOOF Recordings] [3:30:55] 42. Jon Mangan - Aether (Extended Mix) [Borderline] [3:35:36] 43. Three Drives On A Vinyl - Sunset In Ibiza [Massive Drive Recordings] [3:39:00] 44. Will Atkinson - Last Night in Ibiza (Extended Mix) [Black Hole Recordings] [3:45:00] 45. OceanLab - I Am What I Am (Lange Remix) [Anjunabeats] [3:51:52] 46. J Lauda, D.J. MacIntyre - Strange Wonders (Nomas Remix) [SLC-6 Music] [3:56:46] 47. Tracid, Koal_53 - Dreams [KOAL RECORDS]

What can actually hold your life together?We spend a lot of time building on things that feel secure—our beliefs, our accomplishments, our certainty, even our opinions. But Jesus asked a different question: What kind of foundation will still be standing when the storms come?This week, we explored the apostle John's simple but profound declaration: "God is love." After decades of walking with Jesus and reflecting on everything he had seen, John came to believe that those three words are the foundation for understanding God, reading Scripture, and living faithfully.Because the strongest foundation isn't found in having all the right answers.It's found in learning to build your life on the love of God.Chapters00:00 Intro00:22 Four Gospels, Four Beginnings01:38 In the Beginning Was the Word03:12 John Looks Back07:50 God Is Love12:00 Belief or Trust?13:23 The Problem with Defining Christianity by Beliefs Alone16:14 A Hermeneutic of Love23:18 Building on Bedrock27:48 The Foundation That Lasts30:16 Communion: Embodying the Love of ChristKey TakeawaysJohn's understanding of God was formed by walking closely with Jesus."God is love" is the foundation for interpreting Scripture and understanding reality.Christianity is about becoming more than simply believing.Jesus consistently invited people to trust him and follow him before asking for certainty.Love requires humility, growth, and the willingness to keep learning.The strongest lives are built on the bedrock of God's love, not merely on having the right beliefs.Scripture ReferencesJohn 1:1–141 John 1:1–41 John 4:7–21Matthew 5–7Matthew 7:24–27

Can a church really shepherd people it doesn't know? In this episode, Brody sits down with Jonge Tate to challenge the way we think about church, leadership, and success.They talk about the problems with chasing numbers, the multi-site church model, and why healthy churches need real pastors, real relationships, and shared leadership. Jonge also shares the lessons behind Bedrock's church planting journey, including why sending your best leaders away may be one of the hardest and most faithful things a church can do.This is a conversation about church planting, shepherding people, multiplying leaders, and trusting God with the results.Send us Fan MailPlease leave a review on Apple or Spotify to help improve No Sanity Required and help others grow in their faith. Click here to get our Colossians Bible study.

Send us Fan MailJuly Episode — What's New in Cloud FinOpsEpisode SummaryIn this July episode of What's New in Cloud FinOps, Frank and SteveO cover a packed set of cloud and AI cost-management updates across AWS, Azure, Google Cloud, Oracle, Alibaba, and OpenAI. The conversation focuses on how cloud vendors are changing pricing models, improving observability, and adding new ways to optimize compute, storage, and AI workloads.The standout theme is that AI and cloud economics are becoming more dynamic: pricing is shifting by usage pattern, time of day, workload type, and capacity model. The hosts also dig into how organizations can use these changes to improve governance, control costs, and make smarter architecture decisions.Top Topics CoveredAWS EKS and ECS GPU management fee reductionsAmazon CloudWatch intelligent tiering for logsAmazon S3 removing the 30-day minimum for storage class transitionsAWS Billing and Cost Management adding a cost efficiency widgetAWS Data Exports adding standardized Bedrock metadataAWS Lambda publishing logs for managed capacity providersAzure reservation exchange changes and legacy VM RI renewalsAlibaba Cloud time-of-day pricing for frontier AI modelsAlibaba model routing reference architectureOpenAI GPT model price cuts and Bedrock matching those ratesOracle bringing Gemini models into OCIGoogle Data Stream free tier for CDC writesAmazon OpenSearch Service optimized for log analyticsMicrosoft Marketplace single-click SaaS purchasesAWS sustainability dashboard adding water withdrawal dataKey TakeawaysAI pricing is getting more sophisticated.Providers are increasingly using variable pricing, off-peak discounts, and routing strategies to steer usage and improve margins.Cloud optimisation is moving deeper into the platform.Vendors are surfacing more native tools for cost visibility, efficiency scoring, and policy-driven automation.Storage and logging are becoming more cost-aware.New features in S3, CloudWatch, and OpenSearch help teams store more data for less while keeping observability usable.Governance and procurement remain critical.

P.M. Edition for Aug. 6. Meta said today one of its AI models hacked a third-party service during cybersecurity testing. WSJ tech reporter Sam Schechner discusses why these incidents are becoming more common. Plus, billionaire Dan Gilbert has already revived Detroit's downtown. Now he's set his sights on a $1.6 billion project that would transform the city's waterfront—but, as Journal real estate reporter Nicholas Miller explains, it's a gamble for him, and for the city. And SpaceX investors brace for more volatility after $100 billion of the company's shares were unlocked today. We hear from markets reporter David Uberti about what investors should expect. Alex Ossola hosts. Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Of all commercial real estate investors, only 3% are women – and when Beth Azor learned that stat, she decided to frickin' change it. In this episode, Erin sits down with the woman affectionately known as The Canvassing Queen®: founder and CEO of Azor Advisory Services, owner of three Florida shopping centers valued at over $75 million, and founder of the Women's Real Estate Investment Summit, where Erin has had the joy of speaking – and riding the famous four-hour bus tour of the town Beth practically owns. Beth's story starts with an $11,000-a-year nonprofit salary and a real estate license she'd had since age 18. It took a boss literally marching her to a bank to co-sign a $50,000 note – on the condition she invest 20% of every commission from then on – to turn a high-earning spender into an investor. Eight LP deals later, she went out on her own as a GP, and today she's the co-GP of a $32 million asset she waited thirteen years to buy. Yes, thirteen. That story alone (Mr. G, the quarterly "no," and the pivot to pure relationship-building) is worth the listen – and so is the one about crashing a utility company's remote HQ with cupcakes. Whether you've never heard the terms LP and GP or you're ready to raise capital for your first deal, Beth breaks the path down into steps any woman can start this week: pick an asset class, find an expert, invest passively first, and watch how it's done. Because as Beth's community proves – 68 women stood up at this year's summit having invested with someone in the room – you don't have to do it alone. Listen in as Erin and Beth discuss: The boss who called her "a freaking idiot" (with love), co-signed her first $50K investment, and made her bank 20% of every commission The stat that lit the fire: only 3% of commercial real estate investors are women – and most inherited it or signed on a husband's guarantee Why "we don't know any other women doing it" is the real barrier – and how the Women's Real Estate Investment Summit is dismantling it LP vs. GP, explained in plain English: preferred returns, refinances, and why LPs end up "playing with house money" Beth's starting playbook: pick your asset class, find an expert, LP first, and watch the GP How GPs raise capital – including the empty Wells Fargo bank deal where Beth raised $3.2M from 22 people in four days Beth's non-negotiable: never invest with a GP who has no skin in the game The 13-year Mr. G story: how persistence plus genuine relationship turned "no, click" into co-GP of a $32M asset The cupcake story: how a Friday-afternoon delivery did what eight men yelling couldn't Women Investor Wednesdays, the March 2027 summit, and how to get in the room About Affectionately known as The Canvassing Queen®, Beth Azor is the founder and CEO of Azor Advisory Services (AAS), a leading commercial real estate advisory and investment firm based in Davie, Florida. As its principal, Beth currently owns and manages three shopping centers in Florida valued at over $75M. She travels the U.S. consulting with, brokering deals for, and training associates in the commercial real estate industry, with clients including Phillips Edison & Co., Brixmor Properties, The Shopping Center Group, Urban Edge Development, DLC Management Group, and Bedrock. Beth is the author of Don't Say No for the Prospect (2019) and The Retail Leasing Playbook (2020), the founder of the Women's Real Estate Investment Summit – on a mission to get more women investing in real estate and growing their families' wealth – and co-founder of the South Florida Independent Retailer Awards®. Her newly created AI bot "Ask Beth" is a compilation of her 900 YouTube videos and 300 podcast episodes. A graduate of FSU, Beth is founder and past Chairwoman of the FSU Real Estate Foundation, past President of HOPE Outreach Center in Davie, and co-founder of 100+ Women Who Care in South Florida. She is a single mom to a superhero movie podcaster and an aspiring pro golfer – and she recently walked 250 miles of the Camino de Santiago across Spain. How to Connect With Beth Azor Website: https://www.bethazor.com LinkedIn: https://www.linkedin.com/in/bethazor/ Facebook: https://www.facebook.com/azoradvisoryservices Instagram: https://www.instagram.com/bethazor/  Recommended Resources Women's Real Estate Investment Summit – March 3 – 5, 2027, registration opens September; only ~60 of 250 seats left: https://thewomeninvestmentsummit.com/  Women Investor Wednesday podcast – Beth interviews a woman investor every Wednesday (want to be a guest? She wants startup stories, even your first VRBO): https://www.bethazor.com/beths-podcasts/ Don't Say No for the Prospect by Beth Azor: https://www.bethazor.com/product/dont-say-no-for-the-prospect-how-1-went-from-a-sales-rookie-to-a-retail-leasing-rockstar/ The Retail Leasing Playbook by Beth Azor: https://www.amazon.com/Retail-Leasing-Playbook-Beth-Ratzan/dp/0578224208 "Ask Beth" AI bot – 900 videos and 300 podcast episodes' worth of answers: https://www.bethazor.com Happiness & Fulfillment Assessment: https://pursuingfreedom.com/happiness Pursuing Freedom Collective: https://pursuingfreedom.com/collective Get a copy of Pursuing Freedom on Amazon: https://amzn.to/46o7m7z Subscribe to the Pursuing Freedom podcast on Apple Podcasts or Spotify for weekly inspiration and strategies.

Pastor Randy Bradley

Bloomberg reported that Amazon recorded its fifth straight quarter of cloud sales growth, signaling a shift from optimization to new workloads. AWS is emphasizing generative AI services such as Amazon Bedrock, Amazon Q, and Amazon SageMaker, supported by custom chips Trainium and Inferentia. Amazon previously committed up to $4 billion to Anthropic, positioning Claude models on Bedrock against Microsoft's OpenAI alignment and Google Cloud's Vertex AI. AWS continues to use multi-year enterprise agreements, Savings Plans, and reserved capacity while co-selling with partners. Enterprises are focusing on FinOps practices, data gravity, and procurement leverage as AI pilots move to production. Competitive moves by Microsoft and Google are shaping pricing and features as customers plan 2026 cloud budgets.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Hey, it's Alex (yeah, I'm finally back from my vacation!) What a freaking week to come back to! Just after our last episode was published, Anthropic releases Opus 5, Jensen joins X and drops the “Open Weights & AI Leadership” open letter, Kimi K3 is released the following Monday beating expectations, and then the AI hack (OpenAI model breaking sandbox and infiltrating HuggingFace) is on everyone's mind, another Open Letter, this time from over 1K employees inside the frontier AI companies all talk about pacing the pace of frontier AI development. We played with Opus 5 and Kimi K3, and had the great pleasure to chat with friends of the pod Elie Bakouch (Prime Intellect) and Philip Kiely (BaseTen) about this important open weights release, then covered our general thoughts on Opus 5, and made order of all the different open letters that came out this week. Finally we chatted with Max from Pangram about the next version of AI writing detection (their biggest yet) and finished with Zuckerbergs (also on X! what's going on with everyone joining X) op-ed on the vision of personal superintelligence for everyone. Let's dive into this (as always, all the links and sources at the end, please don't forget to sub to our podcast on your favorite podcast app!) Open Weights AIKimi K3 the king of open weights - 2.8T chonker MoE near frontier model (X, HF, Blog, Tech report)This has got to be the biggest news of this week, and maybe the open weights AI news since GLM 5.2. MoonShot came back with Kimi K3, and we haven't seen any models quite this large in the open. Even Grok 4.5 is around 1.5T, this model is nearly 2x the size. Coming in at close to 3T parameters (and 2.5terabytes of weights at MXFP4 format), this model comes in very close to frontier! This was such an important release that I invited 2 friends of the pod, Elie Bakouch (prev HuggingFace, now Prime Intellect) and Philip Kiely (Author of Inference Engineering book, BaseTen) to dive deep into what makes this special! Elie's take, from reading the tech report, there's no single secret sauce, it's a combination of already available in the open techniques. Like KDA (Kimi Delta Attention) that has been out for a while, attention residuals, NVIDIA's latent MoEs. The highlight for Elie was the scaling work they did that reported a 2.5x scaling efficiency over Kimi K2.5 (2.5 performance at the same compute)! They also skipped RoPE entirely in favor of NoPE (the report calls it No Positional Encoding) for long context.Serving 1.4TB on eight GB300s (Baseten blog)Philip's team at Baseten was a day-zero provider (we're still working on bringing this model to CW Inference, stay tuned!) so I invited him to tell us behind the scenes of hosting this beast. Philip said that just loading the weights takes about 1.5TB!! of VRAM, and that's before the KV cache allocation + 1M token windows, so they're serving it on 8 GB300s where NVL72 . Baseten worked with the vLLM and SGLang teams on kernels and he also said they contributed patches back upstream! The model was trained with MXFP4, which, unlike Nvidia's own NVFP4 is a more standard format per Philip. I enjoyed his deep dive analysis into the differences, but because of this and because they trained the model with quantization awareness, it's “only” 1.5TB vs the would-be 5-6 TB if that this model in FP16 would demand. One of the more favorite nerd snipes moments, Philip pointed out that his colleague discovered that with over 99% of the usage being cached (think harnesses that send millions of the same cached tokens back and forth), tokenization actually starts to become a bottleneck. So they released a custom “basetenkenizer” that reduces the latency to serve the first token significantly! Great job!The harness in question is very importantOne important callout with 2 evidence pieces - the way you inference this model really matters. Kimi trained K3 with preserving thinking history, so when your harness uses it, it must send back the full thinking and tool use into the API to get the best next response. If your harness strips that out, you're not getting the most intelligence out of Kimi (shoutout to Niels from HF team for pointing this out). Additionally, the Composio folks, tested K3 on 3 harnesses, Kimi Code, Hermes and Claude Code. The difference in outcome was negligible, but the different in cost and number of tokens is definitely surprising! Claude Code (as a harness only) took 9x more Kimi tokens to get the same responses! This is also why Kimi Vendor Verified exists, their own held back benchmark of how well model providers serve Kimi across different quantization, tokenizer and KV cache settings. Benchmarks and the license! Ok let's start with the ugly... this isn't MIT, not remotely. This model is suspiciously served by all providers with exactly the same price (check OpenRouter) and requires inference companies to sign a contract with Kimi (I've no internal knowledge of this except that CW folks are working on it). Not something I particularly like, but hey... we're still advancing the frontier here! Speaking of frontier, this model approaches the frontier very closely. On DeepSWE, K3 sits just behind Fable 5 and GPT-5.6 Sol at 67%, beating GPT-5.5 & Opus 4.8. On Terminal-Bench 2.1 it takes second place behind GPT 5.6 Sol! It's 4th overall on Agentic Arena, with frontend design being genuinely good across the board - 1st on Design Arena

Des pelleteuses SANS CHAUFFEUR construisent les data centers de l'IA

The Jareds Stern talk about best voted two-time Best of DC, a literal court date, and what the hell are M&Ms thinking? (Recorded 7/29/26)We're Best of DC! - https://jaredstern.com/2026/07/16/four-score-2/BUY THE BOOK!⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://shorturl.at/9Ob5J⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Listen to past episodes!⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://jaredstern.com/between-two-sternsSee Jared Stern live!⁠⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://jaredstern.com/laugh-at-me/

Knowledge Project Key Takeaways The dating site story is systems thinking in one anecdote: a large dating site hypothesized longer profiles would drive more engagement They tested it. It was true. They rolled it outMany, many months later they discovered it was negative for conversion — when people knew that much about each other, they converted lessThat is a second-derivative effect, and you only find it way after the factThe combination is the whole point: know the really old stuff (the history of your field, which signals passion) and really understand the new edge. Do both and you're a power playerApplying for a marketing job at P&G or Pepsi? Know the legends of marketing and genuinely get TikTok. That's a wildly differentiated candidateThe built-in filter: if learning the history of your field sounds tedious, you're in the wrong lane. Tedium is the signal that the passion isn't thereWall Street is the buyer of the product venture capitalists create, since liquidity is either M&A or an IPO and that's where the price gets set — so even at two people on a PowerPoint, you're asking whether this thing grown up is something that buyer gets excited aboutChina has roughly 10 good open source models and the farmer metaphor explains why that matters: two agricultural societies, in one the farmers come to market, sell goods, go home. In the other, they're forced to share best practices with every other farmer. Which one evolves faster? They're open sourcing weights and publishing the new techniques. Meanwhile a lot of US startups are quietly forking those modelsThe chart that tells the AI funding story: losses of the category-leading company before going cash flow positive. Amazon was two or three billion. Uber was around 15 billion. These AI companies will be way bigger than thatBurn rate used to be the risk metric: ten years ago a million a month was terrifying. Now companies burn five billion a year, over a hundred million a month, and at that speed it's genuinely hard to know what your unit economics even areStablecoins are functionally a workaround for regulation the banks lobbied into existence. ACH takes three days, a same-day wire costs $25 and a page of forms, and credit cards charge 2 to 2.5% — “there's zero reason why it should cost two or three percent, just zero” UK Faster Payments did instant bank-to-bank 20 years ago, Pix hit 60-70% of transactions within six years, and the US built FedNow but it went nowhereBill once asked Jeff Bezos how his angel portfolio was so successful given he has zero free time. The answer: when he meets an entrepreneur there is only one question he asks himself, is this person going to do this no matter what, come hell or high water?Benchmark made the partnership flat — no lead partner, no king, no president, just five equal partners, which makes recruiting easy, actually develops new partners, and eliminates all the annual comp politics. The one huge negative: with no CEO, nobody owns anything firm-wide, which is why the site is still a single splash page 15 years laterRead the full notes @ podcastnotes.orgBill Gurley spent years on Wall Street, built his career as a partner at Benchmark, worked through Uber's hypergrowth era, and now serves on the board of the Santa Fe Institute, where he studies complexity and systems thinking. In this episode, Bill shares the mental models he returns to most, including systems thinking, second- and third-order effects, and the importance of understanding both the bedrock of your field and the bleeding edge. He explains what separates great founders, why storytelling and product instincts matter, how he uses AI across different models, and what he sees coming in open source, China, stablecoins, tokenization, payments, and venture capital.  ------ Timestamps: (00:00) Key Mental Models (02:02) Investing Journey and Key Players (05:21) Knowing the Bedrock of the Industry (08:50) Obsessive Learning in Founders (10:04) The Silent Edge (11:44) Surprising AI Use (13:13) The Future of AI Models (14:17) Global AI Regulation (18:12) Impacts of AI on Investing (19:53) Are There Limitations on Training AI Models? (23:04) Would You Sit in the Back Seat While Your Tesla Drives? (24:15) Non-Consensus Opinions (24:53) Are We Overfunding this Buildout? (29:40) The Role of Retail Investors and Tokenization (34:26) What is a Stablecoin? (37:58) Competitive Mode: Visa and Mastercard (39:55) AI and Debt Analysis (45:05) The Craft of Storytelling and Writing (48:07) Founder Advantage: Product Instinct (50:12) Real World Lessons from Working With Uber (52:10) Inside Benchmark's Success (59:42) What is Success for You? ------ Newsletter: The Brain Food newsletter delivers actionable insights and thoughtful ideas every Sunday. It takes 5 minutes to read, and it's completely free. Learn more and sign up at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠fs.blog/newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ------ Follow Shane Parrish: X: ⁠⁠⁠⁠⁠⁠https://x.com/shaneparrish⁠ Insta: ⁠https://www.instagram.com/farnamstreet/⁠ LinkedIn: ⁠https://www.linkedin.com/in/shane-parrish-050a2183/⁠ Follow Bill Gurley LinkedIn: https://www.linkedin.com/in/billgurley/ X: https://x.com/bgurley?lang=en Check out Runnin' Down a Dream: How to Thrive in a Career You Actually Love ------ Thank you to the sponsors for this episode: +CoinShares: Delivering Reason to Digital Asset Investing. ⁠https://coinshares.com/⁠ +Granola AI, The AI notepad for people in back-to-back meetings: https://www.granola.ai/shane Check out the Granola Notes +HeyGen is a message-first AI video platform that helps people and AI agents turn ideas into professional video in minutes. Try for free at https://www.heygen.com/ +LMNT: My go-to zero sugar electrolytes — get a free LMNT Sample Pack here: DrinkLMNT.com/TKP Learn more about your ad choices. Visit megaphone.fm/adchoices

                                                                                                                                                           Bedrock Wine Company In this episode, Rob and Scott review one of Bedrock's top Cabernets from the Montecillo Vineyard in Sonoma County.  So come join us, on The Wine Vault.

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

Feliks Banel's guest on this BONUS EPISODE of CASCADE OF HISTORY is Paula Johnson, Senior Archaeologist and Director-at-Large based in the Seattle office of WillametteCRA (CRA stands for “Cultural Resources Associates”). In this conversation, recorded on July 23, 2026, Johnson describes how the federal historic preservation regulations known as Section 106 work, and then outlines the threat facing 60 years of precedent in how the federal government treats historically significant resources. She also describes how the historic preservation community is mobilizing to push back against what amounts to a gutting of the rules - including elimination of public notice and input and tribal consultation - and how concerned citizens can get involved. A federal body known as the Advisory Council on Historic Preservation is scheduled to vote on the proposed changes on Friday, July 24, 2026. National Trust for Historic Preservation Section 106 website: https://savingplaces.org/stories/section-106-under-threat WillametteCRA website: https://willamettecra.com/ Advisory Council on Historic Preservation website: https://www.achp.gov/ Links to more information as well as images related to most topics discussed on the show are often available at the CASCADE OF HISTORY Facebook page: http://www.facebook.com/groups/cascadeofhistory CASCADE OF HISTORY is broadcast LIVE most Sunday nights at 8pm Pacific Time via flagship station SPACE 101.1 FM in Seattle and gallantly streams everywhere via www.space101fm.org. The radio station broadcasts from studios at historic Magnuson Park – located in the former Master-at-Arms' quarters in the old Sand Point Naval Air Station - on the shores of Lake Washington in Seattle. Subscribe to the CASCADE OF HISTORY podcast via most podcast platforms and never miss regular weekly episodes of Sunday night broadcasts as well as frequent bonus episodes. "LIKE" the Cascade of History Facebook page and get updates and other stories throughout the week, and advance notice of live remote broadcasts taking place in your part of the Old Oregon Country.

The bedrock of all paranormal belief is the foundational aspects of storytelling. Since human beings could tell stories, we have shared in some form or another our experiences. These experiences range from the bland, everyday tidbits of the normal to the unexplainable experiences of dreams or encounters with the unknown. With the accelerated advent of generative AI, the world of paranormal evidence has become so cluttered with grifters, pranksters and tricksters, that is becomes hard to rely on our eyes. This brings us back to the beginning once again - the shared experience though stories. My aversion to the AI movement leaks through as always, too. I also share a recent Ouija Board experience I had and why I have always loved using this tool since when I was a young kid. Hope to see you at a show sometime this summer and exciting announcements are on the horizon! You can find more on my stand-up schedule, short films and more at: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ryansingercomedy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Commercial Free episodes here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ SpectreVision Radio is a bespoke podcast network at the intersection between the arts and the uncanny, featuring a tapestry of shows exploring creativity, the esoteric, and the unknown. We're a community for creators and fans vibrating around common curiosities, shared interests and persistent passions. Learn more about your ad choices. Visit megaphone.fm/adchoices

Domo and AWS reveal how AI agents freed sales reps from 20 hours of weekly busywork, turning scattered data into real-time coaching and forecasting.Topics Include:Domo and AWS teams introduce today's session on AI agents in sales.Topic: using AI agents to transform sales operations, from insight to action.IT teams increasingly asked to turn data into actionable outcomes, not just access.Domo's CRO wanted AI agents to boost sales rep efficiency significantly.Reps act like "archaeologists," digging through scattered systems for basic context.This digging eats roughly 20 hours weekly, half of reps' time.Goal: personal AI agent per rep, understanding their book of business.Live demo begins: agent app surfaces urgent items needing attention.Agent tracks deal milestones, timelines, and forecasts from call and email data."Deal coach" feature grades rep performance and suggests next actions.Agent tone can be tuned from gentle to direct, aiding tough feedback.Architecture overview begins: building an AI-ready data foundation first.Data from CRM, calls, and emails flows into a cloud warehouse.Two agents built: automated deal analysis and personalized deal coach.Agents write insights back to CRM, preserving human edit control.Recipe: build foundation, activate with agents, distribute to people.Governance must be embedded throughout, not bolted on afterward.Second example: Fogo do Chão uses AI to analyze restaurant reviews.AWS architecture explained: Domo runs on Bedrock, defaulting to Anthropic models.Q&A: sales team adoption was immediate and enthusiastic post-rollout.Participants:Jason Longhurst – Head of Product Marketing, DomoAman Tiwari - Sr Solutions Architect, ISV, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Is the light of liberty still shining in the Western Hemisphere? In this episode of Mining the Media, G.K. examines the encouraging democratic trends emerging across North, Central, and South America, highlighting nations where the cause of freedom appears to be gaining ground. Using the United States as a beacon whose light has remained steady through generations, he explores why liberty continues to inspire people throughout the Americas. In Crusty's Corner, Dave tackles one of the most important yet often overlooked pillars of a constitutional republic: judicial integrity. Using recent headlines as a starting point, he examines seven biblical and constitutional principles that explain why equal justice under the law is essential to preserving freedom, maintaining public trust, and protecting every citizen. Whether discussing international developments or the integrity of our own institutions, this episode reminds us that liberty is sustained not merely by elections, but by truth, justice, and the moral principles upon which free societies depend. Please visit us at www.miningthemedia.com and share with your friends, relatives, associates, and neighbors.

Sheffield taps former Bedrock exec to attract national retailers You can get unlimited Olive Garden pasta for the first time in years

                                                                                                                                                                          Bedrock Wine Company In this episode, Rob and Scott review a rare white from one of their favorite wineries, Bedrock, and their Alta Vista Gewurztraminer from Moon Mountain in Sonoma County.  So come join us, on The Wine Vault.

In this episode, Ray Cochrane breaks down AI distillation, the teacher-student technique frontier labs now lean on to train smaller, cheaper models. He also covers GPT-5.6’s government-vetted rollout, Claude Sonnet 5 landing on AWS, Maryland’s two-year data center pause, and Microsoft’s climbing carbon numbers. Finally, he wraps with Apple’s $30 billion Broadcom deal, Meta’s tamper-proof recording light, Michigan’s parasite outbreak, and a simulation that erased a super El Niño. – 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 Ray 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 Full Summary Cochrane opens with a quick personal update. Longer days have him outdoors, including a float trip on the Sandy River at Dabney State Park, where he found clearer water, clay-like sand, and easy footing. Next week brings both a move and a trip home, so he is stocking up on Trader Joe’s “Power Berries” and IKEA bags at his mom’s request. Then he turns to the lead story. AI Distillation Explained: How Frontier Models Teach Each Other Cochrane’s featured story comes from Hugging Face engineer Sergio Paniego. Distillation is teacher-student training for AI: a capable model generates the training signal, and a smaller student learns to match it. The classic off-policy version compresses giant models into cheap students, either through soft labels or piles of worked answers. Google’s Gemma models and DeepSeek’s R1-Distill line were built exactly this way. However, the industry is now converging on multi-teacher on-policy distillation, or MOPD. Labs build reinforcement-learning specialists for math, coding, and agentic work, then have them grade a single student, word by word, as the student generates its own answers. DeepSeek-V4, MiMo-V2-Flash, and NVIDIA’s Nemotron 3 Ultra all run versions of the recipe, and the Qwen3 team reported better results at roughly a tenth of the GPU hours of raw reinforcement learning. Finally, self-distillation lets models like Cursor’s Composer 2.5 learn from better-prompted versions of themselves. 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. GPT-5.6 Arrives With a Government-Vetted Rollout OpenAI shipped GPT-5.6 as a three-tier family: Sol, Terra, and Luna. Sol costs five dollars in and thirty dollars out per million tokens, half of Claude Fable 5’s rate. The benchmarks split: Sol Ultra wins Terminal-Bench at 91.9 percent, while Claude Fable 5 still leads SWE-Bench Pro. Notably, the API launched in limited preview to roughly 20 partners vetted by the U.S. government, though the model went live in Microsoft 365 Copilot on day one. Claude Sonnet 5 Lands on AWS, Plus Quick AWS Wins Claude Sonnet 5 arrived on AWS through Bedrock, pitched as top-tier intelligence at Sonnet pricing. Additionally, Amazon WorkSpaces for AI agents reached general availability, enabling agents to drive full desktop applications securely. OpenSearch gained a log-analytics engine claiming four times the price-performance, and SageMaker now scales inference about twice as fast. Cochrane also flags that Kendra and Q Business move to maintenance mode at the end of July. Anthropic Wants You to Reflect on Your Claude Habits Anthropic launched Reflect, a beta feature that analyzes your past Claude conversations and visualizes how you actually use the assistant. It requires Memory, excludes incognito and health-related chats, and keeps its insights inside the tool. Cochrane loves the idea. He reviews his own transcripts to extract prompt patterns and turn them into reusable skills, and he suggests listeners simply ask their AI to do the same. AlphaEvolve Goes GA on Google Cloud Google made AlphaEvolve generally available to Google Cloud customers on the Gemini Enterprise Agent Platform. The agent acts as an evolutionary collaborator: provide a baseline algorithm and your goals, and it searches for better, human-readable code. BASF, JetBrains, and Kinaxis are the named early adopters. Meanwhile, Cochrane renews his standing wish that DeepMind release AlphaGo as a playable teacher. Google Adds “How This Ad Was Made” AI Labels Google is adding a “How this ad was made” section to My Ad Center across Search, YouTube, and Discover. Ads built with Google’s own AI tools automatically get the disclosure, backed by invisible watermarks. However, ads made with outside tools rely on advertiser self-declaration. Cochrane points out the limits of voluntary disclosure in an AI-flooded content economy. Microsoft’s Carbon Emissions Climb 25 Percent Microsoft’s new sustainability report shows emissions up 25% in 2025, driven by a data center construction spree. The gross figure is 34 million metric tons before offsets, while other coverage puts the net figure at around 20 million. Water consumption also jumped thirty-four percent, even as Microsoft claims its first water-positive year. Cochrane argues regulation needs to catch up, since Google and Amazon report similar increases. Prince George’s County Pauses Data Centers for Two Years Prince George’s County adopted a two-year moratorium on new data center development, the longest pause in Maryland so far. The resolution blocks new applications, including hyperscale projects, until the council passes real regulations. Water and energy impacts remain open questions the county intends to study. Cochrane gives kudos to residents for making their voices heard. Apple and Broadcom Ink a $30 Billion U.S. Chip Deal Apple is expanding its partnership with Broadcom with a multiyear agreement expected to exceed $30 billion. The deal covers custom silicon and wireless components, with more than fifteen billion chips to be made on American soil. Broadcom’s Fort Collins, Colorado plant anchors the work with a $1.5 billion equipment expansion. Tim Cook framed the deal as accelerating Apple’s commitment to American manufacturing. MSI and Intel Ship the First Arc G3 Extreme Handheld Intel detailed how it co-engineered the MSI Claw 8 EX AI+, the first handheld on the Arc G3 Extreme processor. Highlights include a heat-spreading board layout and game-tuning loops that Intel says run Cyberpunk 2077 up to thirty-seven percent faster. The device is on sale now in void purple for around $1,500. At that price, Cochrane jokes he would rather buy a computer. Meta’s Glasses Get a Tamper-Proof Recording Light Meta answered the most common privacy questions about its AI glasses. Photos stay private on the device until the wearer imports or shares them, and a white capture LED blinks during any recording with no off switch. Moreover, newer glasses disable the camera if the LED is blocked, tampered with, or destroyed. Cochrane reminds listeners these claims are Meta grading its own homework, but the blink signal is worth recognizing in public. Michigan’s Parasite Outbreak Tops 1,200 Cases Michigan’s cyclosporiasis outbreak reached 1,251 cases since June 22, with roughly forty hospitalizations along the way. Northwest Ohio adds more than five hundred cases. The parasite typically spreads through contaminated fresh produce, and investigators still have not found the source. Cochrane’s advice: wash your produce, and get tested if your symptoms fit. AI Finds the San Andreas Fault’s Silent Slips Researchers paired AI with borehole strainmeters to detect dozens of hidden slow-slip events beneath the San Andreas Fault’s Parkfield section. Each silent slip releases stress within hours and is reliably followed by low-frequency earthquakes. Together, the findings support a continuous spectrum from silent creep to destructive quakes. The study appears in Nature Communications, and Cochrane hopes it will lead to better earthquake prediction. Cloud Brightening Erased a Super El Niño, in a Simulation Finally, a Science Advances study simulated marine cloud brightening in response to the 1997 and 2015 super El Niño events. Seeding clouds over the eastern Pacific erased the events entirely inside the model. Real deployment would take roughly 2,400 ships spraying continuously, and the simulations showed side effects like extra warming over Europe and Asia. Cochrane finds the weather-machine concept fascinating, yet he questions the consequences of altering cycles the planet runs for a reason. The post AI Distillation: How Frontier Models Teach Each Other #1870 appeared first on Geek News Central.

It's a tops off, windows open, fans blaring, seasonly hot edition of the Radio Therapy Broadcast this month as Dave brings the heat once again, shining a light on the very best the dance floor has to offer. Be warned, you may even need a hydration break! Tracklist... 1. Liva K & Hot Lap ‘Dance Baby' [Abracadabra] 2. Ivory (IT) ‘Bringing Me Joy' [Blessings] 3. Franky Wah ‘Closer To My Dreams' (Larrosa, Sparvieri & Sack) [SHÈN] 4. Steve Lawler ‘Sass' [Bedrock] 5. D-SHIFT ‘Where You Need To Be' [Tronic] 6. Dave Seaman ‘Nightfalls' (Cioz) [Selador] 7. Anrey & Yannek Maunz ‘Kreuzberg' [Sanctuary] 8. Emi Galvan ‘I Wish' [Sudbeat] 9. AIKON ‘Mama' [ICONYC] 10. Christian Smith ‘Mileage Run' [Mango Alley] 11. Andrea Oliva ‘Neon Hearts' [Habitat] 12. Anthony Pappa & Fauxplay ‘Forever Seeking' [Early Morning] This show is syndicated & distributed exclusively by Syndicast. If you are a radio station interested in airing the show or would like to distribute your podcast / radio show please register here: https://syndicast.co.uk/distribution/registration

It's a tops off, windows open, fans blaring, seasonly hot edition of the Radio Therapy Broadcast this month as Dave brings the heat once again, shining a light on the very best the dance floor has to offer. Be warned, you may even need a hydration break! Tracklist... 1. Liva K & Hot Lap ‘Dance Baby' [Abracadabra] 2. Ivory (IT) ‘Bringing Me Joy' [Blessings] 3. Franky Wah ‘Closer To My Dreams' (Larrosa, Sparvieri & Sack) [SHÈN] 4. Steve Lawler ‘Sass' [Bedrock] 5. D-SHIFT ‘Where You Need To Be' [Tronic] 6. Dave Seaman ‘Nightfalls' (Cioz) [Selador] 7. Anrey & Yannek Maunz ‘Kreuzberg' [Sanctuary] 8. Emi Galvan ‘I Wish' [Sudbeat] 9. AIKON ‘Mama' [ICONYC] 10. Christian Smith ‘Mileage Run' [Mango Alley] 11. Andrea Oliva ‘Neon Hearts' [Habitat] 12. Anthony Pappa & Fauxplay ‘Forever Seeking' [Early Morning] This podcast is hosted by Syndicast.

FERNANDA COMORA e MADELEINE LACSKO são jornalistas e RICARDO MARCÍLIO é especialista em geopolítica. Eles são os âncoras do Notícia I-LTDA, o programa de notícias do Inteligência Ltda. Eles vão comentar, junto do Vilela, as notícias recentes do Brasil e do mundo com os convidados CARLOS BEZERRA JR., THIAGO LIMA, LINCOLN FRACARI, PROF. MICHELE RAMOS, JORGE NICOLA, GUGA FIGUEIREDO e ANDRÉ MARSIGLIA. O Vilela desenhava quadrinhos pro jornal de Bedrock.

Learn how Vercel's "self-driving infrastructure" vision pairs with AWS databases to eliminate backend friction, securely cutting Aurora Serverless creation time from minutes to seconds.Topics Include:Hedieh Zandi (Vercel) and Manbeen Kohli (AWS) introduce prompt-to-production sessionVercel powers 18 million developers, maintains Next.js and AI SDKVercel's agentic infrastructure runs on AWS Lambda, CloudFront, and S3AI now generates frontend, APIs, and workflows for small teamsBackend friction remains: credentials, provisioning, database configuration still hardVercel envisions "self-driving infrastructure" that adapts automatically to appsNew AWS partnership brings native Aurora DSQL and Postgres integrationManbeen explains databases now built into Vercel Marketplace and v0Aurora Serverless database creation sped up from minutes to secondsAurora Postgres, DynamoDB, and DSQL scale prototypes without rewritesPre-configured templates help builders start RAG or shopping AI appsDatabase security uses OIDC and IAM tokens, no stored passwordsAWS chosen for agents: low latency, autonomy, one-click simplicityskills.sh gives agents reusable instructions, mirrors AWS Kiro's "powers"v0 lets users build full-stack apps using natural language promptsv0 uses Bedrock models and deploys directly on Vercel infrastructureLive demo: v0 builds restaurant app, provisions database, adds Stripe checkoutDemo ends at AWS console; Rauch quote and hackathon close sessionParticipants:Hedieh Zandi - Product Lead, VercelManbeen Kohli - Director of Product Management, Aurora and RDS Databases, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Have you wondered what’s behind the energy in the polygamy revisionist movement? Are these the same as RLDS polygamy arguments? Back in 2010, Newell Bringhurst wrote a chapter in “Persistence of Polygamy” on the evolution of RLDS thinking on Joseph Smith’s polygamy, how it got forgotten, and recycled among a new generation of LDS members who weren’t aware of the previous history. https://youtu.be/wD-ZHracUnA Are the modern arguments that Joseph Smith was a strict monogamist actually new? In this deep dive, we explore the “layered archive” of historical claims surrounding Joseph Smith's polygamy, revealing that many of today's “polygamy revisionist” arguments are actually recycled 19th-century RLDS positions. The presentation breaks down the 150-year evolution of the Reorganized Church of Jesus Christ of Latter Day Saints (now Community of Christ) through four distinct phases: The Early Concessions (1852–1860): Contrary to popular belief, early leaders like William Marks and Isaac Sheen did not initially deny Joseph Smith’s involvement. Instead, they claimed he had been involved in “spiritual wifery” but recognized his error and repented shortly before his death in Carthage. The Era of Absolute Denial (1860–1960): Under the leadership of Joseph Smith III, the strategy shifted toward establishing the absolute innocence of the founding prophet. This century-long “institutional mandate” framed D&C 132 as a “Brighamite forgery” and placed the blame for polygamy entirely on Brigham Young. The Academic Shockwave (1960s–1980s): The “dam broke” when mid-century historians like Robert Flanders and Lawrence Foster published research that eroded the bedrock of the church’s denial. This led to a painful institutional reckoning, eventually forcing the church to acknowledge Joseph Smith's foundational role in plural marriage. The Modern Revisionist Resurgence: Today, a new wave of LDS members—facing their own faith crises—are adopting these older RLDS arguments to resolve cognitive dissonance regarding the prophet’s moral legacy. Drawing on works like Newell Bringhurst's The Persistence of Polygamy and Robert Flanders' Nauvoo: Kingdom on the Mississippi, this presentation weighs revisionist claims against the preponderance of contemporaneous evidence. We also discuss the psychological toll of these shifting narratives and the ongoing battle to control the legacy of Joseph Smith. RLDS Polygamy Arguments Chapters 0:00 Introduction and the RLDS Foundation 07:53 Anatomy of a Narrative Shift 08:19 The Bedrock: 1852 to 1960 12:30 Early Concessions 16:42 Joseph Smith III and the Strategy Shift 21:09 D&C 132: Brighamite Forgery? 27:35 Historical Reckoning (1960s to Present) 51:12 Modern Skeptic Movement 1:26:44 Three Paradigms of Polygamy 1:29:17 Conclusion: The Enduring Battle for Legacy

In this episode, Joe returns to share how AI has changed his life... but first, his entrepreneurial journey from childhood to becoming a successful business owner, best selling author, and podcaster. He discusses the impact of books, podcasts, and mindset shifts on his success, as well as his experiences working abroad and leveraging AI.“You have to get out of the competitive mindset. There's enough opportunity in the world for everybody.”0:00–0:48 — Opening and guest intro: The host introduces Joe, his background, and the episode's focus.0:48–4:53 — Early entrepreneurial roots and CPA path: Joe shares how he became entrepreneurial as a kid and how his father pushed him toward accounting.4:53–11:05 — Mindset shift, books, and life direction: Joe explains how reading changed his thinking, helped him define his goals, and led to the Philippines.11:05–15:32 — Writing books, credibility, and his recommended book: He talks about how writing books helped his business and recommends From Myths to Money.15:32–17:34 — Support systems and historical entrepreneur choice: The conversation shifts to mentorship, resilience, and why Joe would choose Benjamin Franklin.17:34–19:49 — Podcasting, rebrand, and closing: Joe explains how podcasting supports business growth and shares the Bedrock 360 rebrand.“I believe that some of us are born to be entrepreneurs.”Other Takeaways*The influence of 'Think and Grow Rich' and 'The Science of Getting Rich'*Success often starts with mindset shifts and giving more value than you receive.*How working abroad and in AI transformed Joe's business*The importance of tenacity and support systems in entrepreneurship*Persistence and tenacity are crucial for overcoming entrepreneurial challenges.Enjoy his last appearance with us to follow his journey after this episode here!Send us Fan MailSupport the showRemember to subscribe for the next episode. Show Sponsor: ComingAlive PodcastProduction.com (Download your Podcast Launch Checklist for only $1 here)Music Credits: Copyright Free Music from Adventure by MusicbyAden.

クリエイティブディレクター・小橋賢児が、様々なジャンルのイノベーターをお迎えするトークセッション「FROM THE NEXT ERA」。対話の中からイノベーションの種を導き出します。今回は、「ゼロから世界を築く」を掲げる宇宙スタートアップ、Bedrock Space 創業者・代表 杉原海さんをお迎えして、「宇宙文明の土台＝岩盤（Bedrock）をゼロからつくる」とはどういうことなのか、に迫ります。

Mohnish Pabrai's Interview with Shaan Puri at My First Million on May 5, 2026. (00:00:00) - Introduction (00:00:30) - Value investing in the US; Importance of patience in investing (00:02:15) - Mental models: The mistress is always hotter than the wife (00:04:58) - Introduce randomness in your life; Peter Lynch's One Up on Wall Street (00:08:12) - Elon Musk (00:09:42) - From admiring to executing; Sam Walton & Cloning (00:13:24) - Tesla; Blue Origin vs. SpaceX (00:14:10) - Randomness & Cloning; Farm Con & Kevin Van Trump to Milk road (00:16:40) - McDonald's vs. Burger King (00:17:05) - The Bedrock model: Take a simple idea and take it seriously; Turkey vs. Indian markets (00:20:39) - Mental model conflicts; Circle of competence (00:23:13) - The salad oil crisis; Buffett's stake in AmEx and Disney (00:26:12) - Traits of great investors: Keep investing simple; Warren's Too Hard Pile (00:30:27) - Aksarben racetrack and Buffett's tickets adventure; Moody's Manual (00:33:02) - Japanese Company Handbook; Look for needles in haystacks  (00:34:40) - Stock market: Church with a Casino (00:38:42) - Lunch with Warren Buffett; Leverage lesson from Rick Guerin (00:41:39) - Inner scorecard vs. Outer scorecard (00:43:25) - Cash and capital allocation at Berkshire Hathaway (00:45:14) - My best investments; Investing in Turkey - Reysas & TAV Airports (00:54:57) - Active vs. Passive investing (00:57:22) - Business Moats; McDonald's & FICO (00:59:25) - Investing with AI (01:02:58) - Constellation Software Services; Mark Leonard (01:09:45) - GLP-1 (01:10:48) - Bitcoin vs. Gold (01:11:32) - Do not die at 25 and get buried at 75; Get your music out (01:15:37) - Studying great investors: Ed Thorp (01:20:45) - Ken Griffin: Citadel (01:23:01) - Advice to listeners: Lead an aligned life - My owner's manual by Jack Skeen (01:28:08) - Guy Spier's letter to me The contents of this website are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation or needs and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser. Views expressed on Chai with Pabrai are exclusively those of Mohnish Pabrai and not of any affiliated firm or organization.

Good news: After six months, my girlfriend's hospital odyssey is wrapping up for now.Bad news: My visiting professorship at GVSU has finally ended, and the job hunt continues.For better and for worse, I have more time to return to the podcasts. Bedrock and Hometown Geology will alternate on Wednesdays. The Brisbane episode of Hometown Geology will air June 24 for Patreon subscribers, and Episode 53 of Bedrock will air July 1. Thank you all for your patience once again, I'm excited to get behind the microphone again!If you want to donate:Monthly donations on PatreonOne-off donations on Paypal

Dave's back again this week but this time with a special live mix recorded at the very last Skyline party in Glasgow a couple of months ago. Billed as ‘One Last Dance In The Sky', here's an hours excerpt from his closing set at the end of the night. Enjoy! Tracklist.. 1. Ivory ‘Keep Moving On' [Spectrum] 2. Frankey & Sandrino ‘Matar' [Sum Over Histories] 3. Yunus Guvenen ‘Space 6AM' [Bedrock] 4. Dave Seaman ‘Nightfalls' (Cioz Remix) [Selador] 5. Dave Seaman feat. Polaroid 'So Damn Beautiful' [Selador] 6. Fordal ‘Luminize' [Selador] 7. Monojoke & Amaare ‘Departure' [Univack] 8. Quivver & Dave Seaman ‘Cowbells Of Nuneaton' [Sudbeat] 9. Shameka ‘Solune' (Weird Sounding Dude) [Solivara] 10. Bicep ‘Apricots' (Baunder) [Ninja Tune] 11. Danny Howells & Lloyd Barwood ‘One More Sky' [Mango Alley] 12. Butch ‘When I Was Young' [Mahool] 13. Zuccasam ‘Jomi' [Selador] This podcast is hosted by Syndicast.

Dave's back again this week but this time with a special live mix recorded at the very last Skyline party in Glasgow a couple of months ago. Billed as ‘One Last Dance In The Sky', here's an hours excerpt from his closing set at the end of the night. Enjoy! Tracklist.. 1. Ivory ‘Keep Moving On' [Spectrum] 2. Frankey & Sandrino ‘Matar' [Sum Over Histories] 3. Yunus Guvenen ‘Space 6AM' [Bedrock] 4. Dave Seaman ‘Nightfalls' (Cioz Remix) [Selador] 5. Dave Seaman feat. Polaroid 'So Damn Beautiful' [Selador] 6. Fordal ‘Luminize' [Selador] 7. Monojoke & Amaare ‘Departure' [Univack] 8. Quivver & Dave Seaman ‘Cowbells Of Nuneaton' [Sudbeat] 9. Shameka ‘Solune' (Weird Sounding Dude) [Solivara] 10. Bicep ‘Apricots' (Baunder) [Ninja Tune] 11. Danny Howells & Lloyd Barwood ‘One More Sky' [Mango Alley] 12. Butch ‘When I Was Young' [Mahool] 13. Zuccasam ‘Jomi' [Selador] This show is syndicated & distributed exclusively by Syndicast. If you are a radio station interested in airing the show or would like to distribute your podcast / radio show please register here: https://syndicast.co.uk/distribution/registration

Knowledge Project: Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- Bill Gurley spent years on Wall Street, built his career as a partner at Benchmark, worked through Uber's hypergrowth era, and now serves on the board of the Santa Fe Institute, where he studies complexity and systems thinking. In this episode, Bill shares the mental models he returns to most, including systems thinking, second- and third-order effects, and the importance of understanding both the bedrock of your field and the bleeding edge. He explains what separates great founders, why storytelling and product instincts matter, how he uses AI across different models, and what he sees coming in open source, China, stablecoins, tokenization, payments, and venture capital.  ------ Timestamps: (00:00) Key Mental Models (02:02) Investing Journey and Key Players (05:21) Knowing the Bedrock of the Industry (08:50) Obsessive Learning in Founders (10:04) The Silent Edge (11:44) Surprising AI Use (13:13) The Future of AI Models (14:17) Global AI Regulation (18:12) Impacts of AI on Investing (19:53) Are There Limitations on Training AI Models? (23:04) Would You Sit in the Back Seat While Your Tesla Drives? (24:15) Non-Consensus Opinions (24:53) Are We Overfunding this Buildout? (29:40) The Role of Retail Investors and Tokenization (34:26) What is a Stablecoin? (37:58) Competitive Mode: Visa and Mastercard (39:55) AI and Debt Analysis (45:05) The Craft of Storytelling and Writing (48:07) Founder Advantage: Product Instinct (50:12) Real World Lessons from Working With Uber (52:10) Inside Benchmark's Success (59:42) What is Success for You? ------ Newsletter: The Brain Food newsletter delivers actionable insights and thoughtful ideas every Sunday. It takes 5 minutes to read, and it's completely free. Learn more and sign up at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠fs.blog/newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ------ Follow Shane Parrish: X: ⁠⁠⁠⁠⁠⁠https://x.com/shaneparrish⁠ Insta: ⁠https://www.instagram.com/farnamstreet/⁠ LinkedIn: ⁠https://www.linkedin.com/in/shane-parrish-050a2183/⁠ Follow Bill Gurley LinkedIn: https://www.linkedin.com/in/billgurley/ X: https://x.com/bgurley?lang=en Check out Runnin' Down a Dream: How to Thrive in a Career You Actually Love ------ Thank you to the sponsors for this episode: +CoinShares: Delivering Reason to Digital Asset Investing. ⁠https://coinshares.com/⁠ +Granola AI, The AI notepad for people in back-to-back meetings: https://www.granola.ai/shane Check out the Granola Notes +HeyGen is a message-first AI video platform that helps people and AI agents turn ideas into professional video in minutes. Try for free at https://www.heygen.com/ +LMNT: My go-to zero sugar electrolytes — get a free LMNT Sample Pack here: DrinkLMNT.com/TKP Learn more about your ad choices. Visit megaphone.fm/adchoices

Today, I have the pleasure of speaking with Sarah Thorpe Scott, an executive coach and advisor working at the intersection of leadership, capital, and family enterprise systems. She supports executives, investors, and multigenerational families navigating the moments that matter, including succession, wealth transfer, leadership transitions, governance decisions, and spouses marrying into family systems. Her work focuses on the emotional and relational dynamics that often sit beneath these moments, helping families prepare the next generation for leadership and stewardship while strengthening the dialogue and trust required for long-term success across generations. Sarah is the Founder of Thorpe Scott Coaching & Advisory, Coach-in-Residence at Bedrock, a global multi-family office with offices in Geneva, London, and Monaco, and a Special Advisor to Horizons, a member network of millennial next-generation leaders and investors. Sarah began her career in investment banking at Credit Suisse in New York and later worked across leading media organizations including CNBC and Forbes. She went on to hold senior leadership roles at The New York Times, where she became Managing Director, EMEA, leading global teams and executing dozens of complex, multi-million-dollar partnerships with multinational organizations across virtually every major industry. Married into a fifth-generation family enterprise herself, Sarah brings both professional rigor and lived experience to her work with family offices, next-generation leaders, and the executives and advisors who work alongside them. She has served as Chair of the Young Vic Development Board and the Duke UK Alumni Board. We delve into the topic of spousal integration into UHNW families and the experiences of spouses within the broader family enterprise. We start by having Sarah sharing her observations on how family structures see and treat spouses today, and how enterprise family systems are organized to receive and engage spouses and in-laws. Sarah describes how spousal integration works presently, outlining the typical experience of a spouse joining a multigenerational family of wealth. She highlights some of the common challenges faced by spouses entering these sometimes-complex family systems. One common, and often controversial, practical tool that is part of the spousal integration process is the prenuptial agreement. Sarah shares her thoughts and lived experiences on how well prenups work and offers her views on where there may be room to improve and enhance the experience of the soon-to-be-married couple going through the process. Finally, Sarah lays out her vision and roadmap for a better spousal integration process, including the elements, the approach, and the spirit that can provide a more positive, engaging, and pleasant experience for spouses and the entire family. Enjoy this illuminating conversation with a highly regarded family member-turned-practitioner providing thought leadership in the spousal integration topic that impacts every enterprising family.

Bill Gurley spent years on Wall Street, built his career as a partner at Benchmark, worked through Uber's hypergrowth era, and now serves on the board of the Santa Fe Institute, where he studies complexity and systems thinking. In this episode, Bill shares the mental models he returns to most, including systems thinking, second- and third-order effects, and the importance of understanding both the bedrock of your field and the bleeding edge. He explains what separates great founders, why storytelling and product instincts matter, how he uses AI across different models, and what he sees coming in open source, China, stablecoins, tokenization, payments, and venture capital.  ------ Timestamps: (00:00) Key Mental Models (02:02) Investing Journey and Key Players (05:21) Knowing the Bedrock of the Industry (08:50) Obsessive Learning in Founders (10:04) The Silent Edge (11:44) Surprising AI Use (13:13) The Future of AI Models (14:17) Global AI Regulation (18:12) Impacts of AI on Investing (19:53) Are There Limitations on Training AI Models? (23:04) Would You Sit in the Back Seat While Your Tesla Drives? (24:15) Non-Consensus Opinions (24:53) Are We Overfunding this Buildout? (29:40) The Role of Retail Investors and Tokenization (34:26) What is a Stablecoin? (37:58) Competitive Mode: Visa and Mastercard (39:55) AI and Debt Analysis (45:05) The Craft of Storytelling and Writing (48:07) Founder Advantage: Product Instinct (50:12) Real World Lessons from Working With Uber (52:10) Inside Benchmark's Success (59:42) What is Success for You? ------ Newsletter: The Brain Food newsletter delivers actionable insights and thoughtful ideas every Sunday. It takes 5 minutes to read, and it's completely free. Learn more and sign up at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠fs.blog/newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ------ Follow Shane Parrish: X: ⁠⁠⁠⁠⁠⁠https://x.com/shaneparrish⁠ Insta: ⁠https://www.instagram.com/farnamstreet/⁠ LinkedIn: ⁠https://www.linkedin.com/in/shane-parrish-050a2183/⁠ Follow Bill Gurley LinkedIn: https://www.linkedin.com/in/billgurley/ X: https://x.com/bgurley?lang=en Check out Runnin' Down a Dream: How to Thrive in a Career You Actually Love ------ Thank you to the sponsors for this episode: +CoinShares: Delivering Reason to Digital Asset Investing. ⁠https://coinshares.com/⁠ +Granola AI, The AI notepad for people in back-to-back meetings: https://www.granola.ai/shane Check out the Granola Notes +HeyGen is a message-first AI video platform that helps people and AI agents turn ideas into professional video in minutes. Try for free at https://www.heygen.com/ +LMNT: My go-to zero sugar electrolytes — get a free LMNT Sample Pack here: DrinkLMNT.com/TKP Learn more about your ad choices. Visit megaphone.fm/adchoices

AWS Morning Brief for the week of June 8th, with Corey Quinn. Links:AWS Interconnect - multicloud now offers a free 500 Mbps tierOracle Database@AWS is now available in twenty AWS RegionsAmazon Cognito now supports multi-Region replicationAmazon EKS and Amazon EKS Distro now supports Kubernetes version 1.36Amazon SES now supports tenant-level suppression listsAWS Compute Optimizer now supports 32-day lookback for EBS volume and ECS service rightsizing recommendationsAWS Cost and Usage Report 2.0 now supports Athena and Redshift integrationAmazon ElastiCache for Valkey now supports durabilityUnderstanding how backups work in Amazon AuroraOpenAI models and Codex on Amazon Bedrock are now generally availableHow Bedrock Streaming optimizes its AWS costsFrom Monolith to Multi-Account: Pinterest's AWS Organization Transformation JourneyGain visibility into DDoS attacks with flow logs in AWS Shield AdvancedIdentify unused AWS KMS keys and prevent accidental key deletionsCVE-2026-10591 - Kiro IDE Insufficient File Write Restrictions to Execution-Sensitive PathsCVE-2026-10584 - HTTPS Fallback to HTTP in Graph Explorer

In the book of Hebrews we are told scripture living and active, how have you experienced this?Let's connect! Text "connect" to 513-216-9896 or click the link below: https://connect-card.com/41p3h89OBidharwIMUHR

In the book of Hebrews we are told scripture living and active, how have you experienced this?Let's connect! Text "connect" to 513-216-9896 or click the link below: https://connect-card.com/41p3h89OBidharwIMUHR

Mental Models for Exceptional Capital Allocation by Mohnish Pabrai at Heilbrunn Center for Graham and Dodd Investing on April 21, 2026. (00:00:00) - Introduction (00:02:03) - Charlie Munger's mental models (00:03:54) - Model 1: The Bedrock model: Take a simple idea and take it seriously (00:04:51) - Model 2: Ben Graham's three ideas on markets (00:05:28) - Model 3: Do not overdose on Ben Graham; Poor Charlie's Almanack, Philip Fisher, and Pulak Prasad (00:06:27) - Model 4: Buffett's lifetime 20-punch card (00:07:15) - Model 5: Stay in the epicentre of your circle of competence; John Arrillaga (00:09:09) - Model 6: A high error rate is guaranteed in investing (00:09:26) - Model 7: Circle the wagons: the 4% rule (00:10:36) - Berkshire's 12 best decisions in 60 years (00:12:02) - Mistakes in investing: Ferrari, Progressive Insurance & Goldman Sachs (00:12:55) - Model 8: Do not cut flowers and water weeds; The Nifty 50 crash in the 1970s & Walmart (00:15:34) - Model 9: Be a shameless cloner; VIC & Dataroma; Gimat Gross (00:16:43) - Model 10: History does not repeat itself; Investing in Turkey & Reysas (00:19:50) - Model 11: Explain your investment thesis in 3-4 sentences to a 10-year old (00:19:58) - Model 12: You always need a rope to get out of the deepest well (00:23:14) - Model 13: Nick Sleep; Zen and the Art of Motorcycle Maintenance (00:26:52) - Model 14: Thou shall not use Excel (00:27:17) - Model 15: Use a pre-investment checklist (00:28:06) - Model 16: Be singularly focused like Arjuna (00:29:27) - Read the footnotes; Turn every page: Robert Caro (00:31:16) - Model 17: Enjoy hunting for needles in haystacks; Buffett's childhood entrepreneurial adventures (00:33:40) - Japanese Company Handbook; My introduction to Charlie Munger & Debbie Bozanek (00:37:27) - Model 18: Your deepest desire is your destiny (00:38:53) - Model 19: You should always have someone to discuss your investment ideas with; Li Lu (00:40:45) - Model 20: The mistress is always hotter than the wife!  (00:41:12) - Model 21: Neither a short-term borrower nor a long-term lender be (00:41:33) - Model 22: Introduce randomness into your life; Peter Lynch's One up on Wall Street (00:43:11) - Model 23: Be a Swiss Army knife (00:43:24) - Model 24-26: Focus on spin-offs, uber cannibals & spawners; Alpha-Metallurgical Resources (00:44:02) - Model 27: Arbitrage is wonderful; Transocean vs. Valaris (00:44:17) - Model 28: Heads I win, Tails I don't lose much!; IPSCO and CONSOL Energy (00:46:10) - Model 29: Focus on low-risk; high uncertainty bets (00:46:45) - Model 30: Do not skim off the top (00:47:23) - Book recommendations: Poor Charlie's Almanack, Influence & Excellent advice for living (00:47:41) - Investing in Turkish vs. Indian markets (00:50:17) - Follow your passion  The contents of this website are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation or needs and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser. Views expressed on Chai with Pabrai are exclusively those of Mohnish Pabrai and not of any affiliated firm or organization.

Episode Summary Cooper Freeman, Alaska director for the Center for Biological Diversity, returns to the Rewilding Earth podcast and joins host Jack Humphrey for a transparent, gritty, and surprisingly hopeful update from the frontlines of Alaskan conservation. Navigating a relentless onslaught of fast-tracked industrial projects and regulatory procedures gutted under the current administration, Cooper outlines […] Read full article: Episode 175: How Bedrock Environmental Laws Are Holding the Line in Alaska (For Now) with Cooper Freeman

Mental Models for Exceptional Capital Allocation by Mohnish Pabrai at The University of Nebraska, Omaha on May 1, 2026. (00:00:00) - Introduction (00:02:11) - Charlie Munger's mental models (00:03:43) - Model 1: The Bedrock: Take a simple idea and take it seriously (00:04:06) - Model 2: Ben Graham's Fundamentals (00:04:59) - Model 3: Do not overdose on Ben Graham; Poor Charlie's Almanack, Philip Fisher, and Pulak Prasad (00:05:16) - Model 4: Buffett's lifetime 20-punch card (00:06:05) - Model 5: Stay in the epicentre of your circle of competence; John Arrillaga (00:07:52) - Model 6: A high error rate is guaranteed in investing (00:08:06) - Model 7: Circle the wagons: the 4% rule (00:08:44) - Berkshire's 12 best decisions in 60 years (00:09:41) - Mistakes in investing: Ferrari, Progressive Insurance & Goldman Sachs (00:12:10) - Model 8: Do not cut flowers and water weeds (00:13:02) - Model 9: Be a shameless cloner; VIC; Dataroma & SumZero (00:15:11) - Model 10: History does not repeat itself - but it does rhyme (00:16:16) - Model 11: Explain your investment thesis to a 10-year old in 3-4 sentences (00:16:41) - Model 12: You always need a rope to get out of the deepest well (00:20:50) - Model 13: Pursue quality intensely; Nick Sleep, Zen and the Art of Motorcycle Maintenance (00:25:31) - Model 14: Thou shall not use Excel (00:25:52) - Model 15: Develop and use a pre-investment checklist (00:27:54) - Model 16: Be singularly focused like Arjuna (00:29:31) - Read the footnotes; Turn every page: Robert Caro (00:31:44) - Model 17: Enjoy hunting for needles in haystacks; Buffett's childhood entrepreneurial adventures (00:33:41) - Japanese Company Handbook; My introduction to Charlie Munger & Debbie Bozanek (00:38:02) - Model 18: Your deepest desire is your destiny (00:41:15) - Model 19: You should always have someone to discuss your investment ideas with; Li Lu (00:42:53) - Model 20: The mistress always looks hotter than the wife!  (00:43:30) - Model 21: Neither a short-term borrower nor a long-term lender be (00:43:54) - Model 22: Introduce randomness into your life; Peter Lynch's One up on Wall Street (00:46:28) - Model 23: Be a Swiss Army knife (00:46:36) - Model 24-26: Focus on spin-offs, uber cannibals & spawners (00:47:18) - Model 27: Arbitrage is wonderful; Rupert Murdoch (00:48:26) - Model 28: Heads I win, Tails I don't lose much!; IPSCO and CONSOL Energy (00:51:43) - Model 29: Focus on low-risk; high uncertainty bets (00:52:56) - Model 30: Do not skim off the top (00:53:37) - Book recommendations: Poor Charlie's Almanack, Influence & Excellent advice for living (00:54:48) - Importance of the Bedrock model (00:55:30) - Finding great businesses (00:58:11) - Focusing on my deepest desire (00:59:23) - Berkshire Hathaway A-shares (01:00:35) - Intrinsic value of a company (01:02:17) - The Founders Podcast & Value Investors Club (01:05:34) - Pursue your passion (01:07:49) - Making of a Great American Capitalist by Lowenstein (01:09:05) - Family-run businesses; Walmart (01:10:24) - The Dakshana Foundation & Giving back (01:12:45) - Micron (01:13:43) - Warren's Too Hard pile & Charlie's pie-counter trips The contents of this website are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation or needs and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser.

Our election is unconditional in the strictest sense. Neither our faith nor our obedience is the basis of it. It is free and utterly undeserved.

John Pollock and Brandon Thurston cover the Rousey vs. Carano viewership stats, ring boys to remain anonymous in lawsuit, Tony Khan addresses WBD-Paramount stories, and Ludwig Kaiser is arrested for battery. 00:00:00 Start00:03:11 Brandon's roof 00:09:32 Judge rules to continue “John Doe” anonymity for ring boys in WWE lawsuit00:13:01 Tony Khan dismisses speculation on AEW's television future00:20:09 Is AEW profitable? 00:28:25 All In London attendance00:31:40 Netflix touts success of Ronda Rousey vs. Gina Carano00:43:30 Ludwig Kaiser arrested on battery charge00:55:15 AEW promotes ESPN feature on Darby Allin00:58:25 EventsDC loses Motions to Dismiss in DC Superior Court to Bedrock and Thurston01:05:13 WWE - Made in America doc01:12:34 Nielsen changesMusic courtesy: “Panic Beat” by Ben TramerPOST WrestlingSubscribe: https://postwrestling.com/subscribePatreon: http://postwrestlingcafe.comForum: https://forum.postwrestling.comDiscord: https://discord.com/invite/Q795HhRTwitter/Facebook/Instagram/YouTube: @POSTwrestlingBluesky: https://bsky.app/profile/postwrestling.comWrestlenomicsSubscribe: https://wrestlenomics.com/podcast/Patreon: https://patreon.com/wrestlenomicsSubstack: https://wrestlenomics.substack.com/Twitter/Facebook/Instagram/YouTube: @WrestlenomicsBluesky: https://bsky.app/profile/wrestlenomics.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Ethan Thornton is the Founder and CEO of Mach Industries, a defense technology company developing next-generation unmanned systems and hydrogen-powered weapons platforms to redefine modern warfare and energy logistics. Ethan left MIT after one semester in 2023 to focus on building Mach full-time. Originally from Texas, Ethan grew up on a farm where he began prototyping weapons in high school. Funded through car tech jobs, knife and furniture sales, and small engineering projects. His early hands-on ingenuity and deep sense of urgency, intensified by the war in Ukraine—motivated him to accelerate U.S. innovation in unmanned aircraft and weapon systems to help deter China and strengthen national defense capabilities. Under his leadership, Mach Industries has attracted backing from top venture firms including Sequoia Capital, Khosla Ventures, and Bedrock, and has secured major U.S. Army contracts while expanding manufacturing operations. Ethan's work reflects a broader vision beyond defense. He frequently speaks on the strategic significance of Taiwan's semiconductor industry, the U.S. dollar's reserve status, the challenge of social decay and neo-feudalism, and the need for productive, post-partisan solutions that secure America's technological and economic future. Shawn Ryan Show Sponsors: Go to https://calderalab.com/SRS. Use code SRS for 20% off your first order. Go to https://helixsleep.com/SRS for 25% off. Make sure you enter our show name after checkout so they know we sent you! Go right now to https://hillsdale.edu/SRS to enroll. There's no cost, and it's easy to get started. Ethan Thorton Links: X - https://x.com/ethanrthornton Mach Industries X - https://x.com/mach_industries Mach Industries - https://www.machindustries.com Learn more about your ad choices. Visit podcastchoices.com/adchoices