Podcasts about limitations

In this episode, Craig Jeffery and Arjun Krishnan discuss five key limitations of enterprise AI in treasury: data quality, pattern-based reasoning without true understanding, difficulty handling novel situations, variable outputs, and limited explainability. They also explore the opportunities and solutions behind these constraints, including AI-assisted data cleanup, anomaly detection, stronger controls, human oversight, and safer agent-based system design. Enterprise AI for Treasury: A Guide to Agentic Implementation: https://amzn.to/4vTH8Fv AI: The Automation Spectrum and Examples in Treasury (Valorean Technologies) (2026): https://strategictreasurer.com/420-ai-automation-spectrum-and-examples-in-treasury/ Valorean Technologies: https://valorean.ai/ Timestamps: 00:00 Introduction 00:34 AI limitations and opportunities 02:05 Data quality shapes AI quality 05:35 AI correlates but does not understand 09:29 Arjun Krishnan's background 10:18 AI and genuinely novel situations 12:37 Why AI outputs can vary 16:29 Explainability and audit trails 19:14 The most critical limitations 20:32 Final thoughts and book 20:54 Outro ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ABOUT STRATEGIC TREASURER ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Download The Strategic Treasurer: A Partnership for Corporate Growth by Craig A. Jeffery in Kindle, or hardcover: https://amzn.to/4uqwFQq As an Amazon Associate, we earn from qualifying purchases. Strategic Treasurer is recognized as a top tier consulting firm in the area of treasury and risk management. Corporate clients, banks, and technology vendors all rely on their industry leading advisory services that are backed by a deep awareness of current needs, practices, and budgeting priorities of treasury professionals through Strategic Treasurer's annual industry surveys and decades of treasury experience. Strategic Treasurer utilizes a senior consultant model where every project is managed by senior consultants with actual practitioner experience in corporate and/or banking roles. Visit us today at http://strategictreasurer.com. Or join in the discussion at one of our leading LinkedIn groups: http://strategictreasurer.com/linkedin/

In this episode of the Prolonged Field Care Podcast, Dennis sits down with Alex, CEO of Shannon Mechanics, a Ukrainian company that has produced more than 8,000 drag stretchers and over 330,000 immobilization splints for the front line since the full-scale invasion.Alex shares the raw story of how the company started with scrap materials and construction-store aluminum during the early chaos of 2022, scaled production while operating physically underground with independent power and battery-powered equipment, and refined products based on real soldier feedback. They discuss the BM splint (a more rigid, radiolucent alternative designed for Ukrainian conditions), the philosophy behind their rollable plastic drag stretcher optimized for one-person extraction under drone threat and complex terrain, quality control under resource constraints, the transition from pure volunteering to a sustainable business, and the deeper questions of dignity in life and death, PTSD, and long-term rehabilitation.This is practical, unfiltered insight into how medical manufacturing adapts when supply lines collapse, borders close, and every piece of gear has to work in the worst conditions imaginable.Key TakeawaysMedical equipment designed for true one-person drag evacuation becomes critical when vehicles and multi-person teams are unavailable under drone threat and destroyed terrain.Starting with simple, locally available materials (construction-store aluminum for splints) allowed rapid production when imports were impossible.Operating underground with independent power, internet, and battery-powered tools enables continuity during blackouts and air raids.Visual quality control plus a “donation pile” for minor cosmetic defects keeps functional gear moving to the front while supporting community needs.Sustainable production requires paying people and covering costs—pure volunteering burns out and collapses.Feedback loops from soldiers drive continuous product improvement (rigidity, size options, packing for NATO pouches).Beyond the gear itself, the conversation highlights the need for dignity in recovery of the wounded and the fallen, plus long-term psychological and prosthetic support for survivors.Chapters00:00 – Introduction & Disclaimer00:26 – Meet Alex: CEO of Shannon Mechanics01:15 – Company origins: Revolution of Dignity to 2014–202202:41 – Humble beginnings, scrap materials, and the siege of Kyiv04:55 – Building supply chains under closed borders07:18 – Starting with BM splints, then the Utah/drag stretcher08:06 – Material challenges and community-driven solutions11:20 – Learning the craft, teaching production, and favoring people over full automation13:26 – From volunteering to a sustainable business model16:21 – Quality control process for splints23:20 – Introducing the drag stretcher design philosophy24:02 – Why rigid NATO litters fail in modern Ukrainian conditions25:01 – One-person drag, complex terrain, drones, and secondary injury prevention28:15 – Limitations (sniper fire) and real-world evacuation stories (8 km drag, quad bike integration)30:32 – Hypothermia protection, mud/snow durability, and recovery of the fallen34:40 – PTSD as generational trauma and the need for long-term support40:00 – Managing supply chain volatility and building Ukrainian supplier capacity42:39 – What has allowed the company to succeed during warFor more content, go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.prolongedfieldcare.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Consider supporting us: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠patreon.com/ProlongedFieldCareCollective⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.lobocoffeeco.com/product-page/prolonged-field-care⁠⁠⁠⁠

Pastor Taylor Shippy - Daniel 2:1-49As we continue our series "Daniel: Faithfulness in Exile", we continue following Daniel as he learns what it means to trust God in a world ruled by powerful empires. In Daniel 2, a restless king, an impossible dream, and a towering statue reveal a truth that God's people desperately needed to hear in Exile—and still need today.When the kingdoms of this world seem overwhelming and the future feels uncertain, how do we remain faithful without compromising, fighting for control, or retreating in fear? Join us as we discover that every earthly kingdom has its limits, why God's Kingdom alone endures forever, and what it looks like to live with hope—even in the shadow of the statue.

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This energy update includes essential spiritual guidance to support you on your healing journey. It includes a meditation focus to inspire your positive mindset & enhance your manifesting & prosperity work. I include an overview of the dominant energetic influences at play, and explore ways we can overcome any challenges.Enjoying Mayastar? There's much, much more! Explore over 100 attunement-based energy healing courses at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.mayastar.net⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ & enjoy my latest mystical musings at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.blog.mayastar.net⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Our Exploration speaker this Sunday is Rob Fischer, a long time friend of Redemption. He is a spiritual director, leadership coach, licensed MFT, and founder of Inflection Point. We will be exploring Jesus' invitation to not worry in Matthew 6 25-34.

Is there something in your life that feels absolutely impossible right now?In today's episode, we'll dig into the heart of Matthew 19:26 and learn what it truly means to trust that with God, all things are possible. The conversation focused on letting go of our own limitations, surrendering impossible situations to God, and finding deeper faith when we feel weakest. We'll explore practical truths about admitting when things are bigger than us and discovering how those impossible places can actually strengthen our dependence on God.Join us as we pray and encourage one another to stop rehearsing our challenges and instead start declaring God's power over every impossible moment. Let's lift up our prayers together and step into the day with renewed confidence in what God can do.Tap HERE to send us a text! Our podcast family is growing - introducing Men's Morning Devotional! Our dad, Steve Alessi, and all of our husbands are now hosting their own 5 minute devotionals every weekday - for men who want to become better husbands, fathers, and leaders! Listen, follow and subscribe here - MensMorningDevotional.comSupport the showNEW VIDEO EPISODES! You can watch our new video episodes on YouTube! Watch Our Video DevotionalsNEW TO MY MORNING DEVOTIONAL? We're so glad you're here! We're the Alessis, a ministry family working together in a church in Miami, FL, and we're so blessed to partner with the My Morning Devotional community and continue the great work done by the show's creator and our friend, Alison Delamota.We pray our personal reflections and devotions will empower you to grow your faith in God, and that you'll join us every morning in prayer! JOIN THE MMD COMMUNITYSubscribe to the show on this appShare with a friendJoin our newsletter Follow Us on ⁠Instagram⁠ and ⁠Facebook⁠⁠Leave a reviewMORE PODCASTS YOU'LL ENJOYEnjoy more podcasts from Metro Life Studios, including:The Family Business with The AlessisMen's Morning DevotionalThe Mary and Martha Show Available on our Metro Life Studios app!Get The Metro Life Studios app 

SummaryIn this episode, Chris McShanag, CEO of Virtual Teammate, shares insights on building remote teams, leveraging AI, and scaling businesses effectively. Discover practical strategies for hiring, leadership, and creating a cohesive remote culture.TakeawaysRemote team building and managementAI-enabled virtual teams and productivityHiring strategies and cultural fitLeadership and communication in remote settingsBusiness scaling and operational efficiencyChapters00:00 Introduction to Chris McShanag and Virtual Teammate01:19 Chris's Entrepreneurial Journey and Healthcare Focus03:01 The Shift to Remote Support in Healthcare04:11 Controlling Your Destiny as an Entrepreneur06:04 Parallels Between F1, Fighter Pilots, and Business07:07 Creating Clear Processes and Outcomes for Remote Teams08:04 Hiring and Building the Right Virtual Team09:06 Common Mistakes in Remote Hiring10:00 Aligning Tasks with the Highest Value10:57 Effective Communication and Performance Tracking12:05 Team Roles and Specialization in Remote Settings12:54 Building Company Culture with Virtual Teams13:51 Delegation Framework: Do, Defer, Delete15:09 Overcoming Challenges in Scaling with Virtual Support16:05 Assessing When Virtual Teams Are a Good Fit17:04 Lessons Learned from Entrepreneurial Challenges17:59 The Role of AI in Virtual Teams18:47 Ensuring Human Accountability with AI Tools20:08 Maximizing Productivity and Engagement in Remote Teams 21:03 Building a Cohesive Global Culture21:53 Success Traits of Effective Remote Leaders23:04 Limitations of AI and Human Intervention23:50 Avoiding Premature Outsourcing25:12 The Power of Personal Connection in Virtual Teams26:06 Encouraging Virtual Team Engagement and Leadership27:04 Impact of Virtual Teams on Global Economy28:11 The Future of AI and Human Collaboration28:54 Building a Legacy and Positive Impact in Healthcare30:05 Final Advice for Entrepreneurs and Business Leaders30:58 Where to Connect with Chris and Virtual TeammateLearn more: https://www.linkedin.com/in/christopher-mcshanag/https://virtualteammate.com/Credits:Hosted by Ryan RoghaarProduced by Ryan RoghaarTheme music: "Perfect Day" by OPM  The Eggs Podcast Spotify playlist:bit.ly/eggstunesThe Plugs:The Show: eggsthepodcast.com@eggsthepodcast on X and InstagramMike "DJ Ontic": Shows and info: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠djontic.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@djontic on twitterRyan Roghaar:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠rogha.ar⁠⁠

John is big-headed, Alison has a right of reply, and Liz explains how voting should work. An uncorrected transcript of this episode is available here. Please email your letters of comment to comment@octothorpecast.uk, join our Facebook group, and tag @OctothorpeCast (on Bluesky or on Mastodon) when you post about the show on social media. Letters of comment Brian Nisbet (Facebook) Chris Garcia (email) Eurocon 2029: Bid for Warsaw, Poland Constanze Hofmann (email) ESFS ESFS statues Gareth Kavanagh (Facebook) Ivaylo Alekseev (email) Leigh Edmonds (email) Digger by Ursula Vernon Neil Ottenstein (Facebook) Renay (Bluesky) Shi Lala (Facebook) Alison: Scott Edelman (Bluesky) We also heard from Constanze Hofmann, Farah Mendlesohn, Ivan Sinha, Jonathan Cowie, Kev McVeigh, Lilian Edwards, and Paul Weimer Worldcon 2026: LAcon V The WSFS Business Meeting Agenda WSFS Business Meeting Recaps from LAcon V F.7 Limitation on Listed Contributors for Hugo Award Finalists This was withdrawn by Olav Rokne at the Preliminary Business Meeting “Role Creep in the Hugo Awards Semiprozine Category” by Tammy Coxen on File 770 “Role Creep and the Numbers Game: A Measured Response” by Kat Kourbeti on File 770 F.8 Purposeful Proportionality [Alison told me that we should link a CGP Grey video here but then didn't put it in the show notes, so good luck finding it, listeners—John] History of Hugo Category Definitions by Tammy Coxen Unofficial WSFS Business Meeting Browser by Andrew January Locus Locus Best Science Fiction & Fantasy of the Year: Volume 1 on Kickstarter Picks John: Mothership Companion Alison: Strange Horticulture Liz: Moderation by Elaine Castillo Credits Cover art: “Stand Up and Be Counted”, found art détourned by Alison Scott Alt text: 72 assorted LEGO minifigs in a 6x12 grid. Words above read “Octothorpe 165” and below read “WSFS Business Meeting Register: Stand up and be Counted”. The title case on the part of the sentence after the colon is, frankly, bananas, and it certainly isn't aligned with the Octothorpe style guide. Theme music: “Surf Shimmy” by Kevin MacLeod (CC BY 4.0)

People are calling the new ChatGPT Voice their “AGI moment.”

Catrina M. Craft, a top-tier tax strategist, shares essential insights on avoiding common tax mistakes, structuring your business effectively, and the importance of proactive planning to protect your assets and maximize wealth.“Inaction is really dangerous because what happens is it just builds up, it compounds.”Chapters00:00 Risks of Asset Seizure and Frozen Accounts01:09 Introduction of Katrina Kraft and Episode Overview02:21 Misconception: Tax Professionals Save You Money03:44 The Role of a Tax Strategist in Planning05:07 Importance of Business Structure and Goals07:20 Holistic Approach to Business Formation and Strategy09:57 Common Frustrations of Entrepreneurs11:16 The Limitations of AI and the Importance of Human Expertise13:21 Using AI Tools Responsibly in Tax Planning15:45 The Dangers of Paralyzing Fear and Not Filing17:08 Consequences of Not Filing Taxes and Asset Seizure18:36 How to Connect with Katrina for Tax Strategies“If you need help, pay for that because it's cheaper to pay for help than to pay the IRS those penalties and interests.”Additional Key Takeaways*Differences between bookkeepers, CPAs, and tax strategists*Avoiding Tax Seizures: Protect Your Assets Now*The importance of proactive tax planning*Risks of not filing taxes and how to avoid penaltiesEPISODE #1/10: Unlocking Tax Strategies for Entrepreneurs: January 26, 2026: https://thatentrepreneurshow.buzzsprout.com/737252/episodes/18570362-unlocking-tax-strategies-for-entrepreneursEPISODE #2/10: Unlocking Home Office Deductions February 9, 2026: https://thatentrepreneurshow.buzzsprout.com/737252/episodes/18646020-unlocking-home-office-deductionsEPISODE #3/10: Hidden Tax Strategies Revealed: March 9, 2026: https://thatentrepreneurshow.buzzsprout.com/737252/episodes/18814431-hidden-tax-strategies-revealedEPISODE #4/10: Strategic Family Travel & Tax Benefits: June 10, 2026: https://thatentrepreneurshow.buzzsprout.com/737252/episodes/18814431-hidden-tax-strategies-revealedSend 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.

Our guest on the podcast today is Ryan Frederick, an author, speaker, and entrepreneur focused on the intersection of place and healthy longevity. As founder and CEO of HERE, he provides content courses and coaching in place planning, a holistic, research-based approach to help people find the right place for every stage of life. He's also the author of Right Place, Right Time, a book that helps individuals think through housing decisions for the second half of life. In addition, Ryan provides strategy consulting to organizations looking to create better places, including Fortune 500 companies, institutional investors, health systems, and housing developers and operators. He's also a member of the Advisory Council for the Stanford Center on Longevity. He previously served on the National Advisory Board of Johns Hopkins University School of Nursing and was a member of the Bipartisan Policy Center Task Force on Health and Housing. He's a graduate of Princeton University and Stanford Business School. Episode Highlights 00:00:00 Senior Living, Longevity, and 100-Year Life Planning 00:08:39 How Place Influences Health as You Age 00:10:37 The Four Pillars of Place Planning 00:16:19 Income's Impact on Housing, Home Equity, and Limitations of Aging in Place 00:30:01 Place Planning Tools and Building Community 00:41:43 New Housing Models, Cohousing, and Age Diversity 00:49:13 Continuing Care Retirement Communities and Healthy Aging Dashboards More From Morningstar Your Retirement Countdown, With Christine Benz Harry Margolis: How to Confront Aging Challenges Head-On Joy Loverde: Planning Ahead for Care Needs as You Get Older If you have a comment or a guest idea, please email us at TheLongView@Morningstar.com. Follow Christine Benz (@christine_benz) and Ben Johnson (@MstarBenJohnson) on X, and Christine Benz, Amy Arnott, and Ben Johnson on LinkedIn. Visit Morningstar.com for new research and insights from Christine, Ben, and Amy. Subscribe to Christine's weekly newsletter, Improving Your Finances. If you want more Morningstar podcasts, check out The Morning Filter and Investing Insights. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode with Joshua Luke Smith we sit with the idea that the life in front of you is the main event – even when it is limited, when it is unglamorous, when it's not what you hoped for. We talk about lament and shame, consecration and wonder, what the cell and limits teach us about freedom, how we need to put ourselves in the way of beauty to bring us back to who we are. It's a conversation about attention and presence.Joshua Luke Smith is a poet for the people. He's a songwriter, community builder, podcaster and author. He's the founder of The Psalmists, a charity cultivating creative practice in prisons and among those on the margins of society.His new book "This is The Main Event" comes out in July 2026.He lives with his family in London, UK.Joshua's Book:This is the Main EventJoshua's Recommendation:The OdysseyConnect with Joshua: jjohnson@shiftingculturepodcast.comGo to www.shiftingculturepodcast.com to interact and donate. Every donation helps to produce more podcasts for you to enjoy.Follow on Facebook, Instagram, Twitter, Threads, Bluesky or YouTubeSupport the podcast and the ministry that my wife and I do around the world. Just click on the support the show link below Support the show

Exodus 7-8 is the first time in the Bible where we find there are people who exercise supernatural powers apart from the power of God. Pastor Wikler talks about the nature of magic: it's power and its limitations.

Greetings Glocal Citizens! This past spring, a volunteer from the Digital Democracy Project reached out to me about its founder and this week's guest, Ramon Perez. I'd never heard of the project and as a podcast about the many dimensions of citizenship, this was a very necessary conversation exploring innovative ways to enhance citizen participation and reform democracy using technology like AI, blockchain, and mobile voting. As an AI technology executive and US military veteran, Ramon founded the Digital Democracy Project as an accountability system and mobile voting platform that connects voters directly with their legislative representatives to address systemic problems in the electoral system that result in hyper-partisanship and widespread voter alienation. To achieve better outcomes, he believes technology can be used as a catalyst for building a more modern, representative system of government giving greater control directly to voters. #Listenandlearn more as we discuss systemic challenges in US politics and how technology can create a more transparent, accountable, and inclusive democratic process. Where to find Ramon and the Digital Democracy Project? On LinkedIn On Instagram On Facebook On Bluesky On YouTube What's Ramon Listening to? The Lord of the Rings Series Audiobooks Empire Podcast Other topics of interest: On the Enduring Legacy of 9/11 About OneVirginia21 What is Gerrymandering? About ranked-choice voting What is Hyperledger Fabric? Homomorphic encryption explained Chapters 00:06 Intro and Guest Bio 01:54 Ramon Perez's Background and Motivation 03:14 Defining Democracy in the 21st Century 03:22 Limitations of Current Electoral Systems 05:30 Civic Responsibility and Voter Engagement 07:16 Educating Voters with Technology 09:50 AI's Role in Legislative Summaries 13:57 Systemic Problems in US Elections 16:16 Verifiability, Accountability, and Transparency 17:54 Ramon Perez's Career Path and Transition to AI 20:06 Public Perception of AI and Ethical Use 21:14 Tools and Innovations in Digital Voting 26:07 Global Examples of Democratic Success 35:34 Glocal Speak: Transparency as a Key Value 37:19 Organizing and Operating a Nonprofit Project 42:51 Historical Progress of Voting Rights 44:34 Future Goals and Expansion of the Project 46:44 Ramon Perez's Leisure and Personal Interests 47:42 Reflections on the Evolution of AI and Democracy Special Guest: Ramon Perez.

Welcome to Dark Work Daily—the podcast for those willing to do the work no one sees. Here, we dive into resilience, discipline, and perseverance required to unlock your full potential when motivation fades.

Highway spokesperson Mindy Peterson details the reopening of I-65 in Louisville this week, albeit with a few changes. There'll be lane limitations, slower speeds, and other measures to accommodate ongoing repairs to the city's oldest interstate highway.Mindy Peterson and Terry Meiners discuss the overall aging infrastructure and the state highway department's plans to continue upgrading various highways.

Series: Unforced Rhythms of Grace — Preacher: Michael Young

In this episode, we sit down with Dr. Jared Ball and Dr. Todd Steven Burroughs to break down the myth of the "Anti-Obama Race Traitor." They dig into how that label was often weaponized to silence policy-based critiques of Barack Obama, forcing people to choose between unquestioning support or being ostracized. The conversation centers on how Obama's public identity was a carefully constructed media strategy. The guests talk about how he learned to code-switch and use symbolic language to appeal to both Black voters and white power structures, all while avoiding any real challenge to the status quo. They reflect on how even among Black activists and intellectuals, there was an intense pressure to fall in line, often at the expense of independent political analysis. They also get into how this era set a template for other Black political figures to follow, prioritizing access and symbolism over radical policy demands. It's a pretty candid look back at a time when criticizing the first Black president could get you labeled a traitor, even when those criticisms were based on his actual record, his cabinet appointments, and his imperialist policies. Ultimately, they discuss how the Obama years are being rebranded today as a "golden era," which they argue obscures the reality of what actually went down and the long-term consequences for Black political thought. Jesse Jackson and Black People https://www.firstofthemonth.org/jesse-jackson-and-black-people-redux/ What Is The Message? ft. Todd Steven Burroughs https://www.youtube.com/watch?v=9-SfX6_XLRQ Rising Star: The Making of Barack Obama and the Limitations of Liberal Criticism https://www.youtube.com/watch?v=tMeiPgJe0qo The Great Harlem Debate of 2008! Is the Election of Barack Obama Good for Black People? https://www.youtube.com/watch?v=hWx-SZmgwn8

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

Dive into the evolving role of AI in design and collaboration as Danny Wu, Head of AI Products at Canva, shares insights on how the platform is transforming creative workflows, democratizing design, and leveraging large language models and diffusion techniques.In this episode:How Canva redefined abstraction layers in design, moving from pixel edits to object-based workflowsThe evolution of AI at Canva: from traditional ML to transformers and large language modelsThe impact of ChatGPT integration on Canva's user experience and business growthAgentic AI: Canva's approach to AI that acts as a collaborative partner in designChallenges and misconceptions about AI generative models in creative industriesFuture plans: video content tools and more AI-powered featuresTimestamps:00:00 - Introduction to Canva's innovation in abstraction layers01:12 - Danny Wu's background and journey at Canva02:46 - Transition from software engineer to Head of AI Products04:25 - How diffusion and large language models accelerate Canva's AI capabilities06:50 - The dominance of transformer models in Canva's AI strategy08:51 - Shift from pixel to object, then conceptual design with AI09:19 - Limitations of chat-based creativity vs direct manipulation11:01 - The future of design involving AI-generated, editable content13:30 - Launch of Canva AI and the platform's new architecture15:22 - Use cases and limitations of AI in visual and video content17:05 - Canva's diverse user base and how AI personalization fits different needs18:48 - Challenges of AI aesthetics and user customizations20:32 - Amazing AI features like Magic Layers for editable image designs22:16 - Comparing models: diffusion, open source, and proprietary tech27:36 - Exploring agentic AI: Canva's vision of AI as a collaborative partner30:37 - How ChatGPT and similar tools boost Canva's reach and usability34:26 - Personalizing AI output for users and reducing generated “cookie-cutter” content37:38 - Managing AI's creative style and avoiding homogenization42:39 - Canva's feature development process and testing workflows46:36 - Surprising use cases, like self-grading quizzes in education48:59 - Overlap and differentiation between Canva and other design tools like Figma50:54 - Future video tools and content creation enhancements at Canva

God revealed a clear and specific vision for Abraham and Sarah: they would become the parents of many nations through a son born to them together. Even after reaffirming His covenant and changing their names to reflect their future, Abraham allowed his natural limitations—his age and Sarah's barrenness—to shape his expectations. Instead of embracing God's promise, he suggested that Ishmael fulfill the vision, attempting to reduce God's plan to something that seemed more realistic. But God refused to lower His promise to match Abraham's doubts. He affirmed that while Ishmael would be blessed, His covenant would come through Isaac, the promised son. This example reminds us that God's vision is not determined by our limitations. Faith requires us to trust His power rather than adjust His promises to fit what seems possible in our own strength. __________ Genesis 17:1-7 NLT, Genesis 17:15-21 NLT __________ Partner with Us: https://churchforentrepreneurs.com/partner Connect with Us: https://churchforentrepreneurs.com __________    

This Episode is Sponsored by StayFi Your ultimate tool for Vacation Rental WiFi marketing allowing you to collect guest emails automatically via custom captive WiFi login splash pages. Drive repeat direct bookings and convert your OTA bookings to book direct for their next visit. Visit https://stayfi.com/vrsuccess/ and use code VRSUCCESS for 50% off 3 months of StayFi service. ________________________________________________________________________________________________________________________________________ Paul Anderson first appeared on the Vacation Rental Success Podcast in 2021, when he was running a guest house in Oxford and building a following on Instagram. Five years on, he returns as a coach and strategist working across dozens of short-term rental operators, and now a commissioned officer in the Royal Air Force reserves. That mix of experience gives this conversation its shape: what the military taught him about briefing, decision-making and slowing down, and how those same lessons apply to the way operators are using AI in their marketing. The central argument is one Paul delivered on stage at Scale in Brighton, and it is worth sitting with. AI did not break marketing, it exposed the people who were never really marketing in the first place. When content becomes cheap and fast to produce, the operators who understood their audience all along pull ahead, and the ones who mistook activity for strategy get found out. Heather and Paul talk through his 10-80-10 protocol, his SMERLAC briefing structure borrowed from military planning, and why the real work in AI happens before you ever ask it to write a word. It is a practical, funny and genuinely useful conversation for any operator who has looked at their AI-generated captions and felt that something was missing. Key takeaways AI has not broken marketing. It has exposed the operators who were producing content without ever really marketing, because publishing posts, writing blogs and appearing in magazines are not the same as moving a guest from "I might visit" to "I want to stay there." The real work happens before you prompt. Paul's 10-80-10 protocol puts the first 10 percent into defining exactly who you want and briefing the AI properly, lets AI do 80 percent of the heavy lifting, then reserves the final 10 percent for inspecting and personalising the output. Define your perfect potential guest in granular detail. Go beyond age and income to how many cars they have, where they shop, even the colour of their hair, so you can picture one real person on the other side of the screen rather than a demographic. Use a proper brief, not a wish. "Write me 10 captions about my holiday cottage" is a wish. Paul's SMERLAC structure (Situation, Mission, Execution, Resources, Limitations, Ask questions, Check understanding) gives the AI what it needs to produce something useful. Followers are not the same as money. A client with 28,000 followers built through giveaways had almost no buying intent, while 52 people who genuinely want to book beat 52,000 who just think you are funny. Feed AI your real voice. Heather uploaded the handwritten manuscript of a book she wrote in 2005, unedited, and it changed everything the AI produced. Your idiolect, the phrases only you use, is what cuts through the sameness. ________________________________________________________________________________________________________________________________________

Stasia Steinhagen sheds light on the importance of responsible self-prescribing at home. In this episode, Stasia will highlight the need to stay within our limits of expertise and recognize when it's appropriate to seek professional guidance. She will also provide insights into the programs available at the Northwestern Academy of Homeopathy.   Episode Highlights: 01:26 - Stasia's initial exposure to homeopathy 03:25 - Understanding homeopathy as a healing modality 06:30 - Avoiding medication during pregnancy and childbirth with homeopathy 09:28 - The Northwestern Academy of Homeopathy 12:56 - Online participation option for those unable to attend in person 14:35 - What's special about Northwestern Academy 16:52 - The importance of taking the time to learn about homeopathy 19:19 - The role of apprenticeship in learning and practice 23:13 - Understanding the systematic approach of homeopathy when applying remedies 27:21 - What's the key to safety for a home prescriber 29:19 - Advice for those aspiring to study homeopathy 32:30 - What it's like to be a homeopath   About my guest: Stasia Steinhagen is an experienced educator, homeopath, herbalist, coach, and researcher. She is passionate about helping individuals and organizations reach their full potential. With over 30 years of coaching certification, a 4-year homeopathic training from the Northwestern Academy of Homeopathy, and a Master of Holistic Health Studies focusing on nutrition education. Stasia has a depth of experience in working with complex cases, gifted individuals, and those with special needs. She is a member of a multi-disciplinary faculty research team and has published peer-reviewed articles on integrative medicine. Stasia also serves as the chair of the board of directors for NAH and is an adjunct faculty member and lecturer in graduate research and homeopathic studies programs.   Find out more about Stasia https://www.linkedin.com/in/2degreesnorthstasiasteinhagen/ https://www.homeopathictraining.org/   Learn more about CHE Australia and its homeopathy training programs: https://chehomeopathy.com.au/   If you would like to support the Homeopathy Hangout Podcast, please consider making a donation by visiting www.EugenieKruger.com and click the DONATE button at the top of the site. Every donation about $10 will receive a shout-out on a future episode.   Join my Homeopathy Hangout Podcast Facebook community here: https://www.facebook.com/groups/HelloHomies   Follow me on Instagram https://www.instagram.com/eugeniekrugerhomeopathy/   Here is the link to my free 30-minute Homeopathy@Home online course: https://www.youtube.com/watch?v=vqBUpxO4pZQ&t=438s   Upon completion of the course - and if you live in Australia - you can join my Facebook group for free acute advice (you'll need to answer a couple of questions about the course upon request to join): www.facebook.com/groups/eughom

On this episode of the Getting Smart Podcast, Mason Pashia sits down with Gersom de Koning to explore theater education as a transformative practice. Their conversation examines how drama builds confidence, collaboration, leadership, and transferable skills while also offering a powerful model for gamification, exploration, and learning through productive failure. From arts integration and technical theater to the future of live performance in an AI world, this episode highlights why theater belongs at the center of meaningful learning. Outline (0:00) Introduction & Background (2:01) Gersem's Journey into Theater (5:41) What Makes Great Theater Education (13:40) Drama, Gamification & Learning (26:03) Failure, Collaboration & Leadership (40:32) The Future of Theater & Drama Education Links Read the full blog here LinkedIn Gersom de Koning Official Site Proper Job Gradcast Episode 19 Creatively Christian: The Creative Power of Limitations  

If you've struggled with questions about God, suffering, or whether faith can make sense of the world's brokenness, you're not alone. Poet Christian Wiman joins Amy Julia Becker to talk about pain, joy, doubt, and the lifelong hunger for God. Together, they explore a faith honest enough to hold both questions and hope.S10 E10:00:00 The Genesis of Glimmerings04:41 Navigating Differences in Faith12:24 The Role of Personal Encounters18:45 The Intersection of Suffering and Longing for God23:33 The Relationship Between Love, Suffering, and Hope36:37 Sustaining Faith in Everyday Life39:53 Cutting Through the Noise: Faith Beyond DifferencesMENTIONED IN THIS EPISODE:Glimmerings: Letters on Faith Between a Poet and a Theologian by Miroslav Volf and Christian Wiman My Bright Abyss by Christian WimanMiroslav Volf on Reimagining the Good Life: The Cost of Ambition_SUBSCRIBE to Amy Julia's Substack: amyjuliabecker.substack.comWATCH this conversation on YouTube: Amy Julia Becker on YouTubeJOIN the conversation on Instagram: @amyjuliabeckerLISTEN to more episodes: amyjuliabecker.com/shows/_ABOUT OUR GUEST:Christian Wiman is the Clement-Muehl Professor of the Arts at Yale Divinity School. He is the author, editor, or translator of fifteen books, including Zero at the Bone: Fifty Entries Against Despair and Hammer Is the Prayer: Selected Poems.  His work appears regularly in Harper's, The New Yorker, and Commonweal.More about Chris: https://divinity.yale.edu/profile/christian-wimanGlimmerings: Letters on Faith Between a Poet and a TheologianWe want to hear your thoughts. Send us a text!Connect with me:InstagramFacebookYouTubeWebsiteThanks for listening! 

Most buyers no longer trust marketing claims at face value. They investigate, compare, and verify before they believe. In this solo episode of StrategyCast, Lori Jones explains the four layers of proof that help brands build buyer trust in an increasingly skeptical marketplace.As AI makes content easier to create, unsupported claims are losing value. Buyers now examine leadership behavior, customer experiences, thought leadership, consistency, and visible expertise before they speak with a sales team. The brands that stand out will not simply make stronger claims. They will make their credibility easier to observe.And don't forget! You can crush your marketing strategy with just a few minutes a week by signing up for the StrategyCast Newsletter. You'll receive weekly bursts of marketing tips, clips, resources, and a whole lot more. Visit https://strategycast.com/ for more details.==Let's Break It Down==00:00 Introduction and 600th episode milestone03:31 Understanding the trust shift08:44 Evolving role of proof in marketing10:00 Limitations of borrowed proof15:37 Future of Marketing Trends17:13 Developing a trust architecture framework20:01 Closing thoughts and contact info==Where You Can Find Us==Website: https://strategycast.com/Instagram: https://www.instagram.com/strategy_cast/Facebook: https://www.facebook.com/strategycast==Leave a Review==Hey there, StrategyCast fans!If you've found our tips and tricks on marketing strategies helpful in growing your business, we'd be thrilled if you could take a moment to leave us a review on Apple Podcasts. Your feedback not only supports us but also helps others discover how they can elevate their business game!

Matt Putra, managing partner at 8X, shares insights on leveraging AI in e-commerce, the importance of organizational frameworks like EOS, and strategic financial management for growth and exit readiness.AI's impact on marketing and product developmentUsing EOS to organize and prepare for exitFinancial hygiene and data organization for acquisitionStrategic use of AI in ad creation and testingScaling and managing growth in consumer goodsChapters00:00Introduction and Guest Introduction03:44AI in Product Prototyping and Marketing Acceleration07:10Challenges and Limitations of AI in Content Creation08:49Role of 8X as Fractional CFOs and Data Management11:11Upstream Metrics and Data Organization for Growth13:28Using EOS for Business Operations and Exit Preparation15:35Case Study: Two Large Exits and Preparation Strategies17:55EOS and Strategic Frameworks for Large-Scale Growth20:06Financial Habits for Scaling to 8X and Beyond21:49Monitoring and Optimizing Conversion Funnels23:25The Future of AI in CFO and Financial Roles26:03Governance and Security in AI-Driven Financial Processes28:45Automation, Testing, and Human Oversight in AI Systems32:44Lessons from Metal Recycling and Business Resilience36:36Killing Underperforming SKUs and Product Management39:22Retail Strategies and Entry Points for Brands41:50Trends in Global Trade and Market Expansion44:21The Role of Research Content and AI45:02Soccer in Vancouver and Local Sports Culture46:09Contact and Closing Remarks

Hey there, hero!Richard Bach once said, "Argue for your limitations and, sure enough, they're yours."Wow…does that resonate with me.And I hear examples all the time…from my students, my colleagues and my peers.People can convince themselves of almost anything, and they often do.I hope you check on all the assumptions you have taken as gospel, and in this episode, I want to help remind you to question the “facts” you “know” about yourself.Do you question your limiting beliefs? Do you ask whether what you tell yourself is actually true? How do you combat this and give yourself more options? Let me know in the comments below.REQUEST: Please join this video's conversation and see the full episode on VOHeroes, where the comments are moderated and civil, at https://voheroes.com/on-arguing-for-your-limitations/#Acting #Voice #VoiceOver #Performance #Productivity #Tips #Art #Commerce #Science #Mindset #Success #Process #Options #BestPractices #MarketingWant to be a better VO talent, actor or author? Here's how I can help you......become a VO talent (or a more successful one):     https://voheroes.com/start     ...become an audiobook narrator on ACX (if you're an actor or VO talent):     https://acxmasterclass.com/     ...narrate your own book (if you're an author):     https://narrateyourownbook.com/     ...have the most effective pop filter (especially for VO talent):     https://mikesock.com/     ...be off-book faster for on-camera auditions and work (memorize your lines):     https://rehearsal.pro/...master beautiful audiobook and podcast audio in one drag and drop move on your Mac:     https://audiocupcake.com/The VOHeroes Podcast is heroically built with:BuddyBoss | LearnDash | DreamHost | SamCart | TextExpander | Buz...

In the second episode of Season 5 on the Neuroethics of Psychedelics, we speak with Eiko Fried about psychedelic research, the facts, the myths, and the hype. Professor Fried is a professor of Mental Health & Data Science at Leiden University. He is the co-Founder of the local Open Science Community, and a previous member of the Young Academy Leiden. In this episode, we discuss the limitations of existing psychedelics research, the implications of exaggerated claims, and the role of hype in moving this research forward, at a risk. Relevant references:• Checklist to vet psychedelic science - Eiko Fried BlogEpisode contributorsHost: Dr. Lavinia Uscatescu Audio editor: Sarah Schultz

Bun quitte Zig pour Rust en 11 jours à coups de Claude Code, pour 165 000$ payés par Anthropic : la réaction du créateur de Zig ne se fait pas attendre. TypeScript 7 débarque, réécrit en Go, 8 à 12x plus rapide. Entre les deux, Vidocq réimplémente Jakarta EE en souverain, le COBOL met un uppercut aux microservices, et un CTO demande à son équipe combien de temps il lui faudrait pour revenir à sa vélocité antérieure sans Claude Code. De quoi réfléchir avant le prochain rewrite. Enregistré le 17 juillet 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-342.mp3 ou en vidéo sur YouTube. News Langages Est-ce qu'on peut aussi utiliser des double, des longs, ou autre pour gérer les montants monétaires en Java ? https://blog.frankel.ch/bigdecimal-vs-double/ double (IEEE 754) Usage : Calculs scientifiques, métriques, statistiques. Avantages : Très performant (matériel), idéal pour l'approximatif. Risques : Erreurs d'accumulation, égalité (==) trompeuse, NaN / -0.0. Bonnes pratiques : Utiliser une tolérance (epsilon ou ULP) pour comparer ; utiliser des algorithmes de sommation compensée (Kahan/Neumaier) pour la précision. BigDecimal Usage : Finance, comptabilité, fiscalité (précision décimale stricte). Avantages : Contrôle total des arrondis et de l'échelle. Risques : Lent (allocations), immutabilité (risque de mauvaise réaffectation), confusion equals() vs compareTo(). Bonnes pratiques : Initialiser via String ou valueOf() ; utiliser compareTo pour l'égalité. Point fixe (long) Usage : Trading, systèmes haute performance, paiements. Avantages : Très rapide, déterministe, zéro allocation. Risques : Gestion manuelle de l'échelle et des débordements (Math.addExact). Points de vigilance en production Sérialisation (JSON) : Préférer les String pour BigDecimal pour éviter la perte d'échelle. Atomicité : double n'est pas atomique ; utiliser volatile ou DoubleAdder (pour les compteurs). Tests : Toujours définir un delta ou Offset pour les tests de flottants. Bibliothèques recommandées Moneta (JSR 354) : Standard bancaire complet. decimal4j : Optimisé pour le point fixe haute performance. Apache Commons Numbers : Outils robustes pour la précision et les sommations. Typescript 7 est de sortie devblogs.microsoft.com/typescript/announcing-typescript-7-0 Performance majeure : Portage natif en Go offrant des gains de vitesse de 8x à 12x et une consommation mémoire réduite. Architecture optimisée : Utilisation du multithreading (mémoire partagée) et parallélisation native (analyse, vérification de types,émission). Nouvelles options de contrôle : Introduction des flags –checkers, –builders (parallélisation) et –singleThreaded (mode mono-cœur). Nouvel observateur de fichiers : Passage à une solution basée sur @parcel/watcher pour une meilleure réactivité et stabilité du mode –watch. Compatibilité et transition : Compatible avec les bases de code TypeScript 6.0. Utilisation du package @typescript/typescript6 recommandée pour maintenir des outils dépendants de l'ancienne API. Changements de configuration : Durcissement des défauts (ex: strict activé par défaut) et suppression de nombreuses options obsolètes (target: es5, baseUrl, etc.). Amélioration de l'expérience éditeur : Serveur de langage (LSP) plus stable avec une réduction de 80 % des erreurs et 60 % des crashs. Limitations actuelles : Support incomplet pour les frameworks utilisant des plugins de langage (Vue, Svelte, Astro, Angular) en attendant une API stable. "Java, the documentary" est sur YouTube, retraçant l'histoire du langage youtube.com/watch?v=… La vidéo n'était pas encore disponible à l'heure de l'enregistrement. Sortie officielle le 17 juillet. Avec des interviews de James Gosling, Brian Goetz, Venkat Subramaniam, et bien d'autres. Librairies What's New in 8.0 - Hibernate docs.hibernate.org/orm/8.0/whats-new L'intégration de Jakarta Persistence 4.0 apporte des nouveautés majeures comme EntityAgent (qui standardise la StatelessSession), les mappings de result set en SQL natif, et de nouvelles options de configuration de session et de requêtes (Session Creation Options, Query Options). Le support de Jakarta Data 1.1 est ajouté pour les Hibernate Data Repositories, incluant l'intégration avec les requêtes statiques JPA4, les projections @Select, et les repositories asynchrones via Jakarta Concurrency ou Hibernate Reactive. L'introduction du Graph-based Flushing remplace l'ancienne approche basée sur des heuristiques par un modèle de dépendances utilisant les contraintes relationnelles, afin d'améliorer la fiabilité des tris, la gestion des batchs et les performances globales (bien que l'ancienne méthode reste temporairement disponible). L'API ProcedureCall a été améliorée pour faciliter le casting des résultats (asResultSetOutput) et permettre la déclaration paresseuse (lazy) du mapping des ResultSet. Hibernate supporte désormais la sécurité au niveau de la ligne (Row-Level Security) de manière native pour les bases de données compatibles (PostgreSQL, Db2, SQL Server, CockroachDB) afin de gérer la visibilité en contexte multi-tenant. Une nouvelle méthode getReference() permet dorénavant de récupérer la référence d'une entité directement à partir de son natural id. Le mode Safe Mode Validator (hibernate.query.safe_mode_enabled=true) fait son apparition pour bloquer les opérations risquées comme sql(), function() ou column() dans les requêtes HQL et Criteria, ce qui est particulièrement utile pour les applications exposées aux LLMs. La gestion des associations bidirectionnelles lors de la phase de flush peut maintenant être prise en charge automatiquement par Hibernate (hibernate.bidirectionality_management=true), synchronisant la référence côté inverse de l'association. Le Subselect Fetching est considérablement amélioré, supportant dorénavant les associations "to-one" pour le bulk select fetching (au lieu de se limiter aux collections) et devenant une option de premier ordre via FetchMethod.BY_SUBQUERY. Un des papas de Cucumber et Gherkin lance Var, une alternative pour le test et le BDD var.oselvar.com Lancement de Vár : Nouvel outil de test créé pour pallier les défauts de Cucumber. Limites de Cucumber : Syntaxe Gherkin trop rigide, intégration difficile avec les exécuteurs de tests et support éditeur limité. Usage avec l'IA : Conçu spécifiquement pour vérifier que les agents IA respectent les intentions et spécifications de l'utilisateur. Fonctionnement : Utilisation du Markdown plutôt que du Gherkin ; sert à la fois de guide et d'outil de vérification. Développement assisté : Code et documentation générés en grande partie par Claude sous supervision humaine. Appel aux retours : Projet ouvert aux tests et aux critiques de la communauté. Web Une nouvelle méthode HTTP : QUERY https://kreya.app/blog/new-http-query-method-explained/ Méthode HTTP QUERY (RFC 10008) pour les recherches complexes. Problème : GET (limité par l'URL) vs POST (sémantique inadaptée). Avantages : Permet un corps de requête, sûr, idempotent et cacheable. Limites : Support infrastructurel faible, non partageable par lien, cache complexe. Usage : À réserver aux requêtes complexes si l'environnement le permet. Comment je fais du design en tant que dev backend eventuallycoding.com/p/comment-je-fais-du-design-en-tant-que-dev-backend Hugo Lassiège retrace l'évolution de son workflow de création d'interfaces en tant que développeur backend, depuis ses débuts avec Bootstrap jusqu'à l'ère de l'intelligence artificielle. L'article explique comment la structuration des éléments visuels a progressé grâce à l'Atomic Design, l'émergence des design systems et l'adoption des design tokens via un framework comme Tailwind. L'auteur détaille son processus actuel qui s'appuie fortement sur Claude Design pour générer et itérer sur des maquettes à partir d'un brief, d'un screenshot ou d'un design system de référence. Il aborde également le risque de slopification et de standardisation extrême apporté par ces outils, rappelant que si l'IA simplifie la technique, il reste crucial d'injecter de l'identité et de l'originalité pour éviter un web trop aseptisé. Data et Intelligence Artificielle De l'utilisation de SKILL.md et de "loop engineering" pour augmenter sa productivité glaforge.dev/posts/…/of-skills-and-loops-with-ai-assistance Les skills permettent d'encoder une procédure de manière répétable et automatisable Le loop engineering enlève l'humain de la boucle afin que l'agent atteigne un objectif donné de façon plus autonome Pour écrire des Codelabs (sorte de tutoriel guidé pas à pas) Guillaume a transformé une séance de création de codelab avec son agent préféré (Antigravity) en skill réutilisable pour l'écriture de ses prochains codelabs Il a également utilisé l'approche de "loop engineering" à la mode en ce moment pour que son agent IA compile, exécute, teste les instructions et le code de son codelab, pour qu'il soit complètement fonctionnel Gain estimé : passer de 2 jours de travail à moins de 2 heures ! Redeploying Claude Fable 5 anthropic.com/news/redeploying-fable-5 Anthropic a annoncé le rétablissement de l'accès à ses modèles Claude Fable 5 et Mythos 5, qui avaient été suspendus suite à des restrictions d'exportation imposées par le gouvernement américain le 12 juin 2026. Cette suspension faisait suite à un rapport d'Amazon démontrant une méthode pour contourner les garde-fous de Fable 5, lui permettant d'identifier et d'exploiter une vulnérabilité logicielle (un jailbreak). Pour y remédier, Anthropic a renforcé ses mécanismes de sécurité en déployant un nouveau classifieur capable de bloquer cette technique spécifique dans plus de 99 % des cas, acceptant en contrepartie une augmentation des faux positifs sur des requêtes bénignes. Face à l'absence de consensus sur l'évaluation des jailbreaks, Anthropic s'associe à Amazon, Microsoft, Google et d'autres partenaires pour développer un standard industriel évaluant la sévérité de ces failles selon quatre critères : gain de capacité, étendue du gain, facilité d'arsenalisation et découvrabilité. L'entreprise s'engage également à approfondir sa collaboration avec le gouvernement américain, notamment via des évaluations pré-déploiement, un partage rapide d'informations sur les failles, et des ressources dédiées à la recherche conjointe sur la sécurité de l'IA. Outillage La réécriture de Bun en Rust et la réaction du créateur de Zig bun.com/blog/bun-in-rust et andrewkelley.me/post/my-thoughts-bun-rust-rewrite.html Bun, le runtime JavaScript et TypeScript écrit à l'origine en Zig, a été entièrement réécrit en Rust pour des raisons de stabilité et de gestion de la mémoire. Cette migration massive d'un demi-million de lignes de code a été bouclée en seulement 11 jours grâce à l'utilisation intensive de Claude Code fonctionnant en parallèle, pour un coût d'API estimé à 165 000 dollars financé par Anthropic. Andrew Kelley, le créateur de Zig, a réagi publiquement en qualifiant l'ancienne base de code de Bun de "slop" remplie de hacks et de fuites mémoire accumulées par une course aux fonctionnalités. Kelley exprime son soulagement face à ce départ, expliquant que les plantages incessants de Bun devenaient un passif réputationnel toxique pour le langage Zig et sa fondation. Le rachat de Bun par Anthropic fin 2025 avait déjà mis fin aux donations financières de Bun envers la Zig Software Foundation, facilitant cette séparation. La nouvelle version Rust de Bun passe désormais la quasi-totalité des tests, réduit la taille du binaire et est déjà déployée de manière transparente en production dans Claude Code. Nouveautés de Git 2.55 github.blog/open-source/git/highlights-from-git-2-55 Support natif de FSMonitor sous Linux via inotify pour accélérer les commandes comme git status sur les grands dépôts Intégration de la compaction incrémentale MIDX (multi-pack index) dans git repack pour optimiser la réécriture des métadonnées Amélioration drastique des performances de génération des bitmaps et des pseudo-merge bitmaps lors des tâches de maintenance Nouvelle commande expérimentale git history fixup pour intégrer facilement des modifications locales dans un commit antérieur Possibilité d'exécuter des hooks configurés en parallèle pour optimiser le temps de build et de validation Utilisation d'un autostash automatique lors d'un git checkout -m en cas de conflit de fusion pour éviter de bloquer l'espace de travail Nouvelle commande git format-rev permettant de formater rapidement des commits reçus via l'entrée standard (stdin) Support du push simultané vers un groupe de remotes configuré Protection contre l'exécution de séquences de contrôle de terminal malveillantes via les flux de progression distants Vidocq, une réimplémentation souveraine et sans dépendance de Jakarta EE et Microprofile vidocq.dev/posts/vidocq-a-sovereign-jakarta-ee-and-microprofile-runtime Lancement de Vidocq : Runtime Java open source complet, compatible Jakarta EE Core Profile et Souveraineté numérique : Projet européen hébergé sur Codeberg, sous licences EUPL 1.2, EPL 2 et GPL 2.0. Standardisation totale : Implémentation fidèle des spécifications (CDI, REST, JSON, etc.), validée par 5 650 tests TCK officiels. Sécurité radicale : Zéro dépendance externe et aucune bibliothèque tierce. Aucune manipulation de bytecode à l'exécution (« magie » générée à la compilation via JDK 25). Compatible JPMS, AOT, GraalVM et Leyden CDS. Disponibilité : Projet en phase alpha, code et documentation accessibles sur vidocq.dev. Article complémentaire qui revient sur la genèse de Vidocq, en utilisant l'IA et les TCKs pour driver l'aspect spec-driven development vidocq.dev/posts/the-story-of-vidocq Le "selfware" : Guillaume s'est fait plais' en vibe-codant son propre éditeur de texte glaforge.dev/posts/…/selfware-building-my-own-text-editor-without-knowing-swift Concept de « Selfware » : création de logiciels conçus exclusivement pour soi-même, sans monétisation ni contraintes liées aux utilisateurs tiers. Le rôle de l'IA : les agents de programmation (comme Antigravity) suppriment la barrière technique de l'apprentissage des langages (Swift, APIs) pour les non-développeurs. Développement minimaliste : privilégier la performance et l'utilité directe (démarrage instantané, interface native) au détriment des fonctionnalités complexes (plugins, télémétrie, gestion de comptes). Absence de pression : libération des contraintes liées à la compatibilité, à la maintenance logicielle et aux retours utilisateurs ; le logiciel n'a besoin d'être « assez bon » que pour ses propres besoins. Incitation à l'autonomie : encourager la création d'outils sur mesure pour résoudre les frictions quotidiennes plutôt que de subir les limitations des logiciels commerciaux. Architecture Le cobol a donné un uppercut au microservices https://freedium-mirror.cfd/@maahisoft20/your-microservices-lost-to-cobol-let-that-sink-in-8ce2e236d007 Retour d'expérience sur la migration d'un système COBOL vers des microservices cloud-native qui s'est soldée par un retour en arrière après avoir constaté que le traitement batch initial était plus rapide, moins cher et plus fiable Là où le batch COBOL traitait 2.4 millions d'enregistrements en 11 minutes, le système distribué modernisé à base de message queues, retries et Kubernetes prenait 47 minutes et tombait sous la charge COBOL brille par ses caractéristiques conçues spécifiquement pour la finance comme le calcul décimal précis sans floating point errors et l'absence totale d'overhead réseau, de conteneurs ou de cold starts Rappel que distribuer un système multiplie les points de défaillance silencieux et complexifie la gestion de la cohérence transactionnelle par rapport à une exécution locale séquentielle Une invitation à se demander si les projets de décomposition en microservices apportent réellement un gain de performance de bout en bout pour l'utilisateur final ou s'ils optimisent seulement le diagramme d'architecture Méthodologies Ma meilleure question d'entretien Spring beaufume.fr/articles/spring-interview Florian beaufumé partage sa question d'entretien favorite pour évaluer des développeurs Spring de niveau intermédiaire à avancé : "Que pouvez-vous me dire sur le paramètre spring.jpa.open-in-view ?". Ce paramètre détermine l'activation du pattern Open Session In View (OSIV) qui, lorsqu'il est à true (la valeur par défaut dans Spring Boot), maintient l'un EntityManager JPA ouvert durant toute la requête HTTP. Si l'OSIV facilite le développement en évitant les fameuses LazyInitializationException lors de la sérialisation des entités en JSON, il pose d'importants problèmes de performance en provoquant des requêtes SQL non maîtrisées (comme le problème du N+1 select) en dehors de la couche service. Maintenir l'OSIV actif augmente également le temps de rétention des connexions au sein du pool de la base de données, limitant la scalabilité de l'application. La recommandation est de désactiver ce comportement en le positionnant à false, et de gérer explicitement le chargement des données requises au sein des transactions (via des DTOs, des requêtes JOIN FETCH ou des Entity Graphs) pour garder le contrôle sur les accès à la base de données. 10 points à retenir du rapport AI Engineering 2026 : The Acceleration Whiplash faros.ai/blog/ai-acceleration-whiplash-takeaways L'IA a franchi un cap et est devenue l'auteur principal du code : le taux d'acceptation du code généré est passé de 20% à 60% dans les équipes étudiées par Faros AI. La vélocité métier est bien réelle, avec une augmentation de 66% des epics livrées et une hausse de 33,7% du throughput des tâches par développeur. Ce volume cache un code churn massif (+861%), ce qui signifie qu'une quantité énorme de code est supprimée ou remplacée peu après avoir été ajoutée. La qualité en aval se dégrade fortement : les bugs par développeur ont augmenté de 54% et le nombre d'incidents par pull request a explosé de 242,7%. Le processus de code review est complètement saturé, entraînant un temps médian de relecture multiplié par cinq et une augmentation de 31,3% des PRs mergées sans aucune revue. Le système repose de plus en plus sur les développeurs seniors qui subissent une "senior engineer tax", devant relire un volume insoutenable de code à l'apparence correcte mais structurellement fragile. Contrairement à certaines hypothèses récentes de DORA, une forte maturité DevOps ne protège pas les entreprises contre cette détérioration ; le "Acceleration Whiplash" frappe de la même manière les équipes très performantes. En résumé, les outils d'IA inondent les pipelines de livraison avec un volume de code pensé pour un rythme machine, alors que les systèmes de vérification reposent toujours sur un rythme de validation humain. Loi, société et organisation Le coût d'une equipe d'engineering qui ne sait plus ce qu'elle fait dans un contexte d'augmentation de coût des coding agents https://freedium-mirror.cfd/@developer_programmer/i-spent-47-000-on-claude-code-in-90-[…]-asked-me-one-question-and-i-couldnt-answer-it-af3b203f81bb Une équipe de 8 ingénieurs a vu sa vélocité de développement exploser en utilisant Claude Code de manière intensive, jusqu'à recevoir une facture d'API salée de 47 213 $ pour seulement trois mois d'utilisation. Face à cette dépense, la question piège du CTO n'était pas sur le montant, mais sur la dépendance : "Si nous arrêtions Claude Code demain, combien de temps faudrait-il pour que notre vélocité revienne à son niveau initial ?". L'auteur s'est rendu compte qu'il était incapable de répondre car son équipe, en particulier les profils juniors, avait commencé à perdre l'habitude de concevoir et d'implémenter des fonctionnalités complexes sans l'aide permanente d'un agent. Le deuxième risque stratégique soulevé est celui de la dépendance tarifaire et du vendor lock-in : si l'outil devient une infrastructure indispensable au quotidien, l'entreprise perd tout pouvoir de négociation face aux augmentations de prix de l'éditeur d'IA. Pour éviter que l'IA ne devienne une béquille qui atrophie les compétences de l'équipe, l'article suggère de poser des limites budgétaires strictes, d'organiser régulièrement des sprints sans IA ("AI-free sprints") et de concevoir des processus de développement portables. Retour de Nicolas Delsaux sur jqwik qui donne une perspective plus complète concernant jqwik, il me semble que vous oubliez (comme tous les gens qui parlent de LLM dans "l'industrie") que l'auteur n'a pas fait ça juste pour faire chier le monde, mais parce que ces outils ont des externalités incroyablement négatives, ce dont l'auteur s'explique dans son blog (blog.johanneslink.net/2026/06/09/the-jqwik-anti-ai-affair) Vous oubliez également de signaler que le ticket (github.com/jqwik-team/jqwik/issues/708) par lequel un utilisateur se plaint de cette fonctionnalité a été écrit par un agent. N'oubliez pas non plus que l'enthousiasme pour ces technologies n'est en fait pas universel, et que ces technologies sont loin d'être inévitables (les gains de vitesse ne sont, d'après circle CI - circleci.com/resources/2026-state-of-software-delivery, pas des gains de productivité ) OkHttp, Okio, Retrofit et SQLDelight rejoignent Commonhaus ! commonhaus.org/activity/315.html La fondation Commonhaus, via une publication de Andres Almiray, annonce l'arrivée de quatre projets majeurs de l'écosystème Java et Kotlin : OkHttp, Okio, Retrofit et SQLDelight. Ces projets, initialement créés chez Square (devenu Block), sont désormais regroupés et gérés sous la bannière lysine.dev au sein de la fondation. Jesse Wilson et Jake Wharton, créateurs et mainteneurs historiques de ces outils, rejoignent Commonhaus en tant que leaders de lysine.dev. Suite à leur départ de Block, ils expliquent avoir choisi Commonhaus pour offrir à leur immense communauté d'utilisateurs un cadre de gouvernance pérenne, stable et digne de confiance. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 28-30 août 2026 : State of the Map - Champs-sur-Marne (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 10-11 septembre 2026 : Nantes Craft - Nantes (France) 17 septembre 2026 : dotAI - Paris (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 18 septembre 2026 : dotJS - Paris (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 22 septembre 2026 : Salon Data 2026 - Nantes (France) 22-23 septembre 2026 : Agile en Seine & IA 2026 - Paris (France) 24 septembre 2026 : aMP Day Montpellier 2026 - Montpellier (France) 24 septembre 2026 : OWASP AppSec Days France 2026 - Paris (France) 24 septembre 2026 : PlatformCon Paris - Paris (France) 24 septembre 2026 : React Native Connection 2026 - Paris (France) 24-26 septembre 2026 : Paris Web 2026 - Paris (France) 25 septembre 2026 : SAP Inside Track Paris 2026 - Paris (France) 28-29 septembre 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 2 octobre 2026 : DevFest Perros-Guirec 2026 - Perros-Guirec (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 8-9 octobre 2026 : Forum PHP 2026 - Marne-la-Vallée (France) 12 octobre 2026 : Dev With AI - Paris (France) 22-23 octobre 2026 : Agile Tour Bordeaux 2026 - Bordeaux (France) 26 octobre 2026 : Agile Tour Montpellier - Montpellier (France) 27-29 octobre 2026 : Directions EMEA 2026 - Paris (France) 29-30 octobre 2026 : Campus Agile Grenoble - Grenoble (France) 29-30 octobre 2026 : BDX I/O 2026 - Bordeaux (France) 29-30 octobre 2026 : Agile Tour Nantais 2026 - Nantes (France) 29 octobre 2026-1 novembre 2026 : Pycon FR - Biarritz (France) 30 octobre 2026 : Cloud Nord 2026 - Lille (France) 4-5 novembre 2026 : Devoxx Morocco - Casablanca (Morocco) 14-15 novembre 2026 : Capitole du Libre - Toulouse (France) 19 novembre 2026 : DevFest Toulouse 2026 - Toulouse (France) 19 novembre 2026 : Agile Laval 2026 - Laval (France) 19 novembre 2026 : OVHcloud Summit - Paris (France) 19 novembre 2026 : Codeurs en Seine - Rouen (France) 27 novembre 2026 : DevFest Paris 2026 - Paris (France) 1-3 décembre 2026 : Apidays Paris - Paris (France) 2-3 décembre 2026 : Cloud Native AI Summit Europe - Paris (France) 4 décembre 2026 : DevFest Lyon 2026 - Lyon (France) 4 décembre 2026 : DevFest Dijon 2026 - Dijon (France) 9-10 décembre 2026 : OpenSource Expérience - Paris (France) 9-10 décembre 2026 : DevOps REX - Paris (France) 10 décembre 2026 : KCD Provence - Aix-en-Provence (France) 10 décembre 2026 : DevCon 28 : sécurité | post-quantique | hacking édition 2027 - Paris (France) 14-16 janvier 2027 : SnowCamp 2027 - Grenoble (France) 7-9 avril 2027 : Devoxx France 2027 - Paris (France) 3 juin 2027 : Cloud Native Days France 2027 - Paris (France) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

Our West Executive Pastor, Ryne Isaac, continues week seven of our teaching series, Summer in the Psalms.

Your expectations affect your experiences far more than you might imagine.Donate to Moody Radio: http://moodyradio.org/donateto/todayssinglechristianSee omnystudio.com/listener for privacy information.

In conjunction with the ILRN conference, which starts later this week in Athens, I am hosting a limited series I'm calling "Immersive Learning in the Cradle of Western Civilization."  I'll be talking with educators, developers, business leaders, museum leaders, and others from across Greece to highlight all of the amazing things that are happening in immersive learning in and around Greece.  In this episode, I speak with Georgios Gkoutis about this work "Teachers' Insights into Mixed Reality in Education: Challenges, Opportunities, and Limitations"

We were at the dress rehearsal of BIRDY, one Hung Dance's most critically acclaimed productions, which will be premiering in New York at NYU Skirball on July 17 & 18th. BIRDY explores the relationship between freedom, limitation, and identity through a distinctive movement language inspired by Tai Chi, traditional Peking opera and contemporary dance. Related Links: https://talkingtaiwan.com/birdy-explores-universal-themes-of-freedom-limitation-and-identity-relevant-to-taiwan-ep-353/ We sat down to speak with Lai Hung-Chung, Choreographer and Artistic Director of Hung Dance about his artistic vision for BIRDY and Wei-Ping Chen the dance company's Operations Director. Lai told us that the themes of freedom, limitation, and identity not only reflect his personal struggles but also relate to Taiwan's situation.   Hung Dance is an award-winning Taiwanese contemporary dance company founded in 2017. The troupe's name comes from the Chinese character 翃 (Hóng), meaning to fly.   Related Links: https://talkingtaiwan.com/birdy-explores-universal-themes-of-freedom-limitation-and-identity-relevant-to-taiwan-ep-353/

Grazia Aleppo, MD, discusses the evolving role of continuous glucose monitoring (CGM) in non-insulin-treated Type 2 diabetes, drawing from her recent review published in Diabetes Technology and Therapeutics that examines evolving evidence and real world implications.Practical considerations for integrating CGM into routine care are also reviewed, with an emphasis on early intervention and data-driven decision-making.Key Topics• Evidence supporting CGM use in non-insulin-treated type 2 diabetes • Limitations of A1C and the role of time in range • Impact of CGM on patient engagement and self-management • Clinical and real-world outcomes associated with CGM adoption • Practical applications for endocrinology and primary careLearn more about Northwestern Medicine Endocrinology 

So I got this comment on my last video about Brittany — the one where she made $50,000 in 30 days off five DMs selling a $10,000 offer. ”Nobody's paying you $10,000 for that. I'm calling total BS on this one.” ...If you say so. Here's the thing — this comment isn't dumb. It's actually really common. And it comes from a broke belief that's keeping a lot of you stuck right where you are. So let's talk about it — because he's not wrong that it sounds crazy. He's wrong about why. Chapters 00:00 Introduction: Debunking the Myth of Success01:01 Brittany's Success Story: $50,000 in 30 Days02:22 The Two Responses to Success Claims03:19 Addressing Skepticism About High-Ticket Offers04:14 The Belief System Behind 'BS' Claims05:22 Imagination Limits and Possibilities07:15 The Power of Visualizing Success08:16 Overcoming Self-Imposed Limitations12:24 Trust as the Foundation of Business13:22 Fighting for Limitations or Success15:49 Building Trust Through Consistent Effort17:53 The Role of Imagination in Achieving Goals20:44 The Danger of Personal Experiences as Universal Rules22:31 Expanding Your Imagination to Unlock Possibilities24:42 Final Thoughts: Demand More for Your Life

Arguing for Your Limitations: 9 Self-Limiting Beliefs Keeping You Stuck in OCD, Anxiety & FearIf you feel like you've been "trying to heal" for years but keep landing in the same place, this episode is for you. Matt Codde, LCSW breaks down the most common ways we unconsciously build a case for why we can't change, grow, or heal, and why admitting "I was wrong" might be the most powerful phrase in your recovery.This isn't about blame. It's about awareness. Matt walks through nine self-limiting beliefs he's seen (and lived through himself) that keep people trapped in OCD, anxiety, fear loops, and chronic suffering, from blaming time and money to hiding behind personality, family, and spirituality.If you or someone you love has been stuck in the OCD or anxiety cycle and can't figure out why nothing seems to stick, this conversation will help you see what's really in the way.

When a child grows up with a sibling with a disability, what defines their childhood? Author Brian Trapp reflects on life with his twin brother with cerebral palsy. Brian shares how his childhood was a source of unexpected gifts, deep connection, and lifelong formation. He joins Amy Julia Becker to consider:Limits of labels like “glass child” and “mental age”Family and flourishing togetherThe complexity of identitySibling and caregiving relationshipsRange of Motion by Brian TrappS10 E9: 00:00 The Bond of Brotherhood: Growing Up with Disability 08:55 Glass Child Syndrome and Sibling Experiences 18:23 Analogies for Family Life with Disability 23:27 The Complexity of Identity within Relationships and Caregiving 32:07 Fiction vs. Memoir: Exploring Personal Narratives 35:31 Understanding Mental Age and Its Implications 40:23 Reimagining Disability: A House of LaughterMENTIONED IN THIS EPISODE:Cognoscenti essay by Brian Trapp: My twin brother was disabled, but I don't consider myself a 'glass child'Julia Miele Rodas' essay: “Limited Visibility; Or Confessions of a Satellite”Range of Motion: A Novel by Brian Trapp_SUBSCRIBE to Amy Julia's Substack: amyjuliabecker.substack.comWATCH this conversation on YouTube: Amy Julia Becker on YouTubeJOIN the conversation on Instagram: @amyjuliabeckerLISTEN to more episodes: amyjuliabecker.com/shows/_ABOUT OUR GUEST:Brian Trapp is the author of Range of Motion. He is director of Disability Studies at the University of Oregon, where he also teaches creative writing and edits the Northwest Review. His essay about being a sibling to his twin brother Danny was featured on NPR's Here and Now. Find out more at: https://briantrappwriter.com/__We want to hear your thoughts. Send us a text!Connect with me:InstagramFacebookYouTubeWebsiteThanks for listening! 

A CMO Confidential Interview With Aarron Spinley, Director at the Field Bell Institute and Author of "The Customering Method." Aarron discusses his belief that customer management deserves its own discipline as part of marketing, that understanding customer needs is the foundation of sales, and why popular metrics can be misleading. Key topics include:- Why you should "learn the asset versus learn instead of the measures"- The need to focus on "unnatural churn"- The difference between automation and serviceTune in to hear why he's not fully on board with NPS and a story about "trendy rabbit holes." ⏱️ Chapters00:00 - Introduction to CMO Confidential and Guest Aarron Spinley01:23 - Defining Customer Experience and "Customering"04:35 - Limitations of Marketing Concepts and Survey Data in Customer Management06:53 - Drivers of Customer Loyalty vs. Net Promoter Score (NPS)10:11 - The Relationship Between Customer Satisfaction and Loyalty in Banking12:30 - The Correct Sequence of Customer Management: Asset, System, Measurement13:59 - Market Orientation vs. Sales Orientation within a Customer Base27:28 - What Effective Customer Interaction Looks Like30:02 - The Role of AI and Centralized Decisioning/Orchestration32:52 - Career Advice: Focus on Fundamentals Over Tools34:36 - Conclusion and Closing RemarksSubscribe for weekly episodes featuring world-class marketing leaders, board members, and C-Suite executives.#CMOConfidential, #MarketingLeadership, #BrandStrategy, #CorporateActivism, #MarketingStrategy, #CMO, #AIinMarketing, #ExecutiveLeadership, #BrandReputation, #ConsumerTrust, #DigitalMarketing, #MarketingInsights, #ThoughtLeadership, #BusinessStrategy, #CustomerCentricSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Dr. Alan Castel, PhD, is a professor of psychology at the University of California, Los Angeles (UCLA) and one of the world's foremost experts on human memory and cognitive aging. We discuss what science actually tells us about how to improve our learning ability and memory at any age. We also discuss how memory works, why all planning and imagination about the future is based on the past, false memories, and how to leverage curiosity, emotion, and self-testing retrieval practice to stamp in memories for the long term. We discuss what "superagers"—people who actually improve their cognitive capacity with age—do differently than everyone else. This episode is for anyone interested in the science of memory and tools to maintain and improve your memory across the lifespan. Thank you to our sponsors AG1: https://drinkag1.com/huberman Wealthfront*: https://wealthfront.com/huberman Helix Sleep: https://helixsleep.com/huberman Function: https://functionhealth.com/huberman Lingo: https://hellolingo.com/huberman Timestamps (00:00:00) Dr. Alan Castel (00:02:41) What Is Memory?, Reconstruction & Metacognition (00:04:49) Mnemonics, Remembering Names & Deeper Learning (00:08:22) The Penny & Apple Logo, Noticing vs Seeing, Learning Through Mistakes (00:10:43) Sponsors: Wealthfront & Helix (00:14:05) Neuroplasticity, Frustration, Curiosity & Mindset (00:17:42) Maintaining vs Learning New Things, Habits, Novelty & Emotional Memory (00:24:28) "Mental Photographs," Photo-Taking & Imagining the Future (00:29:28) Eyewitness Memory, the Ronald Cotton Case, Confidence vs Accuracy (00:35:07) Medium-Term & Prospective Memory, Hotel Fire Exits (00:40:28) Sponsor: AG1 (00:41:47) When Habits Turn Lethal, Aviation & Human Error (00:49:01) Why Memory Changes With Age; Alzheimer's & the Nun Study (00:52:34) Exercise & Hippocampal Volume, Falls & Balance (00:57:14) SuperAgers & Athletes; Regret, Balance & Being Driven (01:12:08) Sponsor: Function (01:13:45) Age Stereotypes, Subjective Age & Positive Age Beliefs (01:20:02) Goals & Plans, Scams; Anterior Midcingulate Cortex & SuperAgers (01:26:23) Culture, Resilience, Blue Zones & COVID (01:29:18) Adversity, the Positivity Effect & Intergenerational Learning (01:36:31) Sponsor: Lingo (01:38:00) Limitations & Purpose; Time, Family & Connection (01:44:58) Deliberately Building Memories; the ABCs of Successful Aging (01:51:02) Following Your Interests; Castel's Path & Older Adults (01:57:16) Mental Simulations, Curiosity Studies & Selectivity (02:01:19) Socioemotional Selectivity Theory; Steve Jobs & Lifespan (02:07:10) The Secret to Successful Aging; State vs Trait Curiosity (02:11:04) Scams & AI Voice Cloning (02:14:31) John Wooden, Wisdom, Love & Balance (02:17:41) Learning Through Mistakes; Does the Brain Get Better With Age? (02:25:00) Conclusion, Better With Age (02:26:00) Zero-Cost Support, YouTube, Spotify & Apple Follow, Reviews & Feedback, Sponsors, Protocols Book, Social Media, Neural Network Newsletter Disclaimer & Disclosures *This experience may not be representative of other Wealthfront clients, and there is no guarantee of future performance or success. Experiences will vary. Andrew Huberman receives cash compensation from Wealthfront Brokerage for paid testimonials in his podcast, creating a conflict of interest. The Cash Account, which is not a deposit account, is offered by Wealthfront Brokerage LLC, member FINRA/SIPC. Wealthfront Brokerage is not a bank. The base APY is 3.30% on cash deposits as of January 30, 2026, is representative, subject to change, and requires no minimum. If eligible for the overall boosted rate of 4.05% offered in connection with this promo, your boosted rate is also subject to change if the base rate decreases during the 3 month promo period. Additional terms and conditions apply, which can be found on Wealthfront.com/Huberman. Funds in the Cash Account are swept to program banks, where it earns the variable APY. Same-day withdrawal or instant payment transfers may be limited by destination institutions, daily transaction caps, and by participating entities such as Wells Fargo, the RTP® Network, and FedNow® Service. New Cash Account deposits are subject to a 2-4 day holding period before becoming available for transfer. Investment advisory services are provided by Wealthfront Advisers LLC, an SEC-registered investment adviser. Securities investments: not bank deposits, bank-guaranteed or FDIC-insured, and may lose value. Learn more about your ad choices. Visit megaphone.fm/adchoices

What if your best customers are already sold — before you ever say a word? That's the promise of the PULL framework, and in this episode, startup advisor and author Rob Snyder breaks down exactly why smart, well-resourced founders keep pushing brilliant products onto people who aren't ready to buy — and what to do instead. If you've run the demos, done the research, and still watched prospects disappear, this episode is your wake-up call. You'll discover: Why doing everything "right" can still leave you in the pain cave The four-part PULL framework (Project, Unavoidable, List of options, Limitations) that predicts who will buy before you even pitch How to describe your product in the fewest possible words — and why less is always more Why your sales call should be a "see if they try to buy" call, not a convince-them-to-buy call How a repeatable customer success story does more selling than any pitch deck ever will Rob Snyder is a Harvard MBA, former McKinsey consultant, startup founder, and author of The Power of Pull. He has advised hundreds of startups on how to find product-market fit faster by stopping the push and building for pull. Connect with Rob: Website: robsnyder.org LinkedIn: linkedin.com/in/robsnyder Get the book: The Power of Pull — available on Amazon Learn more about the Story Cycle System™: businessofstory.com #576: The Power of Pull: Find Customers Who Are Already Sold, With Rob Snyder

Is hiring a person still your default answer to a problem? For a growing number of startups, it shouldn't be.In this episode, Yaniv Bernstein is joined by returning guest Matt Cook, co-founder of Scouut, one of Australia's most respected engineering recruiters for early-stage startups, to unpack what's changed in engineering hiring since Matt's last visit. They cover ‘team debt' (the AI-era sequel to tech debt), why AI is acting as an appetite suppressant for headcount, why the best engineers are now worth $400K+, and why the ‘software factory builder' is becoming the most sought-after hire in tech.In this episode, you will:Understand "team debt", and why teams built to solve today's problems are already becoming obsoleteLearn why "removing the crutch" (like scrapping a QA gating function) forces the kind of AI-native change that timid, incremental adjustments never willDiscover why most startups were already overstaffed before AI, and why AI now acts as an ‘appetite suppressant' for hiring rather than just an efficiency toolHear why hiring should now be treated as a last resort, and what that actually means in practice for foundersUnderstand why compensation banding and headcount-based budgeting are breaking down, and what smart companies are replacing them withLearn about the rise of the ‘software factory builder': the rare, highly-paid engineer who uses their judgment to build airtight, AI-based infrastructure the rest of the team can lean onTimestamps00:00 Coming Up...00:58 On Today's Show: Matt Cook on 'Team Debt'02:36 What is Team Debt?05:12 Refocusing on AI Native Solutions10:40 Unlearning Old Hiring Ladders12:04 Why Companies Are Oversized14:59 AI, the 'Corporate Ozempic'19:25 Layoffs and Team Composition24:02 Disrupting Yourself at Series B28:12 The Salary Race for AI Engineers34:01 Limitations of Headcount Budgeting38:16 How Downsizing Can Help You Grow41:13 ClickUp's 'Hail Mary' Pivot44:26 Software Factory Builders and 'Forward Deployed' Automation49:14 Maintaining Safe Internal Software54:25 Closing ThoughtsResources mentionedScouut (Matt Cook's engineering recruitment firm for early-stage startups): https://scouut.com.au/Matt Cook on LinkedIn: https://www.linkedin.com/in/matthewmarkcook/ 'Corporate Ozempic' by Scott Galloway — the essay behind the AI/hiring analogy Yaniv references: https://www.profgalloway.com/corporate-ozempic/The PactHonor the Startup Podcast Pact! If you have listened to TSP and gotten value from it, please:Follow, rate, and review us in your listening appSecure your official TSP merchandise at https://shop.tsp.show/Follow us on YouTube for full video episodes: https://www.youtube.com/@startup-podcast Give us a public shout-out on LinkedIn or anywhere you have a social media followingKey linksThis episode of the Startup Podcast is sponsored by .tech domains. Forget weird prefixes and creative misspellings; the availability for .tech domains is simply way better than .com. For a clean and memorable name, go to https://⁠get.tech/tspThis episode of the Startup Podcast is sponsored by Vanta. Vanta helps businesses get and stay compliant by automating up to 90% of the work for the most in demand compliance frameworks. With over 200 integrations, you can easily monitor and secure the tools your business relies on. For a limited time offer of US$1,000 off, go to ⁠⁠⁠⁠https://⁠www.vanta.com/tsp⁠⁠⁠⁠⁠ The Startup Podcast website: https://www.tsp.show/episodes/Learn more about Chris and YanivWork 1:1 with Chris: http://chrissaad.com/advisory/Follow Chris on Linkedin: https://www.linkedin.com/in/chrissaad/Follow Yaniv on Linkedin: https://www.linkedin.com/in/ybernstein/Producer: Justin McArthur https://www.linkedin.com/in/justin-mcarthurAssistant Producer: Steph Hefferan https://www.linkedin.com/in/steph-heff/Intro Voice: Jeremiah Owyang https://web-strategist.com/

In this return episode, Anika and Dick Wybrow break down the exact mindset shift required to transition between industries, why independent publishing outperforms traditional deals for genre fiction, and the counterintuitive truth about building a sustainable creative business: it's not about chasing trends—it's about finding your authentic voice and the people who love it.In This EpisodeCareer pivot from broadcast to books: How 20 years in comedy and TV prepared him for writing—and why it required complete humilityThe case for indie publishing: Why control, speed, and 70% royalties beat traditional publishing's marketing machineFinding your niche in a crowded market: Why humor is his unfair advantage against AI and how to defend your work in 2026Building a dedicated readership from zero: The power of responding to every email and comment—and why word-of-mouth beats paid adsThe narcolepsy reframe: How he transformed a "disability" into a creativity superpowerAudience growth without the hype: Why you don't announce your book—you quietly build your team of readersThe Substack experiment: Serializing a prequel, building SEO, and getting two revenue bites from one storyBalancing workaholic tendencies with life: Calendar blocking, time limits on creative tasks, and why delegation mattersFive actionable tips for aspiring authors: The exact framework for finishing your first book (no perfectionism allowed)The faith factor: How becoming a Christian shifted his storytelling toward hope and championing marginalized charactersTimestamps07:11 Learning humor writing: Mentors, analysis, and understanding structure11:35 Why independent publishing was the only option for his stories19:22 Why humor is the ultimate AI defense23:11 Finding your first dedicated readers: Authenticity and personal response31:06 Substack as serialization, SEO, and future revenue34:17 Calendar blocking and reframing narcolepsy as a creativity superpower40:09 Five tips for aspiring authors: The exact framework for finishing your book46:15 How faith shifted his storytelling toward hope and purposeKey Insights & TakeawaysInsight 1: Skill Transfer Requires Humility, Not ConfidenceDick had 20 years of broadcast success. Writing books is a different craft entirely. Rather than assuming he knew how to write, he became a student—finding mentors, analyzing structure in published works, and accepting he had "permission to be stupid" about the new medium. Confidence kills learning.Insight 2: Independent Publishing Wins for Niche VoicesTraditional publishers reject stories that are "strange" or don't fit market categories. Independent publishing gives you three critical advantages: (1) Complete creative control, (2) 3-4x faster release schedules, (3) 70% royalties instead of $1 per $14.99 book. For passionate authors with unique voices, this isn't a trade-off—it's freedom.Insight 3: Humor Is Your Unfair Advantage Against AIAI can generate content at scale. AI cannot be funny. Humor is what Dick calls "cultural voodoo"—nobody can define it precisely enough to code it. By writing humor-forward genre fiction, he's built immunity to AI competition that pure genre writers don't have. Authenticity and voice beat volume every time.Insight 4: Your Readers Are Built One Person at a TimeDick didn't build a following through viral moments. He built it by responding personally to every email and comment. Readers who feel seen become advocates. One person from 2019 (Neil) has been a loyal reader for seven years. That's lifetime value that no algorithm can buy.Insight 5: Five Steps to Actually Finish Your BookWrite the book you want to read (don't chase trends)Don't announce it (avoid the dopamine hit that kills momentum)Find 2-3 trusted readers (not family)Set a daily writing goal (even one sentence counts)Finish before you fix (first drafts are garbage—that's the point)The real permission slip: Give yourself permission to write badly. Every author writes garbage first drafts. Revision is where the work happens.Insight 6: Your Limitations Are Often Your Unfair AdvantagesDick has narcolepsy—he's half-asleep most of the time. Rather than fighting it, he reframed it as his creativity engine. Being in a dream-state means his mind wanders into places other people don't naturally go. That's where his best story ideas come from. The lesson: reframe, don't resist.Resources & Links MentionedDDUB Publishing (Dick's independent publishing company)Hell Inc. and Wolfwear series (bestselling collections)The The Facilite Con (sci-fi heist comedy, August 2026 release)Substack (serialization platform for prequel content)Facebook Ads Library (research tool for ad copy analysis)Joe Abercrombie's "The Devils" (humor writing structure reference)About Dick WybrowDick Wybrow spent 20 years as a standup comedian, radio host, and TV producer (including work on CNN's early comedy newscast with Pete Dominic). He pivoted to full-time authorship and founded DDUB Publishing, building a multi-book catalog of supernatural thrillers blended with humor. He's financially supported his wife's retirement through author earnings, relocated to New Zealand, and is now exploring teaching online from Thailand while maintaining a prolific writing schedule. He's a self-taught expert in ad copywriting, graphic design, and marketing—and is committed to mentoring aspiring authors.Connect with DickWebsite: https://www.dickwybrow.com/Social:facebook.com/dickwybrowtiktok.com/@dickwybrow_ Open to mentorship: Authors working on projects can reach out for guidanceSubstack newsletter: dickwybrow.substack.comWeekly humorous newsletter + prequel serializationRelated EpisodeRejection to Recognition: The Tenacity Behind Dick Wybrow's Best-Selling SuccessSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Doomscrolling vertical videos is just brain rot, right?

Guest: Dr. Jennifer Collins & Dr. Nadia BloemendaalWhen it comes to hurricanes, we often rely on a familiar scale to understand their strength — categories based largely on wind speed. But anyone who's experienced a storm knows that impacts go far beyond just how fast the winds are blowing. Today, we're joined by Jennifer Collins and Nadia Bloemendaal, whose work is helping to rethink how we model and measure tropical cyclones. They're the minds behind the STORM model, a powerful tool for simulating thousands of storms across the globe, and the developers of the Tropical Cyclone Severity Scale — a new way of looking at storm risk that goes beyond traditional categories. In this episode, we'll explore how large-scale modeling is changing our understanding of hurricane risk, why current classification systems may fall short, and how new approaches could improve the way we communicate danger and prepare for future storms.Chapters00:00 Introduction to the New Storm Model00:20 The Storm Model's Impact on Various Sectors01:02 Challenges in Developing a Global Storm Model02:21 Modeling Hazards Beyond Wind: Rain and Surge04:31 Motivation for a New Hurricane Severity Scale06:42 Limitations of the Wind-Only Scale08:35 The New Scale: Incorporating Multiple Hazards10:27 Break 111:28 The Concept of a Category 6 Storm13:05 Public Perception and Education Strategies15:22 Localized and Personalized Risk Communication17:27 Real-World Applications and Case Studies19:20 Future Directions and Climate Change Impacts21:54 Inertia and Adoption Challenges in Meteorology23:49 Break 225:00 Public Perception of Storm Categories27:33 The Path Toward Standardization and Adoption30:12 Future Collaborations and Final ThoughtsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

As endovascular technologies evolve, how are IRs maximizing control while minimizing metal in modern venous embolization? In this episode of the BackTable Podcast, Dr. Harris Chengazi joins host Dr. Sabeen Dhand to discuss the clinical impact, deployment techniques, and imaging advantages of modern vascular point embolization devices, focusing on targeted venous occlusion and variceal obliteration. --- Get the BackTable apphttps://www.backtable.com/app --- This podcast is supported by Okami Medicalhttps://okamimedical.com/ --- Timestamps 00:00 - Introduction02:22 - Multidisciplinary Vascular Practice Model05:51 - Understanding Plugs in Embolization08:34 - Comparing Plugs, Coils, and Liquid Embolics13:48 - LOBO Occlusive Device16:12 - Plug Sizing and Vessel Measurement20:45 - Deliverability and Catheter Selection24:28 - Limitations of Point Embolization29:03 - PARTO Access and Technique32:46 - Other LOBO Use Cases34:33 - LOBO Deployment Mechanism36:23 - Cost Effectiveness of Embolics41:08 - Final Thoughts and Closing Remarks --- More about this episode Dr. Chengazi shares insights from incorporating vascular plugs into his embolization practice after training predominantly with coils. He highlights the features that distinguish modern devices like the LOBO from traditional plugs, including their unique nitinol weave, rapid occlusion, and predictable sizing and landing configuration. The physicians explore the utility and limitations of plugs in clinical scenarios including retrograde transvenous variceal obliteration, antegrade variceal embolization during TIPS creation, and tract embolization following percutaneous procedures. Dr. Chengazi furthermore shares specific procedural pearls for point embolization of varices, explaining how to facilitate deployment using a parallel access technique as well as how to position multi-lobed plugs across venous outflow segments to reliably treat large shunts with a single device. Finally, the physicians review the benefits of reduced imaging artifact on follow-up scans compared to traditional coil packs and liquid embolics as well as the impact of modern plug technology on healthcare costs, reinforcing the value of evidence-based device selection for optimal patient outcomes. --- Resources Kundaragi NG, et al. Measurement of Inferior Vena Cava to Shunt Distance in Deciding Access Route for Balloon-Occluded Retrograde Transvenous Obliteration Procedure: A Pilot Study. Am J Interv Radiol. 2018 Sep 19;2(16):1-9.https://dx.doi.org/10.25259/AJIR-22-2018 --- BackTable Vascular & Interventional (VI) is the go-to podcast for interventional radiologists, vascular surgeons, and interventional cardiologists. Download the free BackTable app to get early access to new episodes, cases, and courses curated by physicians in your specialty. ► https://www.backtable.com/app

Lately, I've been thinking a lot about identity and how we lose pieces of ourselves over time, and what it looks like to slowly come back to who we've always been. In this episode, I'm sharing what I've been rediscovering about myself, why old things have been speaking to me so deeply, and how I'm creating a home with secondhand pieces.I'll also share practical tips for finding the best vintage pieces, defining your own style, working with limitations, using what you already have, and why paint might just be the most underrated tool in your home. This is part reflection, part practical guide, and a little love letter to old things, imperfect homes, and becoming more yourself.Podcast Episode Highlights:Thinking about our identities and coming back to ourselvesWhat I've rediscovered about myself recentlyWhat I've been finding latelyMy thoughts on old thingsTip #1: where I'm buying the best piecesTip #2: figuring out a styleTip #3: Limitations as a guide and not a roadblockTip #4: Look around your homeTip #5: Paint is the bestTip #6: Ask aroundFinal thoughtsResources Mentioned in This Podcast Episode:Stay tuned for my new cookbook: The Old-Fashioned on Purpose Cookbook: Timeless Recipes That Fit Your Modern Life, coming SOON in October 2026!!Find photos of some of my recent old finds in this post: https://www.theprairiehomestead.com/2026/06/the-girl-who-saw-treasure-in-junk.htmlOTHER HELPFUL RESOURCES FOR YOUR HOMESTEAD:Sign up for weekly musings from my homestead: https://jillwinger.substack.com/Get my free homesteading tutorials & recipes here: www.theprairiehomestead.comJill on Instagram: @jill.wingerJill on Facebook: http://facebook.com/theprairiehomesteadApply to be a guest on the Old-Fashioned on Purpose podcast: https://www.theprairiehomestead.com/podcast-guest-applicationDid you enjoy listening to this episode? Please drop a comment below or leave a review to let us know. This can help other folks learn about this podcast and we also really appreciate the feedback! 

Homily from the Thirteenth Sunday in Ordinary Time The necessary risk we must take. Jesus calls us to love Him first. To place Him ahead of every other goal or desire in our lives. In order to answer the question "What are you living for?", we have to take the risk of knowing what we are NOT living for. Mass Readings from June 28, 2026:2 Kings 4:8-11, 14-16a Psalm 89:2-3, 16-17, 18-19Romans 6:3-4, 8-11 Matthew 10:37-42