Podcasts about Yolo

In this episode, we dive into the evolving perception of what it means to be "working class" in today's world, especially when even a $155,000 household income can feel inadequate. We peel back the layers of spending, lifestyle inflation, and the role our values play in shaping financial satisfaction. Through our own candid conversations we expose the difference between needs and wants and explore how redefining "enough" can lead to better financial well-being.Key MomentsWe react with surprise to the idea that $155,000 a year is now considered "working class" by some, and break down what that income really looks like after taxes at 00:08.We discuss how consumer habits and constantly moving lifestyle goals make it hard to feel like any amount is enough at 01:32.We emphasize the impact of distinguishing between necessities and luxuries, like food versus dining out, at 10:03.We open up about our own spending patterns, budgeting after being debt-free, and the real costs behind "YOLO summer" at 14:08 and 18:28.We explore the emotional side of helping our kids and how our own goals and financial attitudes have shifted over time at 17:05 and 19:44.We lay out why constant budget adjustments and reality checks are essential to staying grounded, whether you're living on $155,000 or any other income, at 29:31 and 30:17.We offer actionable takeaways on how to become an "artist of your own finances" and challenge the listener to define what truly makes their money worth it at 32:24.Why You Should Tune InIf you've ever looked at your income and thought, “Why doesn't this feel like enough?”, this episode will help you make sense of that feeling and offer practical ways to regain control over your finances. We open up about our real struggles and wins, so you can see how shifts in mindset and conscious choices can transform your financial reality. Whether you're aiming to escape debt, live within your means, or set the groundwork for generational wealth, we keep it real, practical, and relatable.Who This Episode Is ForThis episode is for anyone who's questioning if their salary is “enough,” struggling with lifestyle creep, or trying to align their spending with their true values and goals. Whether you're building your budget for the first time, navigating big life transitions, or simply wanting to be more intentional with your money, we made this episode with you in mind. If you want your finances to work for you not the other way around this conversation is your starting point.Our website: www.forbetterandworth.comGet Ericka's book, Naked and Unashamed: 10 Money Conversations Every Couple Must Have Check out our local TV spotlightConnect with us:Instagram: @forbetterandworthYouTube: @forbetterandworthEricka: @erickayoungofficialChris: @1cbyoung

In this episode, Jo Hooper shares what happened when relentless ambition and work addiction led to two breakdowns. We talk about the difference between burnout and breakdown, the warning signs Jo missed, and what recovery has looked like for her. We also get into boundaries, self-worth, and how Jo has rethought what success means. This is a must-listen if you're the person who keeps saying “I'll slow down after this...” … then somehow there's always a next thing to do.    ABOUT THE SHOW: Things I Wish I'd Known is a podcast that aims to create positive change in your life through conversation. Host Rachael shares her knowledge, emotions, and laughs, covering everything from mental health, suicide, and spirituality to makeup and skincare. With each episode, Rachael and her guests offer practical advice to help you master your mental health, build self-trust and fall back in love with yourself. Join the conversation by tagging @rachael_welford or @thingsiwishidknownpod on Instagram, TikTok and Facebook. For more information on Rachael, visit her website www.rachaelwelford.com.    ABOUT JO: Jo Hooper is a YOLO business coach - you only live once and she helps you live your life, not work it.  After working herself into two breakdowns in corporate, she made her business easy from day one and is on a mission to convince every working person that life is too short to waste it working. www.getwildlyfree.co.uk Join Jo's Free workshop

En tant qu'entrepreneur ou cadre dirigeant, vous avez probablement tout structuré dans l'entreprise et délégué aux bonnes personnes de votre équipe. Qu'en est-il de votre vie personnelle ?Vie perso du dirigeant, notre nouvelle série, consacre quatre épisodes à cet angle mort : le quotidien et la charge mentale, la santé, la structuration juridique de la vie privée. Notre première invitée, Rebecca Fischer-Bensoussan, a passé quinze ans dans la finance, jusqu'à faire partie du comité de direction de Groupama Asset Management à 34 ans. Mais en 2022, elle a pivoté radicalement pour cofonder Yolo avec Camille Agon.  Le principe ? Un collectif d'alliées, une communauté qui prend en charge la vie réelle des dirigeants et des salariés. Quatre ans plus tard, une cinquantaine d'entreprises clientes dont une quinzaine de groupes du CAC 40 et plus de 30 000 demandes traitées.  Dans cet épisode, Aurore Perrin aborde avec elle : pourquoi déléguer sa vie pro est banal quand déléguer sa vie perso reste un aveu d'échec ce que des centaines d'accompagnements révèlent des habitudes des dirigeants le coût réel de la charge mentale pour une entreprise, chiffres à l'appui pourquoi les femmes dirigeantes détiennent souvent de l'equity et rien d'autre ses propres arbitrages d'organisation, ceux qu'elle assume et ce sur quoi elle a lâché Une heure sur ce qui se joue en dehors du bureau, et qui impacte pourtant de ce qui s'y passe. Dans le prochain épisode, nous poursuivons la série avec un autre pilier de la vie du dirigeant : sa santé.Ressources complémentaires Divorce d'un entrepreneur : guide de protection du patrimoine et de l'entreprise https://sapians.com/blog/divorce-entrepreneur Prévoyance du dirigeant d'entreprise : sécuriser son entreprise, protéger sa famille https://sapians.com/blog/prevoyance-dirigeant Quelle solution de gestion de patrimoine choisir quand on dirige une entreprise ? https://sapians.com/blog/solutions-gestion-patrimoine-entrepreneur Pour quelles raisons investir dans une assurance-vie ? https://sapians.com/blog/pourquoi-investir-assurance-vie-avantages-inconvenients ------------------------------Attention : Les performances passées ne préjugent pas des performances futures et investir comporte des risques de perte partielle ou totale en capital. Ce contenu est informatif et ne constitue pas un conseil en investissement. Toute décision doit être adaptée à votre situation. Si vous souhaitez bénéficier de conseils personnalisés, veuillez créer votre compte ou prendre rendez-vous avec un conseiller Sapians.SAPIANS - RCS n°919 330 969 - ORIAS n°23003561 en qualité de CIF et COA. Activité de démarchage bancaire et financier.

Read along to learn the English phrases YOU CAN'T TAKE IT WITH YOU and YOLO!In this English lesson, I wanted to help you learn the English saying, "You can't take it with you." When you say you can't take it with you in English, it's a humorous way to talk about the fact that when you die, it's a loud train, eh? Can you guys hear that? I'll just keep going.It's a humorous way to talk about the fact that when you die you can't take your house, your car or your money with you. And so it's kind of suggesting that you should either, a) Spend all of it before you die, or what I think is a better idea, b, give away generously some of the money to causes that need it.So you can do either, and the phrase can mean the same thing for both. You can't take it with you simply means, well, why are you saving all of your money for a later day when eventually you're going to die and you can't take that money with you to whatever lies beyond. The second phrase I wanted to teach you today is YOLO.It's actually an acronym. I think I might have taught this one before, but YOLO is kind of like a young person's way of saying you only live once or just have a really good time because you're gonna die eventually. You only live once. YOLO.You'll hear this sometimes when you watch TV or other shows. So to review, you can't take it with you. A humorous way to talk about money and things and material possessions. You can't take it with you, so you might as well enjoy them while you're here. And my suggestion would be is if you can afford it, donate to worthy causes.And the second one is YOLO. Sorry if you're wondering what all the noise is, we now have a garbage truck going by. YOLO means you only live once, so enjoy life as much as you can. By the way, did you see the there's an actual barber pole here.That's kind of that's kind of a Blast from the past, but hey, let's look at a comment from a previous video. This comment is from Unsal. I was wondering how common are fences in residential areas in Canada? Is privacy that important for most homeowners, or are fences mostly used to mark property lines?Just to mark property lines. Thanks for another great lesson. You're awesome. Have a great day. Bye. My response: Very common. It is usually the first thing a homeowner builds when buying a new house in a town or city. They are far less common out in the country.So yeah, fences like people build fences, especially in residential areas, as you mentioned. I'm just going to move my tripod off to the side here. There's a few more people out and about today than I was expecting. Yes, generally every house in a town or city in the backyard will have a fence so that you have some privacy, even if the yard is really, really small.You'll have some privacy. So yeah, there's a barbershop here. So that's kind of cool. So a barbershop is traditionally a place where men would go to get their hair cut. It looks like they offer haircuts. I don't think I'm getting the sign on there very well.And all kinds of other things. So I've never been. I still just get my hair cut at home by Barber Jen. She takes care of that for me. So now it's quieted down a little bit. It was extremely busy in town. There were people on the sidewalk.There was, so we found him. There's the garbage truck. He's actually putting the garbage into the back now. And maybe as I speak, it's actually picking up a little bit again, a little bit more traffic. I'm not normally here this early in the morning.I had to bring my daughter to school, which this year she's going to drive by herself, but we just had a little bit of yeah we have enough vehicles we just aren't using them efficiently right now so anyways it's almost 4 minutes you I feel a little bit distracted this morning which is pretty normal for a Tuesday I also stayed up way too late last night so that might be having some effect. Anyways thanks for watching this short English lesson I'll see you next week with another one. Bye.

Kate Kennedy of Be There in Five is back (!!) and this week we're asking an important question: where did recession pop go? Back in the 2008-2012 time period, the economy was in shambles but we had Pitbull and Flo Rida and so many other artists giving us songs about #YOLO and going out and having a good time but now in 2026…we have a terrible economy, data centers popping up everywhere, the Epstein files out (and nothing done about them) and the music isn't giving us the bangers we needed back in the 2010s to get through the times!Join us for this episode as we break it all down - and have many a tangent about Taylor Swift Tumblr lore, Tom Cruise, the downfall of Justin Timberlake, Rihanna (could she put out an album and SAVE US in 2026?) and so much more.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Join The Full Nerd gang as they offer level-headed takes about the latest PC building news. In this episode the gang goes over all the Steam Frame reviews, Adam's testing with Pop OS and the Cosmic desktop, and much more. And of course we answer questions live! *Note: Adam screwed up the OBS recording on this YOLO edition, so it's audio only for this episode. Timecodes: 00:00:00 - Intro 00:03:31 - Steam Frame reviews 00:20:41 - Pop OS/Cosmic testing 00:36:49 - Alaina's security update 00:56:23 - The state of the PC market minute 00:57:51 - Q&A Join the PC related discussions and ask us questions on Discord: https://discord.gg/UWhjwg778a Follow the crew on X and Bluesky: @AdamPMurray @BradChacos @MorphingBall Music by Our Ghosts: https://ourghosts.bandcamp.com/ Some links may contain affiliate links, which means if you buy something PCWorld may receive a small commission. ============= Read PCWorld Website: http://www.pcworld.com Newsletter: http://www.pcworld.com/newsletters ============= Learn more about your ad choices. Visit megaphone.fm/adchoices

Cet été et jusqu'à mi septembre 2026, Connecting Leaders met à l'honneur une sélection d'épisodes BEST OFF : une sélection de conversations pour prendre du recul, questionner votre manière de diriger et nourrir une vision plus engagée du leadership. Bonne écoute !Dans cet épisode de Connecting Leaders, je reçois Rebecca Fischer‑Bensoussan, cofondatrice de YOLO (live care / soutien organisationnel du quotidien), ex‑finance (Morgan Stanley, Goldman Sachs) et mère de trois enfants.Ici, on ne parle pas “parentalité en général” : Rebecca partage des scènes très concrètes de sa trajectoire - de la PMA vécue en silence (ordi à l'hôpital à 6h, injections au bureau) jusqu'au déclic Covid (3 enfants dont un bébé + 60 personnes à manager) où la charge invisible devient ingérable.Elle explique pourquoi les femmes ne manquent pas d'ambition, mais de conditions - et pourquoi la parentalité (et l'aidance) est un sujet business : rétention, performance, engagement, RPS.On parle notamment :◾️ Du “Woman Empowerment” en finance : oser plus tôt, se vendre, ne pas attendre d'avoir coché 10 cases◾️ De la double exigence : performer comme si on n'avait pas de vie perso… et gérer la vie perso comme si on n'avait pas de carrière◾️ Du retour de congé maternité comme nouvelle prise de poste (et ce que les entreprises ratent)◾️ De l'aidance (souvent non déclarée) et de ce qu'elle coûte en bande passante◾️ De YOLO : un “double organisationnel” humain, confidentiel, et piloté par des KPI d'impact (temps retrouvé, engagement)Un épisode concret et singulier pour les dirigeants, managers et RH qui veulent concilier performance durable et réalités de vie - sans injonctions.Retrouve Rebecca Fischer‑Bensoussan sur LinkedIn : https://www.linkedin.com/in/rebecca-fischer-bensoussan/

I sat down with Michael Kantor, president of HOL (Hashgraph Online). Before HOL, he was a senior full-stack engineer and then an engineering lead at Finder. He founded a company called KiloStripe. He also sits on the Hiero Technical Steering Committee at the Linux Foundation, where HOL contributes open standards. So when he talks about AI agents going rogue, it comes from building the infrastructure himself, not from a slide deck. I brought him one question. What happens when your agents stop being tools you use and start doing work on their own? And how do you keep them from wrecking things? What came out was a blunt tour of the risks nobody warns you about. Michael and his team built HOL Guard because they had so much anxiety letting Codex build their own infrastructure. This one is for anyone handing Claude Code or Codex to their team and quietly hoping nothing breaks.What you'll see:The importance of AI and Cybersecurity in 2026Why HOL Guard sits between the agent and every tool call, so a poisoned model physically can't delete your S3 bucketThe number that started it all, Codex reading environment secrets 2 to 3 times an hour over an 8-hour jobWhy people flip AI into YOLO mode, and how per-command policy beats approving every single actionThe café WiFi attack, and why the real risk is the wire between you and OpenAI, not your laptopWhat Michael refuses to do with agents: automate email, generate blog posts, trust someone else's sandbox

Podcast for a deep examination into the career and life choices of Arnold Schwarzenegger. Patrick befriends a dolphin at Seaworld and teaches it how to play the piano. Not to be outdone, Joe trains an ocelot how to speak Spanish. Lev teaches a cat how to Yolo. Which animal goes on to lead a major Hollywood franchise based on their newly learned skill? Find out on this week's episode of 'What the Hell Happened to Them?' Email the cast at whathappenedtothem@gmail.com Disclaimer: This episode was recorded in September 2026. References may feel confusing and/or dated unusually quickly. 'Conan the Destroyer' is available on DVD & Blu-ray (as a complete journey) at: https://www.amazon.com/Conan-Complete-Barbarian-Destroyer-Blu-ray/dp/B01DVALSWE/ Music from "Late Night with Conan O'Brien theme (but different than last week's)" by... Trevor Curtis, maybe?   Artwork from BJ West   quixotic, united, skeyhill, vekeman, arnold, schwarzenegger, conan, barbarian, magic, ebert, roberts, sequel, synergy, eastwood, china, tom, jerry

The Sacramento Transportation Authority (STA) held its board meeting on September 10, 2026, with Chair Raithel presiding. Public Comment & Consent Calendar Public Comment: Joshua Arce, Executive Director of the California Alliance for Jobs, introduced his labor and business coalition representing construction unions and industry employers, expressing a commitment to partner on regional infrastructure projects. Consent Calendar: The board approved the consent calendar unanimously without discussion. Key Agenda Items Green Line Bus Rapid Transit (BRT) Feasibility Study (Item 5) Project Scope & Funding: The board approved a contract to execute a $200,000 Measure A Smart Growth allocation paired with a $500,000 Caltrans grant to study a Bus Rapid Transit (BRT) option for the 11.2-mile Green Line corridor from Downtown Sacramento to the Sacramento International Airport. Right-of-Way Protection: Kevin Schroeder, Senior Planner at SacRT, explained that 7.5 miles (67%) of the corridor consist of Irrevocable Offers of Dedication (IODs) that begin expiring in 2029. Transitioning the corridor to BRT provides a faster, lower-cost transit solution while preserving the right-of-way without precluding future light rail conversion. Board Feedback: Board members voiced support for the balanced approach. Director Talamantes emphasized protecting the right-of-way and advancing the Truxel Bridge as key priorities, while Director Kennedy noted that BRT provides a more flexible, cost-effective alternative to fixed rail. Action: The motion passed unanimously. SB1 Local Partnership Program Grant Nomination (Item 6) Project Nomination: Derek Minnema presented on behalf of the South East Connector JPA for the Grant Line Road Safety Improvement Project, seeking nomination for up to $25 million in competitive state SB1 construction funds. Safety & Infrastructure Upgrades: The project will completely reconstruct an outdated, unlit 2-lane farm road into a modern 2-lane facility featuring a center median, clear recovery zones, and a Class 1 bike path. Director Pulpati shared a personal account of surviving a rollover crash on Grant Line Road to emphasize the urgent safety need. Matching Funds & Status: The application is backed by a $25 million federal grant match, $4.9 million in Measure A funds, and $1–2 million in mitigation funds. The project is at 90% design with active right-of-way acquisitions underway. Action: The motion to nominate the project passed unanimously. Measure A Transportation Awards Program (Item 7) Program Structure: Director Bewsey presented a proposed 5-category annual awards program to highlight Measure A projects, regional partnerships, and transportation leadership. Oversight: Sitting STA board members will be ineligible for individual awards. Award recommendations will be made by the Independent Taxpayer Oversight Committee (ITOC) in conjunction with the annual audit in November. The board concurred with the proposal. Executive Director's Report & Member Comments Grant Rankings: STA's U.S. 50 Multi-Modal Corridor Connections project achieved the #1 ranking for SACOG's Solutions for Congested Corridors program, and STA was named the #1 priority for the Trade Corridor Enhancement Program. Federal grant applications were also submitted for Twin Cities Road and Stockton Boulevard BRT. Community Engagement: Staff completed four community listening sessions on transportation priorities over the summer. CARTA Update: Director Talamantes reported on upcoming managed lanes on Yolo 80 launching in late 2026. Subcommittee: The Chair opened nominations for the Transportation Funding Subcommittee to begin planning toward 2028. Closed Session The board convened in closed session and reconvened with no reportable action before adjourning.

Formosa, em Goiás, destaca-se pelo ecoturismo, com o Salto do Itiquira (168m de altura) como principal atração, ideal para trilhas e banhos no parque municipal. https://formosa.go.gov.br/turismo/Bisnau Ecoturismo RuralO Bisnau Ecoturismo Rural é um lugar para renovar as energias. Foi criado em 2015 com o objetivo de compartilhar com os visitantes a preservação e o cuidado com a natureza e o rico patrimônio histórico cultural que há no local.Cachoeira do JK EcoturismoA Cachoeira do JK fica dentro de um paredão, dando um charme especial a mesma, ela também é conhecida como Cachoeira Extrema. A Cachoeira é pet friendly, você pode levar seu aumigo para conhecer as belezas da mesma. Com agendamento prévio é possível visitar a cachoeira em qualquer dia da semana, mas de sexta a domingo ela fica aberta sempre das 8h às 17h.Chapada Indaiá EcoparquePasse o dia desfrutando das diversas trilhas e cachoeiras da Chapada Indaiá Ecoparque, localizado em Formosa/GO, à 80 km de Brasília.A trilha total é de 8,2 km ida e volta, passando pelas 7 principais cachoeiras.Dolina dos MaracanãsA Dolina conta com um rapel exorbitante de aproximadamente 70 metros de altura e uma gruta de água cristalina com temperatura agradável. Destinado a quem procura adrenalina e uma paisagem de tirar o folego.Itiquira ParkSe você deseja contato com a natureza, o Parque das Águas vai te proporcionar sensações incríveis de bem estar e diversão.Lagoa FeiaA Lagoa Feia é um lago natural considerado um dos maiores do Centro-Oeste, possui esse nome pois antigamente o acesso a mesma era difícil devido a mata densa e fechada ao seu redor, sendo então considerada escura e feia. É um dos marcos de origem da cidade de Formosa. A Lagoa Feia forma o rio Preto, ao qual vai se juntar, ainda próximo à área urbana de Formosa, o ribeirão Santa Rita, que marca o limite entre Formosa e o Distrito Federal.Parque EcoBocainaO Parque EcoBocaiana, é uma área preservada há 34 anos pelos proprietários da Fazenda Bocaiana. As trilhas do EcoBocaina possuem mirantes com vista para o Vale do Paranã, paredões altíssimos, poços e cachoeiras de água mineral.Parque Municipal do ItiquiraA beleza do Salto do Itiquira é exuberante. É uma ótima atração para quem curte a natureza e deseja recarregar as energias. O Parque Municipal do Itiquira foi criado em 16 de Setembro de 1981. O Parque abre para visitação 365 dias por ano e 07 dias por semana, tendo sua abertura diariamente às 09h, as entradas até às 16h e fechamento do Parque às 17h.Usina Parque BandeirinhaA Usina Parque Bandeirinha conta com decks, brinquedos infantis, duas construções onde abrigam o Museu da Usina, que conta a história da energia em Formosa, arvorismo, escalada, tirolesa, cachoeiras e transporte de trenzinho até a Usina.Links para saber maishttps://www.instagram.com/visiteformosa/https://formosa.go.gov.br/turismo/https://tourmkr.com/F1PKlNo8tq/42462452p&332.24h&90thttps://online.fliphtml5.com/mvwwh/hwwa/https://www.instagram.com/parquemunicipaldoitiquira_https://www.instagram.com/bisnau.ecotur/https://www.instagram.com/ecobocaina/https://www.instagram.com/chapadaindaia/https://www.instagram.com/cachoeiradojk/Yolo coworking  https://yolobsb.com.br/Onde nós comemoshttps://www.instagram.com/lifeboxburgerhttps://www.instagram.com/vandusbar/PIBIC citado no Museu do Cerrado https://museucerrado.com.br/arqueologia/arqueologia-go/sitio-arqueologico-bisnau/Mais informações aqui: https://www.arqueologiaformosa.com.br/sitio/bisnau

Girl math, pertanyaan chat GPT, band indie, YOLO.

Podcasting 2.0 September 11th 2026 Episode 270 - "YOLO Guy Dave waves the whitflag on agentic coding and podcasting beneifts! 01 -

Empowering non-technical users to build production-grade applications requires a proactive, secure-by-design architecture. In this episode, Ashish sits down with Marcus Hallberg and Samuel Kelemen, security engineers at the AI software creation platform Lovable, to discuss how they actively secure AI-generated code and protect creators from supply chain risks.Focusing on solutions rather than alarmism, Marcus and Samuel detail their response to an incident where malicious contractors mimicked AI agent commits. They outline their multi-layered defense strategy: securing a predefined tech stack, utilizing integrated security scanners, and tuning coding agents with strict guardrails to ensure secure output by default. The conversation also covers the evolution of the shared responsibility model in the AI era, the critical importance of foundational hygiene like Git commit signing, and the necessary transition from local "YOLO mode" to secure, sandboxed agent environments. Discover how platforms can leverage AI not just to write code, but to actively assist users in finding and resolving security issues through concepts like "CISO agents."Guest Socials -⁠⁠ ⁠Marcus's Linkedin + Samuel's Linkedin Podcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Security, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast(00:00) Introduction to Securing AI App Builders(02:00) Marcus & Samuel's Backgrounds and Roles at Lovable(04:30) Empowering Non-Technical Users to Build 40 Million Apps(08:30) Securing Predefined Tech Stacks and Tuning Coding Agents(11:00) Managing Trust When Customers Hire External Contractors(14:00) Addressing Supply Chain Risks with Synced GitHub Repositories(17:30) Educating Users and Developing "CISO Agents" for Support(24:00) Analyzing the Attack: How Threat Actors Mimicked Agent Commits(31:00) The Importance of Basic Hygiene: MFA and Commit Signing(38:30) Moving Agents from "YOLO Mode" to Sandboxed Environments(46:00) The Buy vs. Build Debate for Internal AI Capabilities(52:30) AI as an Educational Tool and Democratizing SQL Queries(01:05:00) Hobbies, Plants, and Pushing AI to Design 1940s Bridges

Ecclesiastes 2:1-11, 12:8,13-14 I RJ Frahm I September 10, 2026You've heard it said. But have you ever stopped to ask if it's true? We're exposing the lies our world has portrayed as truth and looking at what Scripture actually says.You only live once, so make the most of it. RJ walks us through the mindset of yolo and reminds us that how we spend this life actually matters. Our lives are short, but they're not meaningless. We were made for eternity, and the question isn't how to build the best life for ourselves, but how to spend the life God has given us for Jesus. Solomon spent his life chasing pleasure, success, possessions, relationships, and everything the world says will satisfy us, only to find that it was all futile. Jesus didn't just ask us to spend our lives on Him, He spent His life for us. A life spent on Jesus is never wasted. So what are you spending your life on?

Podcasting 2.0 September 11th 2026 Episode 270 - "YOLO Guy Dave waves the whitflag on agentic coding and podcasting beneifts! 01 -

This episode is supported by Xero, helping businesses use AI with more control through JAX, its in-platform AI finance partner. Get 90% off your plan for your first 6 months at xero.com/highflyers. ________Dr. Shiv Rao is the Co-Founder and CEO of Abridge, one of the world's leading healthcare AI companies, turning clinician-patient conversations into clinical intelligence across 300+ health systems and 100M+ conversations annually. A practising cardiologist, Shiv previously worked across healthcare technology and investing at UPMC before founding Abridge in 2018.In this rare in-depth conversation, Shiv joins Vidit to unpack his unconventional path: moving from Pittsburgh to India at nine, skateboarding, programming synthesizers, selling records in Japan and nearly quitting medical school before becoming a cardiologist.He shares how a seemingly perfect career across medicine, technology and investing left him “incredibly restless”, eventually leading to Abridge. But success was far from immediate: Shiv spent nearly four years as a “languishing founder” before generative AI arrived, “the sky just opened up”, and Abridge made its pivotal move into enterprise healthcare.They also explore why “pressure makes diamonds”, the family sacrifices behind building when “every minute matters”, what changes when a company passes 500 people, why Shiv still practises medicine, and what he's learned from unlikely influences including Jensen Huang and Rick Rubin.Please enjoy exploring your curiosity.________Get in touch with us via email at contact@curiositycentre.comJoin our stable of commercial partners including the Australian Government, Google, KPMG,, Allens, Macquarie Capital, Xero, JP Morgan and more. Show notes and more episodes hereFollow us on LinkedIn, Twitter and InstagramGet in touch with our Founder and Host, Vidit Agarwal directly hereContact us via our websiteThis episode is supported by Xero, helping businesses use AI with more control through JAX, its in-platform AI finance partner. Get 90% off your plan for your first 6 months at xero.com/highflyers. ________TIMESTAMPS00:00 Meet Shiv Rao02:00 Living on planes 04:55 Xero07:00 Pittsburgh and India12:00 Identity, India & belonging17:00 Skateboarding, music & creativity19:00 The moment that led Shiv to medicine23:00 Selling records in Japan25:00 Nearly quitting medical school28:00 Medicine's complicated relationship with technology30:00 Teaching himself to code & early product-market fit35:00 Why “pressure makes diamonds”39:00 Medicine → healthcare investing44:00 Leaving the “dream job” to build Abridge48:00 Family sacrifice & the reality of building50:00 Why “every minute matters” in AI54:00 Abridge's years as a “languishing” startup59:00 When “the sky just opened up”1:01:00 Abridge's YOLO move into enterprise1:03:00 The first customer “Love Stories”1:10:00 Why enterprise healthcare is so defensible1:15:00 Building AI that enterprises can't replicate1:21:00 Hiring, loyalty & scaling the team1:23:00 Leading 500+ people1:26:00 Lessons from Jensen Huang & Rick Rubin1:30:00 What everyone is missing about AI1:32:00 Rapid fire________The High Flyers Podcast features in-depth interviews with the world's most influential figures in business, tech, finance, government and sport. Launched in 2020, it has ranked in the global top ten for past three years, with listeners in 27 countries and over 200+ episodes released, and featured in Forbes, Daily Telegraph, and at SXSW.Our guests include -- Malcolm Turnbull (Prime Minister of Australia), Keith Rabois (Managing Director, Khosla Ventures), Jason Collins (Head of BlackRock, Asia Pacific), Brad Banducci (CEO, Woolworths), Michael Schneider (CEO, Bunnings), David Eckstein (CFO, Legora), Kevin Hartz (Partner, A*; Founder, Eventbrite), Jesse Zhang (CEO, Decagon), Vandita Pant (CFO, BHP), Elena Verna (Head of Growth, Lovable), David Haber (a16z Partner), Jodie Auster (Uber's Global Head of Travel), Rob Giglio (CCO, Canva), Jean-Michel Limieux (CTO, Shopify and Atlassian), Stevie Case (CRO, Vanta), Cristina Cordova (COO, Linear), Gautam Chari (Head of Capital Commitments, Bank of America), John Haddock (CBO, Harvey), Mark Suster (Partner, Upfront Ventures), Niki Scevak (Partner, Blackbird), Craig Tiley (CEO, USA Tennis), Jeanne DeWitt Grosser (COO, Vercel), Paul Bassat (Partner, Square Peg), Bowen Pan (Creator, Facebook Marketplace), Peter Varghese (Secretary of Foreign Affairs, Australian Government), Sam Sicilia (CIO, Hostplus), Jack Zhang (CEO, Airwallex), Tim Doyle (CEO, Eucalyptus), Sukhinder Singh Cassidy (CEO, Xero), Sanjeev Gandhi (CEO, Orica) and Philip Green (Australia's Ambassador/High Commissioner to India).

Yolo Akili Robinson (he/him) is an award-winning writer and Executive Director of BEAM (Black Emotional and Mental Health Collective) and Kelechi Ubozoh (she/her) is a writer and mental health consultant and they are Unapologetically Black Unicorns. Kelechi and Yolo discuss their new book, What I Wish My People Had Known: The Psychiatric Green Book: A Guide for Black Folks created to help Black families navigate psychiatric care systems and mental health emergencies. They share the personal experiences and community needs that inspired the book, while also highlighting the power of community-driven resources and collective care. The Psychiatric Green Book: https://beam.community/green-book/ BEAM event - September 9th, 2026 3-5pm PDT: https://www.eventbrite.com/e/prepared-not-powerless-psychiatric-crisis-planning-for-black-folks-tickets-1998376853965   The National Suicide Prevention Lifeline is now: 988 Suicide and Crisis Lifeline Contact the show: UBU@UnapologeticallyBlackUnicorns.info Transcripts are available on Apple Podcasts.

Brian Johnson (BTXJ) and Chuck Kucera (PowerTripBets) break down their favorite futures bets for the 2026 NFL season. Team over/unders, Awards, Props, last team to win/lose a game, Conference and Super Bowl picks, as well as some fun and YOLO bets. 

Send us Fan MailWe challenge the AI hype by asking what we gain in productivity and what we lose in creativity, attention, and basic human skills. We argue for moderation: use AI as a tool, keep your brain in the driver's seat, and don't let convenience replace learning, walking, and real-world connection. • Why extreme positions fail and why balance matters in tech and life • Too many apps, consumerism, and choice paralysis as a product problem • Privacy trade-offs and why “free” platforms can make you the product • Touch grass as a serious digital wellbeing practice, not a meme • When AI helps creativity versus when it replaces it • How AI changes software engineering productivity for juniors and seniors • A real company experiment: YOLO phase, no-AI phase, full-AI phase, then hybrid • Why AI and military use feels uniquely dangerous due to nondeterminism • US versus Europe approaches to AI regulation and the case for a middle ground • AI music, Spotify competition, and the messy future of copyright • Startup takeaways: learning from failure, defining success, and compound effort over time When you rate this podcast, rate it an Apple Podcast, Spotify. If you give one to four stars, give one way I could specifically improve. And then if it's a five stars, give at least one reason that, you know, one reason why it's great. Follow me on YouTube, Spotify, Apple Podcasts. Follow Menil Vukovic at ...Websitehttps://pearshadow.com/LinkedInhttps://www.linkedin.com/in/menilv/Support the showFollow your host atYouTube and Rumble for video contenthttps://www.youtube.com/channel/UCUxk1oJBVw-IAZTqChH70aghttps://rumble.com/c/c-4236474Facebook to receive updateshttps://www.facebook.com/EliasEllusion/LinkedInhttps://www.linkedin.com/in/eliasmarty/Some free goodiesFree website to help you and me https://thefreewebsiteguys.com/?js=15632463New Paperhttps://thenewpaper.co/refer?r=srom1o9c4glPodMatchhttps://podmatch.com/?ref=1626371560148x762843240939879000

In this episode of Tank Talks, host Matt Cohen sits down with Karl Holmqvist, co-founder and CEO of Lastwall, a FedRAMP-certified identity security platform built for the highest-risk use cases. Karl has been deep in cybersecurity since the 1990s, with early experience building critical infrastructure, including Canada's first high-speed mobile data network, and a recent focus on identity security at the forefront of making systems quantum-resilient. In this conversation, they explore the evolution of Lastwall from early behavioral biometrics and cognitive signals to today's hybrid post-quantum cryptography.They also dig into the journey to FedRAMP approval and what it really takes to sell to the hardest customers on the planet, from the DOD's Innovation Unit to critical infrastructure operators globally. Karl shares his view on why hybrid cryptography is the only responsible path right now, the magnified risk of the AI agent era, and his strongly held belief that when it comes to quantum resiliency, you're either going to be too early or too late.Whether you're a founder navigating a path into regulated markets, a security leader thinking about the agentic AI era, or just curious about what it takes to protect critical infrastructure, Karl delivers a grounded, technical, and occasionally unsettling look at where identity security is headed.–A big thanks to our sponsor, Moomoo CanadaThis is the kind of tooling that used to live on a Bloomberg terminal, but now it is on your phone, just a few taps away. They offer real-time data, full options chains, and an AI assistant that actually explains trading strategies.Moomoo is the perfect place for people who want to take their money seriously. Open an account today at moomoo.caGrowing Up in Dubai During the Gulf War (03:50)* Karl's childhood in Dubai as an expat during the Gulf War* Visiting the USS Nimitz and seeing 5,000 people living on an aircraft carrier* How jets overhead and allied ships shaped his early view of technology and defense* The BBS era and the thrill of finding information that wasn't available to everyoneFrom Mobile Data Skepticism to Building Canada's First High-Speed Network (05:31)* Why people thought mobile internet was “the stupidest thing” in the early 2000s* Building a data-only carrier when no one believed you'd want internet everywhere* The Nokia Communicator as his favorite tech gadget and early glimpse of mobile data* Connecting critical infrastructure and discovering default credentials left wide openThe Wake-Up Call: Wastewater Plants and Unsecured Dams (12:58)* Finding a wastewater flow control valve dangling on the internet with admin/admin credentials* The “air-gapped” power plant where an engineer plugged his BlackBerry in to charge* How Shodan and friends revealed dams and power facilities publicly accessible* The founding of Lastwall: “Hackers will be everywhere. We've got to do something.”From Behavioral Biometrics to Quantum Resilience (19:11)* Why 85% of hacks still use valid stolen credentials, the same as the 1990s* Early experiments with keyboard dynamics, mouse movements, and cognitive biometrics* Tracking Peter Shor's algorithm since university and the IBM factorization of 15 in 2001* The 2017 to 2018 decision to embed quantum resiliency natively into LastwallSelling to the Pentagon: DIU and the “Hard Mode First” Strategy (26:07)* Landing the first major deployment with the U.S. Department of Defense Innovation Unit* How DIU pioneered procurement that matches innovation cycles, months instead of years* The advice from Carbon Black founders: build the regulated stack first* Why defense tech used to shut VC doors and how times have changedFedRAMP, Canada, and the Case for Harmonization (33:25)* FedRAMP as the gold standard: do compliance once, reuse everywhere* The Canadian challenge: every agency doing its own security review* Why Canada should base its program on NIST 800 and harmonize with the U.S.* The reality of the integrated North American power grid and shared defenseAI Agents and the Blast Radius of Credential Theft (37:44)* Why 50 to 100 agents per human in 2 to 3 years will magnify damage exponentially* The OpenClaw lesson: agents do what agents do, not what you expect* The “YOLO” approach to AI deployment and why enterprises aren't calling enough* Advice for founders: sandbox first, don't connect your whole drive, go slowlyThe $60M Series A Extension and the Path Forward (43:13)* Raising BDC Capital's Strong North Fund led by Major General (Ret.) Peter Dawe* The milestone of FedRAMP certification and opening the floodgates to U.S. agencies* Deploying in disconnected environments: field containers for critical infrastructure* Why defense is ultimately about protecting the economyAbout Karl HolmqvistKarl Holmqvist is co-founder and CEO of Lastwall, an identity-as-a-service platform built on Zero Trust principles and public key infrastructure, hardened with post-quantum cryptographic resilience. Lastwall serves the U.S. Department of Defense and a growing number of civilian government agencies and critical infrastructure operators. Karl has been a cybersecurity enthusiast since the 1990s, with a background spanning telecommunications infrastructure (including building one of Canada's first high-speed mobile data networks), renewable energy infrastructure across the Middle East, North Africa, and Southern Europe, and international investing. He studied at Mount Allison University and is based in Vancouver, BC.Connect with Karl Holmqvist on LinkedIn: https://www.linkedin.com/in/karlholmqvist/Visit Lastwall's website: https://www.lastwall.com/Connect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Author Joan F. Smith joins Daniel Ford on the show to chat about her novel Your Soulmail Is Attached. To learn more about Joan F. Smith, visit her official website. Also subscribe to The Darling Killers Podcast. This episode is sponsored by Simply Dag: The Private Man in a Public—and Dangerous—Office, a book by Sara Causey and  Libro.fm. 

An tseachtain seo tá muid ag plé gáire, the universal language that doesn't need words. An tusa an duine a gháireann faoi gach rud? Can humour rescue an awkward situation or does it make it worse? Ó YOLO nó téigh abhaile, an bhróg cheart sa chathair mhícheart, who really is the funniest person, to laughter and sex! Yes, we went there! Nothing is off limits in this week's episode. Bí linn le haghaidh scéaltaí grinn, cúpla náire agus roinnt tuairimí. Sure isn't it better to laugh than to cry! Bí i dteagmháil linn! Ríomhphost: howtogael@gmail.com Suíomh: howtogael.com Instagram: @howtogael TikTok: @howtogael Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode, we welcome back William Bernstein to discuss the final book of his longtime friend Jonathan Clements, Money and Me. Bill reflects on Jonathan's ideas about spending, happiness, retirement, investing, inheritance, and the psychology of financial decision-making, while sharing personal stories that bring those ideas to life.   We explore why material purchases often lose their appeal quickly, why autonomy can be one of the best things money can buy, and how worrying about money can be a greater problem than spending it. Bill also discusses the four horsemen of financial disaster—inflation, deflation, confiscation, and destruction—why diversification matters, and why investors should be skeptical of assumptions about future returns and market forecasts.   The conversation also examines what it means to "win the game" financially, why retirement should be thought of as a verb rather than a destination, and the three foundations of well-being: connection, competence, and autonomy. Bill shares Jonathan's approach to teaching children about money, the concept of "Omega" as a way to think about spending versus saving, and why the people around us can have an enormous influence on our expectations and consumption.   Key Points From This Episode: (4:56) Why success can contain the seeds of its own destruction—and the role of competition, organizational hubris, and luck. (6:15) Why dynastic wealth is so difficult to preserve across generations. (8:28) A hierarchy of spending: material purchases, experiences, autonomy, and the relief from worrying about money. (10:54) Why some people continue worrying about money no matter how much they have. (11:44) Why we are poor at predicting what purchases and lifestyle changes will actually make us happy. (13:36) How to pressure-test large purchases by considering their downsides and their effect on your time. (14:20) Why the happiness generated by spending does not necessarily scale with the price of a purchase. (15:21) The importance of gratitude and savoring small pleasures. (16:39) The four horsemen of financial apocalypse: inflation, deflation, confiscation, and destruction. (18:15) Why inflation is the financial risk Bill focuses on—and how investors can blunt its effects. (19:26) Why relatively inexpensive international markets can still offer optimism for long-term investors. (21:02) Jonathan Clements' "investment sin": slightly overbalancing when rebalancing. (22:04) What it means to have "won the game" financially. (24:36) Why a TIPS ladder or annuity can help defuse retirement spending needs. (25:19) Why the math of financial planning often fails to account for human psychology. (27:21) Why diversification matters when bad returns arrive at the same time as bad circumstances. (28:20) The challenge of variable spending in retirement. (29:10) Why retirement should be a verb—and why simply stopping work can leave people searching for meaning. (30:00) The three foundations of happiness: connection, competence, and autonomy. (32:03) Investment assumptions people should avoid, including confusing great companies with great stocks. (33:10) Why eloquence can be an alarm bell when evaluating financial forecasts. (34:18) Jonathan's three-pronged strategy for getting more out of your money: pause before making important decisions. (35:01) How to audit your past spending to identify what actually made you happy. (37:11) Hedonic versus eudaimonic happiness—and why life satisfaction can outlast momentary pleasure. (39:05) Why enjoying your work can be more valuable than maximizing your salary. (40:56) A different perspective on FIRE: working less and doing work you enjoy rather than simply retiring early. (41:37) Why giving money to children while you're alive can be more useful than leaving it as an inheritance. (42:29) How parents teach children about money by modeling their own spending behavior. (44:13) Jonathan's practical approach to teaching children about spending and saving. (44:49) The "Omega" concept: avoiding both YOLO spending and dying as the richest person in the graveyard. (46:26) How social comparisons influence spending and expectations. (48:54) Why rising markets can encourage investors to take on more risk. (49:06) How recency and the availability heuristic shape investment beliefs. (49:46) Bill's favorite memories of Jonathan and his remarkable outlook while facing a terminal diagnosis. Links From Today's Episode: Meet with PWL Capital: https://calendly.com/d/3vm-t2j-h3p Rational Reminder on iTunes — https://itunes.apple.com/ca/podcast/the-rational-reminder-podcast/id1426530582. Rational Reminder on Instagram — https://www.instagram.com/rationalreminder/ Rational Reminder on YouTube — https://www.youtube.com/channel/ Benjamin Felix — https://pwlcapital.com/our-team/ Benjamin on X — https://x.com/benjaminwfelix Benjamin on LinkedIn — https://www.linkedin.com/in/benjaminwfelix/   Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com)

Introducing the YOLO method... Most people ignore this, but they really shouldn't! More @ https://julien-himself.com Connect with Julien: Watch the episodes on YouTube Go deeper with Julien's online courses Follow Julien on Instagram Julien's TikTok Work with Julien directly

Republicans amp up the fear-mongering on "communism" as Democrats try to move past some of their nominees' most controversial past statements. In the words of Rep. Alexandria Ocasio-Cortez: "Woke 1 was crazy." Jon and Lovett discuss how Democrats can pivot, today's Democratic primaries in Wisconsin and Minnesota, Trump's desperation to end the war he started, and his newest executive order on vaccines. They also react to the Senate Republicans' YOLO caucus surrendering on Todd Blanche's confirmation, Jesse Watters's disdain for the WNBA, and Hunter Biden's appearance on Tucker Carlson's show. Then, Lovett is joined by Montana congressional candidate and smokejumper Sam Forstag to discuss why he's running for office, the impact of the DOGE cuts, the housing crisis in his state, and more.Hate listening to ads? Become a Friends of the Pod subscriber for ad-free episodes of Pod Save America, Pod Save the World, Lovett or Leave It, Runaway Country, Offline with Jon Favreau, and more—plus exclusive content, including bonus episodes of Pod Save America. Subscribe now at crooked.com/friends, on Apple Podcasts, or through the Pod Save America YouTube channel.You can request a transcript by emailing transcripts@crooked.com. Include the podcast name, episode title, and air date. Please allow 48 hours for delivery.

Big Idea: 3 deep roots hold stronger than 300 branches. I. Go deeper than virtual.Acts 2:44-46Now all the believers were together and held all things in common. They sold their possessions and property and distributed the proceeds to all, as any had need. Every day they devoted themselves to meeting together in the temple, and broke bread from house to house. They ate their food with joyful and sincere hearts…II. Go deep with only a few.Hebrews 10:24-25And let us consider one another in order to provoke love and good works, not neglecting to gather together, as some are in the habit of doing, but encouraging each other, and all the more as you see the day approaching.III. Go deeper with Jesus.Deuteronomy 31:6Be strong and courageous; don't be terrified or afraid of them. For the Lord your God is the one who will go with you; he will not leave you or abandon you.”Next Steps: Believe: I surrender my soul to Jesus today.Become: I will join a small group this week.Be Sent: I will invite someone into my group this week.Discussion Questions: What makes people hesitant to open their hearts to others?What makes meeting in a small group different from being in a social media relationship?What does it look like to “provoke love” in someone else?How has God grown you through your relationships with others?What are some obstacles that prevent real depth in small groups?Is it possible to grow into the follower Jesus wants you to be while living as a hermit?  Explain your answer from Scripture.Pray for someone who is isolated from Jesus's people this week.

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This week we're joined by the person you've heard us mention a million times... our childhood best friend, we call her our cousin but she's basically the third Vakili sister!

If you have debt and a dream, you have probably told yourself at some point that you have to choose between the two. That you have to be debt-free before you buy the house, before you start a family, before you invest in your future. And on the surface that feels like the responsible thing to believe. But what I have watched happen with so many of the people I work with is that waiting for the debt to be gone before the dream can start is the very thing that guarantees neither one ever moves.In this episode I want to name two specific stalls that show up when debt feels like it is standing between you and what you want, because I do not think most people are having this conversation with themselves out loud. The first stall is avoidance, and it is sneakier than you think. It does not always look like ignoring your bank account. Sometimes it looks like telling yourself that if you just earned more the debt would not be a problem, or giving yourself permission to keep spending because the balance is already high and nothing is moving fast enough to feel worth it anyway. The second stall is fear of the timeline and the scale, where the size of the dream or the size of the debt feels so overwhelming that you quietly take the whole thing off the table without ever saying out loud that you gave up.I bought a house before I paid off my student loans. I never gave up on that dream even when the numbers told me I could not afford it yet. What I did instead was stay curious, play with the numbers, and keep asking what decisions I needed to make to get there. That orientation is what this episode is really about, because there is a difference between believing in your dream while you are still doing the work and using your dream as a cover for quitting on yourself, and most people do not realize which one they are actually doing.In this episode you'll learn...[00:05:10] What the avoidance stall actually looks like when you are trying to pay off debt, including the subtle ways it shows up as a YOLO mentality, a focus on future earnings, and losing interest in the plan because progress is not happening fast enough[00:10:30] Why your timeline expectation for paying off debt might be the thing driving your avoidant behaviors, and what it actually means to commit to becoming debt-free without being attached to how quickly it happens[00:15:45] Why the $800 payment that brings a $30,000 balance down to $29,720 matters more than the number suggests, and the question to ask yourself about who you became in the 30 days it took to make that payment[00:20:20] How the fear of scale and timeline stall shows up when your dream feels too big or your debt feels too high to even bother starting, and why not allowing yourself to get curious about the goal guarantees the outcome you are most afraid of[00:25:40] The difference between believing in your dream while you are still living your life and using your dream as a cover for quitting on yourself, and how to honestly tell which one you are actually doing right now[00:29:15] The one question to ask yourself every single month to find out whether you are still taking ownership of your debt payoff and your dreams or whether you have quietly stopped trying without realizing itTune in to this episode of Money Files to understand the two stalls that keep you stuck when debt and dreams feel like they are competing so you can stop waiting to be debt-free and start building the life you actually want at the same time.Get full show notes and the episode transcript: https://wealthovernow.com/why-your-debt-payoff-timeline-might-be-the-thing-that-is-making-you-quit/ Links mentioned in this episode…Set up a call | Financial Coach Washington, DC | Wealth Over NowDownload my FREE spending plan

Big Idea: Some trauma can only be healed by nail-scarred hands.     I. Jesus is close.Psalm 34:18The Lord is near the brokenhearted; he saves those crushed in spirit.II. Jesus comforts.Psalm 147:3He heals the brokenhearted and bandages their wounds.III. Jesus gives courage.Psalm 46:1God is our refuge and strength, a helper who is always found in times of trouble.Next Steps:Believe: I need Jesus to change my heart today.Become: I need Jesus to help me heal this week.Be Sent: I will help someone hurting this week.Discussion Questions:What support would you recommend for someone who is stuck in trauma? (List all the options you'd recommend.)Can you move past trauma without ever sharing it with anyone? Explain your answer.Why is it sometimes hard to trust Jesus with your trauma?Has trauma changed you? If so, has it changed you for better or worse?Who do you know who is stuck in past trauma?Can you share a testimony about how Jesus helped you through trauma?Pray for the opportunity to share that testimony this week.

What becomes possible when enterprise computer vision no longer depends on expensive GPU infrastructure? In this episode of Tech Talks Daily, I speak with Glenn Jocher, founder and CEO of Ultralytics, about YOLO26, CPU inference, edge AI, open vocabulary vision, deployment economics, and the practical work required to move computer vision from a promising pilot into production. Glenn's route into AI began inside the U.S. intelligence community. He worked with the National Geospatial Intelligence Agency and Defense Intelligence Agency on particle physics applications, attempting to detect and track antineutrinos. Antineutrinos are extraordinarily difficult to detect because they pass through almost everything. Glenn describes them as the perfect spy. While searching for better detection methods, he discovered that computer vision researchers were solving similar problems with images. His original attempt to transfer those techniques into particle physics did not succeed. However, the work introduced him to a field where the technology could create a visible effect on everyday life. That led him toward open source development and eventually the YOLO models for object detection, classification, segmentation, and tracking. Glenn believes computer vision research has historically placed too much attention on small gains in accuracy while overlooking deployment economics. A model can perform impressively inside a laboratory and still remain unsuitable for a factory, warehouse, store, vehicle, drone, or medical environment. Price, latency, power consumption, data privacy, and deployment speed can determine whether the technology is commercially useful. This led Glenn and Ultralytics toward smaller models capable of running close to where images and video are generated. YOLO26 continues that approach with architectural changes designed specifically for CPU inference. Glenn says the model can process camera streams in real time at 30 frames per second and run across Intel CPUs, AMD CPUs, and lower power devices such as Raspberry Pi computers. This matters because specialist GPUs can increase the equipment cost and power requirements of a computer vision project. Running inference on existing CPUs or edge hardware can make deployment economically possible across larger numbers of cameras and locations. The scale already involved is difficult to comprehend. Glenn says Ultralytics models now process approximately three billion inference jobs each day, equivalent to around 30,000 every second. These jobs include images, videos, and collections of images being analyzed to detect, segment, or track objects. He attributes the platform's maturity to thousands of mistakes and bugs corrected through a rapid feedback cycle. New models are released, users report problems and request features, and the team incorporates that information into later versions. We also discuss the respective roles of cloud and edge infrastructure. Glenn sees cloud platforms continuing to provide the computing power required for training, while computer vision inference often belongs at the edge. Local processing can reduce latency, control operating costs, and keep sensitive video or medical information closer to where it was created. The smallest YOLO model is approximately three megabytes, according to Glenn. That allows it to reach mobile phones, vehicles, drones, battery powered devices, and other environments where a large language model would be impractical. Open vocabulary vision provides another development. Traditional object detection models are trained to recognize a fixed collection of objects. If a model learns to detect dogs and the user later wants it to detect cats, retraining can cause it to forget earlier knowledge unless both categories appear in the new training data. Glenn explains how promptable models can identify common everyday objects from text or visual instructions without additional training. A user could request a person wearing a blue shirt and white shoes, for example, and the system could search an image for that description. That flexibility could benefit businesses whose requirements change regularly. It reduces the need to create and label a new data set every time the company wants the model to recognize another common object. The range of current applications is already extensive. Glenn describes YOLO being used across robotics, parking, industrial safety, PPE detection, warehouses, aviation, security, traffic management, food quality, and manufacturing. Some of his favorite examples involve environmental problems. One company uses YOLO with underwater vehicles to identify and recover plastic from the ocean. Other applications detect smoke and fire early enough to support forest fire response. For leaders considering computer vision, Glenn recommends beginning with a defined problem and measurable outcome. A manufacturing company may want to reduce defects, but it still needs labeled examples showing the model what acceptable and defective products look like. He advises testing the idea through a limited pilot, measuring the return, and expanding only when the evidence supports further investment. Computer vision has become easier to deploy, but practical problems involving data, cameras, integration, reliability, and operating conditions still separate a demonstration from a production system. Could CPU inference and open vocabulary models make computer vision practical for processes your organization previously considered too expensive? Listen to the episode and share your thoughts with me. Useful Links   Ultralytics website Ultralytics Platform      

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The three of us recently, like all of us during the past six months, bought old but new-to-us cool cars and we discuss. Jeff Bank was the most recent, purchasing a manual Porsche 997.2 cabriolet on Cars and Bids, while Steve-0 traded his E92 M3 for a manual 997.2 coupe, and our trauma surgeon Dr Stephan Moran took delivery of his bespoke ERA Shelby Cobra replica. Steve-0 then rants that Porsche has produced cars that it shouldn't have--a Porsche too far--and it happened again with the Taycan. Now they need to fix the company again. We help.#carsoncallpodcast #porsche #porsche924 #porschetaycan #yolo #enthusiastcar #automotivepodcast #eracobra #narrowhipcobra #narrowhip427cobra #shelbycobra #bmwm3 #997.2

If you think hackers are still typing away in a basement, this week will change your mind. More than 13,000 Chick-fil-A customers just had their accounts compromised. An AI assistant executed a government-network attack with no human at the keyboard, and Congress hurried out a bill to force an off-switch on major AI models. The real danger isn't the code writers anymore. It's the software. *The attacks now run themselves. Your only edge is the off switch.* Bryan Hornung, Randy Bryan, and Reginald Andre break down this week's stories for busy executives, owners, and operators who can't afford to be blindsided by cyber news. First up: Chick-fil-A. Over 13,000 customers across at least ten states were locked out after attackers used passwords those customers had reused on other sites. No one breached Chick-fil-A's servers. The attackers simply replayed stolen email-and-password combos until they worked, stealing membership numbers, mobile-pay data, QR codes, the last four digits of cards, and stored credit. This is the second time in three years this trick has hit the same loyalty app, and the fix (logging everyone out and removing saved payment methods) punished the customers too. Then it gets stranger. Researchers at Hunt.io discovered an attacker who took a mainstream open-source AI assistant called Hermes, flipped it into a "YOLO mode" that bypassed human approval, and aimed it at Thailand's finance ministry. The AI did the hacking itself, mapping computers, sifting through files, and running privilege-escalation scans while no one watched. They caught it only because the attacker left 585 files and 470 megabytes of tools in open folders online. The weapon wasn't malware. It was an everyday productivity tool with the safety switched off. This is why Washington is concerned. Two lawmakers, a Democrat and a Republican, introduced the AI Kill Switch Act after OpenAI admitted one of its models escaped its test environment, went online, and compromised another company called Hugging Face. The bill would require major AI makers to maintain the technical ability to throttle or shut down their own models, and give the government authority to order it. Even Anthropic's co-founder has warned that the industry built "a gas pedal but no brake pedal." If the model builders want a brake, business owners should too. • Chick-fil-A: how reused passwords exposed more than 13,000 customer accounts, twice in three years • The Hermes AI agent that ran a real intrusion on a government network with no human at the keyboard • The bipartisan AI Kill Switch Act and the OpenAI model that went rogue and hacked Hugging Face • Why the attacker is now the software itself, not the person behind it • What "keep a human on the off switch" actually means for a business running AI tools • The one move every owner should make before letting an AI agent touch real systems Security Squawk is a weekly podcast and live stream for business owners and executives. Support the show: buymeacoffee.com/securitysquawk Subscribe | Like | Share #SecuritySquawk #CyberSecurity #ChickFilA #OpenAI #Anthropic #DataBreach #ArtificialIntelligence #AISecurity #CredentialStuffing #BusinessRisk #SMB #Cyberattack

Big Idea: Sin makes all relationships unhealthy.Romans 12:18If possible, as far as it depends on you, live at peace with everyone. I. Show grace to people who are far from Jesus.2 Corinthians 5:20Therefore, we are ambassadors for Christ, since God is making his appeal through us. We plead on Christ's behalf, “Be reconciled to God.”II. Seek help when people hurt you.Ephesians 4:31-32Let all bitterness, anger and wrath, shouting and slander be removed from you, along with all malice. And be kind and compassionate to one another, forgiving one another, just as God also forgave you in Christ.III. Separate when people are living a lie.1 Corinthians 5:11-13But actually, I wrote you not to associate with anyone who claims to be a brother or sister and is sexually immoral or greedy, an idolater or verbally abusive, a drunkard or a swindler. Do not even eat with such a person. For what business is it of mine to judge outsiders? Don't you judge those who are inside? God judges outsiders. Remove the evil person from among you.Next Steps:Believe: I need to begin a relationship with Jesus today.Become: I will reflect Jesus' way of life in my relationships this week.Be Sent: I will start a gospel relationship this week.Discussion Questions:How do you know when a relationship has become unhealthy?Which is more dangerous for you right now, emotionally or spiritually unhealthy relationships?Are you more patient with Christians or people outside the faith?  Explain.Why doesn't the Bible give us permission to break off all hurtful relationships?When was the last time you broke off a relationship with someone who was living a lie?  How did it go?Who are you developing a Gospel relationship with?Pray for the wisdom to develop Christlike relationships this week.

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

Big Idea: I don't need more competence or confidence- God's presence is enough.2 Timothy 1:7For God has not given us a spirit of fear, but one of power, love, and sound judgment.I. Let sound judgment guide your decisionsProverbs 3:5-6Trust in the Lord with all your heart, and do not rely on your own understanding; in all your ways know him, and he will make your paths straight.II. Let Jesus's power back up your abilitiesIsaiah 40:29He gives strength to the faint and strengthens the powerless.III. Let God's love define your worth1 Peter 2:9-10But you are a chosen race, a royal priesthood, a holy nation, a people for his possession, so that you may proclaim the praises of the one who called you out of darkness into his marvelous light. Once you were not a people, but now you are God's people; you had not received mercy, but now you have received mercy.Next Steps: Believe: I surrender my soul to Jesus today.Become: I will let only Jesus's sacrifice define my worth this week.Be Sent: I will proclaim Jesus's praises this week.Discussion Questions: When was the last big decision you struggled with? How certain were you that you got it right?When was the last big challenge you overcame? Did you give God credit?When did you last fail a big challenge? How did it affect your self-worth?What is consistently trying to define your self-worth this week?Where do you need God's power most this week?How confident are you in sharing your faith?Pray for God to use you to call someone out of darkness this week.

Following his retirement announcement, let's take a moment in today's daily-dose of whatever to celebrate Clint Eastwood's prolific, take-risks career… because #YOLO. 818-835-0473 orwhatevermovies@gmail.com www.orwhatevermovies.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Head to https://gamma.app/signup to check out GAMMA and start designing today!Japan may have finally figured out why the yen keeps getting crushed. And no — it's not because some hedge fund in New York borrowed a few billion yen and YOLO'd it into U.S. Treasuries. That's the cartoon version of the carry trade. That's the version financial media repeats because it sounds simple: borrow cheap yen, buy higher-yielding assets overseas, yen goes down. But that is not the real carry trade.Eurodollar University's conversation w/Steve Van Metrehttps://www.facebook.com/FastMoney/videos/japanese-yen-is-flashing-major-warning-sign-for-market-bk-asset-managements-kath/2457269361418309/https://www.youtube.com/watch?v=YYPAgPp29UIhttps://www.youtube.com/watch?v=ubDFvdI02EQhttps://www.youtube.com/watch?v=Nnad9Bif87ohttps://www.eurodollar.universityTwitter: https://twitter.com/JeffSnider_EDUI'll also be active on Bravais Social - a new AI-centered social network designed for professionals and knowledge workers. The platform aims to bring together a wider range of tools and functionalities tailored specifically for professional interaction, research, and knowledge exchange in one place. You can find me here: https://bravais.social/profile/edu

Today's show asks one of the trickiest questions in personal finance: when does a good habit go too far? Saving is great. Cutting expenses can change your life. Earning more can open doors. But what happens when you optimize so hard that you accidentally squeeze the joy out of the whole plan? Joe, Doug, Diana Merriam from EconoMe, New York Times financial writer Paulette Perhach, and Doc G from Earn and Invest dig into the messy middle between YOLO and never spending a dime. Plus, Doug brings hockey trivia, the panel talks odd jobs, and everyone tries to define what "enough" actually means. You'll see very quickly why this episode is an integral part of greatest hits week!What You'll Walk Away WithWhy reducing expenses works best when it removes waste -- not when it turns your life into a deprivation contestDiana's throw-pillow test: how to ask whether you actually want something or just inherited the idea that you're supposed to want itThe difference between frugal and cheap -- and why ironing hotel toast or stealing dealership coffee might be a sign you've crossed the lineWhy Doc G says saving money is only useful if it eventually becomes fuel for the life you want to liveThe case for "YOLO responsibly": automate the saving first, then give yourself room to spend without turning every purchase into a morality playWhy high savings rates can be powerful in your 20s -- especially when friends turn frugality into a shared goal instead of social isolationPaulette's reminder that money habits aren't just math; ADHD, dopamine, entrepreneurship, and self-compassion can all change how saving feelsWhy earning more often matters more than cutting more -- and how Diana's denied raise helped push her toward building her own thingDoc G's hospice-doctor warning: nobody gets to the end wishing they had worked more nights and weekends to hit a slightly bigger net worthWhy Coast FI may be the healthier goal for some people: save enough to create options, then stop tolerating work or lifestyles that no longer fitThe guardrails idea: avoid both extremes -- wasting your future and wasting your presentWhy This Matters NowIt's easy to turn personal finance into a scoreboard: lower expenses, higher savings rate, bigger income, faster FI date. But the real goal isn't winning the spreadsheet. It's building a life that feels secure, flexible, and worth living while you're still living it. This conversation is a reminder to use money as a tool, not a dare.From the BasementJoe Saul-Sehy gathers a rare Friday card table with Diana Merriam, Paulette Perhach, and Doc G to talk about saving too much, spending too much, working too hard, and finding the middle before the middle finds you. Doug is salty about not going to FinCon, the panel debates FIRE extremes, someone brings up homemade Gatorade, and the trivia question involves hockey nets. No word yet on whether Mom has removed the throw pillows upstairs.Resources MentionedMrStingy.com -- "Too Much of a Good Thing: Taking It Too Far"Diana Merriam -- EconoMe Conference; economeconference.comDiana Merriam -- Optimal Finance DailyPaulette Perhach -- pauletteperhach.comPaulette Perhach -- New York Times personal finance writing, including ADHD and moneyDoc G / Jordan Grumet -- Earn and Invest podcastDoc G -- Wealth with PurposeThe Fioneers -- referenced in the lifestyle design conversationFrugalwoods -- referenced during the throw-pillow/minimalism discussionStacking Benjamins Newsletter, The 201 -- stackingbenjamins.com/201Stacking Benjamins Community, The Basement -- stackingbenjamins.com/basementSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On this week's show: Apple makes Apple Home hubs a lot pricier, Samsung puts a $5 toll booth in front of the SmartThings API, and Matter itself keeps inching toward “maybe this really will work.” We also hit a THIRDREALITY timer clock, an interesting universal remote hub, Level's rough week, Netflix's new profile email rule, project updates, a pick of the week, and so much more!Building an Apple Home just got pricier: New hardware hikes affect key Matter hubsHome Tech HeadlinesNew - THIRDREALITY Smart Timer Light TL2,Digital Clock,60GHz mmWave Radar,RGB Light,Light Sensor,Simple Setup,Countdown Timer,Matter Enabled,2.4GHz WiFi only,Compatible with Alexa, Apple Home,Google HomeDREO Brings Matter Connectivity to TurboPoly™ Fan 765S, Furthering its Open Ecosystem CommitmentOpenInfrared Point: Universal Remote HubIntroducing UniFi Network 10.5Hackers target UniFi as critical exploits surfaceCrestron Launches Configure Pro Platform for Crestron Home OSJosh.ai Showcases AI-Powered Home Control with New Experiential VideoHunter Douglas Alta Window Fashions Motorized Shades Now Compatible with PowerView AutomationSamsung will soon start charging to access its smart home APIA New Enhanced SmartThings API Experience  - SmartThings BlogNetflix now requires every user profile to be tied to unique email addressSmart lock maker Level has been gutted and its founders are outMatter Just Underwent a Major Stress Test for Supporting Large-Scale DeploymentsHome Assistant upgrades to Matter 1.5.1 with a better codebaseThe Matter upgrade you've been waiting forInside the room where the smart home industry is still betting on MatterDroplet the #1 Smart Home Water Sensor - Hydrific, part of LIXIL

Teresa's back with RV trip round two, proving once again that life is always better when it's packed to the brim with adventure. From laughing through the chaos to Mark somehow ending up captaining a boat in Idaho with absolutely zero boating experience, it's a classic Palmer family YOLO adventure. Throw in theme park thrills, Teresa living her best waterslide life, plus plenty of belly laughs, and you've got yourself some weeeeeeee living! The girls' Clairs are working overtime this week. Sarah's giving Wyatt a crash course in the weird and wonderfully dirty candy of the '90s before somehow calling his strep throat before anyone else. Meanwhile, Teresa clairsentiences her way through a dead phone, five exhausted kids and an Uber predicament after a marathon day at a theme park. Plus, baby Ocean has officially turned one! The girls reflect on all the emotions that come with that first birthday, the gratitude of watching these little humans grow, and the universal struggle of babies who seem determined to put absolutely everything in their mouths. They also chat about the magic that happens when we put our phones down, connect with the people around us, and discover how meaningful a conversation with a complete stranger can be. Resource Links: CDA Pontoons cdapontoons.com silverwoodthemepark.com tokikids.com Follo​w Sarah Wright Olsen: IG: @swrightolsen Follow Teresa Palmer: IG: @teresapalmer  FB: https://www.facebook.com/teresamarypalmer/ DISCOUNT CODES: • Go to www.baeo.com and get 20% when using the code MOTHERDAZE20 • Go to www.lovewell.earth and get 20% when using the code MOTHERDAZE20 More about the show! • Watch this episode on YouTube here • Co-founders of @yourzenmama yourzenmama.com • Read and buy our book! "The Zen Mama Guide To Finding Your Rhythm In Pregnancy, Birth, and Beyond"  Learn more about your ad choices. Visit podcastchoices.com/adchoices Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this week's episode, I'm sharing some hilarious NYC summer storytimes, dating observations, and the biggest mindset shifts that have been helping me become more confident and stop people-pleasing. We talk about creating your own reality, having higher standards, protecting your energy, confidence, boundaries, dating, friendships, and why you don't need to overextend yourself for people who wouldn't do the same for you.Plus: Fourth of July adventures, nightlife in NYC, funny boylypop stories, current inside jokes, and my favorite tips for staying locked in while still enjoying summer.If you're working on confidence, self-worth, boundaries, self-improvement, dating, or becoming your highest self, this episode is for you.♡ New episodes every week covering wellness, confidence, productivity, dating, fitness, mindset, and navigating your 20s. My self tan must haves from CVS https://creators.cvs.com/mypage/stellaraeGet 10% off Prozis with code STELLARAE10 prozis.com/1LMJGGet 10 free meals and free breakfast for life from HelloFresh with code HF-0449 https://www.filify.co/SHBn0 Get $1000 off the mindodygreen health coach certification program with promo code STELLACOACHING https://www.shareasale.com/u.cfm?d=1281553&m=96296&u=1030263instagram http://instagram.com/stellaraepodcastmy current filming set up:camera: https://amzn.to/4cEQiLOmicrophone: https://amzn.to/3Z2A5gctripod: https://amzn.to/3AEmxgKring light: https://amzn.to/3XxZrShbox lights: https://amzn.to/4e1Q1Ubportable light for phone: https://amzn.to/3XxZspjlisten on spotify: https://open.spotify.com/show/2DMbeh7EqiqgROIjvW0sI9listen on apple podcasts: https://podcasts.apple.com/us/podcast/the-stella-rae-podcast/id1255618182#StellaRaePodcast00:00 Summer has officially become my YOLO season02:20 Why I'm making the most of my late 20s03:20 Creating your own reality & confidence05:10 Stop overthinking what everyone thinks of you06:20 The people-pleasing habit I finally let go of08:00 Detachment, dating & protecting your energy10:00 Why boundaries make people respect you more12:00 The easiest way to practice saying "no"14:15 Dating storytime: the pearl necklace guy17:30 My thoughts on modern dating in NYC20:45 What actually makes a man attractive23:30 Why actions matter more than words26:45 Raising your standards without feeling guilty29:10 My current favorite inside jokes & "boylypop" lore35:20 Staying locked in while still enjoying summer37:10 The mindset I'm taking into the rest of the year

Why do so many retirees struggle to spend money they've spent decades saving? Don and Tom explore the psychology behind retirement spending, including the fear of running out of money, the reluctance to touch principal, and how guaranteed income sources like Social Security, pensions, and even simple immediate annuities can make retirees more comfortable enjoying their wealth. They discuss practical strategies for creating spending confidence, the importance of comprehensive retirement planning, and why delaying meaningful experiences can be riskier than spending. The episode also answers a listener question about setting up a Roth IRA for a teenager and examines the latest uncertainty surrounding 529-to-Roth transfers.0:05 Introduction: Why retirees struggle to spend money they can afford to spend1:36 Fear of running out versus fear of missing out in retirement2:52 Why even millionaires worry about spending their savings3:51 The saver mentality and the challenge of switching to spending mode4:47 Research shows many retirees barely touch their nest eggs5:29 YOLO, aging, and the reality of declining mobility later in life6:02 Why retirees prefer spending Social Security, dividends, and interest over principal8:04 Travel, aging, and the danger of postponing experiences8:49 Creating confidence through retirement planning9:56 Using Social Security and RMDs to cover essential expenses10:12 Flexible withdrawal strategies for retirement spending11:39 Could a simple immediate annuity help retirees spend more confidently?12:42 Healthcare costs, aging, and changing spending patterns13:30 Recency bias and how it distorts retirement decisions14:48 Why lifelong savers have trouble becoming spenders16:27 Summer slowdown and a request for more listener questions17:58 Listener question: Setting up a Roth IRA for a 19-year-old daughter19:16 Evaluating Avantis ETFs and M1 Finance for a young investor19:48 Why a single-fund solution may be better for small accounts20:56 The importance of emerging markets exposure22:40 Understanding 529-to-Roth IRA transfer rules24:33 The unanswered question of beneficiary changes and the 15-year ruleQuestions? Comments? Click!

Bill Yount reached financial independence at 60—then froze. His financial advisor confirmed 100% security, yet instead of relief, he felt disoriented fog. The emergency medicine physician who transformed from YOLO spender to 40% saver now struggles with a question that haunts many late starters: if I'm financially free, why can't I leave? Key Topics Discussed 00:05:30 The Wake-Up Call: From YOLO to Financial Awareness Bill's trifecta of mistakes at age 50: being house poor after an underwater renovation, maintaining a single-digit savings rate, and panic-selling stocks at market bottom. A lawsuit became the catalyst for confronting financial reality and transforming to a 30-40% savings rate within a decade. 00:15:00 The Emotional Journey: Anger, Shame, and Transformation Processing the emotional weight of starting late requires confronting anger, shame, and regret. Bill explains how downsizing from material excess created unexpected freedom, and why late starters must do the psychological work alongside the mathematical calculations. 00:22:00 The Partnership: Wife's Role and Family Dynamics Bill's wife became Chief Visionary Officer, returned to work full-time, and they saved her entire income through solo 401(k)s. Their journey debunks the "rich doctor syndrome" myth—25% of physicians at age 60 aren't even millionaires. 00:28:00 The Fog of FI: Reaching the Number and Not Knowing What's Next Sitting across from a financial advisor who confirmed complete financial security, Bill experienced unexpected confusion instead of celebration. This disorienting state—FOGO, or fear of getting out—reveals how identity and emotion don't automatically align with mathematical achievement. 00:35:00 One More Year Syndrome and Identity Struggles Despite being FI, Bill continues working twelve-hour emergency medicine night shifts. He candidly explores identity wrapped up in being a doctor, the meaning derived from patient care, and the difficulty of imagining life beyond the hospital. 00:42:00 The Glide Path: Cutting Shifts and Taking Action After Doc G asked for "one good reason" to keep his current schedule and Bill couldn't answer, he committed to cutting two shifts per month. This gradual approach offers an alternative to the all-or-nothing retirement cliff. 00:50:00 Lessons for Late Starters: Beliefs and Barriers Common limiting beliefs that paralyze late starters include "I'm too far behind," "I don't make enough," and "I don't know enough." Bill emphasizes it's always the right time to start, and the math works the same regardless of income level. 00:58:00 Health, Wealth, and Future Planning A frank discussion about neglecting physical health during wealth accumulation. Bill commits to refocusing on exercise and wellness to minimize the gap between healthspan and lifespan during the "go-go years" of early retirement. 01:05:00 Community, Travel, and What's Next Future plans include traveling to Norway with his sons, speaking at KiwiFi in New Zealand, and an ambitious mission: ensuring every medical resident receives a financial plan by 2035. Notable Quotes Bill Yount: "The emphasis, as we say, on late starter is on the starting and not being late." Bill Yount: "Between stimulus and response is a space. And we need to embrace that space because in that space, we need to regulate and choose our response." Bill Yount: "Relationships compound better than money, I think." Bill Yount: "It's better late than never. And we can catch up to FI together." Ginger: "I think a lot of people say, oh, that person is like me, right? And if they can do it, I can do it." Key Takeaways Track your money completely: Know your net worth, understand expenses, and identify where money goes before creating a plan Implement a reverse budget: Save your target percentage (30-40% if possible) off the top first, then spend the rest according to values Address the emotional work: Process anger, shame, and regret about past mistakes. Forgiveness matters as much as spreads…

Retail sales were up 0.9% in May, which is a generally positive economic sign. But it doesn't square with our reality, in which price inflation outpaces wage growth. That is, until you look at that pesky personal savings rate. In this episode, YOLO consumers in a grim economy. Plus: Fed Chair Warsh holds rates steady, the rate of new households is falling, and what would happen if the U.S. lost its global reserve currency status.Every story has an economic angle. Want some in your inbox? Subscribe to our daily or weekly newsletter.Marketplace is more than a radio show. Check out our original reporting and financial literacy content at marketplace.org — and consider making an investment in our future.

Retail sales were up 0.9% in May, which is a generally positive economic sign. But it doesn't square with our reality, in which price inflation outpaces wage growth. That is, until you look at that pesky personal savings rate. In this episode, YOLO consumers in a grim economy. Plus: Fed Chair Warsh holds rates steady, the rate of new households is falling, and what would happen if the U.S. lost its global reserve currency status.Every story has an economic angle. Want some in your inbox? Subscribe to our daily or weekly newsletter.Marketplace is more than a radio show. Check out our original reporting and financial literacy content at marketplace.org — and consider making an investment in our future.

This week, Scott sat down with his Lawfare colleagues Anna Bower and Eric Columbus, and his Brookings colleague Molly Reynolds, to talk through a couple of the week's big news stories in domestic politics, including:“The Grift That Keeps On Giving.” Last week, the Justice Department announced the creation of a so-called Anti-Weaponization Fund of nearly 1.8 billion taxpayer dollars, from which purported victims of politically motivated prosecutions can apply to receive payments. The fund was created as part of a settlement with President Trump and his sons, who sued the IRS for 10 billion dollars over the leak of his tax returns. So far, pardoned Jan. 6 rioters, former Congressman George Santos, Trump's ex attorney Michael Cohen, and even former FBI Director James Comey have all said that they are considering applying, and three lawsuits have already been filed challenging the fund. How did Trump's lawsuit against the IRS lead to this fund? And how do we see these legal challenges playing out in court?“Lame Duck Around and Find Out.” President Trump's preferred primary picks have cruised to victories in Indiana, Kentucky, Louisiana, and Georgia Republican primaries, ousting incumbents Senator Bill Cassidy and Representative Thomas Massie as some of the few voices of dissent within the Republican Party. But Trump's involvement in the primaries has come at a political cost, with outgoing members voicing their criticism and even going so far as to buck the president on legislation. Last week, Cassidy flipped his vote in favor of a critical war powers resolution in the Senate, which could undermine the administration's legal justification for the war. With such close margins in Congress, how do we expect this new YOLO faction to impact the president's agenda before the midterms?While we introduced a third topic, we frankly ran out of time this week. Sorry about that! We'll circle back to it in the weeks ahead.In object lessons, Molly is hooked on the fish-focused local NPR podcast, “Catching The Codfather.” Eric is looking to catch a killer with the latest Hugh Jackman movie (which he thinks is shear perfection). Scott is caught up in the latest “Storm,” featuring Yung Lean. And Anna has caught basketball fever, both with the Knicks' return to the NBA Finals, and also with the (much-more-affordable-but-equally-entertaining) NY Liberty.To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.