Electrical device that transfers energy through electromagnetic induction from one circuit to another circuit. It may be used to step up or step down the voltage.
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Arrêt des centrales à charbon, fermetures d'usines, suppressions d'emplois : la transition écologique transforme aussi l'économie de nombreux territoires européens. Pour y faire face, l'Union européenne a mis en place un nouvel instrument : le Fonds pour une transition juste. Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.
Vendre du plaisir sans jamais prononcer le mot “sexe”.C'est la règle que Patrick Pruvot s'impose depuis le début.Il ne parle pas de sex-shop ou d'érotisme mais de bien-être intime et de développement durable du couple.Pourtant, rien ne le destinait à l'entrepreneuriat et encore moins dans cet univers.Ancien publicitaire chez Publicis, vers 40 ans, son divorce le force à emménager rue Saint-Denis, au-dessus d'un sex-shop, le Pussy Club.Chaque jour, il passe devant les sex-shops du quartier, observe et repère ce qui pour lui est une aberration marketing. Toutes ces boutiques mélangent deux univers opposés : la pornographie et l'érotisme.Son idée est simple : isoler la partie “noble” pour la rendre fréquentable, "moralement accessible".La sortir des rues sombres pour la coller entre Zara et H&M.Mais personne ne veut de lui : neuf banques sur dix refusent de lui ouvrir un compte, les bailleurs ne signent pas, les investisseurs tournent le dos.Malgré tout ça, Patrick contourne chaque épreuve.20 ans plus tard, Passage du Désir, c'est 25 boutiques en France, près de 35 millions d'euros de chiffre d'affaires, et une première adresse à l'étranger, avec une ambition : exporter l'amour à la française.Mais il ne se contente pas de revendre les marques des autres.Passage du Désir sort un nouveau produit tous les trois jours, uniquement disponible sur son site et dans ses boutiques.Patrick déroule les coulisses d'un commerce que personne n'osait rendre grand public :Pourquoi s'interdire les mots “sexe” et “érotisme” a construit toute la marqueComment banaliser un produit que tout le monde perçoit de façon négativeCombien coûte vraiment l'ouverture d'une boutique, et l'erreur qui tue un lancementL'importance des boutiques pour le retail en ligneL'art de négocier en ChineUne leçon de retail dans le secteur le plus tabou de France. Par celui qui l'a rendu fréquentable.Vous pouvez contacter Patrick sur Linkedin.TIMELINE:00:00:00 - Vendre du plaisir sans prononcer le mot sexe00:12:00 - Où s'arrête l'éthique des banques ?00:21:18 - Créer une gamme pour faire tomber les tabous masculins00:30:57 - Fixer des normes que la loi n'impose pas00:37:42 - Combien ça coûte d'ouvrir une boutique ?00:44:05 - Ouvrir des boutiques pour vendre en ligne00:53:29 - Transformer ses points de vente en attraction01:07:08 - Vendre la French Touch romantique01:16:16 - Lancer un nouveau produit tous les 3 jours01:22:34 - « En France on sait faire un Airbus, pas un sextoy »01:33:08 - L'art de la négociation Chinoise01:38:30 - Le produit qui fidélise autant qu'une marque de cigarette01:48:11 - Les meilleurs jouets à offrirLes anciens épisodes de GDIY mentionnés : #510 - Carole Benaroya - Kujten - La reine du cachemire#405 - Nicolas Santi-Weil - Ami Paris & The Kooples - "Si tu n'arrives pas à en faire un client fais-en un ami"#208 - Marie Comacle - Puissante - Faire vibrer le corps des femmesNous avons parlé de :DNVB et ONVB : les nouveaux modèles de croissance ?Plug Donald TrumpLELO: La Marque Leader Des Accessoires et Produits IntimesClick and Mortar : Définition et guide completModèle « Click and mortar » : définition et avantages pour les entreprises en FranceComment la Chine est devenue imbattable ?Un grand MERCI à nos sponsors : Squarespace : https://squarespace.com/doitQonto: https://qonto.com/r/2i7tk9 Brevo: brevo.com/doit eToro: https://bit.ly/3GTSh0k Payfit: payfit.com Club Med : clubmed.frCuure : https://cuure.com/product-onely (code DOIT)Vous pouvez retrouver la liste de tout le matériel utilisé pour enregistrer nos épisodes sur cette page.Vous souhaitez sponsoriser Génération Do It Yourself ou nous proposer un partenariat ?Contactez mon label Orso Media via ce formulaire.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.
Send us Fan MailIn this episode, Bryn, Rob, Will and Eddie talk about the information necessary to select the right Buck/Boost Transformer for your equipment.Thanks for listening! Please visit www.mitsubishicomfort.comContact us at metustechshow@hvac.mea.com
Nouvel épisode hors-série enregistré en direct de la soirée anniversaire des 5 ans des Trophées RSE AXA. Chaque année, ces trophées récompensent des entreprises et des associations qui, par leur activité, apportent une solution concrète pour répondre aux défis de notre époque. Dans cette série, nous donnons la parole à celles et ceux qui transforment les idées en actions. Et aujourd'hui, nous recevons Bertrand COULON, directeur commercial de TchaoMégot. Il nous explique comment transformer l'un des déchets les plus polluants de notre quotidien en ressource industrielle. Les mégots collectés sont ensuite recyclés pour devenir un isolant éco-conçu utilisable dans le bâtiment ou le textile. Bonne écoute ! Learn more about your ad choices. Visit megaphone.fm/adchoices
Gary Rivlin — Multi-Part, Part One: Gary Rivlin, author of AI Valley: Microsoft, Google, and the Trillion-Dollar Race to Cash In on Artificial Intelligence, explores the origins and personalities driving the modern artificial intelligence revolution. Rivlin begins by demythologizing AI, focusing on central figures such as Reid Hoffman, a polymath whose childhood obsession with strategy games and thirst for human connection led him to co-found LinkedIn and become a premier venture capitalist. Hoffman represents the bloomer perspective, an optimist who sees AI as a co-pilot to amplify human intelligence. The narrative highlights the interconnectedness of Silicon Valley, tracing the friendships and debates between Hoffman, Peter Thiel, and Elon Musk that date back to their early days at PayPal. The history of AI is traced to Frank Rosenblatt's 1950s vision of neural networks, which aimed to create machines that learn as humans do. This approach was long ridiculed by proponents of rules-based computing, leading to decades of AI winters in which funding and interest evaporated. The revival came in the 2010s with Mustafa Suleyman and Demis Hassabis, whose London-based startup DeepMind proved that neural networks could master complex tasks through feedback. After Google acquired DeepMind in 2014, Elon Musk, fearing a corporate monopoly on the technology, co-founded OpenAI as a nonprofit. The discussion concludes with the rise of Sam Altman and the 2017 Transformer paper, a breakthrough from Google researchers that allowed computers to understand context, ultimately enabling generative AI and ChatGPT. (1)
Comment fait-on évoluer une marque née en 1906 sans la trahir ? C'est le défi qu'a relevé Vincent Montalescot, vice-président marketing de Montblanc, mon invité de cet épisode.Rien ne le prédestinait forcément à l'univers de l'écriture : originaire du Limousin, passionné de sport, il rêvait de travailler pour une grande marque. Il fait ses classes à l'agence Havas Sport, passe 17 ans chez Adidas, puis rejoint Montblanc en 2017 avec une mission aussi belle qu'ambitieuse : faire vivre une maison centenaire avec son temps.J'ai choisi de repartager avec vous cette conversation enregistrée en 2020, parce que c'est un véritable cours de marketing dont les principes n'ont pas bougé d'un pouce :Ne pas chercher à réinventer la roue, mais puiser dans les racines historiques de sa marque, avec humilitéFormuler une mission simple et limpide, car en matière de marque la complexité est un aveu d'échecPorter ce renouveau en interne et faire adhérer les équipes, par la pédagogie et la répétitionSur la fin, on aborde des sujets plus personnels : concilier une carrière ambitieuse et une vie de famille épanouie, l'importance des temps de silence, et le besoin d'engagements qui ont du sens au-delà du travail.Ce qui m'a frappée chez Vincent, c'est son amour du produit et de la marque : une force tranquille, élégante et réfléchie, à l'image de la maison qu'il fait rayonner. Une belle leçon de marketing, et de vie.Bonne écoute ✨Chapitrage 00:00 Introduction02:02 Le marketing du luxe et le virage lifestyle de Montblanc08:24 Faire évoluer une marque de 1906 sans la trahir18:48 Choisir ses ambassadeurs et embarquer les équipes32:01 Ses racines, le sport et 17 ans chez Adidas44:42 Luxe ou grand public, et concilier vie pro et perso52:18 Le crible du Podcast58:16 Les livres recommandés par Vincent MontalescotNotes et références de l'épisode ✨ Pour retrouver Vincent :Sur LinkedIn✨ Pour retrouver les livres cités par Vincent :Open, d'André AgassiQu'est-ce qu'un chef, du Général Pierre de Villiers*Liens affiliés FNACHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.
Dans cet épisode rediffusé de la série INNOVATEURS, consacrée à celles et ceux qui font l'innovation, nous vous proposons de réécouter notre rencontre avec Jean-Louis Constanza, cofondateur de Wandercraft, l'une des startups françaises les plus avancées dans le domaine de la robotique humanoïde.Parti d'un projet profondément personnel — aider son fils à remarcher — Jean‑Louis Constanza raconte comment Wandercraft est devenu un acteur majeur des exosquelettes médicaux avant de se lancer dans les robots humanoïdes industriels. Il revient sur la naissance de Calvin, le robot développé avec Renault, capable de porter des charges lourdes dans les usines, et explique pourquoi la robotique représente selon lui un enjeu stratégique majeur pour l'Europe. Il partage également sa vision de l'innovation, son regard sur l'intelligence artificielle, les bouleversements à venir sur l'emploi et les raisons pour lesquelles il estime que les robots humanoïdes vont transformer profondément notre économie et notre société.
THINK WITH FARAH - Entrepreneuriat, développement personnel et émotionnel
Et si ta phase de creux n'était pas le signe qu'il faut te reprendre en main… Mais une opportunité de voir ce que tu es incapable de voir quand tu passes ton temps à courir ?Ces dernières semaines, j'ai été lente. Genre, VRAIMENT lente.Et pour quelqu'un comme moi, qui adore travailler, qui valorise la vitesse, la performance et qui a une énorme capacité d'exécution… ça aurait facilement pu devenir un problème à résoudre.Sauf que cette lenteur m'a donné accès à des réflexions qui pourraient avoir beaucoup plus d'impact sur mon business que plusieurs semaines de productivité intensive.C'est ce que j'appelle le paradoxe de la productivité, dont je te parle dans cet épisode.Parce que plus tu exécutes, moins tu as nécessairement l'espace pour réfléchir.Et si tu cours extrêmement vite dans la mauvaise direction… Tu vas simplement atteindre le mur plus rapidement.Dans cet épisode, je te partage ce que ma propre phase de creux m'a permise de voir, mais surtout comment transformer TES périodes de lenteur en véritables leviers d'expansion.On va parler de :
Dans cet épisode solo, j'analyse le parcours de Geoffrey Laird, Fondateur de Okimia.Son secret ? Suivre vos clients quand ils montent en gamme, et transformer chaque euro qui dort en marge concrète.Au programme :* Suivre vos clients quand ils grandissent, quitte à changer de segment* Passer d'un outil qui montre à un outil qui fait agir* Transformer le temps perdu sur la trésorerie en marge concrète* Vous appuyer sur votre expertise pour créer une vraie barrière face aux outils génériques* Internaliser tout ce qui touche à l'expérience client, à commencer par le premier contactUn épisode 100% actionnable pour tout entrepreneur ou toute entrepreneure.Bonne écoute !Votre entreprise est-elle structurée pour accélérer ? Faites le diagnostic Comment t'as fait ? pour prendre du recul sur vos décisions stratégiques et vérifier si nous sommes la bonne équipe pour vous accompagner : https://diagnostic-commenttasfait.netlify.app/
Tentative de nouveau format : "FAQ Discord". On a récupéré toutes vos questions posées sur notre Discord et on y répond dans cet épisode : indexation, revente de sites, DMCA, Trust Flow, éthique de l'affiliation, YouTube… 1h17 de réponses sans filtre.
Transform and roll... well, sort of!This week on The Insanely Dangerous Retropodshow, Dangerous Dave travels back to the toy-filled world of 1984 to revisit the transforming robots that dared to take on Transformers...GoBots!Were they an underrated part of 80s toy history, or were they always destined to live in the shadow of Optimus Prime and Megatron?Dangerous Dave begins by firing up What Happened Way Back When?, revisiting five songs, five movies and five TV shows from 1984, including Ghostbusters, The Terminator, The Karate Kid, Wham!, Van Halen and The Real Ghostbusters. Then Retro Headlines looks back at some of the major stories making news in Britain and America during an unforgettable year.DANGEROUS DEEP DIVE – THE HISTORY OF GOBOTSDave takes an extended look at the origins of GoBots, beginning with Bandai's Japanese Machine Robo toys before Tonka transformed them into the GoBots brand.We remember the heroic Guardians, led by Leader-1, and the evil Renegades, commanded by the brilliantly named Cy-Kill.There are memories of Turbo, Scooter, Small Foot, Crasher and Cop-Tur as Dave explores why the smaller and often cheaper GoBots became such popular pocket-money toys.We also revisit Challenge of the GoBots, the Hanna-Barbera cartoon that attempted to build a universe around the characters, and examine what happened when Transformers arrived and completely changed the transforming-robot battle.Was Transformers genuinely better...Or did superior characters, storytelling and marketing simply leave GoBots behind?DANGEROUS DETOUR – THE GOLDEN AGE OF 80s TOYSWhy were the 1980s so ridiculously good for toys?Dave remembers an era dominated by He-Man, Transformers, MASK, Thundercats, G.I. Joe, Ghostbusters, Teenage Mutant Ninja Turtles, Visionaries, Bravestarr and, of course, GoBots.It was a time when the cartoon, comic and toy aisle all became part of one enormous childhood universe.BACK IN THE ADSWe're heading back to British television for another classic commercial as Dave remembers the wonderfully bizarre Smash Martians.Why were aliens laughing at humans for peeling potatoes?How did “For Mash Get Smash” become such a memorable slogan?And why do so many people still remember those little metal Martians decades later?RETRO RUMBLE – GOBOTS VS TRANSFORMERSIt's the battle we've been waiting for.GoBots vs Transformers!The two transforming robot giants go head-to-head across toys, characters, cartoons, value for money and pure nostalgia.GoBots put up a surprisingly strong fight...But can Leader-1 and Cy-Kill really defeat Optimus Prime and Megatron?DANGEROUSLY UNDERRATEDDave gives Challenge of the GoBots another chance and argues that the cartoon deserves to be remembered as more than simply “that other transforming robot show.”ONE SEASON WONDERWe also revisit the wonderfully 80s animated series Pole Position, with its high-tech vehicles, crime-solving adventures and connections to the classic arcade game.TOYBOX TIME MACHINEThe Toybox Time Machine opens once again as Dave remembers Voltron and the brilliant idea of taking five robotic lions and combining them into one enormous warrior.And then...It's time for Gaz.GAZ'S RAPID RETRO REVIEW – GOBOTSGaz has heard Dave defending GoBots for most of the episode...And he's having absolutely none of it.GAZ:"GoBots? Sorry, Dange... I didn't like them then and I don't like them now!They were the transforming robots you got when you really wanted Transformers.Leader-1? He's no Optimus Prime.Cy-Kill? He's basically an angry motorbike.And Scooter? Don't even get me started on Scooter.Transformers had Optimus Prime, Megatron, Starscream, Soundwave and Grimlock. GoBots had... well... GoBots.Were the toys cheap? Yes.Were they easy to transform? Yes.But that's because there wasn't much bloody transforming to do!I know you've spent this episode trying to convince everyone that GoBots deserve another chance, Dange, but I'm not buying it.For me... Transformers win every single time.Gaz's Rapid Retro Rating: ⭐⭐ – 2 out of 5"Two stars. And I'm probably being generous. Back to you, Dange!"THE DANGER ZONEAfter Gaz completely destroys them, Dave enters The Danger Zone to mount one final defence of GoBots.They may never have matched Transformers for characters, storytelling or longevity, but Dave argues that history has treated them unfairly.They were affordable.They were fun.They were easy to collect.And for plenty of kids, a GoBot was their first transforming robot.Dave awards the franchise an impressive 27 out of 30 Danger Rating...Even if Gaz would probably like to deduct about twenty points from that score!HALL OF DANGERFinally, despite Gaz's objections, GoBots enter the Hall of Danger for their contribution to the transforming robot craze and their place in 1980s toy history.Love them...Hate them...Or still wish your parents had bought you a Transformer instead...GoBots are part of the story of the 1980s.And that's why they're worth remembering.NEXT TIME...Season 9 continues as Dangerous Dave swaps transforming robots for synthesisers and heads into the incredible catalogue of...PET SHOP BOYSFrom West End Girls and It's a Sin to Always on My Mind, Heart, Being Boring and Go West, we'll explore the history, albums, greatest hits and legacy of Neil Tennant and Chris Lowe.The Insanely Dangerous Retropodshow – keeping the greatest decades alive, one dangerously nostalgic episode at a time.
Après plus de quinze ans dans le développement personnel, voici ce qui m'a réellement aidé à évoluer… et ce qui peut devenir dangereux.Dans cette vidéo, je reviens sur mon parcours avec la timidité, la confiance en soi, l'injustice, l'anxiété, l'argent, les limites et le sentiment d'être différent.Je partage également mon avis sur les promesses magiques, les gourous, la dépendance aux méthodes et les principales dérives du développement personnel.Une transformation durable ne vient pas d'une réponse miracle. Elle se construit en apprenant à se connaître, en expérimentant et en créant progressivement de nouvelles preuves.
Êtes-vous prêt à transformer votre approche marketing et à répondre aux véritables besoins de vos clients ? Dans cet épisode de L'Instant MARKETING COMMUNICATION, Florian Grimault vous plonge dans l'univers de l'utility marketing, une stratégie qui place la création de valeur utile au cœur de la relation client. Dans un monde où les consommateurs sont saturés de messages marketing, cette approche devient essentielle pour les marques souhaitant se démarquer et établir une connexion authentique avec leur audience. Florian explique comment les marques peuvent évoluer d'une simple stratégie de persuasion à une véritable stratégie d'aide, en devenant des facilitateurs qui offrent conseils, outils et services pertinents. L'utility marketing n'est pas qu'une tendance passagère ; c'est un levier stratégique incontournable pour renforcer la légitimité et la confiance envers une marque. À travers des exemples concrets et des études de cas, cet épisode explore les différentes facettes de cette approche, et comment elle peut transformer l'expérience client. En abordant des thèmes tels que les stratégies de vente, l'analyse des tendances et le marketing digital, Florian vous invite à repenser votre plan de communication et à intégrer des pratiques qui répondent réellement aux attentes des consommateurs. Que vous soyez un professionnel du marketing, un expert en communication, un dirigeant d'entreprise ou d'un service ou simplement curieux des tendances du marketing et communication, cet épisode vous fournira des conseils d'experts qui vous aideront à naviguer dans un paysage en constante évolution. Florian aborde également des questions clés pour construire une stratégie efficace d'utility marketing, tout en mettant en lumière l'importance du social listening et de l'engagement sur les réseaux. Avec l'essor des technologies comme le dooh et le sms rcs, il est plus crucial que jamais de comprendre comment ces outils peuvent être utilisés pour améliorer la communication d'entreprise et l'expérience client. Ne manquez pas cet épisode enrichissant qui vous incitera à repenser votre approche et à envisager l'utility marketing comme une clé pour l'avenir de votre marque. Écoutez L'Instant MARKETING COMMUNICATION et découvrez comment devenir un acteur incontournable de la communication moderne, capable de répondre aux défis d'un marché en pleine mutation. suivez L'instant Marketing Communication de Florian GRIMAULT sur les réseaux sociaux LinkedIn linkedin. com/company/l-instant-marketing-communication-de-florian-grimault/? viewAsMember=true">https://www. linkedin. com/company/l-instant-marketing-communication-de-florian-grimault/? viewAsMember=true Site internet : net/">https://albatrosconseil. net/Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.
YOU - The Master Entrepreneur - A Guide to True Greatness with Stan Hustad
There is a whole lot of worrying, waiting and wondering going on. After taking something he rarely takes—a real vacation—Stan Hustad returned to Inconvenient Ideas with five days away from television, computers and the daily digital noise. The occasion was a significant wedding anniversary, celebrated with his wife, Karen, along the North Carolina coast. It also came after a difficult season marked by loss, uncertainty and reminders that life can change very quickly. Perhaps that is why a few days away produced something more valuable than rest: perspective. Stan returned not pretending that the world had suddenly become easier, but recognizing that, despite difficulties, obstacles and a few genuinely frightening possibilities, he was still living in what he calls a POA—a Place of Opportunity. And then he turned the television and computer back on. Welcome back to the world of worrying. Worrying, Waiting, Wondering—and Wishing Stan noticed four activities consuming an extraordinary amount of human energy: Worrying. Waiting. Wondering. Wishing. People worry about their jobs. They wait to see what happens next. They wonder what artificial intelligence will do to their profession. And they wish somebody—or something—would make the uncertainty go away. But wishing is not a business strategy. Worrying is not a career plan. And waiting for the world to become predictable may require a very long wait. The more useful question is: What can you create? That question becomes especially important when so many people feel they are reacting to life rather than creating it. Certainly, much of life is outside anyone's control. But there remains an enormous difference between admitting that reality and surrendering one's ability to act. That distinction may become one of the essential survival skills of the AI age. What Is Real? Artificial intelligence introduces another inconvenient question: What is real? Stan has encountered videos, music, images and performances that seemed completely authentic—until closer examination raised another possibility. Perhaps they weren't. AI is rapidly moving toward a point where ordinary viewers and listeners may have difficulty determining whether something was created by a human being, a machine, or some collaboration between the two. Stan describes the strange sensation perfectly: Sometimes something can seem too fake to be real—and too real to be fake. That changes the questions we may have to ask. Instead of simply asking, "Is it real?" perhaps increasingly we will also ask: Is it true? Is it good? Is it useful? Is it helpful? Is it beautiful? Does it make life better—or worse? That is an inconvenient idea indeed. Even the new Inconvenient Ideas studio provides a playful example. The glowing neon-style sign, the broadcast environment and the visual presentation can now be created with AI assistance. Is the neon sign "real"? Well, you're looking at it. And perhaps the more useful question is whether it helps Stan communicate an idea worth hearing. Become an Author of Ideas Stan also makes a personal confession. He has spent nearly half a century as a broadcaster and communicator. He talks. He teaches. He coaches. He interviews. And perhaps most importantly, he continually comes up with ideas. But traditional writing has never necessarily been his greatest strength. AI has helped him recognize a different possibility: Maybe he is not primarily an author who writes. Maybe he is an Author of Ideas. That changes the relationship with artificial intelligence. The human being supplies experience, judgment, imagination, conviction, stories and ideas. AI can help organize, edit, develop and distribute them. In that arrangement, AI does not necessarily replace the human creator. It can multiply the creator. And that may be one of the great opportunities of this technological revolution. But Beware of Trojan Horses Every gift is not necessarily good for you. The ancient Trojans learned that lesson the hard way. Artificial intelligence may become one of the greatest productivity tools ever created. It may also eliminate jobs, disrupt professions, destroy established business models and make some previously valuable skills considerably less valuable. Both realities can be true. The appropriate response, therefore, is neither blind enthusiasm nor paralyzing fear. It is preparation. Create Your Own Personal Economy Here Stan reaches the central challenge of the program. In an increasingly unpredictable economy, people should learn how to create their own personal economy. That does not mean everyone must quit a job tomorrow morning and launch a company by lunchtime. It means learning to think entrepreneurially. Can you recognize an opportunity? Can you solve a problem? Can you create something useful? Can you communicate its value? Can you develop relationships? Can you serve customers? Can you adapt when technology changes the rules? And can you create another source of usefulness and income when an old one disappears? Those questions are becoming important whether someone is 22, 42, 62—or 82. Everyone Is Also Becoming a Broadcaster There is another skill Stan believes people increasingly need. They need to learn how to communicate through modern media. You may never have a radio program. You may never call yourself a podcaster. You may never dream of becoming a YouTube personality. But we live in a mass-media marketing world. At some point, most professionals, entrepreneurs, leaders and creators will need to become comfortable behind a microphone, in front of a camera, on a screen or communicating through digital media. That makes broadcasting no longer merely a profession. It is becoming a life and business skill. The Four People We May Need to Become Stan's challenge can be reduced to four roles worth developing in this new world: Become an Entrepreneur. Learn to recognize opportunities, create value and build your own personal economy. Become a Broadcaster. Learn to communicate clearly, confidently and persuasively through modern media. Become a Transformer. Develop the ability to change yourself, your work and perhaps the lives of people around you. Become an Author of Ideas. Bring distinctly human experience, imagination, wisdom and judgment to AI tools capable of helping those ideas travel farther and faster. None of this guarantees an easy future. There will still be plenty to worry about. But perhaps there will be considerably less to fear. THINGS TO REMEMBER • Worry is information, not instruction. Fear may tell you that something deserves attention. It doesn't necessarily tell you what to do. • AI is both opportunity and disruption. Refusing to acknowledge either side leaves you unprepared. • Your ideas still matter. AI can generate words, pictures and possibilities, but human experience, judgment, character and wisdom remain extraordinarily valuable. • Entrepreneurship is becoming a basic life skill. You don't necessarily need to own a company to learn how to create value. • Broadcasting is becoming a basic communication skill. The ability to speak effectively through microphones, cameras and digital media will increasingly matter. • Transformation requires action. Waiting for certainty may simply mean waiting forever. THINGS TO SHARE Ask someone you care about: "If your present job disappeared because of AI, what useful thing could you create, teach, communicate or sell?" Then ask a second question: "What could AI help you do that you could not realistically have done five years ago?" Those two questions may start a far more productive conversation than another hour of worrying about artificial intelligence. THINGS TO TAKE NOTE OF Pay attention this week to the places where AI is already changing your life. Notice what it does extraordinarily well. Notice where it makes mistakes. Notice what frightens you. But especially notice where it gives you new capabilities. Instead of merely asking, "What will AI do to me?" begin asking: "What can I now do with AI that I could never do before?" That is the entrepreneur's question. THINGS TO DO Choose one idea you have been postponing. Write it down—or speak it into your phone. Use AI to help organize it. Turn it into something tangible: an article, presentation, proposal, podcast, video, course, business idea or conversation. Then put it into the world. Don't merely worry. Don't merely wait. Don't merely wonder. And don't merely wish. Create. THE INCONVENIENT CHALLENGE The AI digital world will undoubtedly produce disruption. Some careers will disappear. Others will change dramatically. New professions will emerge that hardly anyone can describe today. So the goal cannot be to make the future perfectly safe. The goal is to become the kind of person who is increasingly capable of meeting it. Stan Hustad has spent decades as an entrepreneur, broadcaster, communicator and coach. Today he is helping people learn how to become entrepreneurs, world-class broadcasters, transformational thinkers and Authors of Ideas who can work intelligently with artificial intelligence rather than merely worry about what it might do to them. If the new world has you worrying, waiting, wondering or wishing, perhaps it is time for a different conversation. Stan's invitation is simple: Learn to create your own economy. Learn to communicate your own ideas. Learn to use the new tools. Learn to become more useful. And learn to become less afraid of the future by becoming better prepared for it. Stan Hustad and What It Takes Radio are available to help. Stan@witradio.net Because in the end, the future may belong neither to the people who blindly embrace AI nor to those who desperately resist it. It may belong to the people who learn how to remain fully human while becoming remarkably good at using it. And remember: If an idea is interesting and important, there's a pretty good chance it will also be inconvenient.
One thing I forgot to do as we were pressed for time was to ask all the hosts what their favorite reveal was from the show. I'll keep mine to the four franchises as I usually do, but hopefully we can squeeze that in too on the next show. For Masters, as much as I love build-a-figures and vintage toy artwork homages, it does seem like more of the same. Their Chronicles Clawful is what really caught my eye. All I thought I wanted from Star Wars was a Rotta the Hutt in either Black Series or Vintage Collection. Well, I got both and hate the execution on both. So, even though I won't buy it, my favorite Star Wars thing was the vintage decoed X-Wing. What's funny is my favorite Transformer is something we didn't even talk about. It was hidden in the official pics. Besides the Icons Decepticon symbol Megatron, everything else was pretty lackluster. Even more disappointing was G.I. Joe. I get it, they blow their load with Yo Joe June. Besides some name reveals, we really only got what is looking to be a disappointing Thundermachine.
Transformer des tissus destinés à être jetés en robes porteuses de messages d'unité et d'espoir. C'est l'idée de l'exposition « Transformer la mode, réinventer la paix », qui se tient au Siège des Nations Unies à New York jusqu'au 9 septembre.À l'origine de cette initiative, la créatrice de mode et écologiste Runa Ray, qui souhaite faire de la mode un outil au service des causes sociales et du développement durable. « La mode utilise beaucoup de tissu... J'ai essayé de transformer ces tissus pour des messages d'unité et de paix », explique-t-elle.Inspirée par l'élan de solidarité observé à travers le monde pendant la pandémie de Covid-19, Runa Ray s'est demandé : « Pourquoi ne pourrions-nous pas faire cela tout le temps ? » Cette réflexion l'a conduite à lancer le projet Global Peace Flag, qui réunit des tissus récupérés dans une trentaine de pays, sur lesquels des étudiants et des citoyens du monde entier ont inscrit des messages de paix.Au micro d'ONU Info, elle revient sur la naissance de cette initiative et explique pourquoi elle veut faire de la mode « une voix... pas juste pour le glamour, mais pour les causes sociales ».(Interview : Runa Ray, créatrice de mode et environnementaliste; propos recueillis par Cristina Silveiro et Hisae Kawamori)
Regresamos al álbum clásico Transformer de Lou Reed del año 1972 y específicamente a ese tema definitivo, emocionante y atemporal: Perfect Day. Ricardo Portman nos cuenta su historia. Escucharemos Perfect Day, Perfect Day (acoustic demo) y Walk on the Wild Side. Recuerden que nuestros programas los pueden escuchar también en: Nuestra web https://ecosdelvinilo.com/ La Música del Arcón - FM 96.9 (Buenos Aires, Argentina) miércoles 18:00 (hora Arg.) Radio M7 (Córdoba) lunes 18:00 y sábados 17:00. Distancia Radio (Córdoba) jueves y sábados 19:00 Radio Free Rock (Cartagena) viernes 18:00. Radio Hierbabuena (Lima, Perú) jueves 20:00 (hora Perú) Onda Wantuki (Madrid) semanal
Votre produit a plein de fonctionnalités trop cool… mais vos prospects ne comprennent toujours pas pourquoi ils devraient le choisir ?Le problème vient souvent d'un positionnement flou et d'un messaging centré sur le produit plutôt que sur la valeur apportée au client.Dans cet épisode Décrypte, je vous explique le Benefit Ladder, un framework largement utilisé en Product Marketing pour relier les fonctionnalités d'un produit aux bénéfices qu'il apporte, jusqu'à la valeur émotionnelle perçue par vos clients.Je vous montre également comment utiliser ce framework avec un exemple concret basé sur Granola.Dans cet épisode, découvrez :
What does Buster need after holding onto the Creation Matrix and having his own godhood with machines? Well, not a Transformer of his own to pilot! That's what Optimus Prime thinks anyways.If you'd like to contact the guys, they'd love to hear from you! Morethanmeetstheseguys@gmail.comhttps://discord.gg/sKr8jwaAvhIf you'd like to toss a buck or more per episode, we'd adore and say nice things about you. You don't have to, as we'll still gladly hang out with you guys and gals every week, but we appreciate any help! patreon.com/user?u=69144181
What if grief was not something to get over, but a bridge that leads you to your greatest transformation? In this powerful episode of From Betrayal to Breakthrough, Dr. Debi Silber sits down with Rosalind Price, known as The Grief Gangstress, for an honest, deeply human conversation about cumulative loss, the 5 grief archetypes, and how navigating grief with strategic intention can lead to profound personal transformation. Rosalind Price has experienced grief from the age of four, losing her mother at 16, her father shortly after, members of her support system, and later her former husband. Rather than running from grief, she built an entire framework to move through it strategically, and now helps individuals and C-suite leaders do the same. She shares how grief is not a nemesis or an antagonist, but a bridge that reminds us we are the priority. This episode will change the way you see grief. Whether you are healing from betrayal, loss, or a life-altering shift, Rosalind's 5 grief archetypes will help you understand where you are in your journey and how to move forward with intention and power. Topics Covered: How cumulative grief can feel like life itself has betrayed you, and why that feeling is valid The critical difference between soldiering through grief and actually healing from it Why the people around us sometimes unintentionally add to our grief instead of easing it The 5 grief archetypes: Devoted Soldier, Seeker, Deep Diver, Expander, and Transformer, and how to identify which one you are How grief shows up in the corporate world and what C-suite leaders can do to create space for it Why speaking up for your needs during grief is not a burden, it is a gift to everyone around you How grief and betrayal healing share the same emotional terrain and why Debi's audience will deeply relate The relationship between love and grief: the bigger the love, the bigger the grief, and why that is actually a sign of your greatness Rosalind's personal journey from lifelong loss to becoming The Grief Gangstress Why grieving is the season and the bridge, and grief is the destination that reminds you that you are the priority Resources Mentioned: Grief Archetype Assessment Guide (FREE) — rrrichardprice.com/the-blueprint The Five Stages of Betrayal Recovery — ThePBTInstitute.com Post Betrayal Syndrome Assessment — ThePBTInstitute.com Betrayal to Breakthrough Live — ThePBTInstitute.com/live Connect with Rosalind Price: Website: rrrichardprice.com Free Grief Archetype Assessment Guide: rrrichardprice.com/the-blueprint LinkedIn: Rosalind Price Facebook: Rosalind Price / The Grief Gangstress Instagram: Rosalind Price / The Grief Gangstress Substack: Rosalind Price / The Grief Gangstress Connect with Dr. Debi Silber: Instagram: @debisilber Website: ThePBTInstitute.com Betrayal to Breakthrough Live — ThePBTInstitute.com/live Bring Dr. Debi to your stage — ThePBTInstitute.com/speaking Read the largest research study on betrayal ever conducted — ThePBTInstitute.com/state-of-betrayal-report
L'accélération technologique et l'instabilité mondiale transforment le rôle de l'employeur. Face aux crises extérieures, l'organisation devient un refuge protecteur où la transformation du management doit sécuriser les individus tout en soutenant la croissance.Dans cet épisode, Delphine Zanelli reçoit Mario Ceccon, Directeur des Ressources Humaines du groupe GEODIS.L'intégration de l'intelligence artificielle suscite une dualité émotionnelle sur le terrain. Les collaborateurs oscillent entre la peur du remplacement sur les tâches répétitives et l'espoir de voir les nouveaux outils résoudre des problèmes chroniques. Pour piloter cette mutation, le groupe a hissé le sujet au niveau du comité exécutif. Parallèlement, l'instabilité géopolitique pousse l'entreprise à assumer des responsabilités de protection autrefois dévolues aux institutions publiques. Le futur du travail dépend désormais de cette sécurité psychologique. Le déploiement d'assistantes sociales et de psychologues sur site permet de capter les signaux faibles et de préserver l'équilibre des équipes.Cette nouvelle réalité exige d'outiller l'encadrement à grande échelle pour mener à bien la transformation du management. L'organisation a défini sept principes clairs, allant de l'obligation de débattre et s'aligner jusqu'à l'exemplarité. Pour ancrer ces valeurs, un parcours de quatorze semaines traduit ces règles en pratiques managériales tangibles, telles que la gestion des conflits. L'impact de cet accompagnement est ensuite mesuré annuellement par un index de leadership évalué directement par le terrain, garantissant le suivi précis de l'engagement des collaborateurs.Né en Argentine et fort d'un parcours interculturel, Mario Ceccon identifie la résilience et l'entraide comme des constantes humaines universelles. En délaissant l'obsession de la performance individuelle de ses débuts, il conçoit désormais son métier de manager comme un levier au service du collectif. Il incarne l'approche du "servant leadership", prouvant que la confiance reste le moteur ultime pour accompagner la transformation du management.Cet épisode donne des éléments concrets pour définir des règles communes d'encadrement, intégrer le soutien psychologique sur le lieu de travail et structurer la montée en compétences managériales de milliers de collaborateurs.CHAPITRAGE :(00:00) Les grands enjeux RH d'un groupe mondial (03:29) IA : entre angoisse et cas d'usage (09:02) Sept principes de leadership clairs (14:26) Former massivement l'encadrement (21:01) L'intégration de la santé mentale (24:21) L'entreprise comme refuge protecteur (27:15) Une trajectoire depuis la Pampa (33:02) La bascule vers le servant leadership
durée : 00:02:14 - Le Billet de François Morel - Parler comme un poète, écrire comme Victor Hugo : un projet estival, qui passe à la radio. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France
Le conseil du jour, c'est une minute pour prendre du recul, respirer, et avancer un peu plus sereinement dans votre travail. Un conseil simple, concret, applicable dès aujourd'hui. Un format court de Happy Work, par Gaël Chatelain-Berry.NOUVEAU : retrouvez moi sur WhatsApp sur la chaîne Happy Work... pas de spam, c'est gratuit et il n'y a que du feelgood !!! : https://whatsapp.com/channel/0029VbBSSbM6BIEm0yskHH2gEt pour retrouver tous mes contenus, tests, articles, vidéos : www.gchatelain.comSoutenez ce podcast http://supporter.acast.com/happy-work. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.
Il y a des histoires qu'on aimerait ne jamais avoir à raconter.Christelle Arrighi, est mère de 3 enfants et vient de perdre son mari après 11 années de bataille contre le cancer.Christelle fait partie de ces femmes solaires, qui vous happe quand vous croisez son regard. Et pourtant son histoire est déchirante.En 2007, Christelle rencontre Martin. Coup de foudre immédiat. Mais 3 semaines après l'arrivée de leur premier bébé le sol s'effondre : Martin découvre une tumeur au cerveau de 6 centimètres. Christelle a 30 ans. Elle est en plein postpartum. Et elle va devoir se battre, aux côtés de son mari, pendant onze ans.Aujourd'hui, Christelle vient me raconter cette histoire d'amour et de combat. Cette famille de 5 qu'ils ont construite malgré la maladie.Elle vient aussi honorer la mémoire de Martin, parti le 31 janvier dernier et porter un message urgent : celui d'une maladie qu'on ne finance pas, qu'on ne guérit pas, et dont personne ne parle.C'est un épisode bouleversant. Accrochez-vous.Lien utiles : ==> lien de la pétition pour soutenir la recherche contre les tumeurs cérébrales==> dons à l'institut du cerveau (66% de déduction fiscale ie pour 100€ de don, il n'en coûte que 34€) Au programme :01:09 — Le combat d'une vie11:48 — Onze ans contre le cancer26:28 — Construire une famille malgré la maladie45:13 — Quand le cancer revient01:01:52 — Les derniers mois01:27:36 — Transformer le deuil en combatHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.
Join Phatty316 and Redbeard and returning family member MaximusG420 for a Thursday nite special Live show ,On the sheet for today the will get into the 40th anniversary of the Transformer the movie , The Odyssey's box office and the release of Spiderman Brand New Day
Crise climatique, montée des autoritarismes, IA, fractures sociales, incertitude géopolitique.La peur est partout. Et si, au lieu de la fuir, on apprenait à l'utiliser ?Dans cet épisode solo, je pars de mes propres angoisses – celles qui réveillent à 3h du matin – pour interroger une idée simple mais radicale : la peur n'est pas une faiblesse, c'est un signal.Et parfois, un moteur.Nous vivons une époque de polycrises : climat, eau, biodiversité, inégalités, démocratie, géopolitique, technologie, démographie.Ce n'est pas une impression. Ce n'est pas une hystérie collective.C'est notre réalité.Face à ça, nous avons développé trois réflexes :le nihilisme passif (“on est foutus, autant profiter”),l'indignation permanente (qui donne bonne conscience mais n'engage rien),l'optimisme béat (“la technologie va nous sauver”).Aucun ne tient vraiment.Dans cet épisode solo de Vlan, je propose une autre voie :- prendre la peur au sérieux,- comprendre ce qu'elle nous dit et la transformer en élan d'action.Je m'appuie sur plusieurs penseurs – Thomas Hobbes, Baruch Spinoza, Aristote, Erich Fromm – pour montrer une chose essentielle : historiquement et philosophiquement, la peur a toujours été un moteur de coopération, de création et de civilisation.On parle de :pourquoi notre peur est rationnelle,pourquoi vouloir la supprimer est une erreur,pourquoi nous ne sommes pas égaux face à elle,comment l'action agit comme une catharsis,et comment le conatus – cet élan vital décrit par Spinoza – continue d'agir en nous, même quand tout semble bloqué.Ce n'est pas un épisode de développement personnel.Ce n'est pas un épisode “solutions miracles”.C'est une tentative honnête de répondre à une question centrale de notre époque :que faire de notre peur, quand le monde devient objectivement inquiétant ?Idées centrales discutées
Aujourd'hui dans cette nouvelle leçon, je vous propose de redécouvrir une conversation enregistrée l'année dernière avec Tania, architecte d'intérieur. Son entreprise fonctionne déjà très bien en France. Pourtant, elle nourrit une ambition qui peut sembler démesurée : ouvrir une agence à New York.Comment poursuivre un rêve sans mettre en danger tout ce que l'on a construit ? Faut-il se lancer coûte que coûte ou avancer par étapes ? C'est précisément de cet équilibre dont nous parlons dans cette leçon.Vous verrez que voir grand ne signifie pas prendre tous les risques. Bien souvent, les projets les plus ambitieux sont ceux qui se construisent avec le plus de méthode. J'espère que cette conversation vous donnera l'élan d'oser, tout en vous aidant à avancer avec intelligence.Bonne écoute ✨CHAPITRAGE 00:00:00 – Introduction 00:01:58 – Le rêve de Tania : ouvrir une agence d'architecture d'intérieur à New York00:06:45 – Tester un marché sans tout risquer : la méthode du "premier pas"00:12:45 – Trouver ses premiers clients et construire un réseau local00:18:15 – Transformer une ambition en plan d'action concret00:24:20 – Les derniers conseils pour réussir son implantation et conclusionNotes et références de l'épisode Pour retrouver Tania Liegeon : Sur InstagramSur LinkedInSur PinterestSur FacebookSur son sitePour retrouver Demian : Sur le site Demian.educationReprenez le contrôle de votre temps Pour retrouver Demian sur les réseaux :Sur InstagramSur LinkedInHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.
This is the All Local noon update for July 29, 2026.
What is the cleanest way to connect several microphones, preamps and audio interfaces without constantly crawling behind the rack? AP starts with what sounds like a simple question about expanding the line-level inputs on his Audient iD44. That sends the team down a glorious rabbit hole involving passive switchers, mic splitters, USB converters, matrix routers, the Heritage Audio Synth Buddy, ESI interfaces, Broadcast Tools, Source-Nexus and the Passport VO. Eventually, one decidedly old-school solution keeps winning the argument: the humble patchbay. The team discusses quarter-inch, Bantam and TT patchbays, normalled and half-normalled connections, passive mic splitting, phantom power, transformer isolation, corrosion, loose connectors and why adding another preamp stage may not be the sonic disaster people imagine. Along the way, George mourns the disappearance of simple line-level audio interfaces, Robert explains how passive splitters survive real concert recording jobs, and Robbo demonstrates his remarkable ability to make a straightforward signal path impressively complicated. In this episode: • Switching several microphones and preamps into one interface • Passive line-level switchers versus audio interfaces • Why a patchbay may be the simplest solution • Normalled, half-normalled and open patchbay configurations • Quarter-inch patchbays versus TT and Bantam systems • Passive microphone splitting and phantom power • Transformer isolation and troublesome ground loops • Keeping the signal path clean without overengineering it The Pro Audio Suite is hosted by George Whittam, Robert Marshall, Andrew Peters and Darren "Robbo" Robertson. Thanks to our sponsors: Tri-Booth Austrian Audio, Making Passion Heard Leave a comment, suggest a topic or simply say g'day at theproaudiosuite.com.
La Cédéao a apporté son soutien au gazoduc Nigeria-Maroc, un projet long de 7 000 kilomètres lancé il y a près de dix ans. Destiné à alimenter 13 pays d'Afrique de l'Ouest en gaz avant d'approvisionner le Maroc et l'Europe, il doit entrer en service à partir de 2031. Reste un défi majeur : réunir les 25 milliards de dollars nécessaires à sa construction. L'une des principales ambitions du gazoduc est de renforcer l'accès à l'énergie en Afrique de l'Ouest. La moitié des volumes transportés, soit environ 15 milliards de m3 de gaz par an, sera destinée aux 13 pays situés sur le tracé. L'autre moitié alimentera le Maroc, avant d'être exportée vers le marché européen. Pour Amina Benkhadra, directrice générale de l'Office national des hydrocarbures et des mines (ONHYM) du Maroc, cette double vocation est au cœur du projet. « Il longera 13 pays sur la côte africaine, depuis le Nigeria jusqu'au Maroc et sera connecté à l'Europe, à l'Espagne à travers le gazoduc Maghreb-Europe, qui est déjà en production aujourd'hui », explique-t-elle. Une infrastructure pensée d'abord pour les besoins africains Au-delà de l'exportation, les promoteurs présentent le gazoduc comme un levier de développement pour les économies de la région. L'objectif est d'offrir une énergie plus stable aux ménages mais aussi aux secteurs industriels, notamment les activités minières, particulièrement consommatrices d'électricité. Pour Amina Benkhadra, cette disponibilité énergétique est un préalable à la croissance : « Beaucoup de pays sur le tracé ont besoin d'une énergie durable pour pouvoir assurer leur développement économique et social. Il y a plusieurs industries énergivores. Parlons par exemple des industries minières, qui ont besoin d'une énergie stable et durable comme le gaz. Et donc, c'est d'abord une énergie qui va être mise à leur disposition pour assurer le développement et la croissance dans leur pays. » Le soutien affiché par les 13 États concernés s'explique aussi par leurs intérêts économiques. Les futurs producteurs de gaz, comme le Sénégal ou la Mauritanie, pourraient utiliser cette infrastructure pour exporter une partie de leur production. Pour Francis Perrin, directeur de recherche à l'IRIS à Paris et chercheur associé au Policy Center for the New South à Rabat, l'accord politique de la Cédéao constitue une étape importante, sans pour autant garantir le succès du projet : « Quand l'idée de ce projet a été lancée par le Maroc et le Nigeria, qui sont les deux promoteurs principaux en 2016, ça pouvait apparaître comme un très beau rêve. La décision importante de la Cédéao ne veut pas dire que c'est gagné. Mais le projet a quand même avancé. Chacun des pays sur ce tracé a intérêt à ce que ce projet marche. C'est une base, ça permet d'avancer. Ce n'est pas une garantie absolue de succès, bien sûr. » Le défi du financement Le principal obstacle reste désormais financier. Construire près de 7 000 kilomètres de gazoduc représente un investissement estimé à 25 milliards de dollars. Selon Francis Perrin, le financement devrait associer des institutions internationales, des banques de développement, des agences de crédit à l'exportation et des investisseurs privés. La viabilité économique du projet dépendra avant tout de l'existence d'un marché capable d'absorber le gaz transporté. « Il pourrait y avoir des institutions financières internationales type Banque mondiale qui a déjà commencé à être contacté par le Maroc et le Nigeria, des banques de développement. On peut penser évidemment à la Banque africaine de développement et aux banques européennes. Il y aura aussi des agences de crédit à l'exportation. Des contacts ont commencé avec la US Export Import Bank et évidemment, dernier point, des financeurs privés, c'est vraiment ce couple "marché-financement" qui fonctionne. Il n'y a pas de financement sans marché, et il n'y a pas de marché sans financement. » À ses débuts, le projet avait même suscité l'intérêt du géant russe Gazprom, avant que la guerre en Ukraine ne rebatte les cartes géopolitiques. Les opérateurs nigérians et marocains visent désormais un lancement des travaux d'ici deux ans et les premières livraisons de gaz à partir de 2031. À lire aussiProjet de gazoduc Nigeria-Maroc: «L'accord de la Cédéao marque une étape majeure»
Au Niger, il y a trois ans, le 26 juillet 2023, le président Mohamed Bazoum était renversé par un coup d'État militaire. Il est toujours retenu à Niamey avec son épouse. Depuis, le pays est dirigé par le Conseil national pour la sauvegarde de la patrie (CNSP), avec à sa tête le général Abdourahamane Tiani, officiellement investi président l'an dernier pour une durée de cinq ans renouvelables. L'économiste Kiari Liman-Tinguiri, directeur de l'institut West Africa and Sahel Development Institute (Wasdi), ex-ambassadeur du Niger aux États-Unis sous la présidence Bazoum est l'invité de RFI. RFI : Quel bilan trois ans après l'arrivée du Conseil national pour la sauvegarde de la patrie au pouvoir au Niger ? Kiari Liman-Tinguiri : Les arguments avancés pour expliquer le coup d'État au Niger étaient doubles. Il y avait d'abord la situation sécuritaire et ensuite ce qu'ils ont appelé la gouvernance. Du point de vue de la sécurité, malheureusement, le constat est que la situation s'est détériorée de façon incontestable par rapport à 2023. Quels sont les éléments factuels dont vous disposez pour dire que la situation s'est détériorée au niveau sécuritaire ? Je me base sur des données publiques : les données de la base d'Acled. Il y a eu une hausse du nombre d'incidents sécuritaires. Il y a eu une augmentation du nombre de pertes en vies humaines. Il y a eu une augmentation du nombre d'attaques contre les forces armées. Et il y a eu une extension géographique de la violence jihadiste. Comment peut-on expliquer cette détérioration de la situation sécuritaire ? Est-elle est liée au contexte régional peut-être ? Sauf que dans le même temps, la région de Tillabéri est devenue la région la plus affectée par la violence terroriste. C'est là où il y a le plus de morts. Il n'y a aucune région, ni du Mali, ni du Burkina Faso, qui a enregistré cela. Sur le plan politique, tous les partis ont désormais été dissous au Niger, en 2025. Est-ce qu'il y a encore, à ce jour, une vie politique dans le pays ? Malheureusement, non. Si ce n'était que la dissolution des partis politiques… Il y a au Niger un rétrécissement extrême de l'espace civique. Vous avez des arrestations arbitraires en grand nombre. Vous avez une suppression de toute possibilité de liberté d'expression, l'interdiction des médias même internationaux, le mépris des décisions de justice, la dissolution des organisations non-gouvernementales et leur soumission à un système de contrôle sans précédent. Donc, un régime autoritaire. C'est ça la réalité. Cela fait trois ans que Mohamed Bazoum est détenu à Niamey. Quelles sont les nouvelles que vous avez du président renversé ? Comment se porte-t-il aujourd'hui ? J'espère qu'il se porte bien, mais le président Bazoum n'a pas de contact. Il a été privé des moyens de communication depuis très longtemps. Sa situation est préoccupante. D'abord la détention injuste, injustifiable. Il n'y a pas de précédent dans notre pays. Le président Bazoum, à l'heure où nous parlons, a été détenu plus longtemps qu'il n'a gouverné le pays. Sa détention est arbitraire. Ça, ça a été décidé par un groupe de travail des Nations unies. Il n'a pas été jugé. Il n'y a aucun grief public contre lui. Mais il y a d'autres personnes détenues : son ministre de l'Intérieur Hamadou Adamou Souley, Moussa Tchangari un activiste de la société civile... Il faut libérer tout ce monde. Économiquement en tout cas, malgré les prédictions des plus pessimistes, le pays ne s'est pas effondré. Comment décririez-vous aujourd'hui l'économie nigérienne ? Oui, sur le plan factuel, on a une croissance économique forte. En 2024, c'est remarquable, entre 9% et 10 %. En 2025, les estimations la placent autour de 7 %. Ça, c'est pour la croissance macroéconomique. Grâce au pétrole ? C'est le pétrole. Le pétrole représente à peu près 9 % du PIB. Le deuxième contributeur à la croissance, c'est l'agriculture. Mais les deux sources font qu'il ne s'agit pas d'une performance de politique publique ou de stratégie économique. Qu'il s'agisse du pipeline Niger-Bénin, qu'il s'agisse des investissements pétroliers, tout cela a été négocié et était sur le point d'entrer en production quand le coup d'État est intervenu. Transformer une croissance de nature pétrolière en réduction de la pauvreté ou en bien-être des populations, ça, ça demande des politiques publiques. De surcroît, il y a une baisse drastique de l'aide. Et tout ça, sur fond d'une augmentation de nos dépenses en matière de défense. Il y a quand même un autre élément à prendre en compte, c'est cette fermeture de la frontière entre le Niger et le Bénin. Que fait perdre cette fermeture de la frontière à Niamey ? C'est l'une des choses les plus incompréhensibles. Il semble que la querelle procéderait de la possibilité qu'il y ait des forces françaises basées au Bénin, qui constitueraient une menace. Mais on ne peut pas réclamer pour soi la souveraineté et décider qui peut aller chez les autres. Par bonheur, le pétrole passe quand même par le Bénin. Donc, la frontière est fermée, mais juste la frontière terrestre, au niveau de Malanville. Avec les militaires, le CNSP tient au pouvoir trois ans après. Comment d'après vous et pourquoi ? Il n'y a pas qu'au Niger. C'est le cas au Mali, c'est le cas au Burkina Faso. Je ne pense pas que le fait de perdurer au pouvoir soit lié à la performance. C'est davantage lié à l'atonie de l'élite. Les gens s'accommodent de la situation sur place. Et puis, compte tenu de la situation sécuritaire, ce ne serait un cadeau pour personne, j'imagine. C'est une tragédie de penser comme ça. La pression jihadiste est si forte que je ne sais pas si le pouvoir attire. À lire aussiTrois ans après le coup d'État, le Niger toujours en proie à l'instabilité et au terrorisme
À Madagascar, une enquête de journalistes d'investigation révèle que les revenus des crédits carbone atteignent difficilement les communautés locales censées protéger les forêts. Malgré un premier versement d'environ 9 millions de dollars de la Banque mondiale en 2023, les lourdeurs administratives ralentissent les projets et les transferts de fonds. Transformer la protection des forêts en revenus : c'est l'objectif du mécanisme REDD+, pour « Réduction des émissions dues à la déforestation et à la dégradation des forêts ». À Madagascar, il s'applique à 14 aires protégées appartenant à l'État et gérées par des organisations privées. Lynda Andriatsitonta est journaliste d'investigation au sein du réseau Malina : « Sur le plan de partage des bénéfices du programme, 5% des revenus issus des crédits carbone sont alloués aux communautés locales, en récompense de leurs efforts de protection des forêts. Nous avons constaté que malgré le versement d'une première tranche du financement de la Banque mondiale, de nombreuses communautés locales n'ont toujours pas reçu les bénéfices qui leur sont destinés. Certaines ignoraient même l'existence de ces récompenses. » Des communautés locales tenues à l'écart des retombées La moitié des revenus revient aux gestionnaires d'aires protégées, chargés de les réinvestir dans des projets de reboisement, des activités génératrices de revenus ou de nouveaux services de base, en concertation avec les communautés locales, lesquelles doivent formuler leurs besoins par écrit. « Mais sur le terrain, la consultation semble rester lettre morte », affirment les journalistes de Malina, en pointant « des mécanismes de décision opaques ». Lovakanto Ravelomanana, coordinatrice du système REDD+ au sein du ministère de l'Environnement, avance une explication : « Le souci, c'est l'ampleur géographique. Deux millions d'hectares d'aires protégées sont concernés par ce programme. Au niveau des forêts très denses, des zones ne peuvent être accessibles qu'à moto sur des pistes très mauvaises, d'autres accessibles uniquement à pied. Chaque gestionnaire d'aire protégée doit s'adapter pour voir comment toucher le maximum de communautés locales. » Blocages et lenteurs administratives À cet enclavement s'ajoutent des freins administratifs selon Lovakanto Ravelomanana. Les communes, censées percevoir 13% de l'enveloppe, se plaignent de ne pas toutes avoir reçu l'argent deux ans et demi après le premier décaissement : « La machine administrative est très lourde. Dès qu'il y a des erreurs, on revient toujours à la case départ. Un même dossier peut rester pendant des mois au même endroit, au sein d'un même ministère. C'est vrai qu'on tâtonne, tout le monde apprend sur le tas, mais malheureusement la conséquence, ce sont tous ces retards qui s'accumulent. » L'accord signé entre la Banque mondiale et Madagascar prévoit le versement de deux autres tranches de crédits carbone, représentant ensemble 42 millions de dollars supplémentaires environ. À deux conditions toutefois : la réduction effective des émissions de gaz à effet de serre grâce à la protection des forêts. Et la mise en œuvre du plan de partage des revenus. À lire aussiMadagascar: à Antananarivo, les travailleurs informels très exposés aux pics de pollution
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
Dans cet épisode solo, j'analyse le parcours de Guillaume Mangeot, Directeur Général de Monsieur Cheveux.Son secret ? Transformer une expérience personnelle en une entreprise de services fondée sur la confiance, la pédagogie et l'accompagnement, en s'appuyant sur cinq leviers que tout entrepreneur peut adapter à son activité.Au programme :Construire un business autour d'un problème que l'on connaît intimement.Éduquer son marché avant de chercher à vendre.Savoir saisir une opportunité lorsqu'elle se présente.Grandir en apprenant progressivement à déléguer.Utiliser l'intelligence artificielle comme un levier de productivité plutôt que comme une menace.Un épisode 100 % actionnable pour tout entrepreneur ou toute entrepreneure qui souhaite développer une entreprise durable en créant une véritable valeur pour ses clients.Bonne écoute !Votre entreprise est-elle structurée pour accélérer ? Faites le diagnostic Comment t'as fait ? pour prendre du recul sur vos décisions stratégiques et vérifier si nous sommes la bonne équipe pour vous accompagner : https://diagnostic-commenttasfait.netlify.app/
The Long Cycles ConvergeLast week we named three great cycles being activated simultaneously. This week Jupiter weaves through all three in rapid succession — bringing what is large and transpersonal into the realm of the humanly meaningful. Into story, culture, and the conversations where collective transformation actually takes root.This week also carries a transit that deserves its own moment of recognition. On Friday, Neptune in Aries sextiles Pluto in Aquarius, the two slowest moving planets in flowing alignment, directly activating their own great cycle seeded in the lates 1890s.And the Sun follows. Mercury stations direct. Saturn stations retrograde. A rare threshold is reaching its crescendo.Key Cycles This Week:Monday, July 20: Jupiter in Leo opposes Pluto in Aquarius and trines Neptune in Aries. Jupiter opens the week with two major contacts holding the essential tension of this entire threshold period. The opposition to Pluto, the Expander meeting the Transformer, activates the unfinished Uranus-Pluto Virgo revolution from the mid 1960's around power, identity, and who gets to flourish. Where does the desire to be fully seen meet the power structures that have historically demanded containment?On the same day Jupiter trines Neptune in Aries, activating the Uranus-Neptune Capricorn cycle from the early 1990's, the dissolution of old structures of authority and meaning. Flow rather than rupture. The invitation to imagine what might replace what is dissolving. Hold both transits together. The confrontation with power and the opening of new vision are the same threshold approached from two directions.Tuesday, July 21: Jupiter in Leo sextiles Uranus in Gemini. Jupiter completes its rapid weaving through all three long cycles. This sextile activates the Neptune-Pluto Gemini cycle from the late 1890's, the revolution in mind, communication, and shared reality. New ideas finding unexpected audiences. Connections forming between people and communities that would not ordinarily find each other. In two days Jupiter has touched all three great cycles, bringing each into the realm of collective story and shared meaning.Wednesday, July 22: Sun enters Leo. Leo season begins in a field already charged by everything building around it. The Sun in Leo asks what is genuinely alive in you that wants to be expressed — not performed, not managed, but genuinely offered from the place that knows its own worth. In a collective moment asking so much endurance, Leo season is not a distraction. It is a necessary counterweight.Thursday, July 23: Sun in Leo squares Chiron in Taurus. Mercury stations direct at 16 degrees Cancer. The desire to shine meets the wound of unworthiness in its most embodied form. Where does the body carry the memory of having been seen and found lacking? Let what is tender be visible to yourself at least. That is enough for today.At the same time Mercury stations direct, completing three weeks of retrograde review in the realm of feeling and belonging. What the interior work of these weeks revealed, the emotional truth that found language, the unfinished conversation revisited, is now ready to move forward. Trust what you know now. It is ready to be spoken.Friday, July 24: Neptune in Aries sextiles Pluto in Aquarius.Before Saturn and the Sun's confrontation with Pluto, something quieter and more vast deserves naming. The two slowest moving planets, the Imaginal Field and the Transformer, in flowing alignment. This is the great Neptune-Pluto cycle speaking to itself across time, the revolution in mind and shared reality seeded in the late 1890s now activated through a harmonious aspect between the two planets themselves. In the background of everything else moving through this week, this is a quiet and profound current. What is the imagination of humanity reaching toward beneath the noise and the breakdown? What is being dissolved so that something genuinely new can find its form? These are questions that do not resolve in a day. But they are worth sitting with as the longest arc of the three great cycles makes itself briefly, quietly known.Sunday, July 26: Saturn stations retrograde at 15 degrees Aries. Sun in Leo opposes Pluto in Aquarius. Saturn turning inward begins months of honest interior review. What structures in your life are genuinely serving who you are becoming? On the same day the Sun opposes Pluto, the Conscious Self meeting the Transformer directly. Where are you being asked to step into genuine visibility in the face of systems that have historically demanded your containment? Sun opposing Pluto makes that question undeniable. Sometimes the undeniable question is the most useful gift.Looking Ahead to Next WeekMonday, July 27 the Lunar Nodes change signs. The North Node enters Leo, the South Node enters Aquarius — an eighteen-month shift asking the collective to develop toward genuine creative expression and the courage to offer individual gifts in service of the whole.Wednesday, July 29 brings a Full Moon at 6 degrees of Aquarius conjunct Pluto, opposing Jupiter in Leo, and activating all three long cycles simultaneously. The Sun and Moon in direct contact with Pluto while touching Neptune in Aries and Uranus in Gemini. This is the culmination of everything this arc has been building toward. Its own fuller story is coming in next's week podcast.Reflection QuestionsAs Jupiter weaves through all three long cycles, which thread feels most urgently alive in your own life and community right now?Where does the Sun squaring Chiron illuminate a wound around visibility or worthiness, and what would it mean to let that be seen rather than managed around?As Mercury stations direct and Saturn stations retrograde on the same day, what emotional truth is ready to be spoken, and what inner structure is ready to be honestly assessed?You are being prepared for something. Stay present to the preparation.Podcast poem: Working Together by David WhyteIf what we have been exploring these past two weeks is resonating — the long cycles, the thresholds of becoming, the invitation to shed what is false and remember what is genuinely true in you — I want to offer something that meets exactly that territory.The Inner Alchemist's Path is a private podcast journey through seven thresholds of transformation. Ten beautifully crafted episodes, each five to eight minutes, guiding you through the mythic spiral of genuine inner alchemy. If you are standing at a threshold right now, if something is dissolving and something new is quietly stirring, this was made for that moment.You can find it at my website ontheedgesofchange.com on the Home page under "What's Open Now."Support the showGo to Sheila's website for information for transformational resources: https://www.ontheedgesofchange.comThis episode was co-created with generative AI, engaged as a soul-aligned ally in service of transformation. At the edge where technology meets myth, I choose insight over noise, and alchemy over automation. Thank you for dreaming the future with me.
Play NowEpisode 405 of the Seibertron.com Twincast / Podcast starts up trying to decode the Transformers: The Movie: The Apology Tour's coded message. The crew then shifts discussion to the recently revealed Scooby Doo Collaborative figure and its four head options. "Made to Order" Seekers then take over the discussion before Cyberworld proves that Transformers can still be fun. Hasbro announcements about the future of Studio Series then promptly bring the crew back to Earth. Finally, a batch of listener questions drives discussion to Transformer housing, historical figures as Cybertronians and even mythical alt modes. As always, bragging rights brings this show to a close.
Le conseil du jour, c'est une minute pour prendre du recul, respirer, et avancer un peu plus sereinement dans votre travail. Un conseil simple, concret, applicable dès aujourd'hui. Un format court de Happy Work, par Gaël Chatelain-Berry.NOUVEAU : retrouvez moi sur WhatsApp sur la chaîne Happy Work... pas de spam, c'est gratuit et il n'y a que du feelgood !!! : https://whatsapp.com/channel/0029VbBSSbM6BIEm0yskHH2gEt pour retrouver tous mes contenus, tests, articles, vidéos : www.gchatelain.comSoutenez ce podcast http://supporter.acast.com/happy-work. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.
Un simple grain d'Afrique à mêler aux habitudes, sans tout révolutionner, mais pour découvrir d'autres saveurs, sortir des sentiers battus. Le fonio, micro-flocons légers et savoureux, remplace la semoule de blé, des pancakes au moringa pour la couleur, du mil dont le goût fait penser à la noisette grillée en crêpes ou en gâteau, des haricots cornilles en beignets, ou pâte à tartiner : bons pour la planète, bons pour la santé et bons au goût, ces « grains » sont encore un cadeau de l'Afrique au monde ! Le monde peine à ouvrir les yeux, à remercier l'Afrique qui lui a déjà tant offert. Prenez l'arachide, la cajou ! Le souchet et le baobab attendent leur heure, Aïssatou Mbaye elle poursuit sa mission de conteuse et de passeuse en partageant ses recettes, ses liens avec les différents grains et leur histoire, elle qui a maintenant passé autant de sa vie en France qu'au Sénégal a cœur de transmettre, et que ses enfants partagent à leur tour les recettes et les saveurs de leur double culture. Avec Aïssatou Mbaye, cuisinière, conteuse, autrice, son dernier livre Grain d'Afrique, 60 recettes pour une touche d'Afrique au quotidien est paru aux éditions Marie-Claire. Aïssatou a publié plusieurs livres primés sur la cuisine subsaharienne, elle est aussi fondatrice du keliba Café à Dakar pour la lire sur Instagram. ► Pour aller plus loin - Farines d'Afrique Nathalie Brigault Ngoum et son univers - Mon Afrique, de la cheffe Anto Cogagne - Autour du mil - La petite épicerie du monde - BMK, l'Afrique passionnément. Programmation musicale : Désert, de Jonathan Benisty et Michael Nkouaga.
The mates discuss Mira Murati's 975B Open Model, Ramin Hasani speaks on Post-Transformer AI, and Demi's AI FINRA. Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Salim Ismail is the founder of Open ExO, a GP at Exponential Venture Capital/The Organizational Singularity Fund and a sought after global speaker and thought leader. Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified Ramin Hasani is the Co-founder and CEO of Liquid AI – My companies: Apply to Dave's and my new fund:https://qr.diamandis.com/linkventureslanding Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter _ Connect with Peter: X Instagram Substack Website Xprize A360 Connect with Dave: Web X LinkedIn Instagram TikTok Connect with Salim: LinkedIn X Join Salim's 10X Shift Subscribe to Salim's YouTube channel Exponential Venture Capital Connect with Alex Website LinkedIn X Email Substack Spotify Threads Connect with Ramin Website X LinkedIn Listen to MOONSHOTS: Apple YouTube – *Recorded on July 16th, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices
Un nouveau sommet consacré à l'intelligence artificielle s'ouvrira ce vendredi en Chine. Pendant quatre jours Shanghai va accueillir la Conférence mondiale sur l'IA. Un rendez-vous qui aura un écho particulier en France où la révolution AI Overviews est annoncée pour cet été. Le nouvel outil Google de réponses générées par l'IA doit en effet être déployé. Et les inquiétudes sont grandes chez les éditeurs de presse qui craignent d'être invisibilisés. Quels risques AI Overviews représente-t-il pour les médias ? À quoi ressemblera la presse après l'essor de l'intelligence artificielle ? Avec : - Alexis Levrier, historien des médias, maître de conférences à l'université de Reims - Éric Barbier, journaliste à L'Est Républicain et référent sur l'intelligence artificielle au sein du syndicat national des journalistes (SNJ) - Laurence Devillers, professeur en IA à Sorbonne Université, spécialiste des interactions affectives humain machine, présidente de la fondation Blaise Pascal.
Un nouveau sommet consacré à l'intelligence artificielle s'ouvrira ce vendredi en Chine. Pendant quatre jours Shanghai va accueillir la Conférence mondiale sur l'IA. Un rendez-vous qui aura un écho particulier en France où la révolution AI Overviews est annoncée pour cet été. Le nouvel outil Google de réponses générées par l'IA doit en effet être déployé. Et les inquiétudes sont grandes chez les éditeurs de presse qui craignent d'être invisibilisés. Quels risques AI Overviews représente-t-il pour les médias ? À quoi ressemblera la presse après l'essor de l'intelligence artificielle ? Avec : - Alexis Levrier, historien des médias, maître de conférences à l'université de Reims - Éric Barbier, journaliste à L'Est Républicain et référent sur l'intelligence artificielle au sein du syndicat national des journalistes (SNJ) - Laurence Devillers, professeur en IA à Sorbonne Université, spécialiste des interactions affectives humain machine, présidente de la fondation Blaise Pascal.
The Long Cycles Become VisibleSomething unusual is happening in the sky this month.Not just the weekly transits. Something larger. A convergence of long cycles that rarely become visible all at once — briefly illuminated in ways that allow us to feel their presence and direction. This week I am taking more time than usual to orient you in that larger field before walking the specific transits.The Three Long CyclesIn astrology, when two outer planets meet in conjunction, they seed a cycle that unfolds over generations. Three of these great cycles were seeded in the last 130 years. This month all three are being activated simultaneously.Neptune conjunct Pluto in Gemini, late 1890s. Gemini governs mind, language, communication, and how reality is constructed and shared. This conjunction seeded a revolution in all of those domains — mass media, the discovery of the unconscious, technologies that transformed how human beings perceive and communicate. The AI emergence, the fracturing of shared reality, the speed at which language and information now move — this is that Gemini cycle in its current expression.Uranus conjunct Pluto in Virgo, mid-1960s. Virgo governs the body, healing, and the relationship between the individual and the systems they inhabit. This conjunction seeded a revolution in identity and power — civil rights, feminism, the environmental movement, the questioning of who gets to belong and on whose terms. The wounds of who has been excluded, whose body deemed less valuable, whose labor extracted — this cycle is still metabolizing in the collective body.Uranus conjunct Neptune in Capricorn, early 1990s. Capricorn governs structure, authority, and the institutions that organize collective life. This conjunction seeded the dissolution of old structures of collective meaning — the fall of the Soviet Union, globalization, the beginning of the internet age, the slow unraveling of institutional frameworks that once held collective life together. Those structures have never been fully replaced. What is breaking down in collective systems right now is this Capricorn cycle in its current expression.This month, through flowing trines and sextiles, all three cycles are being activated simultaneously. This is why many people are experiencing this time as both overwhelming and somehow significant in a way they cannot quite name. Something genuinely large is in motion.Key Cycles This Week:Monday, July 13: Venus in Virgo squares Uranus in Gemini. Unexpected friction in relationship. A desire for more authenticity running up against existing patterns and expectations. Hold relationships with spaciousness. What feels like disruption may be asking for genuine realignment rather than repair of what no longer fits.Tuesday, July 14: New Moon at 22 degrees of Cancer. The most personal question in the middle of everything large: what do you need in order to feel genuinely held right now? This New Moon also invites tending the ancestral field. What has been passed down that is being asked to transform? What do you want to consciously seed differently for those who come after?Wednesday, July 15: Uranus in Gemini sextiles Neptune in Aries. The revolution in mind and communication in easy flow with the dissolution of old identities and the emergence of new vision. Genuine creative and visionary potential available today. Stay curious about what arrives in image, in impulse, in what the imagination reaches toward.Friday, July 17: Uranus in Gemini trines Pluto in Aquarius. One of the most significant contacts of the month. The Awakener and the Transformer in flowing alignment, directly activating the Uranus-Pluto Virgo cycle of the 1960s. Where that conjunction was rupturing initiation, this trine offers flow — new pathways for what was seeded in that turbulent decade. Not a transit to merely observe. A transit to act from.Sunday, July 19: Mars in Gemini sextiles Saturn in Aries. A grounding close to an activated week. Inspiration finding form. Insight becoming commitment. What has moved through you this week that wants to become something real and actionable?Looking AheadNext week the activation deepens further — Jupiter opposing Pluto, the Sun entering Leo, Saturn stationing retrograde, and a Full Moon at 6 degrees of Aquarius conjunct Pluto and square Chiron. A culmination moment for everything this two-week arc is building toward. Episode 374 will go deeper into what these cycles are asking of us personally and collectively.Reflection QuestionsOf the three great cycles, which feels most alive and present in your own life right now?At this Cancer New Moon, what does your interior life genuinely need in order to feel held enough to be present to what is unfolding?What does the larger arc of collective transformation seem to be asking of you specifically, given your gifts, your wounds, your particular place in the web of life?You are living inside something genuinely large. That is not a burden. It is, in its own way, a privilege — to be alive at a moment when the long arcs of collective transformation are briefly, unusually visible.Podcast poem: Continue by Maya AngelouIf you are feeling called to work more deeply with these themes of tending, belonging, and rooting yourself within larger cycles of change, I invite you to join me in the Root in the Sacred group coaching program. It's a deeper container for those ready for sustained work with the Earth element, with the body, and with what it means to belong to the living world on your own terms. The program is structured as six-90-minute online sessions and meets on Thursdays, beginning July 16th. You can find details about the program at ontheedgesofchange.com under What's Open Now.Support the showGo to Sheila's website for information for transformational resources: https://www.ontheedgesofchange.comThis episode was co-created with generative AI, engaged as a soul-aligned ally in service of transformation. At the edge where technology meets myth, I choose insight over noise, and alchemy over automation. Thank you for dreaming the future with me.
Join us as we continue in our message series called“Jesus // According to Matthew, where we're going to walk through the Gospel of Matthew.
À certains moments d'une carrière, continuer ne suffit plus. Après quinze ans en IMOCA, trois Vendée Globe et une longue histoire bâtie avec Bureau Vallée, Louis Burton a choisi de repartir presque de zéro en Ultim, lui qui n'a presque aucune expérience en multicoque. Une autre vitesse, une autre culture, une autre manière de naviguer, et la signature d'un marin qui trace depuis ses débuts sa propre route.Cette capacité à rebondir ne date pas d'hier. Rien, dans son parcours, ne le destinait à la course au large professionnelle. Né loin des ports, il découvre la voile enfant en Bretagne, restaure ses premiers bateaux pendant ses études, enchaîne les régates avec les moyens du bord avant de saisir l'opportunité de la Route du Rhum 2010. En quelques années, il passe des courses en flotte en IRC aux pontons du Vendée Globe, porté par une énergie qui compense souvent le manque d'expérience.Au fil des campagnes, Burton construit une méthode. Derrière le skipper se cache un entrepreneur qui refuse d'opposer performance sportive et développement économique. Monter des projets, fidéliser des partenaires, construire une équipe et anticiper les cycles de quatre ans deviennent des leviers aussi importants que les choix météorologiques ou les réglages du bateau. Une approche qui lui permet de s'installer durablement parmi les références de l'IMOCA, jusqu'à finir sur le podium du Vendée Globe 2021.Le projet Armand Thiery s'inscrit dans cette continuité. Plutôt que de rechercher un simple remplaçant à son sponsor historique, Louis Burton imagine un programme sur une décennie, articulé autour d'un Ultim - l'ancien Macif de François Gabart - capable d'évoluer progressivement et d'attirer un club d'entreprises. L'objectif n'est pas de gagner immédiatement, mais de bâtir les conditions de la réussite sur le long terme.L'historie du Malouin d'adoption raconte moins une succession de résultats qu'une façon d'avancer. Transformer les périodes d'incertitude en opportunités, accepter de repartir d'une feuille presque blanche et conserver intact le plaisir d'aller sur l'eau : c'est cette logique qui relie ses premiers bords à L'Île-aux-Moines, ses Vendée Globe et désormais son entrée dans l'univers des géants volants.Diffusé le 10 juillet 2026Générique : In Closing – Days PastPost-production : Théo LevillainHébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.
durée : 00:39:45 - Les Matins de France Culture - par : Guillaume Erner - Le 24 juin 2026 a marqué la journée la plus chaude jamais enregistrée. Entre canicules et sentiment d'impréparation, la France peine encore à s'adapter malgré des décennies d'alertes scientifiques. Comment préparer le pays au changement climatique et mesurer enfin l'ampleur du défi ? - équipe : Félicie Faugère, Yoann Duval, Marie-Lys de Saint Salvy, Emma Lichtenstein, Mathilde Thon-Fourcade, Alice Deschamps, Carolina Sousa - invités : Jean-Marc Jancovici Ingénieur français Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France
Le monde du business adore les recettes toutes faites. Scaler vite, standardiser, lever des fonds, déléguer au maximum. Et pourtant, l'un des projets les plus désirables des 20 dernières années s'est construit exactement à l'inverse.Dorothée Meilichzon et Pierre-Charles Cros travaillent ensemble depuis plus de 20 ans.Elle est l'une des architectes d'intérieur les plus reconnues en France ayant gagné de nombreux prix. Lui est cofondateur de l'Experimental Group. Avec une dizaine d'hôtels dans 6 pays, Experimental est l'un des premiers groupes hôteliers à avoir créé une vision lifestyle et expérientielle de l'hospitalité, bien avant que cela devienne à la mode.Ensemble, ils construisent des lieux qui ont une âme.Des hôtels, des bars, des restaurants où l'on ressent immédiatement quelque chose. Et justement, dans cet épisode, on essaie de comprendre pourquoi certains lieux vibrent… et d'autres restent désespérément froids malgré des budgets colossaux.Ce que j'ai adoré dans cette conversation, c'est qu'elle est profondément à contre-courant.Pierre-Charles assume vouloir grandir toujours plus. Dorothée assume l'inverse : rester à taille humaine pour continuer à focaliser son temps sur la création.Cet épisode est profondément sincère et montre qu'il n'existe pas une seule façon de réussir. Et j'avoue m'être reconnue dans beaucoup de choses qu'ils racontent sur la création, l'exigence et le rapport au travail.Enfin, c'est un bonheur d'avoir 2 amis qui se connaissent depuis plus de 20 ans par cœur et se chamaillent en direct ;)Mais je ne vous en dis pas plus et laisse place à ma conversation avec Dorothée Meilichzon et Pierre-Charles Cros.Bonne écoute ✨Chapitrage 00:00 Introduction01:45 L'amour du beau et la recette de leur confiance13:35 Lassitude après 20 ans et où en sont leurs entreprises21:24 Comment naît un hôtel, de l'idée à l'achat29:48 Pourquoi tant de lieux premium manquent d'âme37:12 Leur quotidien et le secret de leur créativité48:19 Le crible du Podcast1:00:52 Les livres recommandés par Dorothée et Pierre-CharlesNotes et références de l'épisode ✨ Pour retrouver Dorothée : Sur InstagramSur son site✨ Pour retrouver Pierre-Charles : Sur LinkedIn✨ Pour retrouver l'Experimental GroupSur le siteSur Instagram✨ Le livre cité dans l'épisode : Kitchen confidential d'Anthony Bourdain*Liens affiliés FnacHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.
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