Podcasts about Saturation

Your people aren't tired of change — they're saturated. There's a difference, and it's the difference between an AI rollout that lands and one that bounces off your workforce entirely. Kelle Fontenot is the Chief Digital Officer at KPMG US, where the CIO, the CTO, and the Chief Data Officer all report to her. She owns internal innovation, architecture, platform, engineering, and data across a 40,000-person workforce — and she's spent the last four and a half years steering that organization through cloud, data, and now an AI wave reshaping how every one of her people does their job. In this conversation, Kelle reframes 'change fatigue' as 'change saturation,' reveals that KPMG employees built 25,000 AI agents in the last six months alone, walks through the synthetic-data acquisition powering regulated AI testing at scale, and explains the brand-new Anthropic partnership turning a 140-year-old services firm into a products company.   What you'll learn • Why 'change fatigue' is the wrong diagnosis — and what 'saturation' changes about how you roll out AI • Why KPMG refuses to use AI as a head-count lever — and why that decision is actually accelerating adoption • How 40,000 KPMG employees built 25,000 AI agents in six months — and what that means for who counts as a 'builder' • Why the CIO, CTO, and CDO all report to one person — and what would break if they didn't • How synthetic data lets a regulated firm test AI at scale without the breach risk • What KPMG's Anthropic partnership signals about the future of professional services   Connect Kelle Fontenot on LinkedIn KPMG US IT Visionaries Podcast   Chapters 0:00 AI Change Has Become AI Saturation 1:29 Why “Change Fatigue” Is the Wrong Diagnosis 3:27 Prompting Like It's November 4:46 Giving People Space to Innovate 6:38 AI Is Not a Headcount Lever 10:07 Building AI in a Regulated Business 11:24 The Risk Container Around AI 14:12 The AI-Augmented Auditor 17:21 The Agent Governance Problem 20:59 Why Digital, Data, and Tech Sit Together 22:59 Building an Inside Startup 30:04 Innovation Has to Happen at the Edge 36:48 The ROI Math for AI Agents 38:50 Why KPMG Bought a Synthetic Data Company 44:09 KPMG's Anthropic Partnership 51:03 Shipping AI at Scale 52:10 Kelle Fontenot's Advice for Leaders -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Dans un monde saturé d'images numériques, pourquoi les marques de luxe continuent-elles d'investir dans des livres imprimés, coûteux et pensés pour durer ?Dans cet épisode, j'analyse le livre comme objet culturel, symbole de distinction et outil stratégique pour les marques de luxe. Entre coffee table books, storytelling, mécénat culturel et édition de marque, cet épisode explore pourquoi le livre reste un objet puissant à l'ère de TikTok, de l'instantanéité et de l'intelligence artificielle.À travers mes expériences sur des projets éditoriaux pour Gallimard, Fisheye, Bulgari, la Villa Kujoyama ou encore la Fondation Tara Océan, je reviens sur les enjeux culturels, symboliques et stratégiques des livres de marque.Un épisode sur l'image, le luxe, la photographie, le design éditorial et notre rapport contemporain aux objets physiques.Bonne écoute !00:00 : Introduction : pourquoi les marques investissent encore dans les livres00:30 : Le retour des objets physiques à l'ère numérique00:42 : Saturation visuelle, réseaux sociaux et besoin de ralentir le regard01:33 : Coffee table books, identité sociale et distinction culturelle01:39 : Pourquoi le livre est devenu un objet stratégique pour le luxe01:54 : Livres décoratifs, matérialité et désir d'objet02:48 : Les marques de luxe comme acteurs culturels03:39 : Le rôle des éditeurs dans les livres de marque03:39 : Baudrillard, storytelling et construction d'univers03:45 : Qui contrôle le récit des livres de marque ?04:41 : Livres corporate et édition en marque blanche04:09 : Les marques qui deviennent leurs propres médias05:13 : Pourquoi les marques internalisent l'édition07:09 : Le pouvoir symbolique du livre en 202608:38 : Numérique, BookTok et retour de la matérialité09:15 : Conclusion : le livre comme objet culturel durableMon site : https://marinelefort.fr/Pour vous inscrire à la newsletter du podcast : https://bit.ly/lesvoixdelaphotonewsletterLe site du podcast : https://lesvoixdelaphoto.fr/Et vous pouvez retrouvez le podcast sur Instagram, Facebook et LinkedIn @lesvoixdelaphoto Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Making a Scene Presents - Using Saturation for Warmth: The Digital Trick That Feels Analog Why This Matters: Character Without Expensive Gear There is a funny lie that keeps floating around the home recording world. It says that if your mix does not sound warm, rich, and expensive, you must need better gear. A better microphone. A better preamp. A better interface. A better room. A better compressor. A rack full of vintage hardware that costs more than your car. The gear companies love that story because it keeps independent artists chasing the next shiny box instead of learning how sound actually works. Now, let's be honest. Good gear is great. A nice preamp can be beautiful. A real tape machine can be magic. A great room can make recording easier. But none of that changes the truth that most indie artists are working in bedrooms, basements, spare rooms, garages, and small home studios. That is not a weakness. That is the new center of the music business. The home studio is where songs are written, demos become masters, artists build catalogs, and independent careers are built one track at a time. http://www.makingascene.org

In this episode, we speak with Andrew Jensen, founder and director of Fox Jensen, one of Australia's leading contemporary art galleries and Fox Jensen Mccrory, alongside Emma Fox and Sarah Mccrory. Andrew discusses the evolution of the gallery, from its beginnings to its current presence in both Sydney and New Zealand. We explore the changing landscape of contemporary art, what makes a successful artist gallery relationship, and how a commercial gallery balances artistic vision with the realities of the art market.The conversation also touches on collecting, the international art scene, the enduring significance of painting, and the role galleries play in supporting artists throughout their careers. Andrew shares insights from decades of experience working closely with established and emerging artists, offering a candid look at the challenges and opportunities facing contemporary art today.Whether you're an artist, collector, curator, or simply interested in contemporary culture, this episode provides a thoughtful perspective on the business, passion and commitment behind running a leading gallery.'Andrew Jensen opened the gallery in New Zealand in 1988 and over the course of more than thirty-five years it has set itself aside in terms of its seamless presentation of international work alongside the most considered practices from the region. In early 2011 the gallery expanded to Australia opening a second gallery in Sydney.Multiple exhibitions by major artists such as Imi Knoebel, Fred Sandback, Tony Oursler, Helmut Federle, Günter Umberg, Winston Roeth, Lawrence Carroll, Elisabeth Vary and Callum Innes altered and enriched the local conditions. These exhibitions continued to provide the basis for an increasingly expansive approach that has seen the curated aspect of the gallery grow. There have been numerous notable projects over the last decade or more including E=MC2, Naked, The Architecture of Colour, Six Degrees of Separation, Points of Orientation, Detox, Melancholia, The Authority of Death, Farben, Saturation, There's Joy in Repetition, Portrait without a Face, Eros, Permafrost and more recently Raven, Plastic Soul, Terrain, No One's Rose & Rain. The galleries' programs have developed a welcome richness and energy with the inclusion of a newer generation of international artists including Jan Albers, Mark Francis, Hanns Kunitzberger, Sofie Muller, Erin Lawlor, Liat Yossifor, Koen Delaere, Jane Bustin and Gideon Rubin. Alongside this, artists from the region include Aida Tomescu, Tomislav Nikolic, Matthew Allen, Geoff Thornley, Robert Malherbe, Jenny Topfer, Todd Hunter and Gary McMillan. The galleries are also privileged to hold the Estate of Bill & Pip Culbert.With the opening of the major new gallery space in Sydney in late 2025 the galleries have both expanded and consolidated its program. In 2026 the galleries are presenting works by celebrated artists Ian Davenport (UK), Paul Czerlitzki (POL), Ingo Meller (GER), Ulrike Schulze (GER), Gerold Millar (GER) and Lucienne O'Mara (UK). Fox Jensen, Sydney and Fox Jensen McCrory, Auckland are run in close partnership with its artists by Andrew Jensen, Emma Fox and Sarah McCrory. It participates annually in art fairs whilst remaining deeply committed to its galleries' programs and to publishing.' - Fox Jensen Website Thanks for Andrew Jensen and Emma Fox for having us in their home for the converstauon.Fox jensen Gallery, cnr Brennan &, McEvoy St, Alexandria NSW 2015Fox Jensen Mcrorym, 10 Putiki St, Gtey Lynn, AKL 1021 Hosted on Acast. See acast.com/privacy for more information.

Avec : Élise Goldfarb, entrepreneure. Yael Mellul, ancienne avocate. Et Benjamin Amar, professeur d'histoire-géographie. - Accompagnée de Charles Magnien et sa bande, Estelle Denis s'invite à la table des français pour traiter des sujets qui font leur quotidien. Société, conso, actualité, débats, coup de gueule, coups de cœurs… En simultané sur RMC Story.

Saturation du réseau routier : le parc automobile mauricien frôle les 756 000 véhicules by TOPFM MAURITIUS

In this episode, we break down category entry points and how to use LinkedIn ads to plant your brand in buyers minds way before they're even ready to buy.We cover:→ What category entry points actually are and why they matter for B2B demand gen→ Why LinkedIn ads is such a strong fit for targeting these key buyer moments→ Real examples of category entry points you can build campaigns around→ If you are a B2B marketer trying to build pipeline and stay top of mind with your ICP before they enter market.Tune in and learn:→ How to identify the moments that trigger buyers into market→ How to use LinkedIn ads targeting to reach the right people at the right time→ Why cataloging your market beats relying on intent data platforms→ How to turn category entry points into ad creative and thought leadership content-----------------------------------------------------

Jeremy Lee, Joe Poirot, David Chase, Chris McGill, and Josh Adams close out Sports Cards Live with a deep conversation about the modern sports card market, the Fanatics Collect Premiere auction, autograph culture, and whether modern collecting is becoming too concentrated around a handful of superstar athletes. The panel breaks down the dominance of players like Shohei Ohtani, Michael Jordan, LeBron James, Wembanyama, Steph Curry, Tom Brady, and Kobe Bryant in major auctions, while exploring whether modern cards are beginning to feel repetitive compared to vintage and 1990s collecting. Topics include:• Reactions to the Fanatics Premiere auction results• The LeBron James Superfractor auto sale• Why Shohei cards seem to dominate modern auctions• Risk versus stability in current player collecting• The evolution of athlete autographs over time• Why certain players become hobby focal points• Modern card saturation and collector fatigue• Vintage versus modern collecting psychology• Why some collectors prefer retired players and legends The episode also includes discussion about The Hangover on Sports Card Clubhouse, upcoming 90s Auctions, the Hobby Spectrum, and the evolving identity of today's collector market. Subscribe to Sports Cards Live on YouTube for weekly hobby conversations, market discussion, and collector psychology. Take the Hobby Spectrum Assessment and discover your collector archetype:HobbySpectrum.com Get your copy of Pops and Comps on Amazon. Comment below:Do modern card auctions feel exciting to you right now, or are too few players dominating the hobby? Learn more about your ad choices. Visit megaphone.fm/adchoices

Join the Cognitive Dissidents as they examine recent events with an eye toward understanding what's really going on behind the official narratives, planning ahead and keeping the lines of communication open to help keep like-minded people sane! This week the Dissidents discuss the crazy deal the Trump Administration made with the Trump Administration to insulate him and everyone he knows and loves from ever being harassed by the govt for anything they've done or might be accused of doing in the future. We also discuss China, LNG, the North American Union, Cuba, the American election cycle and so much more. Find, Follow, Subscribe & Rate on your favorite podcasting platform AND for video and social & more... Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://monicaperezshow.com/⁠⁠⁠⁠⁠⁠⁠ Rumble:⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠https://rumble.com/user/monicaperezshow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Youtube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/c/MonicaPerez⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter/X: @monicaperezshow Instagram: @monicaperezshow Find Hrvoje Moric: Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://geopoliticsandempire.com/⁠⁠⁠⁠⁠⁠⁠⁠ Substack: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://substack.com/@geopoliticsandempire⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter/X: @HrvojePM Find Parallel Mike and Parallel Systems Broadcast: Parallel Mike Podcast: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://parallelmike.com⁠⁠⁠⁠⁠⁠⁠⁠ Community & Financial Newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/parallelsystems⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@parallelsystems⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter/X: @parallel_mike Substack: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://substack.com/@parallelmike⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Patreon: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://patreon.com/parallelsystems⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Hour 4: Mark Kaboly joins Jeff for the full hour! Is there such a thing as too much football? How many people have a Peacock account? And Mark was texting Ron Cook during a commercial break.

La mode adversariale pour se cacher de quoi ? Par Régis BAUDOUIN « Vous n’êtes plus un humain. Pour l’œil électronique qui vous regarde, vous êtes un zèbre, une girafe ou un simple pixel vide. » En ce mois de mai 2026, alors que la Vidéosurveillance Algorithmique (VSA) s’installe définitivement dans nos espaces publics après des années d’expérimentations, une contre-attaque insolite est née dans les ateliers de la Fashion Tech. C’est l’émergence de la mode adversariale. Des designers de mode et des chercheurs en sécurité informatique s’allient pour créer des collections de vêtements d’un nouveau genre : la mode adversariale ou mode furtive. Leur but ? Saturation, confusion et invisibilité face aux caméras intelligentes de l’État. XY Magazine décrypte la tech qui se cache derrière ces textiles rebelles. Qu’est-ce qu’une attaque adversariale ? Pour comprendre comment un simple sweat-shirt peut paralyser une IA de surveillance à plusieurs millions d’euros, il faut plonger dans le moteur de la vision par ordinateur. Les caméras intelligentes actuelles utilisent des réseaux de neurones convolutifs (comme les célèbres modèles YOLO — You Only Look Once). Contrairement à l’œil humain qui appréhende une forme dans sa globalité, une IA segmente une image en milliers de couches mathématiques. Elle cherche des motifs géométriques spécifiques, des contrastes et des textures pour en déduire, avec un certain pourcentage de confiance : « Ceci est une silhouette humaine. ». L’intelligence artificielle ne reconnait pas elle se base sur un modèle statistique de probabilité. C'est ici qu'intervient l'attaque adversariale via la mode furtive. En imprimant sur le tissu des motifs graphiques hyper-spécifiques — générés par des algorithmes miroirs —, les designers exploitent les failles mathématiques des réseaux de neurones. Ces motifs, baptisés “patches adversariaux”, saturent les capacités d’analyse de l’IA. C’est l’équivalent d’une attaque par déni de service (DDoS) mais appliquée à la reconnaissance visuelle. Concrètement, le motif envoie une information contradictoire si violente à l’algorithme que celui-ci “bugge” : soit il ne détecte plus du tout la silhouette (qui devient invisible pour le système), soit il la catégorise à tort comme un animal ou un objet inanimé. Le cadre vert de détection automatique de la caméra se déplace sur le motif texturé, laissant le porteur du vêtement totalement hors du radar. Source https://www.capable.design/collections/all Avec ce motif de Capable design, à 82% la caméra vous identifie comme une pomme. L’omniprésence vertigineuse de l’œil algorithmique Le déploiement de la surveillance automatisée depuis les jeux Olympique de Paris. On a atteint une échelle qui défie l’entendement sociologique. En 2026, l’œil algorithmique est partout, avec plus d’un milliard de caméras IA actives sur le globe. La France s’est transformée en un véritable panoptique numérique : on y dénombre 100 000 caméras publiques et près de 2 millions de capteurs privés surveillant commerces et transports. Cette numérisation de la place publique transforme nos déplacements physiques en flux de données constants. Le marché de la vidéosurveillance, estimé à 6,8 milliards de dollars en 2025, devrait d’ailleurs exploser pour atteindre les 11 milliards d’ici 2030. Face à cette marchandisation de la silhouette humaine, le vêtement cesse d’être une surface passive pour devenir un bouclier de protection visuelle. C’est le point de vue des militants pour une mode furtive. Déjouer le tracking des citoyens, pouvoir sortir sans être reconnu au départ c’est l’objectif des scientifiques qui travaillent sur ces images de brouillage. Mais aussi cela fait le jeu de ceux qui doivent se cacher par nécessité. Le vêtement comme “bruit mathématique” et bug sémantique Pour contrer cette détection, des designers collaborent avec des data-scientists pour exploiter les failles des réseaux neuronaux via l’usage d’images “adversariales” (adversarial noise). Le principe est fascinant : l’IA ne “voit” pas un humain, elle calcule des contrastes et des probabilités. En injectant un bruit visuel spécifique dans la trame du tissu, on force la machine à commettre une erreur de classification. Il existe ici une distinction technique cruciale que les marques de pointe commencent à maîtriser. Là où des projets comme AdvHat ciblent spécifiquement les modèles biométriques pour empêcher l’identification d’un individu précis, des collectifs comme Cap_able s’attaquent à la détection d’objets. Ci-dessous un simple sticker sur un bonnet et la caméra ne sait plus ce qu’elle filme. Source https://ailb-web.ing.unimore.it/icpr/media/slides/10934.pdf Les vêtements utilisent des configurations visuelles capables de semer le doute dans un algorithme pour qu’il ne reconnaisse plus la catégorie “personne”. En portant ces motifs, vous perturbez des modèles standards comme YOLOv8 ou OpenFace. Pour l’œil humain, vous êtes élégamment vêtu ; pour le serveur, vous n’êtes qu’une erreur de segmentation ou un amas de pixels sans signification biologique. Votre vie privée est protégée. Mais les algorithmes s’adaptent. Pour un humain, vous portez des vêtements de mauvais gout et étranges. Pour l’IA vous êtes une erreur. Résister par le design paramétrique : le paradoxe du leurre Cette nouvelle esthétique de la résistance s’appuie sur le design paramétrique, utilisant des variables mathématiques pour définir des textures optimisées. Des marques comme Cap_able ou le projet HyperFace d’Adam Harvey créent des motifs qui ne se contentent pas de masquer le porteur, mais saturent les capacités de calcul des caméras. Source https://adam.harvey.studio/hyperface/ La stratégie la plus efficace consiste à multiplier les “faux visages” sur un seul vêtement pour créer une sur-saturation algorithmique. En obligeant le système à détecter des dizaines d’humains là où il n’y en a qu’un, on crée une confusion systémique. C’est le paradoxe ultime de notre ère : utiliser les outils de conception informatique les plus sophistiqués pour saboter les systèmes de surveillance de pointe. Le textile arme politique L’essor de cette mode anti-IA n’est pas qu’une prouesse technique, c’est le symptôme d’un climat politique de plus en plus coercitif. Aux États-Unis, le retour d’une administration Trump et le durcissement des politiques migratoires ont agi comme un catalyseur. L’utilisation par l’ICE (police de l’immigration) d’outils mobiles de reconnaissance faciale a transformé le besoin d’anonymat en une urgence de sécurité personnelle. Depuis cette bascule politique, les ventes de vêtements “furtifs” ont doublé chez les principaux revendeurs spécialisés. Même si le marché reste marginal. La peur de l’identification automatisée n’est plus l’apanage des activistes de la vie privée. Elle devient une préoccupation citoyenne générale pour ceux qui refusent que leur visage devienne un identifiant à distance, consultable en temps réel par les autorités. Entre friction et réalité technique : les limites de l’invisibilité Soyons lucides : ces textiles ne sont pas des capes d’invisibilité totales, mais des outils de “friction” visant à réduire la probabilité de détection. L’efficacité varie selon l’angle de vue, la densité de la foule et la sophistication des logiciels propriétaires. Cependant, la panoplie du citoyen furtif s’est considérablement diversifiée : L’identification biométrique : La collection « Faception » d’Urban Privacy utilise des mailles noir et blanc pour briser la symétrie faciale calculée par les algorithmes. T shirt qui trompe les caméras La surveillance nocturne : La ligne « Urbanghost » propose des matériaux spécifiques conçus pour éblouir ou tromper les caméras à infrarouges. source https://urban-privacy.com/products/anti-paparazzi-triangle-scarf-bio-premium-unisex-trianglescarf-for-protection-against-unwanted-photos-more-privacy L’analyse de la démarche (gait analysis) : L’usage de coupes amples (baggy cuts) permet de masquer les estimateurs biomécaniques des articulations, empêchant l’IA de reconnaître un individu à sa démarche. Les leurres lumineux : Des accessoires LED intégrés dégradent la qualité des capteurs dans les zones à faible luminosité. La résistance s’organise. La puissance de calcul des algorithmes et les contres mesures parviennent à déjouer ces tentatives d’invisibilité. Conclusion : Vers une esthétique de la vie privée Le vêtement redevient un espace de liberté individuelle et un rempart contre l’intrusion. Dans un futur saturé de capteurs, l’innovation textile nous permet de négocier notre visibilité face au pouvoir froid des serveurs. La question n’est plus de savoir si nous serons vus, mais si nous serons lisibles. Dans ce monde de surveillance totale, le “bon goût” de demain ne sera peut-être plus défini par notre capacité à nous montrer, mais par notre élégance à rester obstinément indéchiffrables pour les machines. La mode furtive ou adversariale va se développer.The post Ces vêtements conçus pour rendre invisible face aux IA de surveillance first appeared on XY Magazine.

Cognitive Dissidents Parallel Mike Podcast https://parallelmike.com Substack: https://parallelsystems.substack.com/ Monica Perez Show https://monicaperezshow.com Geopolitics & Empire https://geopoliticsandempire.com/

Join the Cognitive Dissidents as they examine recent events with an eye toward understanding what's really going on behind the official narratives, planning ahead and keeping the lines of communication open to help keep like-minded people sane! This week the Dissidents discuss the crazy deal the Trump Administration made with the Trump Administration to insulate him and everyone he knows and loves from ever being harassed by the govt for anything they've done or might be accused of doing in the future. We also discuss China, LNG, the North American Union, Cuba, the American election cycle and so much more. Find, Follow, Subscribe & Rate on your favorite podcasting platform AND for video and social & more... Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://monicaperezshow.com/⁠⁠⁠⁠⁠⁠⁠ Rumble:⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠https://rumble.com/user/monicaperezshow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Youtube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/c/MonicaPerez⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter/X: @monicaperezshow Instagram: @monicaperezshow Find Hrvoje Moric: Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://geopoliticsandempire.com/⁠⁠⁠⁠⁠⁠⁠⁠ Substack: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://substack.com/@geopoliticsandempire⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter/X: @HrvojePM Find Parallel Mike and Parallel Systems Broadcast: Parallel Mike Podcast: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://parallelmike.com⁠⁠⁠⁠⁠⁠⁠⁠ Community & Financial Newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/parallelsystems⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@parallelsystems⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter/X: @parallel_mike Substack: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://substack.com/@parallelmike⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Patreon: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://patreon.com/parallelsystems⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Join the Cognitive Dissidents as they examine recent events with an eye toward understanding what's really going on behind the official narratives, planning ahead, and keeping the lines of communication open to help like-minded people stay sane! This week the Dissidents discuss the crazy deal Trump made to insulate himself and everyone he knows and loves from ever being harassed by the government for anything they've done or might be accused of doing in the future. We also discuss China, LNG, the North American Union, Cuba, the American election cycle and much more. Watch on BitChute / Brighteon / Rumble / Substack / YouTube *Support Geopolitics & Empire! Become a Member https://geopoliticsandempire.substack.com Donate https://geopoliticsandempire.com/donations Consult https://geopoliticsandempire.com/consultation ***Visit Our Affiliates & Sponsors! Above Phone https://abovephone.com/?above=geopolitics American Gold Exchange https://www.amergold.com/geopolitics Escape The Technocracy (15% off w/ GEOPOLITICS!) https://escapethetechnocracy.com/geopolitics Expat Money (FREE “Plan B” Report!) https://expatmoney.com/geopolitics PassVult https://passvult.com Sociatates Civis https://societates-civis.com StartMail https://www.startmail.com/partner/?ref=ngu4nzr Wise Wolf Gold https://www.wolfpack.gold/?ref=geopolitics Websites Parallel Systems https://parallelmike.com Parallel Substack https://parallelsystems.substack.com Monica Perez Show https://monicaperezshow.com Monica Perez Substack https://monicaperezshow.substack.com About Parallel Mike Parallel Mike is an organic farmer, investor and host of both the Parallel Systems Broadcast & Parallel Mike Podcast. He is passionate about living purposefully, natural health and self sufficiency. About Monica Perez The Monica Perez Shows offers analysis of top headlines with an eye to pulling back the curtain on the propaganda, revealing the true agenda behind the news of the day and why it matters. Monica also provides fascinating conversations with principled thought leaders and subject matter experts in areas of interest to the truth & liberty communities.

In this solo episode of Even Better, Sinikka Waugh explores the experience of change saturation and what it feels like when too much change arrives all at once. Drawing on personal insight and coaching conversations, she reflects on how easily we can become overwhelmed, stretched thin, and disconnected from what matters most. Sinikka invites listeners to notice when that sense of saturation begins to take hold and to pause instead of pushing through. She encourages reconnecting with your inner circle as a source of support and perspective, and returning to your deeper why as an anchor in the midst of uncertainty. Through thoughtful reflection and practical encouragement, this episode offers a reset for anyone feeling the weight of constant change, helping listeners slow down, regain clarity, and move forward with greater intention and resilience.   -- Sinikka Waugh - Connect with me on either LinkedIn or send me an email! Founder, Owner, Trainer, and Coach Sinikka Waugh, PMP, President and CEO of Your Clear Next Step, spends her days helping people have better workdays. Trainer, coach, business leader, and difference maker, Sinikka is known for consistently helping people solve problems and get things done at work. With a 20+ year background in languages, literature, and project management, Sinikka has helped over 50,000 people have better workdays since 2008. Her clients value how her professionalism blends seamlessly with her down-to-earth, "try this now" approach and her passion for helping others. Sinikka holds a BA from Central College, an MA from the University of Iowa, and is a certified Project Management Professional through the Project Management Institute (PMI).

Trooper James O'Callaghan joins the show to talk saturation speed enforcement in Western New York. First off, what is it, what results has it yielded after being enacted on the 33, and where else could it be deployed in Western New York?

Guest co-host Mike Schulte joins Dave with 15 years of Pork Tornadoes social media wisdom, and the message is blunt: relentless consistency wins. You literally can’t post too much in 2026—nobody sees everything anymore, so repost that same flyer as a fresh post (not a share) and keep going. Give it 45 days before you judge results. Why invest? More fans mean more bodies at the gig, plus the social proof that signals to newcomers that other people already love you. And remember—you’re not competing with other bands, you’re competing with people’s couches. From there, Dave and Mike dig into the live-show craft. Build a sound check formula so it stops being a nightmare, then cook up a Suno-generated theme song to walk on to—Always Be Performing means the show starts before the first chord lands. Treat your setlist like art: the opener’s a throwaway, but song three is the most important slot of the night. Then think about your saturation—the Pork Tornadoes cap themselves at two ticketed gigs per year inside a 30-mile radius, and the minute they got scarce, their pay jumped tenfold. Simple, not easy. 00:00:00 Gig Gab 533 – Monday, May 11th, 2026 May 11th: National Eat What You Want Day (also Hostess CupCake Day!) Guest co-host: Mike Schulte 00:01:10 Did you ever watch Night Court Dave reminds Mike of Harry Confused Breakfast Shows that were so far ahead of their time: All In The Family Roseanne 00:05:06 Managing your band's social media Relentless Consistency is the key (right now). “You can never post too much” – Mike Schulte, May 11, 2026 Mike has been running social media for Pork Tornadoes for 15 years Everyone doesn't see every post (anymore) It's money-driven Repost the same thing, the same flyer, the same idea (as a new post, not a “share”) 00:09:49 Getting “started” on social media in 2026 I tried to follow your model and nothing changed. In two weeks. You've gotta spend a month or more (Dave says 45 days) 00:14:05 What's the benefit of investing in social media The more fans you have, there WILL be more people who come to your events Also: social proof. Showing people that other people like you. 00:18:55 Social Proof + Bullheaded Persistence = Success. 00:22:00 People don't go out like they used to You're not competing with other bands, you're competing with people's couches 00:24:39 A band retreat! If 2020 hadn't happened, Pork Tornadoes would've probably gone full time 00:26:04 SPONSOR: Claude.ai – Ready to tackle bigger problems? Sign up for Claude today, which includes access to Claude Cowork, too, when you visit https://Claude.ai/giggab 00:27:42 Recent Gig(s) Gab Boston Cream Band at Seacoast Repertory Theater Pork Tornadoes is a 2-hours straight-thru band 00:34:19 Orchestrate your sound check Sound check used to be a nightmare, until we created a formula 00:38:27 Create a musical lead-in for your show For the wranglers in the Gig Gab audience Use Suno to create a theme song for your band 00:42:57 Writing a setlist is an art Your first song is a throwaway The third song is the FIRST most important song in the set (according to Dave) Develop business-like rituals for your band 00:48:32 What's Your Band's Saturation? Self-imposed proximity clauses Pork Tornadoes Proximity Clause: No more than 2 ticketed events in a 30-mile radius per year Plus one free-to-the-public festival gig to pull people in To the venues who don't have proximity clauses: why do you not? The minute we started getting scarce, was the minute our pay increased 10-fold 01:00:12 The Pork Tornadoes formula: simple, not easy. Gig Gab 532 Outtro Follow Mike Schulte Confused Breakfast The Pork Tornadoes Contact Gig Gab! @GigGabPodcast on Instagram feedback@giggabpodcast.com Sign Up for the Gig Gab Mailing List The post Relentless Consistency and the Scarcity Premium with Mike Schulte from The Pork Tornadoes – Gig Gab 533 appeared first on Gig Gab.

If you've ever talked yourself out of going after a product line because the niche felt too saturated, this episode is going to reframe that completely.Saturation is one of the most common reasons sellers hold back on Etsy, and it's also one of the most misunderstood. Every niche is saturated. The entire internet is saturated. And yet new sellers are breaking through in competitive spaces every single day, because saturation doesn't mean there's no room. It means there's demand. In this episode I'm breaking down why saturation is a strategy problem, not a niche problem, and how to find the pockets of opportunity inside even the most competitive categories on Etsy.What You'll Learn:Why a saturated niche is actually a sign that buyers are thereThe difference between competing everywhere in a niche and finding where you actually have room to growWhy the biggest shops in your niche can't dominate every buyer search and what that means for youWhat long-tail keywords have to do with finding your opening in a saturated marketWhat to do next:- Enroll in the Etsy Visibility Accelerator: https://sarahjwaggoner.com/evapod- DM me on Instagram to talk through where your opportunity is: https://www.instagram.com/sarahjwaggoner/

In this episode, Ryan Moyer shares his expertise in short-term rental investments and property management, focusing on operational excellence, high-end experiential rentals, and strategic growth. Learn how to stay competitive, optimize revenue, and build a strong network in the vacation rental industry.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true 'white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a "mini-mastermind" with Mike and his private clients on an upcoming "Retreat", either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas "Big H Ranch"? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

"Geoffrey, comment tu fais pour avoir de tels résultats en si peu de temps ?"C'est une question qui revient souvent. Depuis mon départ de la police en 2020, j'ai formé plus de 250 préparateurs mentaux et accompagné des centaines de leaders. La réalité, c'est que le marché de l'accompagnement explose, mais les exigences des clients aussi.Aujourd'hui, savoir "comment le cerveau fonctionne" ne suffit plus. À l'heure de l'IA, l'information est gratuite. Ce que vos clients achètent, c'est du résultat tangible, du sur-mesure et une incarnation hors norme.Dans cet épisode, je quitte le mode "théorie" pour vous livrer mes 6 protocoles de terrain, ceux qui m'ont permis de propulser l'Académie Puissance Mentale et de transformer des vies, de l'athlète de haut niveau au chef d'entreprise.

In episode 156 of The Side Hustle Experiment Podcast  John (https://www.instagram.com/sidehustleexperiment/ ) and Drew (https://www.instagram.com/realdrewd/)  talk with Miles Longstreth (https://www.instagram.com/mileslongstrethinfo)Miles shares his journey from Amazon FBA to coaching, leveraging AI tools, and building a sustainable online business. Discover his strategies for content creation, community building, and navigating market saturation.Don't forget to Like, Subscribe, and hit the bell so you don't miss future episodes with top entrepreneurs and creators.Chapters00:00 Introduction and Background02:41 Transition to Coaching and Consulting05:30 Utilizing AI in Content Creation08:39 YouTube Strategy for Coaching Success11:36 Adapting to Changes in the Amazon Market14:27 Building a Community for Coaching17:00 Lessons Learned from Coaching Experience19:56 The Importance of Networking22:59 Starting Offers and Unique Selling Propositions25:38 Documenting the Journey28:34 The Evolution of Social Media and Marketing31:29 Authenticity in Coaching and Marketing36:04 Understanding Client Expectations and Realities38:14 The Role of Ads in Business Strategy39:17 Building an Organic Audience42:03 The Importance of Patience in Content Creation44:08 The Long-Term Value of Consistency45:17 Navigating Coaching and Client Relationships48:08 The Challenge of One-on-One Coaching50:40 Lead Magnets and Community Building52:32 Saturation in the Market53:47 Understanding Market Saturation and Personal Action58:22 Leveraging AI in Business01:03:59 Wrapping Up and Future Directions#makemoneyonline #sidehustleexperimentpodcast #sidehustles Follow us on Instagram: https://www.instagram.com/sidehustleexperimentpodcast/ Listen on your favorite podcast platformYoutube: https://bit.ly/3HHklFOSpotify: https://spoti.fi/48RRKcPApple: https://apple.co/4bmaFOk Check out Drew's StuffInstagram: https://www.instagram.com/realdrewdTwitter: https://twitter.com/DrewFBACheck out John's StuffInstagram: https://www.instagram.com/sidehustleexperiment/Twitter: https://twitter.com/SideHustleExp FREE ResourcesFREE Guide: How to Make Money Reviewing Products https://bit.ly/3HIGFSP

Spider-man, Spider-manDoes whatever Superman canBut he can't, he can't flyBut they both live the newspaper lifeLook out, its Spider-Man/SupermanBest BooksSuperman #37Absolute Wonder Woman #19Book BlurbsCyclops #3, Green Lantern #34, Rogue #4, Infernal Hulk #6, Captain America #9B-SegmentIs there too much Avatar? Are they ruining Avatar (the good Avatar)?Spider-man/Superman #1Uncle's One More ThingFinal Fantasy XII The Zodiac AgeWistoria: Wand and Sword

Spain has approximately 42GW of utility-scale solar and 50GW when rooftop is included, yet less than 100MW of grid-connected battery storage. In February, solar capture rates hit €1.30 per megawatt hour, a fraction of the €30–35/MWh needed for a solar project to break even. So why hasn't battery storage followed the solar boom and could it be the key to rescuing solar revenues?Pablo Martinez Serrano, Iberia Industry Lead at Modo Energy, joins Ed Porter to break down why Spain's energy market defies easy assumptions, and what the Iberian blackout changed.They cover:- Why Spain's hydro fleet masked the need for batteries for years, and why that's no longer enough as solar saturation bites.- Why solar developers are earning less and less for every unit of power they generate and what that means for the projects still in the pipeline.- The co-location thesis: why existing solar asset owners are turning to BESS to fix their generation profile and unlock ancillary service revenue- What actually caused the Iberian blackout: voltage instability, cascading disconnections, and why the TSO had already flagged the risk- Spain's new voltage control market: how it works, why priority of dispatch may be more valuable than the reactive service payment itselfWant to model battery revenue stacks in Spain or track Iberian power market dynamics? Ko, Modo Energy's AI analyst, is built for exactly these questions. Free sign up: https://help.modo.energy/en/articles/13335470-ko-your-ai-analyst?utm_source=podcast&utm_medium=podcast_apps&utm_id=pablo_martinez⏱ CHAPTERS00:00:00 Introduction00:00:50 What everyone gets wrong about Spain00:01:54 Spain's generation mix: solar, wind, hydro, gas and nuclear00:04:43 Seasonal demand dynamics and why spring is the problem00:06:03 Solar capture price collapse: €42 to below €30/MWh00:08:19 PPA contracts, negative prices and the solar momentum problem00:11:52 The co-location pivot: why developers are turning to storage00:13:58 Why Spain has less than 100MW of batteries vs GB's 6GW00:15:33 Where the money is coming from: two types of investor00:17:11 The Iberian blackout: what went wrong and why00:20:04 How Spain is rebuilding grid stability after the blackout00:21:04 Spain's new voltage control market and what it pays00:24:43 Grid forming inverters and the future of ancillary services00:26:38 Contrarian take: Spain hasn't actually decoupled from gas00:29:15 The three phases of displacing thermal generators00:30:39 Closing remarksYou can watch or listen to new episodes every Tuesday. Transmission is a Modo Energy production. Your host is Ed Porter - Director EMEA & APAC at Modo Energy.

Spain has approximately 42GW of utility-scale solar and 50GW when rooftop is included, yet less than 100MW of grid-connected battery storage. In February, solar capture rates hit €1.30 per megawatt hour, a fraction of the €30–35/MWh needed for a solar project to break even. So why hasn't battery storage followed the solar boom and could it be the key to rescuing solar revenues?Pablo Martinez Serrano, Iberia Industry Lead at Modo Energy, joins Ed Porter to break down why Spain's energy market defies easy assumptions, and what the Iberian blackout changed.They cover:- Why Spain's hydro fleet masked the need for batteries for years, and why that's no longer enough as solar saturation bites.- Why solar developers are earning less and less for every unit of power they generate and what that means for the projects still in the pipeline.- The co-location thesis: why existing solar asset owners are turning to BESS to fix their generation profile and unlock ancillary service revenue- What actually caused the Iberian blackout: voltage instability, cascading disconnections, and why the TSO had already flagged the risk- Spain's new voltage control market: how it works, why priority of dispatch may be more valuable than the reactive service payment itselfWant to model battery revenue stacks in Spain or track Iberian power market dynamics? Ko, Modo Energy's AI analyst, is built for exactly these questions. Free sign up: https://help.modo.energy/en/articles/13335470-ko-your-ai-analyst?utm_source=podcast&utm_medium=podcast_apps&utm_id=pablo_martinez⏱ CHAPTERS00:00:00 Introduction00:00:50 What everyone gets wrong about Spain00:01:54 Spain's generation mix: solar, wind, hydro, gas and nuclear00:04:43 Seasonal demand dynamics and why spring is the problem00:06:03 Solar capture price collapse: €42 to below €30/MWh00:08:19 PPA contracts, negative prices and the solar momentum problem00:11:52 The co-location pivot: why developers are turning to storage00:13:58 Why Spain has less than 100MW of batteries vs GB's 6GW00:15:33 Where the money is coming from: two types of investor00:17:11 The Iberian blackout: what went wrong and why00:20:04 How Spain is rebuilding grid stability after the blackout00:21:04 Spain's new voltage control market and what it pays00:24:43 Grid forming inverters and the future of ancillary services00:26:38 Contrarian take: Spain hasn't actually decoupled from gas00:29:15 The three phases of displacing thermal generators00:30:39 Closing remarksYou can watch or listen to new episodes every Tuesday. Transmission is a Modo Energy production. Your host is Ed Porter - Director EMEA & APAC at Modo Energy.

Submit to the Courage Files Are you worried if you're making the right moves in your author career (ads, audio, direct sales… all of it)? This episode will help you take a breath and zoom out.I'm joined by returning guest Becca Syme to talk about how publishing has changed—and why so many writers are making decisions from pressure, fear, or misread data. We dig into insights from her new book, Dear Writer, You Still Need to Quit, including why the old “gold rush” advice doesn't apply anymore, what actually matters at different stages of your publishing career, and how to make smarter, more sustainable decisions.If you've been feeling overwhelmed by all the options—or like you're behind—this conversation will help you reset and then move forward with more clarity.Timestamps 00:00 Why Becca Wrote This New Book 01:36 Meet Becca Syme 07:23 The New QuitBook + Publishing Shift 12:55 Saturation, Curation, and Discoverability 17:22 How Writers Misread Sales Data 20:39 The Five Phases of an Author Business 24:37 Investment vs. Speculation 26:36 Author Archetypes 29:24 Early Career Pressure + Market Awareness 32:22 Scaling Demand (Phase Two) 36:09 Later Career Phases 40:36 Decision Fatigue + Cost 42:06 Fear-Based Decisions 42:56 Where to Learn More Guest Bio and LinksBecca Syme holds a master's degree in transformational leadership and has been a success coach (primarily utilizing the Gallup Strengthsfinder®) for over fifteen years. She's coached over 5,000 individual authors and creatives through her Write Better-Faster and Strengths for Writers classes & coaching cohorts: six- and seven-figure authors, major award winners, midlisters, and new authors alike. Becca is the host of QuitCast for Writers, Author of the QUIT BOOKS SERIES FOR WRITERS, and a USA TODAY Best-Selling author of cozy mysteries under the pen name R.L. SYME.WebsiteHave a comment or idea about the show? Send me a direct text! Love to hear from you.Support the show To become a supporter of the show, click here!To get in touch with Stacy:Email: Stacy@writeitscared.cohttps://www.writeitscared.co/wishttps://www.instagram.com/writeitscared/Take advantage of these Free Resources From Write It Scared: Download Your Free Novel Planning and Drafting Quick Start Guide Download Your Free Guide to Remove Creative Blocks and Work Through Fears

In this episode of Home in Progress, Dan Hansen opens with a story about slicing his finger on a new rotary shredder and officially passing cheese-grating duties on to his kids. From there, he wraps up his multi-week series on what the brain wants from the spaces we live in by turning to one of the biggest design decisions of all: color.Dan explains that paint color is not just about personal taste. It also affects us biologically. He explores how color sends signals through the eye and into parts of the brain involved in stress, alertness, and emotional regulation. Along the way, he breaks color down into its three core elements: hue, brightness, and saturation.The episode looks at what research suggests about common color families. Red tends to be stimulating and physiologically activating. Blue is often associated with lower heart rate, lower blood pressure, and better emotional recovery. Green shows especially strong connections to stress reduction and restoration. Dan also explains that saturation works like a volume knob, making colors feel louder or quieter, and notes that very dark spaces can sometimes make us feel more watchful or on edge than mid-range values.Most importantly, he offers a practical framework for choosing paint colors more wisely: do not start with the color itself. Start with the feeling you want the room to create. From there, Dan walks through helpful color guidance for bedrooms, kitchens, living rooms, home offices, and bathrooms. He also reminds listeners that RepcoLite color consultants are available to help homeowners make confident choices.Timestamps00:00 Welcome and sponsor00:12 Rotary shredder mishap01:31 Why color affects us02:59 The biology of color07:15 Hue, brightness, and saturation08:49 What research says about red, blue, and green14:00 Saturation as a volume knob16:02 Brightness and hidden stress18:40 Turning the science into practical advice19:27 When the deeper point finally clicks20:28 Why color affects biology, not just preference21:52 Choose the feeling first24:32 A living room color regret26:52 Room-by-room color guidance28:08 Bedroom colors for calm30:00 Kitchen colors and controlling warmth31:10 Flexible color ideas for living rooms32:47 Home office colors for focus33:37 Bathroom colors for a reset36:49 What the feeling of home really means39:01 Final thoughts and where to get help

Solo Jim: I have nothing left, except Spider-man and Jim. That's right no Mike or Jeff this week but Jim is your savior on this fine Adam Scott Nickelback Birthday Bash.Stand By Me In Theaters: Corey Feldman snuck into a screening of Stand By Me and couldn't stop filming the screen for his social media. Fine this dude for piracy.Crowd Controversy: EROK has launched an investigation into the crowd photos from Goonies and Stand By Me screenings that have been posted by Corey. He has an expert on hand and everything! Is this a thing?COREY FELDMAN!, SHOW STOPPER!, LET'S JUST TALK!, DON CHEADLE!, BOOGIE NIGHTS!, JIM AND THEM IS POP CULTURE!, CHRIS HANSEN!, HAVE A SEAT!, DATELINE!, TO CATCH A PREDATOR!, REAL ONES!, LVL UP EXPO!, HACKAMANIA!, LIVE!, SOLO SHOW!, NO JEFF!, NO MIKE!, ONLY JIM!, JUST JIM!, FAKE FRIENDS!, WHO'S LEFT!?, FRIDAY NIGHT!, 22 NECKLACE!, REAL ONES!, KISS EM IF YOU GOT EM!, AUDITIONS!, NICKELBACK BIRTHDAY BASH!, ADAM SCOTT!, NO MAS!, SIPPING ON SHOTS!, PO BOX!, HOOK!, TEXAS CHAINSAW MASSACRE!, CHRIS HANSEN CAMEO!, FIRST THING IN THE MORNING!, GOBLIN GHOUL!, APRIL FOOLS!, ADAN GONZALEZ!, STAUNCH TV!, DRAFTED!, IRAN!, WAR!, PROTECT ME!, ICP!, MIRACLES!, THE BOY BLUE!, COREY FELDMAN!, STAND BY ME!, SNEAK INTO THEATER!, WEDNESDAY!, WATCHED STAND BY ME TOGETHER!, THEATRICAL RELEASE!, MILES APART!, CHOPPER SIC BALLS!, JIM AND THEM FINALE SPECIAL!, INTERVIEW!, PLAYING WITH YOUR FRIENDS!, AWKWARD!, ANNOYED!, RUDE!, YAWNING!, AARP!, WIL WHEATON!, ANNOYING!, PERFORMATIVE!, SUMMERTIME!, JERRY O'CONNELL!, BORED!, SHALLOW!, RIVER PHOENIX!, VEGETARIAN!, INTO MUSIC!, COPIED!, BREAKING BAD!, STOLE DEAD PEOPLE'S HABITS!, FENIX TX!, KRISTIN!, DENISE RICHARDS!, BRAVE BROWSER!, FAKE CROWD!, PHOTOSHOP!, AI!, EROK!, SATURATION!, CONTRAST!, DOCTORED PHOTOS!, INVESTIGATION!, FIVERR!,

Solo Jim: I have nothing left, except Spider-man and Jim. That's right no Mike or Jeff this week but Jim is your savior on this fine Adam Scott Nickelback Birthday Bash.Stand By Me In Theaters: Corey Feldman snuck into a screening of Stand By Me and couldn't stop filming the screen for his social media. Fine this dude for piracy.Crowd Controversy: EROK has launched an investigation into the crowd photos from Goonies and Stand By Me screenings that have been posted by Corey. He has an expert on hand and everything! Is this a thing?COREY FELDMAN!, SHOW STOPPER!, LET'S JUST TALK!, DON CHEADLE!, BOOGIE NIGHTS!, JIM AND THEM IS POP CULTURE!, CHRIS HANSEN!, HAVE A SEAT!, DATELINE!, TO CATCH A PREDATOR!, REAL ONES!, LVL UP EXPO!, HACKAMANIA!, LIVE!, SOLO SHOW!, NO JEFF!, NO MIKE!, ONLY JIM!, JUST JIM!, FAKE FRIENDS!, WHO'S LEFT!?, FRIDAY NIGHT!, 22 NECKLACE!, REAL ONES!, KISS EM IF YOU GOT EM!, AUDITIONS!, NICKELBACK BIRTHDAY BASH!, ADAM SCOTT!, NO MAS!, SIPPING ON SHOTS!, PO BOX!, HOOK!, TEXAS CHAINSAW MASSACRE!, CHRIS HANSEN CAMEO!, FIRST THING IN THE MORNING!, GOBLIN GHOUL!, APRIL FOOLS!, ADAN GONZALEZ!, STAUNCH TV!, DRAFTED!, IRAN!, WAR!, PROTECT ME!, ICP!, MIRACLES!, THE BOY BLUE!, COREY FELDMAN!, STAND BY ME!, SNEAK INTO THEATER!, WEDNESDAY!, WATCHED STAND BY ME TOGETHER!, THEATRICAL RELEASE!, MILES APART!, CHOPPER SIC BALLS!, JIM AND THEM FINALE SPECIAL!, INTERVIEW!, PLAYING WITH YOUR FRIENDS!, AWKWARD!, ANNOYED!, RUDE!, YAWNING!, AARP!, WIL WHEATON!, ANNOYING!, PERFORMATIVE!, SUMMERTIME!, JERRY O'CONNELL!, BORED!, SHALLOW!, RIVER PHOENIX!, VEGETARIAN!, INTO MUSIC!, COPIED!, BREAKING BAD!, STOLE DEAD PEOPLE'S HABITS!, FENIX TX!, KRISTIN!, DENISE RICHARDS!, BRAVE BROWSER!, FAKE CROWD!, PHOTOSHOP!, AI!, EROK!, SATURATION!, CONTRAST!, DOCTORED PHOTOS!, INVESTIGATION!, FIVERR!,

The provided text outlines the mission of the DEEP Institute to expand the use of saturation diving beyond its traditional role in the offshore oil and gas industry. By establishing a specialized saturation training facility, the organization aims to provide scientists, researchers, and military personnel with the skills necessary to work at depths between 50 and 200 meters. This initiative is designed to overcome the limitations of standard scuba equipment and expensive submersibles, allowing for sustained human presence and productive research within the ocean's critical continental shelf. The training utilizes a closed bell system to prepare divers for living in underwater habitats, which is a key component of the company's broader goal to make humans more aquatic. Ultimately, these advancements seek to improve our understanding and protection of the marine environment by enabling people to stay submerged for extended periods.#SaturationDiving #DEEPInstitute #UnderwaterTraining #OceanScientists #ContinentalShelfResearch #ClosedBellDiving #UnderwaterHabitats #MakeHumansAquatic #MarineProtection #DeepSeaExploration #AquaticFuture #SustainedOceanPresencehttps://discord.gg/W7cy7Tg9http://atlantisseacolony.com/https://www.facebook.com/atlantisseacolony/

In this episode, Travis sits down with his in-studio producer for a candid, behind-the-scenes conversation about writing books, publishing paths, and what it really takes to stand out in an increasingly saturated market. From traditional vs. self-publishing to the surprising statistics around book sales, this episode breaks down the realities most aspiring authors don't consider—and why focusing on quality over speed is the ultimate competitive advantage. On this episode we talk about: Traditional publishing vs. self-publishing and how to choose the right path Eye-opening statistics about book sales and why most books fail to sell The impact of AI and the explosion of low-quality self-published content Why credibility, distribution, and timing matter for first-time authors The philosophy of “there's always room for the best” in any saturated market Top 3 Takeaways The publishing path you choose should align with your current goals—traditional publishing can offer credibility, while self-publishing offers control and flexibility. Most books sell very few copies, so success comes less from publishing and more from creating a genuinely high-quality product. Saturation isn't the problem—lack of excellence is. If your work is truly great, you can still stand out in any market. Notable Quotes "I don't want to write a book just to say I wrote a book—I want it to actually be good." "There's always room for the best." "If you're going to write a book, just make it good—that's what actually matters long term." Connect with Travis Chappell: LinkedIn: https://www.linkedin.com/in/travischappell Instagram: https://www.instagram.com/travischappell Other: https://travischappell.com  Travis Makes Money is made possible by High Level – the All-In-One Sales & Marketing Platform built for agencies, by an agency.  Capture leads, nurture them, and close more deals—all from one powerful platform.  Get an extended free trial at gohighlevel.com/travis Learn more about your ad choices. Visit megaphone.fm/adchoices

April 3, 2026 In this episode, Scott, Mark, and Dr. Ray Painter tackle two areas where interpretation is driving real-world coding challenges: when a prostate biopsy truly qualifies as a stereotactic template-guided saturation biopsy (55706), and whether diagnostic cystoscopy meets moderate risk under E/M guidelines. The discussion breaks down what differentiates saturation biopsy from standard transperineal approaches, including the importance of 3D templating and full-gland sampling, and why payer expectations may not align with physician interpretation. They also revisit the E/M risk table, reinforcing why cystoscopy is generally considered moderate risk—even if not explicitly stated in current guidelines. The key takeaway: as coding becomes more nuanced, balancing guideline interpretation with payer expectations is critical to staying compliant and getting paid correctly. PRS Coding and Reimbursement HubAccess the HubBotox LCD AlertDownload the AlertFree In-Office Prostate Biopsy Calculator (Suppoted by UC-Care)Download NowPRS Coding CoursesFor UrologistFor APPsFor Coders, Billers, and Admins Join the Urology Pharma and Tech Pioneer GroupEmpowering urology practices to adopt new technology faster by providing clear reimbursement strategies—ensuring the practice gets paid and patients benefit sooner.         https://www.prsnetwork.com/joinuptpClick Here to Start Your Free Trial of AUACodingToday.com   The Thriving Urology Practice Facebook group.The Thriving Urology Practice Facebook Group link to join:https://www.facebook.com/groups/ThrivingPractice/ 

Mark Cuban had it all wrong.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

What happens when everyone is talented… but no one stands out?In this episode, we sit down with Chris Denner to unpack one of the biggest challenges in photography right now: a saturated industry full of really good work… that all looks the same.Chris has spent nearly two decades building a brand that refuses to blend in. His work is bold, chaotic, colorful, and unapologetically different — and it's exactly why his clients seek him out.We talk about the rise of the sea of sameness, why saturation isn't the real problem, and how photographers can break out of trend chasing to build something that actually lasts.This is a conversation about identity, disruption, and the courage to create work that doesn't fit inside the box.WHAT YOU'LL LEARNWhy the photography industry feels more saturated than everThe real reason good work isn't enough anymoreHow to stand out when everyone has access to the same toolsThe danger of trend chasing and copy paste creativityWhy personality and perspective matter more than presetsHow to create work that feels personal instead of performativeThe difference between directing moments and forcing themKEY CALLOUTSEveryone is good now… so good isn't the differentiator anymoreThe sea of sameness is real and it's where most photographers get stuckIf everyone is zigging, I've always been the one zaggingI wasn't good… I was cheapDon't impose your ideas on a wedding — build them with the coupleThe goal isn't pretty. The goal is realDisruption is the only way to stand out in a saturated marketSHOW NOTES00:00 – Meet Chris DennerUK-based photographer from LeicesterBackground, personality, and early creative influences05:00 – Is the Industry Too Saturated?Why photography feels overcrowded right nowCOVID's impact on new creatives entering the industryThe low barrier to entry problem10:00 – The Disappearing MiddleThe rise of luxury vs entry-level marketsWhy the middle tier is shrinking15:00 – The Sea of SamenessWhy everyone's work looks similarThe danger of copying Instagram trendsHow inspiration turns into imitation25:00 – Finding Your VoiceChris's shift from cheap work to intentional workChoosing clients that reflect your personality35:00 – Disruption as a StrategyWhy being different is the only real advantageCreative inspiration and pushing boundaries45:00 – Directing Without ForcingThe fine line between guidance and controlHow to create space for real moments55:00 – What NOT to DoCopy paste prompts and viral photo ideasTrend fatigue and recycled concepts60:00 – Final TakeawaysStop chasing trendsBuild work around people, not PinterestIf you want to stand out, you have to be willing to be differentLINKSFollow Chris Dennerhttps://www.instagram.com/chrisdennerphotohttps://www.chrisdenner.co.ukJoin PHOTOCOhttps://www.mileswittboyer.com/photocoMore from Mileshttps://www.mileswittboyer.com-------------If this episode hit you, here's the moveStop scrollingStop savingStart creating something that actually feels like youAnd if you're ready to go deeper into building a business and body of work that stands out, come join us inside PHOTOCOhttps://www.mileswittboyer.com/photocoIf you loved this episode, leave a review, share it with a friend, and tag us on Instagram so we can see what resonated most

Maggie Berghoff is the Founder & CEO of a top business mentorship program for health and wellness business owners.  She is passionate about helping health business owners build their businesses while prioritizing their lifestyles. Top 3 Value Bombs 1. High-ticket programs create deeper transformation because clients are fully committed and supported at a higher level. 2. Saturation isn't the problem; clarity of transformation and personal brand is what makes you stand out. 3. Growth requires a mindset shift from solopreneur to CEO, where your team becomes your new clients. Visit Maggie's website - Maggie Berghoff Website Sponsors HighLevel - The ultimate all-in-one platform for entrepreneurs, marketers, coaches, and agencies. Learn more at HighLevelFire.com. 50 - Join JLD on his free '50 days to something' video series on YouTube and create something special in 50 days!      

Voici les liens pour écouter l'épisode Pourquoi le tapis de course a-t-il été un instrument de torture ?Apple Podcasts:https://podcasts.apple.com/fr/podcast/pourquoi-le-tapis-de-course-a-t-il/id1048372492?i=1000756915527Spotify:https://open.spotify.com/episode/1JZfMJW5Cu88LpK2VQlCSr?si=07106fbff27b41ac---------------------Le “démarketing” touristique, c'est une idée contre-intuitive : au lieu d'attirer toujours plus de visiteurs… certaines destinations cherchent désormais à en attirer moins.Pourquoi ? Parce que le tourisme de masse est devenu, dans certains cas, un problème économique autant qu'un succès.Quelques chiffres permettent de comprendre l'ampleur du phénomène. On compte environ 1,5 milliard de touristes internationaux dans le monde. Le tourisme représente environ 10 % du PIB mondial et plus de 270 millions d'emplois. C'est donc une industrie gigantesque.Mais cette croissance a un revers. Aujourd'hui, 95 % des touristes se concentrent sur seulement 5 % des destinations mondiales.Résultat : certaines villes et sites sont littéralement saturés. À Étretat, par exemple, 1,5 million de visiteurs par an pour un territoire minuscule.C'est là qu'intervient le démarketing.Le concept vient du marketing classique : il s'agit de réduire volontairement la demande. Appliqué au tourisme, cela signifie limiter la fréquentation pour préserver un territoire… et, paradoxalement, sa valeur économique.Concrètement, les destinations utilisent plusieurs leviers :– réduire leur promotion touristique, voire disparaître des campagnes publicitaires– limiter l'accès avec des quotas ou des réservations obligatoires– augmenter les prix ou instaurer des taxes (comme à Venise)– rediriger les visiteurs vers des zones moins fréquentéesL'objectif n'est pas de “faire fuir” les touristes, mais de mieux les répartir et d'augmenter la qualité de l'expérience.Car économiquement, le problème est simple : trop de touristes peut détruire la valeur même d'une destination. Saturation des infrastructures, hausse des prix immobiliers, dégradation de l'environnement… À terme, cela peut faire fuir les visiteurs à forte valeur ajoutée et réduire les revenus locaux.Le démarketing repose donc sur une idée clé : mieux vaut moins de touristes… mais qui dépensent plus et restent plus longtemps.C'est un changement de modèle économique. On passe d'une logique de volume à une logique de valeur.Autrement dit, le tourisme du futur pourrait ressembler davantage au luxe qu'à la grande distribution. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

LINKEDIN CONTENT INTELLIGENCE REPORT Q1 2026   The LinkedIn newsfeed has completely changed how professional content gets seen and shared — and old posting playbooks are now obsolete. In “LinkedIn Content Intelligence Report Q1 2026”, we'll break down how LinkedIn's evolving algorithm prioritises relevance, meaning, and audience interest — not just likes and vanity metrics — and what this means for creators and marketers focused on real visibility and real engagement. Real data, real trends, real tactical takeaways.   What we'll cover:   The New Distribution Logic: LinkedIn now prioritises deep, meaningful engagement and professional relevance over broad viral reach, reshaping who sees your posts and why. Interest-Based Feed Signals: The feed is increasingly driven by topical relevance and user interest, not just network connections or follower count. Engagement Quality Over Quantity: Simple likes matter far less than sustained conversations and comments that signal real value. Post Formats That Win Now: Text, thought leadership, and content sparking discussion are outperforming generic posts — and tactics like saves and comments now carry disproportionate weight. Semantic Content Signals: LinkedIn's new 360Brew AI reads meaning and topical authority, rewarding posts that speak clearly to a defined audience. Saturation and Signal Noise: More posts and noise mean generic content gets buried — creators must craft specificity and relevance to break through. Profile + Content Alignment: Your profile and the themes you post about now act together as signals to the algorithm — misalignment can suppress reach.   Every creator has wondered, why isn't this getting seen anymore? The answer isn't a mystery - the algorithm has shifted fundamentally. This livestream will turn complex technical changes into actionable strategy, outlining where visibility is going, what LinkedIn actually rewards, and how you can reclaim reach and influence on the platform. Whether you're a content creator, marketer, or brand strategist, this session will give you the context and tactics to adapt and win in 2026.     We're on Friday 20th March, 2pm GMT. Register by clicking on the green button (save my spot) and follow the channel here (recommended)       Episode 368 is sponsored by Maki   At Maki, we help organisations like BNP Paribas, PwC, Booking.com, Nestlé, and H&M transform hiring with AI agents for screening, interviewing, and assessment.   By combining automation, behavioural science, and data, Maki enables talent teams to hire faster, fairer, and smarter.   Our global partnership with H&M saved 250 000 recruiter hours, cut time-to-hire by 4×, and reduced turnover by 22 %, delivering $85 M ROI.   Beyond automation, we're building a continuous reinforcement system where every recruiter judgment and employee outcome makes our AI agents smarter; creating a unique data moat in HR.   Learn more: makipeople.com

Dans ce 6ème épisode de notre série Médecine et plongée, le Dr Mathieu Coulange, chef du service de médecine hyperbare, subaquatique et maritime à l'APHM (Marseille), s'attaque à un ennemi sournois du plongeur : le froid.On pense souvent que le froid est juste une question d'inconfort ou de frissons. En réalité, c'est un facteur majeur dans le déclenchement des accidents de désaturation (ADD) et des œdèmes pulmonaires d'immersion. Mathieu nous explique avec une clarté redoutable la mécanique physiologique : le froid "ferme" les vaisseaux périphériques, le sang afflue vers le cœur, le corps élimine de l'eau pour faire baisser la pression... et le plongeur finit déshydraté sans s'en rendre compte.Au programme de cet épisode :L'impact direct de la température de l'eau sur votre circulation sanguine.Le lien méconnu entre froid, diurèse d'immersion et risque d'accident.Pourquoi le frisson est déjà un stade trop avancé d'hypothermie.L'équipement : combinaison humide, semi-étanche ou étancheLe conseil contre-intuitif : pourquoi il ne faut pas prendre une douche très chaude immédiatement après une plongée froide (et comment se réchauffer correctement).Un épisode essentiel pour adapter vos pratiques, que vous plongiez en Méditerranée l'hiver, en lac, ou même dans des eaux tropicales.⚠️ Avertissement importantLes informations présentées dans cet épisode sont fournies à titre général et pédagogique. Elles ne constituent en aucun cas un avis médical personnalisé et ne sauraient se substituer à une consultation auprès d'un professionnel de santé, ni aux formations reconnues en plongée sous-marine.En cas de symptôme, de malaise ou de doute après une plongée, consultez sans délai un médecin (idéalement formé à la médecine de plongée) ou un service d'urgence, et suivez les procédures de sécurité recommandées par vos organismes de formation.Ni les auteurs du podcast, ni l'invité ne pourront être tenus responsables de décisions prises sur la seule base des informations diffusées dans cet épisode.

Today, Mystie, Abby, and Brandy discuss the essay "Saturation Love" by Jim Wilson. And yes! They remembered to discuss the tough questions that naturally arise when reading the essay. You're going to love this conversation! *** Have you bought the Scholé Sisters' new book, Scholé Every Day yet? It is getting great reviews. If you are trying to get a book club off the ground, make this book your first reading assignment because convincing moms that it's okay to read — that they should be reading and thinking — is a large part of what the book does. Scholé Every Day will help cultivate the passion for reading that you want to see in your book club members. Go here to purchase: https://www.amazon.com/Scholé-Every-Day-How-Thinking/dp/B0FRHPS3R5/ *** Go here to access today's show notes: https://www.scholesisters.com/ss170 Go here to join the FREE area of the Sistership: https://www.scholesisters.com/sistership/

Proverbs 4:23, Luke 6:45, John 17:17, Psalm 1:2 | Luke Peterson

Quick SummaryMarketing expert Melissa Dlugolecki shares her unconventional journey from celery juice educator to seven-figure agency owner, revealing why volume and brand consistency are the only strategies that matter in 2025. She opens up about transforming grief into purpose after losing her daughter, and why the lessons from that journey make entrepreneurs unstoppable.In This EpisodeWhy volume is non-negotiable in today's saturated market (and how to achieve it without burnout)The two-person brand persona exercise that instantly clarifies your positioningHow Melissa applies Tom Brady and Bill Belichick's mastery mindset to businessThe parallel between the grief journey and entrepreneurshipWhy "it's too saturated" is just an excuse hiding deeper fearsSystems and strategies for producing 60+ pieces of content daily across multiple clientsThe Kintsugi philosophy: filling your cracks with goldTactical tools from grief work that transform business resilienceKey TakeawaysVolume + Brand = Visibility: Success in 2025 requires showing up everywhere, consistently. Your "rent" is no longer a physical storefront—it's your online presence.Don't Take Anything Personally: Whether it's compliments or criticism, your worth isn't determined by others' opinions. This protects you from emotional rollercoaster decision-making.Mood Follows Action: Waiting to feel motivated means you'll never move forward. Commitment shifts energy, not the other way around.Your Brand Mitigates Risk: Consistency across all touchpoints (not just social media) creates the security buyers need to invest in you.Saturation is a Mindset Problem: The real issue isn't too many voices—it's unclear expectations and resistance to reality.Memorable Quotes"If you want freedom in your life, examine your expectations. Most unhappiness comes from subliminal expectations we never agreed upon.""It's a volume game. You have to be on demand when the buyer is ready to consume—not when you feel like posting.""Your brand is your rent in 2025. Just like brick-and-mortar businesses paid for storefronts, we pay through visibility.""Entrepreneurship is ego death after ego death. The post didn't perform well? That's your ego thinking everyone's watching.""Everyone is carrying a story we know nothing about. When we lead with that, we live more compassionately."Resources MentionedBook: The Four Agreements by Don Miguel RuizBook: Scar Tissue by Melissa Dlugolecki (available on Amazon and Kindle)Documentary: 30 for 30 series on Tom Brady and Bill BelichickPhilosophy: Kintsugi (Japanese art of repairing with gold)Concept: Chop Wood Carry WaterProject Management Tools: Monday.com, Trello, AsanaAbout the GuestMelissa Dlugolecki is a marketing strategist, agency owner, and author who helps entrepreneurs build powerful, cohesive brands. After growing a holistic health business to seven figures in 13 months, she pivoted to solve the marketing pain points she witnessed in her clients. Melissa's approach is informed by her background in psychology and sociology, her experience as a high school educator, and the profound grief journey following the loss of her daughter, Laden, in 2014. She ran the Boston Marathon five times in her daughter's memory and channels a unique blend of optimism and data-driven precision into everything she creates.ConnectMelissa's Instagram: @melissadluMelissa's Website: speakingofmelissa.comMelissa's Book: Scar Tissue (Amazon, Kindle)Kelsey's Website: KelseyReidl.comKelsey's Podcast: Rain or Shine (350+ episodes featuring Canadian entrepreneurs)Instagram/Social: @KelseyReidl

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

WEBINAR LINK:https://shawnmoore.clickfunnels.com/optiniyvvg89sWant to learn more about Vodyssey or start your STR journey. Book a call here:https://meetings.hubspot.com/vodysseystrategysession/booknow?utm_source=vodysseycom&uuid=80fb7859-b8f4-40d1-a31d-15a5caa687b7FOLLOW US:https://www.facebook.com/share/g/16XJMvMbVo/https://www.instagram.com/vodysseyshawnmoorehttps://www.facebook.com/vodysseyshawnmoore/https://www.linkedin.com/company/str-financial-freedomhttps://www.tiktok.com/@vodysseyshawnmooreCONTACT US:support@vodyssey.comChapters00:00:00 Intro00:02:44 Debating the Value of Short-Term Rentals00:07:09 Housing Costs and Market Dynamics00:09:00 Interest Rates and Investment Decisions00:12:39 Risk vs. Reward in Real Estate00:19:42 Saturation in the Market00:25:40 Time Investment for Returns00:30:59 Effort Required in Short-Term Rentals00:35:50 Understanding Costs in Real Estate00:41:59 The Impact of One Property00:45:32 Choosing the Right Coach

In just five years, BROCKHAMPTON became a Hip-Hop comet, shooting by with seven albums and it could've been more! With those works, they went from free-wheeling Mixtape-sounding albums to more mature works lyrically and sonically. Its obvious that they couldn't have existed in previous eras but could it be done ever again?TIMESTAMPS:Weekly Music Roundup - (1:06)Ben:Don Toliver - OctaneLord Jah Monte Ogbon - As of NowLooking at Birds - Living RoomBy Storm x Injury Reserve - My Ghosts go GhostJordan Ward - BACKWARDWale the Sage - Hug Me As If We Were To Die TomorrowNafe Smallz - It's Not You It's MeThe Game x DJ Drama - Gangsta Grillz EMNTZu - Ferrum SidereumSkrillex - KoraLabrinth - COSMIC OPERA ACT ICharlie:Terrace Martin - PASSIONPJ - Why Do Feelings Matter AnywayLabrinth - COSMIC OPERA ACT ITeeZandos - STILL ODDNija - What I Didn't SayXV & MIKE SUMMERS - The Kid With The Green BackpackTopic Intro/Ben's Research House - (15:00)Saturation - (20:26)Saturation II - (26:45)Saturation III - (36:18)Iridescence - (46:00)Ginger - (53:54)Roadrunner: New Light, New Machine - (1:00:26)The Family - (1:05:41)TM - (1:09:48)Lighter Note - (1:18:34)Thanks for listening. Below are the Social accounts for all parties involved.Music - "Pizza And Video Games" by Bonus Points (Thanks to Chillhop Music for the right to use)HHBTN (Twitter & IG) - @HipHopNumbers5E (Twitter & IG) - @The5thElementUKChillHop (Twitter) - @ChillhopdotcomBonus Points (Twitter) - @BonusPoints92Other Podcasts Under The 5EPN:"What's Good?" W/ Charlie TaylorIn Search of SauceBlack Women Watch...5EPN RadioThe Beauty Of Independence

Broad Match - Danny and Adam break down Amazon's financial trajectory ahead of the Q4 2025 earnings call, exploring why Prime has effectively tapped out, where the retail business is heading, and why Rufus may be Amazon's most important bet for the future of e-commerce. Host: Danny McMillan Co-Host: Adam "Heist" Runquist Episode Summary With Amazon's Q4 2025 earnings call on the horizon, Adam digs into the historical financials of Amazon's retail business to understand where the company has been and where it is heading. The picture is clear: Prime membership has reached over 200 million Americans, covering roughly 75% of the adult population, and growth has slowed to just 3-4% annually. The remaining unsubscribed population is largely economically unfeasible to convert. The numbers tell a compelling story across Amazon's retail business units. First-party retail has matured and is effectively flat or declining. Third-party seller fees have grown 190% since 2019, far outpacing the 75% growth in Amazon's own retail — but sellers are now squeezed to single-digit net margins with little room for further extraction. Advertising remains the standout at 56 billion dollars in 2024 with 300% growth over five years, yet its long-term sustainability depends on healthy seller participation. This sets up what Adam describes as Amazon's innovators dilemma. Danny and Adam agree that Rufus represents Amazon's play to shift from a purchase destination to a product discovery and research platform, effectively competing with Google, YouTube, and Reddit for the consideration phase. The episode closes with a rallying call for sellers to focus on extreme efficiency, leveraging AI tools to optimise listings at a level of sophistication that was impossible even a year ago, and to prepare for a market where fewer sellers will survive but those who do will be significantly rewarded. Key Takeaways Amazon Prime has effectively saturated the US market at over 200 million members, with the remaining population largely economically unfeasible to convert, signalling the end of Amazon's biggest historical growth engine. Third-party seller fees have grown 190% since 2019 compared to 75% growth in Amazon's own retail, but sellers operating on single-digit margins means Amazon has limited room to extract further on a per-unit basis. Amazon's advertising business pulled in 56 billion dollars in 2024 with 300% five-year growth, but its future depends on whether enough healthy sellers remain to sustain ad spend. Rufus is positioned as Amazon's answer to the innovators dilemma — shifting from a purchase-only platform to a product discovery and research destination to drive more visits, higher conversion, and larger basket sizes. AI tools now allow sellers to accomplish listing optimisation work in hours that previously took weeks, making sophisticated conversion optimisation accessible to small teams without additional headcount. The market is entering a consolidation phase where fewer sellers will survive, but those who maintain cash reserves, optimise ruthlessly, and adapt to the changing landscape will benefit as competitors exit. Chapter Markers 00:00 - Introduction 00:40 - Why Amazon earnings matter for sellers 03:30 - Prime membership growth and saturation 06:22 - First-party retail maturity and decline 09:30 - Third-party seller fees hitting the ceiling 11:10 - Advertising as Amazon's growth engine 13:28 - Rufus and the discovery play 15:47 - The debate around Rufus and objectivity 19:07 - AI efficiency and listing optimisation 22:16 - Beyond keywords and single-dimension thinking 33:24 - Market consolidation and survival strategy 37:19 - Practical steps for sellers right now Resources Seller Sessions Website Seller Sessions YouTube Adam "Heist" Runquist on LinkedIn Adam Heist YouTube Channel ```

In Episode 79 of Geopolitics with Ghost, Ghost focuses on the growing gap between escalating rhetoric and the absence of decisive geopolitical action. The discussion centers on how information saturation, constant alerts, and emotionally charged reporting create the illusion of imminent global conflict while actual strategic moves remain limited or deliberately restrained. Ghost breaks down recent international signaling, media amplification, and the role of timing in shaping public perception, emphasizing that what is not happening often matters more than what is loudly announced. The episode examines how governments use ambiguity, delay, and narrative noise to manage pressure without triggering escalation, and why observers must resist reacting to every headline as a breaking turning point. Throughout the conversation, Ghost stresses patience, historical pattern recognition, and discipline in analysis, urging listeners to focus on structure, incentives, and long-term positioning rather than surface-level panic. Episode 79 continues the show's steady approach to geopolitics by prioritizing context, restraint, and strategic awareness over emotional interpretation.

JOIN OUR PATREON AND GET ACCESS TO EXCLUSIVE CONTENT: https://mixingmusicpodcast.com/exclusiveI WRITE BOOKS FOR CHILDREN: https://deekeiandkayoko.comHIRE DEE KEI: links.deekeimixes.comHIRE LU: https://soundbetter.com/profiles/1419...Hire James: https://www.jamesparrishmixes.com/Find Dee Kei and Lu on Social Media:Instagram: @DeeKeiMixes @masteredbyluTwitter: @DeeKeiMixes @masteredbyluJoin the ‘Mixing Music Podcast' Discord:   / discord  The Mixing Music Podcast is sponsored by ⁠Izotope⁠, ⁠Antares (Auto Tune)⁠, Sweetwater, ⁠Plugin Boutique⁠, ⁠Lauten Audio⁠, ⁠Filepass⁠, & ⁠Canva⁠The Mixing Music Podcast is a video and audio series on the art of music production and post-production. Dee Kei, Lu, and James are professionals in the Los Angeles music industry having worked with names like Odetari, 6arelyhuman, Trey Songz, Keyshia Cole, Benny the Butcher, carolesdaughter, Crying City, Daphne Loves Derby, Natalie Jane, charlieonnafriday, bludnymph, Lay Bankz, Rico Nasty, Ayesha Erotica, ATEEZ, Dizzy Wright, Kanye West, Blackway, The Game, Dylan Espeseth, Tara Yummy, Asteria, Kets4eki, Shaquille O'Neal, Republic Records, Interscope Records, Arista Records, Position Music, Capital Records, Mercury Records, Universal Music Group, apg, Hive Music, Sony Music, and many others.This podcast is meant to be used for educational purposes only. This show was filmed and recorded at Dee Kei's private studio in North Hollywood, California. If you would like to sponsor the show, please email us at mixingmusicpodcast@gmail.com.Support this podcast at — https://redcircle.com/mixing-music-music-production-audio-engineering-and-music/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy