Podcasts about Google Search

Should doctors have their own Google Business Profile in addition to a location listing? This episode breaks down the difference between practitioner and practice profiles, when a separate doctor Google listing can help, and when it creates duplicate listings, split reviews, and patient confusion. You'll also learn what to do if a doctor leaves your practice but their Google Business Profile still shows your address, and how to protect your main location listing while you clean it up. Episode webpage, shownotes, and blog post: https://propelyourcompany.com/should-doctors-have-a-personal-google-business-profile-and-a-location-google-business-profile/Book a Google Business Profile Audit: https://propelyourcompany.com/google-business-profile-audit/Send in your questions. ❤ We'd love to hear from you!NEW Webinar: How to dominate Google Search, Google Maps, AI-driven search results, and get more new patients.>> Save your spot

This week, we continue to see ongoing heated Google Search volatility. I posted the big Google webmaster report for March 2026. Google AI Mode added more links to recipe sites. Google AI Mode has this recipe widget but is that a good thing. Google AI Overviews...

If Google Analytics (GA4) leaves you feeling confused, but you still want to know which marketing efforts are actually working, this episode is for you. I am walking you through a simple “analytics stack” for clinics where GA4 stays in place behind the scenes, and Clicky becomes your clear, real-time dashboard for quick decisions that lead to more booked appointments.You will hear what Clicky is, how it pairs with GA4, and which six numbers to check each week so your team can spot trends, fix issues fast, and keep your website and marketing moving in the right direction. I also explain how to set Clicky up in just a few minutes and how to turn the data into practical next steps, even if you are not a numbers person.Webpage, blog post, & shownotes: https://propelyourcompany.com/simple-website-traffic-tracker/>> Get Started with Clicky - https://clicky.com/66422350 We are affiliates for Clicky because we genuinely use and recommend it for clinics. There is a free plan you can start with, and on the episode blog and show notes page, you will find screenshots, step-by-step setup visuals, and more.Send in your questions. ❤ We'd love to hear from you!NEW Webinar: How to dominate Google Search, Google Maps, AI-driven search results, and get more new patients.>> Save your spot

Plus - TikTok won't add end-to-end encryption to direct messages; Google Search rolls out Gemini's Canvas in AI Mode to all US users Presented by Adaptive. Learn more at adaptivesecurity.com Learn more about your ad choices. Visit podcastchoices.com/adchoices

---------------Diese Folge wird u.a. präsentiert von claneo.deGenerative Engine Optimization - Inhalte optimieren für ChatGPT & Co - Das Standardwerk für GEO, von Magdalena Mues, Matthäus Michalik, Martin Grahl, Andre Alpar & Franziska Schneider. Erscheint am 5.3.2026 im Rheinwerk Verlag. Jetzt das Buch auf Amazon vorbestellen. Link: ⁠⁠⁠⁠⁠https://www.amazon.de/Generative-Engine-Optimization-aufbereiten-GEO-Ma%C3%9Fnahmen/dp/336711426X/⁠⁠⁠⁠---------------In dieser Episode spreche ich mit Christian B. Schmidt (Digital Effects) über ihre Untersuchung der Verschiebung von Search & Brand Authority in der Reisebranche und die Unterschiede zwischen Google Search und AI Search. Wir analysieren die Auswirkungen von AI Search auf die Reisebranche, mit Fokus auf Marktanteile, Content-Strategien und zukünftige Herausforderungen für Reiseunternehmen. Erfahren Sie, wie sich die Suchlandschaft verändert und welche Strategien notwendig sind, um sichtbar zu bleiben.Chapters00:00 Einführung und Icebreaker02:45 Der Shift von Google zu AI-Search12:22 Veränderungen im Reisefunnel durch AI20:18 Die neuen KPIs im AI-Zeitalter22:21 Gewinner und Verlierer der Studie27:54 Transaktionale Keywords und ihre Bedeutung30:06 AI-Overviews vs. ChatGPT: Unterschiede und Strategien36:14 Konkret Maßnahmen für Reiseunternehmen45:15 Zukunft der Reiseplanung: API und Agentic AI47:29 Messbarkeit in der AI-Suche und Dashboard-Tools

Favour Obasi-ike, MBA, MS dives deep into the art and science of building a high-performing website. The conversation kicks off with a fundamental principle: a high-performing website is built on a foundation of high-quality, structured content that builds a relationship with the consumer. Favour emphasizes that content without consumption is merely information, and the key to engagement is creating content that drives conversation and conversion.The episode explores the importance of starting with a website before diving into social media, establishing a central hub for your brand and content. Favour introduces listeners to the power of Google Advanced Search as a tool for discovering high-volume search phrases and understanding audience intent. This data-driven approach to content creation is presented as the cornerstone of a successful content strategy.The discussion then shifts to the technical aspects of website performance, highlighting the significance of structured data (schema markup) and the Open Graph protocol. Favour explains how these technical elements help search engines understand and display content more effectively, leading to improved visibility and click-through rates. The episode also touches on the latest trends in web design, mentioning innovative tools like PeachWeb, Spline Design, and Dora that are shaping the future of web development.A significant portion of the episode is dedicated to a real-world case study, where Favour shares impressive growth metrics from a client who doubled their website traffic and saw a massive increase in image search impressions by focusing on technical SEO and content structure. This practical example serves as a powerful testament to the effectiveness of the strategies discussed.The conversation also features a guest, Tree, who shares her struggles as a small business owner in the tree service industry. This leads to a valuable discussion on how to overcome marketing challenges with limited resources, with Favour suggesting a podcast as a low-cost, high-impact strategy for building authority and attracting an audience. The episode concludes with a wealth of practical advice and resources for business owners looking to enhance their online presence and build a website that drives sustainable growth.Book SEO Services? Save These Quick Links for Later>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Book SEO Services with Favour Obasi-ike⁠>> Visit Work and PLAY Entertainment website to learn about our digital marketing services>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our exclusive SEO Marketing community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Read SEO Articles>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to the We Don't PLAY Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Purchase Flaev Beatz Beats Online>> Favour Obasi-ike Quick LinksKey Takeaways1. Content is King, but Structure is Queen: A high-performing website is built on high-quality, structured content that is consistently delivered to your audience.2. Start with Your Website: Before you build your social media presence, establish your website as the central hub for your brand and content.3. Leverage Google Advanced Search: Use Google Advanced Search to find high-volume search phrases and understand what your audience is looking for.4. Technical SEO is Crucial: Pay attention to technical details like structured data (schema markup) and the Open Graph protocol to improve your website's visibility and click-through rates.5. Embrace New Technologies: Stay ahead of the curve by exploring innovative web design and development tools like PeachWeb, Spline Design, and Dora.6. Podcasting as a Powerful Marketing Tool: A podcast can be a low-cost, high-impact way to build authority, attract an audience, and drive traffic to your website.7. Focus on Long-Term Growth: Building a high-performing website is a long-term investment that requires a strategic approach and consistent effort.Memorable Quotes[01:03.0 - 01:08.0] "Because if you write content, but nobody's consuming it, then is it really content or is it just information?"[06:49.8 - 06:52.8] "I wouldn't start a social media if I don't have a website."[31:25.1 - 31:26.9] "It's not a one plug fix."[57:18.6 - 57:20.1] "If Google doesn't trust you, Google is not going to trust you with their client or with their customer."[86:08.4 - 86:11.3] "A podcast is free and a lot of people are starting podcasts today..."FAQs1. What is the first step to building a high-performing website? The first step is to focus on creating high-quality, structured content that addresses the needs and questions of your target audience.2. Why is it important to have a website before a social media presence? Your website is the only online property you truly own and control. It serves as the central hub for your brand and content, while social media should be used to drive traffic back to your website.3. What are some key technical SEO elements to focus on? Two crucial technical SEO elements are structured data (schema markup) and the Open Graph protocol. These help search engines understand and display your content more effectively.4. How can I find out what my audience is searching for? Google Advanced Search is a powerful tool for discovering high-volume search phrases and gaining insights into your audience's intent.5. What are some low-cost marketing strategies for small businesses? Starting a podcast is a low-cost, high-impact strategy for building authority, attracting an audience, and driving traffic to your website.Timestamps[00:00.0] Introduction: How to Build a High-Performing Website[01:03.0] The Importance of Content Consumption[02:27.0] Starting from Scratch: No Website, No Social Media[03:07.0] Using Google Advanced Search for Content Ideas[05:01.0] The Equation: High-Performing Website = High-Quality Content[06:46.6] Why You Need a Website Before Social Media[08:16.5] Google Search vs. Google Discover[09:27.3] Understanding the Open Graph Protocol[11:05.1] The Power of Visuals: Thumbnails and Rich Snippets[13:05.0] Case Study: Doubling Website Traffic with Technical SEO[20:01.0] The Importance of a Mobile-First Approach[23:04.0] Building a Website with No-Code Tools[28:32.8] The Future of the Web: AI and Personalized Content[34:41.1] How to Build High-Quality Content[40:01.0] The Role of AI in Content Creation[45:01.0] Overcoming Marketing Challenges with a Podcast[57:10.3] The E-E-A-T Framework: Expertise, Experience, Authority, and Trust[01:00:10.7] The Importance of a Long-Term Perspective[01:15:01.0] Q&A with Tree: A Small Business Owner's Journey[01:23:10.7] Conclusion: Building a Sustainable Online PresenceResourcesHost by Favour Obasi-ike, MBA, MSWe Don't PLAY!™️ PodcastWhat is SEOTechnical SEO CourseTop SEO Strategies to Boost Organic TrafficPeachWebSpline DesignDoraSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, I sit down with Skip Wilson of Draft Advertising to talk about why clicks don't always turn into customers. We break ads down to what actually matters: a specific offer for a specific audience that leads to a specific action. Instead of starting with platforms or trendy tactics, we focus on choosing the action first and building everything around that.We also explore why vague offers like “free consultations” often fall flat and how smaller, clearer entry points can lift conversions. Skip shares practical insight on when Google Search makes sense, when Meta performs better, and how to use AI tools thoughtfully without losing control of your message. If you want your ads to feel more intentional and measurable, this conversation will help you rethink your approach.Draft AdvertisingSend a textSupport the show Show Notes Apply to be featured on My Weekly Marketing! Take the Marketing Clarity Quiz and get instant insights on your marketing strategy.

This week, we covered the competition of the Google Discover core update. Also gave a status update on the Google Search volatility. Google had a brief serving issue with Google Search. Google is testing showing vertical...

Google mejora generación de imágenes con Gemini 3.1 Flash Image más rápido y consistentePor Félix Riaño @LocutorCoGoogle lanza Nano Banana 2, nuevo modelo de imágenes con más velocidad, texto preciso y hasta resolución 4KAyer hablábamos del nuevo Samsung Galaxy S26 y de cómo integra inteligencia artificial de Google. Hoy vamos a mirar otra pieza de ese mismo ecosistema. Google acaba de lanzar Nano Banana 2, que en realidad se llama Gemini 3.1 Flash Image. Es el nuevo modelo para crear imágenes con inteligencia artificial dentro de la app Gemini, en el buscador y hasta en herramientas de edición de video como Flow.¿Qué cambia frente a la versión anterior? Google promete más velocidad, mejor seguimiento de instrucciones y mayor coherencia cuando aparecen varios personajes en la misma imagen. Además, puede generar imágenes desde 512 píxeles hasta resolución 4K. Y eso abre preguntas importantes: ¿es realmente mejor que Nano Banana Pro? ¿Qué pasa con otras opciones como Midjourney, DALL·E o Firefly?Pero más velocidad trae nuevas dudasNano Banana nació en agosto de 2025 y se volvió viral. En solo cuatro días atrajo a 13 millones de usuarios nuevos a la app Gemini. Para octubre ya había generado más de 5.000 millones de imágenes. Luego llegó Nano Banana Pro en noviembre, con mejor calidad y más control en el texto dentro de las imágenes.Ahora Google combina lo mejor de ambos mundos. Nano Banana 2 usa la arquitectura Gemini 3.1 Flash Image. “Flash” significa rapidez. La idea es generar imágenes casi al instante, pero manteniendo buena calidad. Google dice que puede conservar la identidad de hasta cinco personajes en una misma escena y respetar hasta 14 objetos diferentes sin que cambien de forma o estilo en cada intento. Eso es útil para crear cómics, storyboards o campañas publicitarias donde los personajes deben verse iguales en cada imagen.También mejora la escritura dentro de las imágenes. Por ejemplo, si haces una tarjeta de cumpleaños o un anuncio con texto, ahora las letras salen más legibles y con menos errores.El problema es que cada vez es más difícil distinguir una imagen real de una creada por inteligencia artificial. Herramientas como Nano Banana 2 pueden producir paisajes, retratos y escenas con iluminación realista y texturas muy detalladas. Según encuestas citadas por CNET, la mayoría de personas cree haber visto imágenes hechas con IA, pero menos de la mitad se siente segura de poder identificarlas.Esto afecta redes sociales, campañas políticas, publicidad y hasta tareas escolares. Además, existe el debate sobre derechos de autor. Empresas creativas y estudios de cine han expresado preocupación por el uso de imágenes que podrían basarse en obras protegidas.Google intenta responder a esto con marcas de agua invisibles llamadas SynthID y con credenciales C2PA, un estándar que permite verificar si una imagen fue generada con IA y cómo se creó. Pero esa verificación funciona mejor cuando el contenido viene directamente de herramientas de Google.Al mismo tiempo, la competencia no se queda quieta. OpenAI tiene DALL·E y el generador de video Sora. Midjourney sigue siendo fuerte en arte estilizado. Adobe Firefly apuesta por integración directa con Photoshop y herramientas profesionales. Cada plataforma tiene ventajas distintas en estilo, control o integración empresarial.Nano Banana 2 ya está reemplazando a las versiones anteriores dentro de la app Gemini. En los modos “Fast”, “Thinking” y “Pro” ahora se usará este nuevo modelo por defecto. Los usuarios de planes pagos como Google AI Pro y Ultra podrán seguir accediendo a Nano Banana Pro desde un menú adicional cuando necesiten máxima precisión factual.También se integra en Google Search, en el modo IA y en Google Lens, en 141 países. Está disponible en navegadores de escritorio y móviles, y en herramientas como AI Studio, Vertex AI en Google Cloud y Google Ads. Incluso en Flow, la plataforma de edición de video de Google, ahora es el generador de imágenes predeterminado.La apuesta es clara: Google quiere que la generación de imágenes sea rápida, cotidiana y conectada con información en tiempo real desde el buscador. Eso puede ayudar a crear infografías, diagramas educativos o visualizaciones de datos más exactas.La gran pregunta es si esta velocidad masiva va a aumentar el volumen de imágenes artificiales circulando en internet. Y también si los usuarios aprenderán a verificar lo que ven antes de compartirlo.Nano Banana 2 permite elegir diferentes proporciones de imagen. Puedes crear formato cuadrado para Instagram, vertical para historias o panorámico para pantallas anchas. La resolución máxima es 4K, que equivale a 3.840 por 2.160 píxeles. Eso significa más de 8 millones de píxeles en una sola imagen.Otra mejora es el uso de “conocimiento del mundo” en tiempo real. El modelo puede consultar información actualizada desde el buscador para representar lugares o conceptos con mayor precisión. Por ejemplo, si pides una imagen de un museo específico, puede basarse en referencias visuales reales.En el contexto del Galaxy S26 que mencionamos ayer, esto muestra cómo Google está fortaleciendo todo su ecosistema de IA. El teléfono, el buscador, la app Gemini y las herramientas en la nube comparten modelos cada vez más potentes.Para quienes crean contenido, esto significa menos tiempo editando y más iteraciones rápidas. Para educadores, puede facilitar la creación de material visual didáctico. Para empresas, abre nuevas opciones en publicidad digital automatizada.Pero siempre habrá alternativas. Midjourney suele destacar en arte conceptual. DALL·E se integra con ChatGPT. Adobe Firefly apuesta por entornos profesionales con licencias más controladas. Elegir dependerá de qué necesitas: velocidad, estilo artístico, integración empresarial o control legal.Nano Banana 2 combina rapidez y calidad en la generación de imágenes con IA dentro del ecosistema Google. Mejora texto, coherencia de personajes y resolución hasta 4K. La competencia sigue fuerte y el debate sobre autenticidad continúa. Cuéntame qué opinas y síguenos en Spotify en Flash Diario.BibliografíaTechCrunchArs TechnicaCNBCEngadgetCNETBlog oficial de GoogleConviértete en un supporter de este podcast: https://www.spreaker.com/podcast/flash-diario-de-el-siglo-21-es-hoy--5835407/support.Apoya el Flash Diario y escúchalo sin publicidad en el Club de Supporters. 

Mobile healthcare providers face unique SEO challenges without a fixed location to anchor their online presence. This episode explores tailored strategies for mobile massage therapists, chiropractors, and other traveling healthcare professionals to improve their local search visibility despite not having a traditional brick-and-mortar practice.Episode webpage and show notes: https://propelyourcompany.com/local-seo-for-mobile-medical-providers/Send in your questions. ❤ We'd love to hear from you!NEW Webinar: How to dominate Google Search, Google Maps, AI-driven search results, and get more new patients.>> Save your spot

Martin and Gary unpack how HTML parsing really works, why the HTML standard is so lenient, and how messy markup can silently break key SEO signals like hreflang and rel=canonical. They revisit validators and cross‑browser hacks from the Netscape/IE days, and discuss whether semantic HTML and strict validity truly matter for search. You'll also hear when link hints like preload, prefetch, and DNS prefetch help performance (and indirectly SEO), and where meta and link tags really belong. ​ Resources: HTML Living Standard → https://html.spec.whatwg.org/ Episode transcript → https://goo.gle/sotr105-transcript Listen to more Search Off the Record → https://goo.gle/sotr-yt  Subscribe to Google Search Channel → https://goo.gle/SearchCentral Search Off the Record is a podcast series that takes you behind the scenes of Google Search with the Search Relations team.  #SOTRpodcast #SEO #GoogleSearch Speakers: Martin Splitt, Gary Illyes

Épisode 1439 : TikTok est en train de devenir un des canaux les plus stratégiques pour le référencement local en 2026, à la fois moteur de recherche, carte interactive et vitrine vidéo pour les établissements.—On entend parfois le terme TiKtok SEO : Ca veut dire quoi ? TikTok SEO = optimiser tes vidéos et ton profil pour le moteur de recherche interne de TikTok. Il est notamment question d'optimisation des mots-clés, du texte à l'écran, des hashtags, des fonctions de localisation.En 2026, TikTok est considéré comme un véritable moteur de recherche.Dans la pratique, TikTok se substitue bien souvent à Google Search. C'est notamment le cas chez les plus jeunes autour de requêtes du type : “meilleurs restos + ville”, “que faire à + ville”, “bar cocktail + quartier”, etc., où TikTok se substitue à Google Maps ou TripAdvisor chez les jeunes.—Puissance du moteur de recherche TikTokEnviron 40% des jeunes, lorsqu'ils cherchent un lieu pour déjeuner, vont d'abord sur TikTok ou Instagram plutôt que Google Maps ou Search.49% des consommateurs déclarent utiliser TikTok comme moteur de recherche, et près de 10% des Gen Z disent le préférer à Google pour certaines recherches.For You Page vs NearBy La FYP favorise le contenu de ton pays ou de ta région.TikTok tient compte de ta localisation dans ce qu'il pousse. L'algorithme intègre des signaux de proximité. Tu es français tu verras du contenu français. Mais ça ne veut pas dire que ta For You Page devient majoritairement “hyper locale”.L'hyper local de TiKTok tu le trouves dans l'onglet NearbyEn Décembre 2025, TiKtok a lancé un nouvel onglet dédié à une expérience hyper local.Quand je suis sur l'écran d'accueil de Tiktok, j'ai désormais un onglet de localisation en haut à droite. Par exemple : Paris, Lyon, beaujolais…Quand je clic sur cet onglet ma For You Page s'adapte pour ne pousser plus que des contenus localiser très près de moi. De l'hyper local.On y trouve typiquement : restaurants, bars, expériences, événements, shops, activités à faire autour de soi, afin de transformer TikTok en guide temps réel de son quartier.—Penser sa page comme une fiche TikTok (façon Google My Business)Pour un établissement local, ta page TikTok doit être conçue comme une fiche Google Business Profile… mais en vidéo :Nom & pseudo optimisés : intégrer type d'établissement + ville/quartier dans le nom du compte ou la bio (“Pizzeria Napoli – Lyon 7”, “Salon Curly Hair Paris Bastille”).Bio et mots-clés : inclure les spécialités, le quartier, les usages (“brunch”, “afterwork”, “terrasse”, “famille”, etc.) pour aider à la compréhension algorithmique.Catalogue vidéo : au lieu de photos statiques, tu crées une “galerie” de vidéos par cas d'usage : visite guidée, coulisses, best-sellers, FAQ, avis clients filmés, comment venir, horaires en situation.Régularité : comme sur Google, la fraîcheur des contenus rassure et alimente l'algorithme; TikTok voit que ton lieu est “vivant” si tu publies plusieurs fois par semaine.—Penser son contenu pour le référencement naturel et local par la vidéoLe local SEO TikTok repose sur des vidéos qui répondent à des requêtes très concrètes : “où bruncher”, “bar à vin cosy”, “meilleur burger pas cher”, “que faire avec des enfants ce week-end”.Les bonnes pratiques SEO vidéo : intégrer le mot-clé parlé dans les 3 premières secondes, l'afficher en texte à l'écran, le mettre dans la description et les hashtags.Exemple de “fiche vidéo” locale…Retrouvez toutes les notes de l'épisode sur www.lesuperdaily.com ! Le Super Daily est le podcast quotidien sur les réseaux sociaux. Il est fabriqué avec une pluie d'amour par les équipes de Supernatifs. Nous sommes une agence social media basée à Lyon : https://supernatifs.com. Ensemble, nous aidons les entreprises à créer des relations durables et rentables avec leurs audiences. Ensemble, nous inventons, produisons et diffusons des contenus qui engagent vos collaborateurs, vos prospects et vos consommateurs. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

How should PR pros react to Reddit's surge in popularity? PRWeek's latest podcast takes a look.Joining the latest episode of Beyond the Noise are Paige Hiley, director of social at Golin; and Jo Bromilow, director of social and influence at MSL.Beyond the Noise looks at some of the biggest issues affecting communications and PR. Download the podcast via Apple, Spotify, or listen on your favourite platform.Speaking to PRWeek UK deputy news editor Evie Barrett, the guests discuss clients taking an interest in social media platform Reddit, and how its community focus requires an entirely different approach to other channels.Reddit's changing userbase, and its links to Google and OpenAI, are also discussed within the episode.Bromilow and Hiley give advice on communicating with Reddit moderators, as well as using the app as a social listening tool.The episode was recorded prior to the news of Reddit being fined £14m by the Information Commissioner's Office for failing to properly check the age of its users, as reported on Tuesday (24 February).Commenting after the announcement, Bromilow said: “Reddit's response – that it intends to appeal and that it feels like it treads a delicate balance between protecting users' privacy and policing its platform – is a consistent one with how it has always operated and helps reinforce some of the points we made about the platform self-policing and the moderators existing to protect the userbase.“There have been lots of headlines recently about various social networks from TikTok to Meta being hit with fines in this regard, so this isn't a Reddit-specific issue. I anticipate a lot of back and forth along these lines as the online safety act comes into effect, and the reality of enforcing it vs the PR power of talking about it.” Hosted on Acast. See acast.com/privacy for more information.

In case you missed the news.... Google search for "Can't Sell House" is at all time high right now. The solution is simple. No one knows about your house for sale if you are just one of the 1000s out there using the same formula. You need to be in front of a MUCH wider audience. That story about what helped you fall in love with that house MUST be part of the gameplan. Too often, the agents don't even ask you what made you pick that house. Another buyer will need to go through that before they ever make a bid. Starting the process here is the key to success in 2026! Sign up now for your detailed report and more info!

AI is changing Google search faster than most salon owners realise, and whether you like it or not, it's going to impact your business. In this episode, I'm joined by returning guest Phil Evans from Salon Guru, and we're diving into what AI is doing to Google search and what that means for your salon business.We break down how Google's AI is already showing up in your potential clients' search results, what's actually driving which salons appear at the top, and why content and reviews still remain the foundation of everything.We also get into the practical stuff, how to use AI tools like ChatGPT without sounding like a robot, why you need to know your traffic and rank numbers before anything else, and the simple phone test you can do today that will tell you exactly where your salon stands. Phil's homework at the end of this episode is worth the listen alone. IN THIS EPISODE:[00:00] Introduction and marketing course waitlist announcement [01:13] Welcoming Phil Evans from Salon Guru back to the show [02:00] What AI has actually changed about Google search [03:15] How AI search works in practice, with real examples[07:57] The numbers behind salon website traffic (and what "good" looks like) [09:13] What determines which salon ranks first for "best balayage near me" [10:00] Why content and reviews are 90% of the job [11:31] The three most important things a small salon owner should focus on [16:00] How to use AI writing tools without sounding like a robot [19:25] What happens if a salon ignores AI and search over the next few years [21:00] How to make your existing content AI-ready [22:39] Three practical takeaways you can action today [23:08] The free rank report from Salon Guru (and how to find your numbers) [25:10] How local search radius works, and the wake-up call most salons need Want MORE to help you GROW?

Adding a second, third, or fourth location changes your SEO fast. This episode breaks down the multi-location upgrade plan that helps clinics rank in each market without creating internal competition, duplicate Google Business Profiles, or thin location pages. You'll learn what to fix on your website, how to structure location pages and internal links, how to lock down NAP and citations, and a simple 30-day rollout plan for scaling without chaos. 

In this week's episode, we take a look at hysteria over AI, and compare it to past religious movements like William Miller's Great Disappointment. This coupon code will get you 50% off the audiobook of Half-Elven Thief, Book #1 in the Half-Elven Thief series, (as excellently narrated by Leanne Woodward) at my Payhip store: RIVAH50 The coupon code is valid through March 2, 2026. So if you need a new audiobook this winter, we've got you covered! TRANSCRIPT 00:00:00 Introduction and Writing Updates Hello, everyone. Welcome to Episode 291 of The Pulp Writer Show. My name is Jonathan Moeller. Today is February 28th, 2026, and today we're looking at AI hysteria and whether or not AI gives any actual benefits to people. We also have Coupon of the Week, progress updates on my current writing projects, and also Question the Week, where we talk to people about AI. But first, let's start off with Coupon of the Week. This week's coupon code will get you 50% off the audiobook of Half-Elven Thief (as excellently narrated by Leanne Woodward) at my Payhip store. That coupon code is RIVAH50. This coupon code will be valid through March 2, 2026. So if you need a new audiobook as we exit winter and come into spring, we have got you covered. Now let's have an update on my current writing and publishing and audiobook projects. I'm pleased to report that the rough draft of Cloak of Summoning is done. It turned out to be just about as long as Cloak of Worlds, maybe a thousand words shorter. I am about 20% through the first round of editing, and I am hopeful that that book will be out sometime in March, probably the first week of March if all go as well. I've also written a short story called Dragon Claw that newsletter subscribers will get for free in ebook format when Cloak of Summoning comes out, which as I said will hopefully be in early March. I'm also 11,000 words into Blade of Wraiths, the fourth book in my Blades of Ruin epic fantasy series, and that will be my main project once Cloak of Summoning is published. In audiobook news, the audiobook of Blade of Shadows (as narrated by Brad Wills) is now out at almost all the stores, so you can get it at Audible, Apple, Google Play, Kobo, and the other main stores. Cloak of Titans (as narrated by Hollis McCarthy) is done and is currently rolling out to the stores. I think as of right now, you can get it at Google Play, Kobo, and my own Payhip store, but it should be showing up on Audible and the other main stores before too much longer. So that is where I'm at with my current writing, publishing, and audiobook projects. 00:01:56 Question of the Week Now let's move on to Question of the Week. For the first Question of the Week of 2026 and this week's question: have you personally derived any benefits or experienced any negatives from the rise of generative AI? And this question was inspired by the topic of this week's post, obviously enough since we're talking about AI. I should note that this is a contentious topic with divergent opinions, and so I asked people to remain civil in the comments and they definitely were, so thank you for everyone for that. Now let's have some opinions on AI before I tell you how AI has positively and mostly negatively affected my life. Joachim says: I have not used AI for private purposes. My Con: My Chromebook might be obsolete rather sooner than later. In my company, we use an AI, which is helpful. It has all the knowledge articles, so you can ask, how do I do this or that? The company's Con: laptop prices are going up. Eddie says: My Cons are much the same as yours. My Pros are using it to create images for tabletop games to help players visualize monsters and NPCs. I have found it effective in turning voice to text meeting notes into meeting minutes and actions. Jesse says: Software engineer here. I have found it helpful when I'm working on something in a language I'm not as familiar with the syntax. As a "how I might do this" learning tool, it's not bad. As a "do this for me/vibe code" thing, no thanks…too much trust. John says: Yes and no. I was in an AI startup that stopped paying me and my team for two months then let us go. We're currently suing them for back pay, but the tech worked and is still working. I also work in ad tech. Devs are trying to get more productive using AI tools. It's hit and miss as far as I can tell, but using traditional machine learning and data science to optimize marketing has worked for decades and still works, but that's not what people consider to be AI nowadays. Also drove across the country last August and used ChatGPT to plan my trip, and that works splendidly. I think John might win here for largest negative in his comment though, to be fair, that's more for business reasons than for AI itself, though I, for his sake, I'm pleased he was able to use ChatGPT to plan his drive across the country and ChatGPT didn't send him driving off a cliff someplace. Jenny says: I'm so over everyone trying to push this "solution" on me. It's like protein enhanced foods. Stop trying to put protein and AI into everything. Just put it where it makes sense or let me choose it. My negative experiences far outweigh anything helpful. Jimmy says: I have quit using Google search. It never tried to find the answer that I asked for. It just returned what it felt like. Its answers usually matched the paid ads it led the list with. Rob says: Okay for meeting notes and rough drafting for job applications, et cetera. Other than that, seems to have limited use for me personally and is a nuisance on my phone, internet browser, et cetera. And finally, Randy says: my biggest Con is that the AI answers that pop up when I'm trying to search range between inaccurate and dangerously wrong. I suspect many people don't realize they aren't reading actual data when they see them. So thank you to everyone who shared their thoughts on that. For myself, I've mostly experienced negative things with AI and a few positive things though to be honest, both the positive and negative things were relatively minor in the greater scheme of things. So I shall list off the Pros and Cons of my experiences with generative AI. I should mention that none of my books, short stories, for sale audiobooks, or book covers contain any AI elements. If it says Jonathan Moeller on the cover and it's not on YouTube, then it is 100% human made. Now, the Pros and Cons. The Pros: Power Director 365, the video editing program I use for YouTube, has an "animated by AI" feature so I've used it to animate some of my book covers for use of Facebook ads with middling results at best. I used Google's Voice AI stuff to create AI voice versions of the Silent Order books and then put them on YouTube because I wanted to understand the technology. I'm not planning to ever do actual audiobook versions of Silent Order since they wouldn't make back any money, so I wasn't screwing a narrator out of work and the voices involved were licensed by Google, so there was no copyright infringement the way there is with companies like Anthropic. That said, I suspect this is less generative AI and simply a more advanced text to speech technology, which has been around forever. I mean, you could do text to speech back on the earliest versions of the Macintosh. I mean, ideally, I would like text to speech to just be a button in your ereader app of choice for accessibility reasons, and then you can purchase the audiobook if the text to speech was too bland. Overall, a lot of people listen to the AI versions on YouTube, but the listeners mostly complained about the synthetic voice and would've preferred a real narrator, unsurprisingly. Now onto the Cons. Facebook ads went from very effective to middling at best on a good day, thanks to their Advantage Plus AI. I am constantly bombarded by AI generated scam emails of several different varieties. I deleted twelve before I recorded this. The price of Microsoft Office went up, the price for RAM and GPUs went up due to data center hoarding them all. The price for electricity has gone up. Windows 11 and Microsoft Office's performance has gone down quite a bit due to forced AI integration. In fact, I got so annoyed at Windows 11, I switched to writing on a Mac Mini, which I suppose was a positive because I like the Mac Mini, but still. Google Search and all Google products in general are much less useful because of AI and the quality of information on the internet (already low) has gone down quite a bit due to the prevalence of AI slop. Admittedly, neither these Pros or Cons are majorly serious to me personally (with the possible exception of electricity prices going up), but the Cons definitely outweigh the Pros. I can confidently say I have derived no real benefit from generative AI, and I suspect a lot of other people could say the same, if they're honest. 00:07:27 Main Topic of the Week: William Miller, The Great Disappointment, and AI Now onto our related main topic this week, AI hysteria, William Miller, and The Great Disappointment. This past week there were numerous articles from and interviews with various AI bros saying that within 12 to 18 months, AI will replace white collar work and humanity must simply adjust. When I read these articles, I wasn't reminded of the Singularity, of AI, of Skynet and the Terminator, or anything technological. Instead, I thought of a preacher named William Miller who died about 190 years ago. William Miller came out of the Second Great Awakening, which was one of the waves of religious vitality and furor that grip America every so often. Miller almost died in combat as an officer in the War of 1812, and saw one of his men killed in front of him, which understandably left a lasting impression. His experiences led him to an examination of mortality that resulted in a fervent Baptist conversion. He also became convinced that he could calculate the date of Christ's return from the Bible and decided that Jesus Christ would return on October 22nd, 1844. By then, he had a substantial following, and on the day his followers gathered in their churches to await the End of Days and the judging of the living and the dead, many of them having already given away their possessions, but nothing happened. Miller's movement collapsed and most of his followers abandoned their beliefs, though some splinter groups eventually involved into the Adventist branch of American Protestantism, of which the Seventh Day Adventists are the most prominent. Nowadays, when Miller is discussed online, the usual tone is to laugh at the religious rubes from the benighted past, so unlike us enlightened and savvy moderns. But I think the truth is that Miller succumbed to a universal human impulse. Every generation thinks that it is going to be the last generation or the generation that will see the culmination of history, whether they're viewing that through a religious lens or a secular lens. For example, when I was in my early twenties, I knew a very religious woman my own age, who was convinced that the world had become so wicked that it would end by the time she was 30. A few years later, I met another woman who thought global warming would ensure the collapse of the ecosystem and the end of the food chain by the time we were 30. However, I have not been 30 for a rather long span of time now, and for better or for worse, the world grinds on. Nor is this an impulse limited to my own generation. People who came of age during the Cold War thought the world would end in nuclear fire during their lifetimes and a little after that from global cooling. Lesser examples could be seen in the Y2K scare in 2000. Throughout the Middle Ages and the early modern period, it was common for peasant revolts to be led by charismatic preachers who predicted that soon all thrones would be overthrown and Christ would return to judge the living and the dead. Because of all these examples, I'm certain there is a universal human impulse to believe that the world will end in our lifetimes. I think this comes partly from a combination of fear and hope, fear of the future and the end of the world and hope that one's life will be lifted out of the mundane in the final fulfillment of history. You don't have to get up and go to school or work tomorrow if the world ends, but the truth is that the world is most likely not going to end, and you and I are probably going to have to get up and go to work tomorrow. I think the hyperbole about AI comes from that same sort of apocalyptic impulse, this idea that one is living to see and participating in the apotheosis of history when what one is in fact doing is using a money losing chatbot that frequently gets things wrong. To be clear, AI isn't going to wipe out white collar work, and it isn't going to cause the collapse of society, though like cryptocurrency, it will cause a lot of harm without very much benefit. AI simply isn't good enough and doesn't do what does boosters say that it can do. There are numerous people who, in my opinion, are accurately explaining and pointing out the many flaws in AI and in the economic bubble it has created, just as there were people who predicted the fall of the Soviet Union, the dot-com bubble, the housing bubble, the criminal activities of FTX and the flaws of cryptocurrency, and were frequently derided as cranks until subsequent events prove them right. So why all the hyperbole around AI? I think part of it is the end of days impulse we discussed above. The rest of it, I'm afraid, is simple crass desire for money and power. Why are all these tech companies burning unfathomable sums of money on AI when it's obvious, painfully obvious, that the bubble is heading for a crash? After the dot-com crash of the early 2000s, the Internet companies that survived eventually evolved into the tech titans of our day (Amazon and Google come to mind). All these different AI companies and boosters are hoping that their company is the one that survives and becomes the next titan conglomerate of the 2030s. Admittedly, I think this is unlikely. I think that while the most probable outcome for the current model of AI, LLMs, and generative AI is that it ends up like cryptocurrency. For a while, crypto advocates thought that it would overthrow central banking and lead to unprecedented freedom and prosperity. However, while there are many valid criticisms to be made of central banking and fiat currency, one of their advantages is that that they do a good job of shutting down the kind of scams that crypto easily facilitates. For all the glowing promises of its boosters, the primary use case for cryptocurrency has been to cause economic disruptions and to facilitate crimes and scams. I suspect AI will probably degenerate down to a similar state once the bubble pops. The technology won't go away, but it can't do all the miraculous things its backers promise. The money is going to run out eventually and it will inflict a lot of economic damage on its way out. And like crypto, AI will mostly have negative uses. Likely its most common use cases will be to help students cheat on exams, make stupid political memes where someone's least favorite politician (whoever that is) is shaking hands with Emperor Palpatine or Thanos or whoever, engage in mass copyright infringement, and to scam seniors out of their savings. So if you are disturbed by the rhetoric around AI, take heart. When you read an article from someone announcing the glories of AI and discussing how all of civilization will have to rework itself around AI, remember that the person in question is most likely seeking money or power, or are like William Miller's followers the day before October 22nd, 1844. So that is it for this week. Thank you for listening to The Pulp Writer Show. I hope you found the show useful. A reminder that you can listen to all the back episodes at https://thepulpwritershow.com. If you enjoyed the podcast, please leave a review on your podcasting platform of choice. Stay safe and stay healthy, and we'll see you all next week.  

dattrax: Welcome my Fellow Brothers and Sisters to where house music resides. How are you? How's the weather where you live? This winter in Toronto, Ontario, Canada has been brutal. LMAO.IF you ever want to visit Toronto, make sure that you only come here from June to Sept.__________________House music and DJ'ing house are endlessly fascinating to me even after 30 yrs.I've loved this art form since I was 14 and started buying records at 18. I'm 51 and still buy new tracks almost monthly. Still hunt through a mountain of shet to find the few gems that MOVE me. __________________WITH EACH HOUSE MIX » First, I always create a house music mix for me. To satisfy my nerdy curiosities and to showcase new tracks that I've bought.. Second, I make mixes for my best friend and DJ partner, Jimmy. We've both LOVED house music since we were 14yrs old and met at 16yrs old.We danced all the time and DJ'ed together in every nice place and shit hole in our Beloved Toronto.If Jim tells me that he likes one of my mixes, then I"m on cloud 9 for the rest of the week.Third, anyone else like a dattrax mix, then that's gravy on top.__________________Thank you for listening to this mix. I appreciate you. You could have chosen to spend your time anywhere else you'd like. Thank you. All the best to you.Much love and respect. May God Bless You and All Who You Love Abudantly.Cheers,dattrax---------------You're on our main site with 200+ mixes. Free mobile app or go to the Podomatic website:https://www.podomatic.com/podcasts/dattrax---------------How do we describe the dattrax sound? Always Fun, Tech-Fused, Funky-Foot Stompin', Carved Deep and Woven & Laced with Sweet Smooth Hands in the Air Vocals... Strictly House Music- always dattrax.---------------DJ Bookings for Canada, the US, or Global: dattrax@gmail.comDonation ETransfers (CAD): dattrax@gmail.comConnect on IG: https://www.instagram.com/house_music_by_dattrax/Connect on FaceCrack: https://www.facebook.com/dat.so.940---------------As always - massive thanks to the fantastic vocalists, producers, DJs, and dancers (even in your homes, driving, in the gym or while walking about or walking your doggie) for their incredible advancement of this beautiful musical genre!! It makes us all feel young, vibrant, and extremely happy!!---------------***Email us at dattrax@gmail.com if you want the playlist for this mix...This mix has 80 Tracks in 3 hrs and 17 mins!!!---------------"Toronto House DJ Mixes"Come and listen to the mixes of over 500 of the BEST House Music DJs in Toronto, Ontario, Canada:https://www.facebook.com/groups/TorontoHOUSEDJMixes---------------IMAGE CREDIT:A few years ago, I sent my oldest daughter, Grace a Google Image of Sunshine Bear from the Care Bears. She's a graphic artist. I asked her to remove the sun emblem from his chest and add a symbol of a house and beside that a music note, for 'house music'.This one I put through a filter and got this cool purple and I LOVE purple.This is what Google Search says about the colour Purple:"Purple is widely recognized as the color of royalty, luxury, power, and ambition, historically derived from rare, expensive dyes. It blends the calming, stable energy of blue with the intense, stimulating energy of red, symbolizing wisdom, creativity, magic, and spirituality."

 Want more acupuncture patients from Google without feeling salesy? This episode breaks down a calm, practical SEO plan to help your practice show up in local search and turn visibility into booked appointments. You'll learn what to optimize first, which website pages to build, how to improve your Google Business Profile, and a simple content strategy that supports rankings without becoming a full-time content creator. Episode webpage: https://propelyourcompany.com/acupuncturist-seo-strategySend in your questions. ❤ We'd love to hear from you!NEW Webinar: How to dominate Google Search, Google Maps, AI-driven search results, and get more new patients.>> Save your spot

In this episode, find out why many sources cited in Google's AI Overviews don't rank in the top ten, and how your business can break through using content marketing built on SEO fundamentals, topical authority, and strategic optimization.Learn more at https://dominateorganicsearch.com/ Dominate Organic Search City: Anthem Address: 41111 North Daisy Mountain Drive Website: https://dominateorganicsearch.com

Marketing attribution often feels like a guessing game where everyone just ends up giving credit to Google Search. Jay Schwedelson connects with Daniel Murray from The Marketing Millennials to break down why you should treat attribution data as a directional compass rather than a precise GPS. They discuss practical ways to validate your data, including the power of holdout groups and geo-testing, plus a random side conversation about whether Olympic curlers are actually the best athletes in the world.Follow Daniel on LinkedIn and check out The Marketing Millennials podcast for sharp, no-fluff marketing insights. Subscribe to Ari Murray's newsletter at gotomillions.co for sharp, actionable marketing insights.Best Moments:(01:50) Why attribution should be viewed as a compass instead of a turn-by-turn GPS(02:22) The problem with giving Google Search all the credit for last-touch conversions(03:10) How to set up a holdout group to measure the true lift of your campaigns(04:09) Using geo-based testing to see if specific channels are actually driving growth(04:45) Why it is a red flag if one channel claims 100% of the credit in a multi-touch world(05:15) The surprisingly effective "How did you hear about us?" form field strategy(06:40) A hot take on whether curling athletes are actually the best in the worldCheck out Jay's YOUTUBE Channel: https://www.youtube.com/@schwedelsonCheck out Jay's TIKTOK: https://www.tiktok.com/@schwedelsonCheck Out Jay's INSTAGRAM: https://www.instagram.com/jayschwedelson/ㅤPre-order Jay Schwedelson's new book, Stupider People Have Done It (out April 21, 2026). All net proceeds are donated to The V Foundation for Cancer Research—let's kick cancer's butt: https://www.amazon.com/Stupider-People-Have-Done-Marketing/dp/1637635206

Attribution is one of the most talked-about topics in marketing…and also one of the most misleading. Jay and Daniel explain why most attribution models are basically garbage, especially last-touch attribution, and why marketers keep over-investing in channels like Google Search simply because they get the final click. Jay walks through one of the most underused measurement tactics in marketing: holdout groups, where you intentionally exclude part of your audience from campaigns to measure real lift. Daniel adds the simplest attribution hack of all: just asking customers where they heard about you. If you're tired of dashboards that tell you what you want to hear instead of what's real, this episode Follow Jay: LinkedIn: https://www.linkedin.com/in/schwedelson/ Podcast: Do This, Not That Follow Daniel: YouTube: https://www.youtube.com/@themarketingmillennials/featured Twitter: https://www.twitter.com/Dmurr68 LinkedIn: https://www.linkedin.com/in/daniel-murray-marketing Sign up for The Marketing Millennials newsletter: https://themarketingmillennials.com/ Daniel is a Workweek friend, working to produce amazing podcasts. To find out more, visit: https://workweek.com/

Voice search and AI-powered search are changing how patients find clinics in 2026, but the winning strategy is still simple, clear local SEO and content that answers real patient questions. In this episode, you'll learn how voice and AI queries differ from typed searches, what to update on your website and Google Business Profile, and five practical upgrades to help you show up more often and turn visibility into booked appointments. Episode Webpage: https://propelyourcompany.com/voice-search/Live Webinar: Fix Your AI Visibility Blind Spots - https://propelyourcompany.com/fix/Send in your questions. ❤ We'd love to hear from you!NEW Webinar: How to dominate Google Search, Google Maps, AI-driven search results, and get more new patients.>> Save your spot

Emmanuel et Guillaume discutent de divers sujets liés à la programmation, notamment les systèmes de fichiers en Java, le Data Oriented Programming, les défis de JPA avec Kotlin, et les nouvelles fonctionnalités de Quarkus. Ils explorent également des sujets un peu fous comme la création de datacenters dans l'espace. Pas mal d'architecture aussi. Enregistré le 13 février 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-337.mp3 ou en vidéo sur YouTube. News Langages Comment implémenter un file system en Java https://foojay.io/today/bootstrapping-a-java-file-system/ Créer un système de fichiers Java personnalisé avec NIO.2 pour des usages variés (VCS, archives, systèmes distants). Évolution Java: java.io.File (1.0) -> NIO (1.4) -> NIO.2 (1.7) pour personnalisation via FileSystem. Recommander conception préalable; API Java est orientée POSIX. Composants clés à considérer: Conception URI (scheme unique, chemin). Gestion de l'arborescence (BD, métadonnées, efficacité). Stockage binaire (emplacement, chiffrement, versions). Minimum pour démarrer (4 composants): Implémenter Path (représente fichier/répertoire). Étendre FileSystem (instance du système). Étendre FileSystemProvider (moteur, enregistré par scheme). Enregistrer FileSystemProvider via META-INF/services. Étapes suivantes: Couche BD (arborescence), opérations répertoire/fichier de base, stockage, tests. Processus long et exigeant, mais gratifiant.   Un article de brian goetz sur le futur du data oriented programming en Java https://openjdk.org/projects/amber/design-notes/beyond-records Le projet Amber de Java introduit les "carrier classes", une évolution des records qui permet plus de flexibilité tout en gardant les avantages du pattern matching et de la reconstruction Les records imposent des contraintes strictes (immutabilité, représentation exacte de l'état) qui limitent leur usage pour des classes avec état muable ou dérivé Les carrier classes permettent de déclarer une state description complète et canonique sans imposer que la représentation interne corresponde exactement à l'API publique Le modificateur "component" sur les champs permet au compilateur de dériver automatiquement les accesseurs pour les composants alignés avec la state description Les compact constructors sont généralisés aux carrier classes, générant automatiquement l'initialisation des component fields Les carrier classes supportent la déconstruction via pattern matching comme les records, rendant possible leur usage dans les instanceof et switch Les carrier interfaces permettent de définir une state description sur une interface, obligeant les implémentations à fournir les accesseurs correspondants L'extension entre carrier classes est possible, avec dérivation automatique des appels super() quand les composants parent sont subsumés par l'enfant Les records deviennent un cas particulier de carrier classes avec des contraintes supplémentaires (final, extends Record, component fields privés et finaux obligatoires) L'évolution compatible des records est améliorée en permettant l'ajout de composants en fin de liste et la déconstruction partielle par préfixe Comment éviter les pièges courants avec JPA et Kotlin - https://blog.jetbrains.com/idea/2026/01/how-to-avoid-common-pitfalls-with-jpa-and-kotlin/ JPA est une spécification Java pour la persistance objet-relationnel, mais son utilisation avec Kotlin présente des incompatibilités dues aux différences de conception des deux langages Les classes Kotlin sont finales par défaut, ce qui empêche la création de proxies par JPA pour le lazy loading et les opérations transactionnelles Le plugin kotlin-jpa génère automatiquement des constructeurs sans argument et rend les classes open, résolvant les problèmes de compatibilité Les data classes Kotlin ne sont pas adaptées aux entités JPA car elles génèrent equals/hashCode basés sur tous les champs, causant des problèmes avec les relations lazy L'utilisation de lateinit var pour les relations peut provoquer des exceptions si on accède aux propriétés avant leur initialisation par JPA Les types non-nullables Kotlin peuvent entrer en conflit avec le comportement de JPA qui initialise les entités avec des valeurs null temporaires Le backing field direct dans les getters/setters personnalisés peut contourner la logique de JPA et casser le lazy loading IntelliJ IDEA 2024.3 introduit des inspections pour détecter automatiquement ces problèmes et propose des quick-fixes L'IDE détecte les entités finales, les data classes inappropriées, les problèmes de constructeurs et l'usage incorrect de lateinit Ces nouvelles fonctionnalités aident les développeurs à éviter les bugs subtils liés à l'utilisation de JPA avec Kotlin Librairies Guide sur MapStruct @IterableMapping - https://www.baeldung.com/java-mapstruct-iterablemapping MapStruct est une bibliothèque Java pour générer automatiquement des mappers entre beans, l'annotation @IterableMapping permet de configurer finement le mapping de collections L'attribut dateFormat permet de formater automatiquement des dates lors du mapping de listes sans écrire de boucle manuelle L'attribut qualifiedByName permet de spécifier quelle méthode custom appliquer sur chaque élément de la collection à mapper Exemple d'usage : filtrer des données sensibles comme des mots de passe en mappant uniquement certains champs via une méthode dédiée L'attribut nullValueMappingStrategy permet de contrôler le comportement quand la collection source est null (retourner null ou une collection vide) L'annotation fonctionne pour tous types de collections Java (List, Set, etc.) et génère le code de boucle nécessaire Possibilité d'appliquer des formats numériques avec numberFormat pour convertir des nombres en chaînes avec un format spécifique MapStruct génère l'implémentation complète du mapper au moment de la compilation, éliminant le code boilerplate L'annotation peut être combinée avec @Named pour créer des méthodes de mapping réutilisables et nommées Le mapping des collections supporte les conversions de types complexes au-delà des simples conversions de types primitifs Accès aux fichiers Samba depuis Java avec JCIFS - https://www.baeldung.com/java-samba-jcifs JCIFS est une bibliothèque Java permettant d'accéder aux partages Samba/SMB sans monter de lecteur réseau, supportant le protocole SMB3 on pense aux galériens qui doivent se connecter aux systèmes dit legacy La configuration nécessite un contexte CIFS (CIFSContext) et des objets SmbFile pour représenter les ressources distantes L'authentification se fait via NtlmPasswordAuthenticator avec domaine, nom d'utilisateur et mot de passe La bibliothèque permet de lister les fichiers et dossiers avec listFiles() et vérifier leurs propriétés (taille, date de modification) Création de fichiers avec createNewFile() et de dossiers avec mkdir() ou mkdirs() pour créer toute une arborescence Suppression via delete() qui peut parcourir et supprimer récursivement des arborescences entières Copie de fichiers entre partages Samba avec copyTo(), mais impossibilité de copier depuis le système de fichiers local Pour copier depuis le système local, utilisation des streams SmbFileInputStream et SmbFileOutputStream Les opérations peuvent cibler différents serveurs Samba et différents partages (anonymes ou protégés par mot de passe) La bibliothèque s'intègre dans des blocs try-with-resources pour une gestion automatique des ressources Quarkus 3.31 - Support complet Java 25, nouveau packaging Maven et Panache Next - https://quarkus.io/blog/quarkus-3-31-released/ Support complet de Java 25 avec images runtime et native Nouveau packaging Maven de type quarkus avec lifecycle optimisé pour des builds plus rapides voici un article complet pour plus de detail https://quarkus.io/blog/building-large-applications/ Introduction de Panache Next, nouvelle génération avec meilleure expérience développeur et API unifiée ORM/Reactive Mise à jour vers Hibernate ORM 7.2, Reactive 3.2, Search 8.2 Support de Hibernate Spatial pour les données géospatiales Passage à Testcontainers 2 et JUnit 6 Annotations de sécurité supportées sur les repositories Jakarta Data Chiffrement des tokens OIDC pour les implémentations custom TokenStateManager Support OAuth 2.0 Pushed Authorization Requests dans l'extension OIDC Maven 3.9 maintenant requis minimum pour les projets Quarkus A2A Java SDK 1.0.0.Alpha1 - Alignement avec la spécification 1.0 du protocole Agent2Agent - https://quarkus.io/blog/a2a-java-sdk-1-0-0-alpha1/ Le SDK Java A2A implémente le protocole Agent2Agent qui permet la communication standardisée entre agents IA pour découvrir des capacités, déléguer des tâches et collaborer Passage à la version 1.0 de la spécification marque la transition d'expérimental à production-ready avec des changements cassants assumés Modernisation complète du module spec avec des Java records partout remplaçant le mix précédent de classes et records pour plus de cohérence Adoption de Protocol Buffers comme source de vérité avec des mappers MapStruct pour la conversion et Gson pour JSON-RPC Les builders utilisent maintenant des méthodes factory statiques au lieu de constructeurs publics suivant les best practices Java modernes Introduction de trois BOMs Maven pour simplifier la gestion des dépendances du SDK core, des extensions et des implémentations de référence Quarkus AgentCard évolue avec une liste supportedInterfaces remplaçant url et preferredTransport pour plus de flexibilité dans la déclaration des protocoles Support de la pagination ajouté pour ListTasks et les endpoints de configuration des notifications push avec des wrappers Result appropriés Interface A2AHttpClient pluggable permettant des implémentations HTTP personnalisées avec une implémentation Vert.x fournie Travail continu vers la conformité complète avec le TCK 1.0 en cours de développement parallèlement à la finalisation de la spécification Pourquoi Quarkus finit par "cliquer" : les 10 questions que se posent les développeurs Java - https://www.the-main-thread.com/p/quarkus-java-developers-top-questions-2025 un article qui revele et repond aux questions des gens qui ont utilisé Quarkus depuis 4-6 mois, les non noob questions Quarkus est un framework Java moderne optimisé pour le cloud qui propose des temps de démarrage ultra-rapides et une empreinte mémoire réduite Pourquoi Quarkus démarre si vite ? Le framework effectue le travail lourd au moment du build (scanning, indexation, génération de bytecode) plutôt qu'au runtime Quand utiliser le mode réactif plutôt qu'impératif ? Le réactif est pertinent pour les workloads avec haute concurrence et dominance I/O, l'impératif reste plus simple dans les autres cas Quelle est la différence entre Dev Services et Testcontainers ? Dev Services utilise Testcontainers en gérant automatiquement le cycle de vie, les ports et la configuration sans cérémonie Comment la DI de Quarkus diffère de Spring ? CDI est un standard basé sur la sécurité des types et la découverte au build-time, différent de l'approche framework de Spring Comment gérer la configuration entre environnements ? Quarkus permet de scaler depuis le développement local jusqu'à Kubernetes avec des profils, fichiers multiples et configuration externe Comment tester correctement les applications Quarkus ? @QuarkusTest démarre l'application une fois pour toute la suite de tests, changeant le modèle mental par rapport à Spring Boot Que fait vraiment Panache en coulisses ? Panache est du JPA avec des opinions fortes et des défauts propres, enveloppant Hibernate avec un style Active Record Doit-on utiliser les images natives et quand ? Les images natives brillent pour le serverless et l'edge grâce au démarrage rapide et la faible empreinte mémoire, mais tous les apps n'en bénéficient pas Comment Quarkus s'intègre avec Kubernetes ? Le framework génère automatiquement les ressources Kubernetes, gère les health checks et métriques comme s'il était nativement conçu pour cet écosystème Comment intégrer l'IA dans une application Quarkus ? LangChain4j permet d'ajouter embeddings, retrieval, guardrails et observabilité directement en Java sans passer par Python Infrastructure Les alternatives à MinIO https://rmoff.net/2026/01/14/alternatives-to-minio-for-single-node-local-s3/ MinIO a abandonné le support single-node fin 2025 pour des raisons commerciales, cassant de nombreuses démos et pipelines CI/CD qui l'utilisaient pour émuler S3 localement L'auteur cherche un remplacement simple avec image Docker, compatibilité S3, licence open source, déploiement mono-nœud facile et communauté active S3Proxy est très léger et facile à configurer, semble être l'option la plus simple mais repose sur un seul contributeur RustFS est facile à utiliser et inclut une GUI, mais c'est un projet très récent en version alpha avec une faille de sécurité majeure récente SeaweedFS existe depuis 2012 avec support S3 depuis 2018, relativement facile à configurer et dispose d'une interface web basique Zenko CloudServer remplace facilement MinIO mais la documentation et le branding (cloudserver/zenko/scality) peuvent prêter à confusion Garage nécessite une configuration complexe avec fichier TOML et conteneur d'initialisation séparé, pas un simple remplacement drop-in Apache Ozone requiert au minimum quatre nœuds pour fonctionner, beaucoup trop lourd pour un usage local simple L'auteur recommande SeaweedFS et S3Proxy comme remplaçants viables, RustFS en maybe, et élimine Garage et Ozone pour leur complexité Garage a une histoire tres associative, il vient du collectif https://deuxfleurs.fr/ qui offre un cloud distribué sans datacenter C'est certainement pas une bonne idée, les datacenters dans l'espace https://taranis.ie/datacenters-in-space-are-a-terrible-horrible-no-good-idea/ Avis d'expert (ex-NASA/Google, Dr en électronique spatiale) : Centres de données spatiaux, une "terrible" idée. Incompatibilité fondamentale : L'électronique (surtout IA/GPU) est inadaptée à l'environnement spatial. Énergie : Accès limité. Le solaire (type ISS) est insuffisant pour l'échelle de l'IA. Le nucléaire (RTG) est trop faible. Refroidissement : L'espace n'est pas "froid" ; absence de convection. Nécessite des radiateurs gigantesques (ex: 531m² pour 200kW). Radiations : Provoque erreurs (SEU, SEL) et dommages. Les GPU sont très vulnérables. Blindage lourd et inefficace. Les puces "durcies" sont très lentes. Communications : Bande passante très limitée (1Gbps radio vs 100Gbps terrestre). Le laser est tributaire des conditions atmosphériques. Conclusion : Projet extrêmement difficile, coûteux et aux performances médiocres. Data et Intelligence Artificielle Guillaume a développé un serveur MCP pour arXiv (le site de publication de papiers de recherche) en Java avec le framework Quarkus https://glaforge.dev/posts/2026/01/18/implementing-an-arxiv-mcp-server-with-quarkus-in-java/ Implémentation d'un serveur MCP (Model Context Protocol) arXiv en Java avec Quarkus. Objectif : Accéder aux publications arXiv et illustrer les fonctionnalités moins connues du protocole MCP. Mise en œuvre : Utilisation du framework Quarkus (Java) et son support MCP étendu. Assistance par Antigravity (IDE agentique) pour le développement et l'intégration de l'API arXiv. Interaction avec l'API arXiv : requêtes HTTP, format XML Atom pour les résultats, parser XML Jackson. Fonctionnalités MCP exposées : Outils (@Tool) : Recherche de publications (search_papers). Ressources (@Resource, @ResourceTemplate) : Taxonomie des catégories arXiv, métadonnées des articles (via un template d'URI). Prompts (@Prompt) : Exemples pour résumer des articles ou construire des requêtes de recherche. Configuration : Le serveur peut fonctionner en STDIO (local) ou via HTTP Streamable (local ou distant), avec une configuration simple dans des clients comme Gemini CLI. Conclusion : Quarkus simplifie la création de serveurs MCP riches en fonctionnalités, rendant les données et services "prêts pour l'IA" avec l'aide d'outils d'IA comme Antigravity. Anthropic ne mettra pas de pub dans Claude https://www.anthropic.com/news/claude-is-a-space-to-think c'est en reaction au plan non public d'OpenAi de mettre de la pub pour pousser les gens au mode payant OpenAI a besoin de cash et est probablement le plus utilisé pour gratuit au monde Anthropic annonce que Claude restera sans publicité pour préserver son rôle d'assistant conversationnel dédié au travail et à la réflexion approfondie. Les conversations avec Claude sont souvent sensibles, personnelles ou impliquent des tâches complexes d'ingénierie logicielle où les publicités seraient inappropriées. L'analyse des conversations montre qu'une part significative aborde des sujets délicats similaires à ceux évoqués avec un conseiller de confiance. Un modèle publicitaire créerait des incitations contradictoires avec le principe fondamental d'être "genuinely helpful" inscrit dans la Constitution de Claude. Les publicités introduiraient un conflit d'intérêt potentiel où les recommandations pourraient être influencées par des motivations commerciales plutôt que par l'intérêt de l'utilisateur. Le modèle économique d'Anthropic repose sur les contrats entreprise et les abonnements payants, permettant de réinvestir dans l'amélioration de Claude. Anthropic maintient l'accès gratuit avec des modèles de pointe et propose des tarifs réduits pour les ONG et l'éducation dans plus de 60 pays. Le commerce "agentique" sera supporté mais uniquement à l'initiative de l'utilisateur, jamais des annonceurs, pour préserver la confiance. Les intégrations tierces comme Figma, Asana ou Canva continueront d'être développées en gardant l'utilisateur aux commandes. Anthropic compare Claude à un cahier ou un tableau blanc : des espaces de pensée purs, sans publicité. Infinispan 16.1 est sorti https://infinispan.org/blog/2026/02/04/infinispan-16-1 déjà le nom de la release mérite une mention Le memory bounded par cache et par ensemble de cache s est pas facile à faire en Java Une nouvelle api OpenAPI AOT caché dans les images container Un serveur MCP local juste avec un fichier Java ? C'est possible avec LangChain4j et JBang https://glaforge.dev/posts/2026/02/11/zero-boilerplate-java-stdio-mcp-servers-with-langchain4j-and-jbang/ Création rapide de serveurs MCP Java sans boilerplate. MCP (Model Context Protocol): standard pour connecter les LLM à des outils et données. Le tutoriel répond au manque d'options simples pour les développeurs Java, face à une prédominance de Python/TypeScript dans l'écosystème MCP. La solution utilise: LangChain4j: qui intègre un nouveau module serveur MCP pour le protocole STDIO. JBang: permet d'exécuter des fichiers Java comme des scripts, éliminant les fichiers de build (pom.xml, Gradle). Implémentation: se fait via un seul fichier .java. JBang gère automatiquement les dépendances (//DEPS). L'annotation @Tool de LangChain4j expose les méthodes Java aux LLM. StdioMcpServerTransport gère la communication JSON-RPC via l'entrée/sortie standard (STDIO). Point crucial: Les logs doivent impérativement être redirigés vers System.err pour éviter de corrompre System.out, qui est réservé à la communication MCP (messages JSON-RPC). Facilite l'intégration locale avec des outils comme Gemini CLI, Claude Code, etc. Reciprocal Rank Fusion : un algorithme utile et souvent utilisé pour faire de la recherche hybride, pour mélanger du RAG et des recherches par mots-clé https://glaforge.dev/posts/2026/02/10/advanced-rag-understanding-reciprocal-rank-fusion-in-hybrid-search/ RAG : Qualité LLM dépend de la récupération. Recherche Hybride : Combiner vectoriel et mots-clés (BM25) est optimal. Défi : Fusionner des scores d'échelles différentes. Solution : Reciprocal Rank Fusion (RRF). RRF : Algorithme robuste qui fusionne des listes de résultats en se basant uniquement sur le rang des documents, ignorant les scores. Avantages RRF : Pas de normalisation de scores, scalable, excellente première étape de réorganisation. Architecture RAG fréquente : RRF (large sélection) + Cross-Encoder / modèle de reranking (précision fine). RAG-Fusion : Utilise un LLM pour générer plusieurs variantes de requête, puis RRF agrège tous les résultats pour renforcer le consensus et réduire les hallucinations. Implémentation : LangChain4j utilise RRF par défaut pour agréger les résultats de plusieurs retrievers. Les dernières fonctionnalités de Gemini et Nano Banana supportées dans LangChain4j https://glaforge.dev/posts/2026/02/06/latest-gemini-and-nano-banana-enhancements-in-langchain4j/ Nouveaux modèles d'images Nano Banana (Gemini 2.5/3.0) pour génération et édition (jusqu'à 4K). "Grounding" via Google Search (pour images et texte) et Google Maps (localisation, Gemini 2.5). Outil de contexte URL (Gemini 3.0) pour lecture directe de pages web. Agents multimodaux (AiServices) capables de générer des images. Configuration de la réflexion (profondeur Chain-of-Thought) pour Gemini 3.0. Métadonnées enrichies : usage des tokens et détails des sources de "grounding". Comment configurer Gemini CLI comment agent de code dans IntelliJ grâce au protocole ACP https://glaforge.dev/posts/2026/02/01/how-to-integrate-gemini-cli-with-intellij-idea-using-acp/ But : Intégrer Gemini CLI à IntelliJ IDEA via l'Agent Client Protocol (ACP). Prérequis : IntelliJ IDEA 2025.3+, Node.js (v20+), Gemini CLI. Étapes : Installer Gemini CLI (npm install -g @google/gemini-cli). Localiser l'exécutable gemini. Configurer ~/.jetbrains/acp.json (chemin exécutable, --experimental-acp, use_idea_mcp: true). Redémarrer IDEA, sélectionner "Gemini CLI" dans l'Assistant IA. Usage : Gemini interagit avec le code et exécute des commandes (contexte projet). Important : S'assurer du flag --experimental-acp dans la configuration. Outillage PipeNet, une alternative (open source aussi) à LocalTunnel, mais un plus évoluée https://pipenet.dev/ pipenet: Alternative open-source et moderne à localtunnel (client + serveur). Usages: Développement local (partage, webhooks), intégration SDK, auto-hébergement sécurisé. Fonctionnalités: Client (expose ports locaux, sous-domaines), Serveur (déploiement, domaines personnalisés, optimisé cloud mono-port). Avantages vs localtunnel: Déploiement cloud sur un seul port, support multi-domaines, TypeScript/ESM, maintenance active. Protocoles: HTTP/S, WebSocket, SSE, HTTP Streaming. Intégration: CLI ou SDK JavaScript. JSON-IO — une librairie comme Jackson ou GSON, supportant JSON5, TOON, et qui pourrait être utile pour l'utilisation du "structured output" des LLMs quand ils ne produisent pas du JSON parfait https://github.com/jdereg/json-io json-io : Librairie Java pour la sérialisation et désérialisation JSON/TOON. Gère les graphes d'objets complexes, les références cycliques et les types polymorphes. Support complet JSON5 (lecture et écriture), y compris des fonctionnalités non prises en charge par Jackson/Gson. Format TOON : Notation orientée token, optimisée pour les LLM, réduisant l'utilisation de tokens de 40 à 50% par rapport au JSON. Légère : Aucune dépendance externe (sauf java-util), taille de JAR réduite (~330K). Compatible JDK 1.8 à 24, ainsi qu'avec les environnements JPMS et OSGi. Deux modes de conversion : vers des objets Java typés (toJava()) ou vers des Map (toMaps()). Options de configuration étendues via ReadOptionsBuilder et WriteOptionsBuilder. Optimisée pour les déploiements cloud natifs et les architectures de microservices. Utiliser mailpit et testcontainer pour tester vos envois d'emails https://foojay.io/today/testing-emails-with-testcontainers-and-mailpit/ l'article montre via SpringBoot et sans. Et voici l'extension Quarkus https://quarkus.io/extensions/io.quarkiverse.mailpit/quarkus-mailpit/?tab=docs Tester l'envoi d'emails en développement est complexe car on ne peut pas utiliser de vrais serveurs SMTP Mailpit est un serveur SMTP de test qui capture les emails et propose une interface web pour les consulter Testcontainers permet de démarrer Mailpit dans un conteneur Docker pour les tests d'intégration L'article montre comment configurer une application SpringBoot pour envoyer des emails via JavaMail Un module Testcontainers dédié à Mailpit facilite son intégration dans les tests Le conteneur Mailpit expose un port SMTP (1025) et une API HTTP (8025) pour vérifier les emails reçus Les tests peuvent interroger l'API HTTP de Mailpit pour valider le contenu des emails envoyés Cette approche évite d'utiliser des mocks et teste réellement l'envoi d'emails Mailpit peut aussi servir en développement local pour visualiser les emails sans les envoyer réellement La solution fonctionne avec n'importe quel framework Java supportant JavaMail Architecture Comment scaler un système de 0 à 10 millions d'utilisateurs https://blog.algomaster.io/p/scaling-a-system-from-0-to-10-million-users Philosophie : Scalabilité incrémentale, résoudre les goulots d'étranglement sans sur-ingénierie. 0-100 utilisateurs : Serveur unique (app, DB, jobs). 100-1K : Séparer app et DB (services gérés, pooling). 1K-10K : Équilibreur de charge, multi-serveurs d'app (stateless via sessions partagées). 10K-100K : Caching, réplicas de lecture DB, CDN (réduire charge DB). 100K-500K : Auto-scaling, applications stateless (authentification JWT). 500K-10M : Sharding DB, microservices, files de messages (traitement asynchrone). 10M+ : Déploiement multi-régions, CQRS, persistance polyglotte, infra personnalisée. Principes clés : Simplicité, mesure, stateless essentiel, cache/asynchrone, sharding prudent, compromis (CAP), coût de la complexité. Patterns d'Architecture 2026 - Du Hype à la Réalité du Terrain (Part 1/2) - https://blog.ippon.fr/2026/01/30/patterns-darchitecture-2026-part-1/ L'article présente quatre patterns d'architecture logicielle pour répondre aux enjeux de scalabilité, résilience et agilité business dans les systèmes modernes Il présentent leurs raisons et leurs pièges Un bon rappel L'Event-Driven Architecture permet une communication asynchrone entre systèmes via des événements publiés et consommés, évitant le couplage direct Les bénéfices de l'EDA incluent la scalabilité indépendante des composants, la résilience face aux pannes et l'ajout facile de nouveaux cas d'usage Le pattern API-First associé à un API Gateway centralise la sécurité, le routage et l'observabilité des APIs avec un catalogue unifié Le Backend for Frontend crée des APIs spécifiques par canal (mobile, web, partenaires) pour optimiser l'expérience utilisateur CQRS sépare les modèles de lecture et d'écriture avec des bases optimisées distinctes, tandis que l'Event Sourcing stocke tous les événements plutôt que l'état actuel Le Saga Pattern gère les transactions distribuées via orchestration centralisée ou chorégraphie événementielle pour coordonner plusieurs microservices Les pièges courants incluent l'explosion d'événements granulaires, la complexité du debugging distribué, et la mauvaise gestion de la cohérence finale Les technologies phares sont Kafka pour l'event streaming, Kong pour l'API Gateway, EventStoreDB pour l'Event Sourcing et Temporal pour les Sagas Ces patterns nécessitent une maturité technique et ne sont pas adaptés aux applications CRUD simples ou aux équipes junior Patterns d'architecture 2026 : du hype à la réalité terrain part. 2 - https://blog.ippon.fr/2026/02/04/patterns-darchitecture-2026-part-2/ Deuxième partie d'un guide pratique sur les patterns d'architecture logicielle et système éprouvés pour moderniser et structurer les applications en 2026 Strangler Fig permet de migrer progressivement un système legacy en l'enveloppant petit à petit plutôt que de tout réécrire d'un coup (70% d'échec pour les big bang) Anti-Corruption Layer protège votre nouveau domaine métier des modèles externes et legacy en créant une couche de traduction entre les systèmes Service Mesh gère automatiquement la communication inter-services dans les architectures microservices (sécurité mTLS, observabilité, résilience) Architecture Hexagonale sépare le coeur métier des détails techniques via des ports et adaptateurs pour améliorer la testabilité et l'évolutivité Chaque pattern est illustré par un cas client concret avec résultats mesurables et liste des pièges à éviter lors de l'implémentation Les technologies 2026 mentionnées incluent Istio, Linkerd pour service mesh, LaunchDarkly pour feature flags, NGINX et Kong pour API gateway Tableau comparatif final aide à choisir le bon pattern selon la complexité, le scope et le use case spécifique du projet L'article insiste sur une approche pragmatique : ne pas utiliser un pattern juste parce qu'il est moderne mais parce qu'il résout un problème réel Pour les systèmes simples type CRUD ou avec peu de services, ces patterns peuvent introduire une complexité inutile qu'il faut savoir éviter Méthodologies Le rêve récurrent de remplacer voire supprimer les développeurs https://www.caimito.net/en/blog/2025/12/07/the-recurring-dream-of-replacing-developers.html Depuis 1969, chaque décennie voit une tentative de réduire le besoin de développeurs (de COBOL, UML, visual builders… à IA). Motivation : frustration des dirigeants face aux délais et coûts de développement. La complexité logicielle est intrinsèque et intellectuelle, non pas une question d'outils. Chaque vague technologique apporte de la valeur mais ne supprime pas l'expertise humaine. L'IA assiste les développeurs, améliore l'efficacité, mais ne remplace ni le jugement ni la gestion de la complexité. La demande de logiciels excède l'offre car la contrainte majeure est la réflexion nécessaire pour gérer cette complexité. Pour les dirigeants : les outils rendent-ils nos développeurs plus efficaces sur les problèmes complexes et réduisent-ils les tâches répétitives ? Le "rêve" de remplacer les développeurs, irréalisable, est un moteur d'innovation créant des outils précieux. Comment creuser des sujets à l'ère de l'IA générative. Quid du partage et la curation de ces recherches ? https://glaforge.dev/posts/2026/02/04/researching-topics-in-the-age-of-ai-rock-solid-webhooks-case-study/ Recherche initiale de l'auteur sur les webhooks en 2019, processus long et manuel. L'IA (Deep Research, Gemini, NotebookLM) facilite désormais la recherche approfondie, l'exploration de sujets et le partage des résultats. L'IA a identifié et validé des pratiques clés pour des déploiements de webhooks résilients, en grande partie les mêmes que celles trouvées précédemment par l'auteur. Génération d'artefacts par l'IA : rapport détaillé, résumé concis, illustration sketchnote, et même une présentation (slide deck). Guillaume s'interroge sur le partage public de ces rapports de recherche générés par l'IA, tout en souhaitant éviter le "AI Slop". Loi, société et organisation Le logiciel menacé par le vibe coding https://www.techbuzz.ai/articles/we-built-a-monday-com-clone-in-under-an-hour-with-ai Deux journalistes de CNBC sans expérience de code ont créé un clone fonctionnel de Monday.com en moins de 60 minutes pour 5 à 15 dollars. L'expérience valide les craintes des investisseurs qui ont provoqué une baisse de 30% des actions des entreprises SaaS. L'IA a non seulement reproduit les fonctionnalités de base mais a aussi recherché Monday.com de manière autonome pour identifier et recréer ses fonctionnalités clés. Cette technique appelée "vibe-coding" permet aux non-développeurs de construire des applications via des instructions en anglais courant. Les entreprises les plus vulnérables sont celles offrant des outils "qui se posent sur le travail" comme Atlassian, Adobe, HubSpot, Zendesk et Smartsheet. Les entreprises de cybersécurité comme CrowdStrike et Palo Alto sont considérées plus protégées grâce aux effets de réseau et aux barrières réglementaires. Les systèmes d'enregistrement comme Salesforce restent plus difficiles à répliquer en raison de leur profondeur d'intégration et de données d'entreprise. Le coût de 5 à 15 dollars par construction permet aux entreprises de prototyper plusieurs solutions personnalisées pour moins cher qu'une seule licence Monday.com. L'expérience soulève des questions sur la pérennité du marché de 5 milliards de dollars des outils de gestion de projet face à l'IA générative. Conférences En complément de l'agenda des conférences de Aurélie Vache, il y a également le site https://javaconferences.org/ (fait par Brian Vermeer) avec toutes les conférences Java à venir ! La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 12-13 février 2026 : Touraine Tech #26 - Tours (France) 12-13 février 2026 : World Artificial Intelligence Cannes Festival - Cannes (France) 19 février 2026 : ObservabilityCON on the Road - Paris (France) 6 mars 2026 : WordCamp Nice 2026 - Nice (France) 18 mars 2026 : Jupyter Workshops: AI in Jupyter: Building Extensible AI Capabilities for Interactive Computing - Saint-Maur-des-Fossés (France) 18-19 mars 2026 : Agile Niort 2026 - Niort (France) 20 mars 2026 : Atlantique Day 2026 - Nantes (France) 26 mars 2026 : Data Days Lille - Lille (France) 26-27 mars 2026 : SymfonyLive Paris 2026 - Paris (France) 26-27 mars 2026 : REACT PARIS - Paris (France) 27-29 mars 2026 : Shift - Nantes (France) 31 mars 2026 : ParisTestConf - Paris (France) 31 mars 2026-1 avril 2026 : FlowCon France 2026 - Paris (France) 1 avril 2026 : AWS Summit Paris - Paris (France) 2 avril 2026 : Pragma Cannes 2026 - Cannes (France) 2-3 avril 2026 : Xen Spring Meetup 2026 - Grenoble (France) 7 avril 2026 : PyTorch Conference Europe - Paris (France) 9-10 avril 2026 : Android Makers by droidcon 2026 - Paris (France) 9-11 avril 2026 : Drupalcamp Grenoble 2026 - Grenoble (France) 16-17 avril 2026 : MiXiT 2026 - Lyon (France) 17-18 avril 2026 : Faiseuses du Web 5 - Dinan (France) 22-24 avril 2026 : Devoxx France 2026 - Paris (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 12 mai 2026 : Lead Innovation Day - Leadership Edition - Paris (France) 19 mai 2026 : La Product Conf Paris 2026 - Paris (France) 21-22 mai 2026 : Flupa UX Days 2026 - Paris (France) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 28 mai 2026 : DevCon 27 : I.A. & Vibe Coding - Paris (France) 28 mai 2026 : Cloud Toulouse 2026 - Toulouse (France) 29 mai 2026 : NG Baguette Conf 2026 - Paris (France) 29 mai 2026 : Agile Tour Strasbourg 2026 - Strasbourg (France) 2-3 juin 2026 : Agile Tour Rennes 2026 - Rennes (France) 2-3 juin 2026 : OW2Con - Paris-Châtillon (France) 3 juin 2026 : IA–NA - La Rochelle (France) 5 juin 2026 : TechReady - Nantes (France) 5 juin 2026 : Fork it! - Rouen - Rouen (France) 6 juin 2026 : Polycloud - Montpellier (France) 9 juin 2026 : JFTL - Montrouge (France) 9 juin 2026 : C: - Caen (France) 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 2 août 2026 : 4th Tech Summit on Artificial Intelligence & Robotics - Paris (France) 20-22 août 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 24 septembre 2026 : PlatformCon Live Day Paris 2026 - Paris (France) 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

This week in search we have more ongoing Google search ranking volatility. Bing Webmaster Tools rolled out new AI Performance reports with a new design. Google AI Overviews tests new overlay cards. Grokipedia is seeing a decline in visibility in Google Search and ChatGPT...

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]:

Therapy practices need SEO, but the usual marketing advice can cross privacy and ethics lines fast. This episode breaks down a privacy-first SEO plan that helps you rank locally and in AI-driven search without relying on reviews, testimonials, or client stories. You'll learn what pages to create, how to structure content for clarity, and how to set boundaries that protect clients, your license, and your peace.Episode webpage: https://propelyourcompany.com/seo-for-therapists/Book a discovery call: https://calendly.com/propelyourcompany/discovery-callCompany website: https://propelyourcompany.com/Send in your questions. ❤ We'd love to hear from you!NEW Webinar: How to dominate Google Search, Google Maps, AI-driven search results, and get more new patients.>> Save your spot

In this episode of Search Off the Record, Martin and Gary from the Google Search Relations team tackle a deceptively simple question: do you still need a website in 2026? Starting from the recurring industry claim that "the web is dead," they explore how the web has evolved through the rise of apps, AI chatbots, and social platforms, and why the answer almost always ends up being "it depends." Tune in for an engaging discussion on how websites remain relevant and what it means for content creation and discovery. Episode transcript → https://goo.gle/sotr103-transcript Listen to more Search Off the Record → https://goo.gle/sotr-yt  Subscribe to Google Search Channel → https://goo.gle/SearchCentral   Search Off the Record is a podcast series that takes you behind the scenes of Google Search with the Search Relations team.  #SOTRpodcast #SEO #GoogleSearch Speakers: Martin Splitt, Gary Illyes

Your website might not need more pages; it may need a cleanup. This 2026 “remove list” walks clinic owners through what to delete, replace, or rewrite on their website to improve SEO, trust, and conversions, including outdated content, generic messaging, weak calls to action, and slow elements that hurt mobile performance. Episode webpage, checklist, & shownotes: https://propelyourcompany.com/what-to-delete-or-rewrite-on-clinic-websites-now/Send in your questions. ❤ We'd love to hear from you!NEW Webinar: How to dominate Google Search, Google Maps, AI-driven search results, and get more new patients.>> Save your spot

In this episode of Around the Desk, Sean Emory, Founder and Chief Investment Officer at Avory & Co., steps back from the AI noise to focus on what actually matters right now.Using recent earnings from Google, Microsoft, Amazon, and Meta, this conversation breaks down what the massive AI CapEx buildout really signals, how different business models monetize AI very differently, and why many of the fears around software disruption may be overstated.This episode explores AI through a capital allocation lens, separating defensive spending from offensive opportunity, and what Big Tech behavior tells us about the true health of the underlying economy.Topics covered include:• The scale of Big Tech AI CapEx and why it matters more than feature launches • Defensive vs offensive AI spending and how to think about moats • Why AI CapEx is also an economic confidence signal • Different monetization paths at Amazon, Microsoft, Meta, and Google • Why Meta may be the cleanest AI beneficiary • The narrative vs data gap around Google Search and AI disruption • Why the “AI breaks software” panic may be overdone • Enterprise security, governance, and why AI rollout feels fast and slow at the same time • Platforms vs single-purpose tools and where risk actually sits • What recent software earnings say about demand, renewals, and long-term contracts • How AI likely becomes embedded inside platforms rather than replacing themThis conversation is for informational purposes only and should not be considered investment advice. Avory & Co. may hold positions in some of the companies discussed. Please do your own research before making any investment decisions._____DisclaimerAvory is not an investor in either company mentioned. .Avory & Co. is a Registered Investment Adviser. This platform is solely for informational purposes. Advisory services are only offered to clients or prospective clients where Avory & Co. and its representatives are properly licensed or exempt from licensure. Past performance is no guarantee of future returns. Investing involves risk and possible loss of principal capital. No advice may be rendered by Avory & Co. unless a client service agreement is in place.Listeners and viewers are encouraged to seek advice from a qualified tax, legal, or investment adviser to determine whether any information presented may be suitable for their specific situation. Past performance is not indicative of future performance.“Likes” are not intended to be endorsements of our firm, our advisors, or our services. While we monitor comments and “likes,” we do not endorse or necessarily share the opinions expressed by site users. Any form of testimony from current or past clients about their experience with our firm is strictly forbidden under current securities laws. Please limit posts to industry-related educational information and comments.Third-party rankings and recognitions are no guarantee of future investment success and do not ensure that a client or prospective client will experience a higher level of performance or results. These ratings should not be construed as an endorsement of the advisor by any client nor are they representative of any one client's evaluation.Please reach out to Houston Hess, our Head of Compliance and Operations, for any further details.

What if search becomes.... proactive?

In this episode, we explore the strange signals people use to interpret global events, from Pentagon pizza orders and satellite data to the Big Mac Index and other unconventional measures of economic reality. We examine the decline of Google search, the rise of AI-powered alternatives, and why new tools are changing how people actually find information. For the “foolishness of the week”, we detail an unfortunate incident involving a piece of World War I artillery, before turning to a broader cultural debate about nostalgia for the 1950s. With guest Andrew Heaton, we unpack myths about work, gender roles, housing, healthcare, and prosperity, comparing mid-century life to modern standards of living. Along the way, we discuss food abundance, technological progress, wage compensation, inequality, and whether people genuinely want to return to the past or simply romanticize it from a distance. 00:00 Introduction and Overview 00:28 Pentagon Pizza Orders and “Pizza Intelligence” 02:51 Proxy Signals, Satellite Data, and the Waffle House Index 04:25 The Big Mac Index and Measuring Cost of Living 05:00 The Decline of Google Search and Sponsored Results 07:19 Switching Search Engines and the Myth of Google Monopoly 09:54 AI Search Tools and Why They Actually Work 11:28 Foolishness of the Week: World War I Artillery Incident 13:43 How Bad Ideas Escalate at Parties 15:51 Introducing Andrew Heaton 16:39 Was the 1950s a Time or a Place? 18:43 Economic Reality vs 1950s Nostalgia 20:58 Women's Work, Household Labor, and Misleading Myths 23:56 Food Costs, Eating Out, and Modern Abundance 25:46 Medicine, Lifespan, and Why 50s Healthcare Was Worse 27:57 Housing Size, Zoning, and the Cost of Homes 30:01 Cars, Air Conditioning, and Quality of Life Improvements 31:17 Mortgage Rates and Why Housing Feels Unaffordable Now 34:02 Manufacturing, Exports, and the “We Don't Make Anything” Myth 35:35 Agricultural Productivity and Modern Farming 37:19 Food Waste as a Measure of Prosperity 37:42 Great Depression Scarcity and Generational Habits 39:59 Transportation Costs and Higher Quality Modern Vehicles 42:50 Car Safety, Seatbelts, and Survival Rates 43:42 Wages, Benefits, and What “Compensation” Really Means 45:29 What the 1950s Actually Did Better 47:52 Inequality, Community, and Social Capital in the 50s 49:44 Technology, Isolation, and Choosing Modern Life 52:05 Longing for Silence from Technology 53:18 The Mythology of Happy Days Learn more about your ad choices. Visit podcastchoices.com/adchoices

Physical therapists are in a competitive industry which means their SEO (search engine optimization) must be up to par to compete online. SEO is a crucial element of your digital marketing campaign and an effective way to get patients through your doors. So, how do you find new patients? Or, should we say: How do new patients find you? Getting your website in front of your target audience is the answer. This is done through a strategic search engine optimization (SEO) campaign. Whether you're a physical therapist new to SEO or have been utilizing it for years, this post will give you invaluable insight into creating a stellar SEO campaign that makes your visitors, Google, and ROI happy. 

In today's MadTech Daily, we cover the DOJ appealing the Google search monopoly ruling, WeChat blocking a Tencent AI chatbot giveaway, and Netflix and Warner Bros struggling over a potential merger.

2/4/26Episode SummaryScott selects several real Shopify stores by searching a specific long-tail query (“Valentine's gift for wife of 30 years”) and examines how they appear in different search experiences (Google Search, Google Shopping, Google Gemini) and how well the stores are optimized for that query.He walks through multiple Shopify stores and product pages to evaluate how they communicate value, structure their content, and use structured data (for things like holiday relevance), highlighting areas where many stores could better tailor for specific shopping scenarios like Valentine's.Throughout the episode he discusses practical aspects such as SEO structured data, visual merchandising, how stores promote seasonal offers, and accessibility (e.g., ADA compliance scores).The episode is focused on actionable insights to help Shopify merchants improve product visibility and on-site experience by learning from real examples.Show LinksProductsArtic Angel - https://articangel.com/products/special-gift-for-wife-i-cant-live-without-you-gold-heart-necklaceBearaby - https://bearaby.com/products/the-napperLola Blankets - https://lolablankets.com/products/rosewaterMaster & Dynamic - https://www.masterdynamic.com/products/mw75-active-noise-cancelling-wireless-headphonesPrime Choice - https://primechoiceshop.com/products/i-cant-live-without-you-to-my-wife-necklace-1Pure Enrichment - https://pureenrichment.com/products/purebliss-luxury-towel-warmerAppsDatify - https://apps.shopify.com/datifyBadgezilla - https://apps.shopify.com/badgezillaLinear Shopping Experiences - https://apps.shopify.com/linear-shopping-experienceVideo & Transcript https://jadepuma.com/blogs/the-shopify-solutions-podcast/episode-178-lets-review-some-shopify-stores

What's your most embarrassing Google search? "Do turtles get itchy?"See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Send us a textThinking about running paid ads in 2026? Pump the brakes.Frazier and Michael are breaking down the truth about paid advertising — what works, what doesn't, and where most loan officers go wrong. Whether you're sitting on a 30K marketing budget or barely spending $300 a month, this episode is a tactical deep dive into interruption vs. intent, social vs. search, and why Google still wins if you're serious about ROI.No fluff. No theory. Just straight talk on what actually drives deals — and what will drain your wallet.What You'll LearnWhy social media ads aren't dead — but most of them are dumbThe difference between passive brand amplification and transactional lead genWhy low-cost leads are usually garbage (and what actually matters)How intent changes everything — and why interruption-based ads have a short fuseWhat to realistically budget for Google or social ads in 2026Real Talk Quotes“You don't own the customer. You're just renting attention.”“Lead cost means nothing if the leads suck.”“You're never gonna get a CTC on your feelings.”“Intent-based leads wait for you. Interruption leads forget you.”“We can all get 50-cent leads. That's not the flex you think it is.”Tactical Takeaways✅ Expect to spend $1,500/month minimum if you want transactional ROI✅ Social media ads work best to amplify brand — not chase cold leads✅ Google Search ads are still the king of high-intent lead generation✅ YouTube & TikTok are the only social platforms with real search intent✅ If you're not following up with automation, you're burning money✅ Don't run your own ads unless you want to waste time and budgetWhy This Episode MattersMost LOs are playing checkers when it comes to paid ads — this episode helps you play chess. Whether you're doing consumer direct or trying to convert your social audience, you'll learn where to invest, what to expect, and how to actually get deals from your dollars.Want Help Running Ads That Work?This episode is powered by Empower LO, the trusted team behind hundreds of top-producing LOs running scalable Google ad campaigns.Learn more at empowerlo.com

​Join Martin and Gary as they dive into Search Off the Record's Episode 103, unpacking the 2025 Year-End report on crawling issues. Discover fascinating insights on faceted navigation, action parameters, irrelevant parameters and more, highlighting the biggest challenges faced by web crawlers last year. With humor and expert analysis, this episode reveals critical takeaways for webmasters and SEO professionals. Don't miss valuable tips to enhance your site's crawl efficiency! ​ Resources: URL structure best practices for Google Search → https://developers.google.com/search/docs/crawling-indexing/url-structure   Crawling December: Faceted navigation → https://developers.google.com/search/blog/2024/12/crawling-december-faceted-nav   Xkcd (programmer humor) → https://xkcd.com/327/     

AI took center stage at NRF 2026, and few moments underscored its importance more than Google CEO Sundar Pichai's keynote, where he outlined how shopping is evolving in an increasingly agentic, AI-driven world.This episode of Retail Remix, recorded live from the show floor, features host Nicole Silberstein in conversation with Anil Jain, who leads Global Strategic Industries at Google Cloud. Anil shares how Google Cloud is working with retailers to reimagine everything from product discovery to post-purchase service and why agentic AI represents a fundamental shift in how consumers will interact with brands.Key TakeawaysWhy AI is becoming the great equalizer, helping smaller companies compete with limited resources;How AI experiences in general-use platforms like Google Search are upping the ante for everyone, and how to keep up;What multimodal search unlocks when consumers can shop using not just text, but also voice, images and video;Why hyper-personalization is finally within reach after decades of promise;The change management that will be required as AI shifts the way we all work; How Google and its Cloud division are building for this future.Related LinksRelated reading: Google Launches Direct Checkout in Search, GeminiLearn how Google Cloud is helping retailers adopt AI at scaleExplore more NRF26 coverage and retail insights from Retail TouchPointsSubscribe so you don't miss more episodes of Retail Remix from the show floor of NRF26

Google is phasing out the classic Q&A section on Google Business Profiles and replacing it with AI-powered "Ask" features in Google Maps, driven by Gemini. For clinic owners, chiropractors, physical therapists, acupuncturists, med spas, and other healthcare providers, this shift means patients now get instant AI-generated answers about your services, insurance, hours, accessibility, and more - pulled from your profile, reviews, website, photos, and beyond.⚡Episode guide, blog & podcast notes:  https://propelyourcompany.com/google-ai-answers/If your info isn't clear and consistent, the AI might say "I don't have enough information" or get it wrong - costing you leads to competitors.In this episode of the Clinic Marketing Podcast, Darcy Sullivan from Propel Marketing and Design breaks down:Why the old Q&A vanished and where the new AI "Ask about this place" button is appearing (especially in Google Maps).Why healthcare categories (like many medical clinics) are rolling out unevenly - but the change is coming.Real patient questions clinics face: "Do you take my insurance?", "Same-day appointments?", "Do you treat kids/sciatica/migraines?", "Parking available?", "Wheelchair accessible?"A clinic-specific AI-feeding checklist: GBP basics, categories/services/attributes, strategic photos/videos, review prompts for detailed language, website FAQs, social posts, and more.7-day action plan to audit and optimize your Google Business Profile + website this week.How to monitor AI answers and avoid misinformation risks.Even if the feature hasn't hit your listing yet, building an "AI-ready" info ecosystem is one of the top local SEO moves for clinics right now - boosting visibility in Maps and search.Tune in for practical steps to make sure Google's AI answers questions the right way... your way.Send in your questions. ❤ We'd love to hear from you!NEW Webinar: How to dominate Google Search, Google Maps, AI-driven search results, and get more new patients.>> Save your spot

AI is everywhere in marketing right now. But here's the truth — it's not a solution. It's a tool. And like any tool, it's only as powerful as the person using it. Our guest, Kaspar Szymanski, knows this better than most. A former Google Search team member and one of the world's leading SEO experts,…

Send us a textAI isn't killing search — it's reshaping how people discover, evaluate, and choose businesses.In this in terview with Crystal Carter of Wix, we unpack how large language models, AI assistants, and emerging “agent” experiences are changing consumer behavior, local search, and brand visibility. From ChatGPT and Google's AI-driven results to personalization, intent modeling, and task completion, we explore what actually changes — and what doesn't.We also dig into:• Why AI acts more like a complement to search than a replacement• How “choice” and “consideration” evolve in AI-first experiences• What happens when interfaces collapse research, comparison, and action into one flow• Why local, reviews, and brand signals still matter — just differently• What businesses should prepare for as agents begin acting on behalf of usersIf you care about search, local, UX, or how consumers actually make decisions, this is the conversation you want to hear.Subscribe to our newsletters and other content at https://www.nearmedia.co/subscribe/

Marc Andreessen is a founder, investor, and co-founder of Netscape, as well as co-founder of the venture capital firm Andreessen Horowitz (a16z). In this conversation, we dig into why we're living through a unique and one of the most incredible times in history, and what comes next.We discuss:1. Why AI is arriving at the perfect moment to counter demographic collapse and declining productivity2. How Marc has raised his 10-year-old kid to thrive in an AI-driven world3. What's actually going to happen with AI and jobs (spoiler: he thinks the panic is “totally off base”)4. The “Mexican standoff” that's happening between product managers, designers, and engineers5. Why you should still learn to code (even with AI)6. How to develop an “E-shaped” career that combines multiple skills, with AI as a force multiplier7. The career advice he keeps coming back to (“Don't be fungible”)8. How AI can democratize one-on-one tutoring, potentially transforming education9. His media diet: X and old books, nothing in between—Brought to you by:DX—The developer intelligence platform designed by leading researchersBrex—The banking solution for startupsDatadog—Now home to Eppo, the leading experimentation and feature flagging platform—Episode transcript: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Marc Andreessen:• X: https://x.com/pmarca• Substack: https://pmarca.substack.com• Andreessen Horowitz's website: https://a16z.com• Andreessen Horowitz's YouTube channel: https://www.youtube.com/@a16z—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Marc Andreessen(04:27) The historic moment we're living in(06:52) The impact of AI on society(11:14) AI's role in education and parenting(22:15) The future of jobs in an AI-driven world(30:15) Marc's past predictions(35:35) The Mexican standoff of tech roles(39:28) Adapting to changing job tasks(42:15) The shift to scripting languages(44:50) The importance of understanding code(51:37) The value of design in the AI era(53:30) The T-shaped skill strategy(01:02:05) AI's impact on founders and companies(01:05:58) The concept of one-person billion-dollar companies(01:08:33) Debating AI moats and market dynamics(01:14:39) The rapid evolution of AI models(01:18:05) Indeterminate optimism in venture capital(01:22:17) The concept of AGI and its implications(01:30:00) Marc's media diet(01:36:18) Favorite movies and AI voice technology(01:39:24) Marc's product diet(01:43:16) Closing thoughts and recommendations—Referenced:• Linus Torvalds on LinkedIn: https://www.linkedin.com/in/linustorvalds• The philosopher's stone: https://en.wikipedia.org/wiki/Philosopher%27s_stone• Alexander the Great: https://en.wikipedia.org/wiki/Alexander_the_Great• Aristotle: https://en.wikipedia.org/wiki/Aristotle• Bloom's 2 sigma problem: https://en.wikipedia.org/wiki/Bloom%27s_2_sigma_problem• Alpha School: https://alpha.school• In Tech We Trust? A Debate with Peter Thiel and Marc Andreessen: https://a16z.com/in-tech-we-trust-a-debate-with-peter-thiel-and-marc-andreessen• John Woo: https://en.wikipedia.org/wiki/John_Woo• Assembly: https://en.wikipedia.org/wiki/Assembly_language• C programming language: https://en.wikipedia.org/wiki/C_(programming_language)• Python: https://www.python.org• Netscape: https://en.wikipedia.org/wiki/Netscape• Perl: https://www.perl.org• Scott Adams: https://en.wikipedia.org/wiki/Scott_Adams• Larry Summers's website: https://larrysummers.com• Nano Banana: https://gemini.google/overview/image-generation• Bitcoin: https://bitcoin.org• Ethereum: https://ethereum.org• Satoshi Nakamoto: https://en.wikipedia.org/wiki/Satoshi_Nakamoto• Inside ChatGPT: The fastest-growing product in history | Nick Turley (Head of ChatGPT at OpenAI): https://www.lennysnewsletter.com/p/inside-chatgpt-nick-turley• Anthropic co-founder on quitting OpenAI, AGI predictions, $100M talent wars, 20% unemployment, and the nightmare scenarios keeping him up at night | Ben Mann: https://www.lennysnewsletter.com/p/anthropic-co-founder-benjamin-mann• Inside Google's AI turnaround: The rise of AI Mode, strategy behind AI Overviews, and their vision for AI-powered search | Robby Stein (VP of Product, Google Search): https://www.lennysnewsletter.com/p/how-google-built-ai-mode-in-under-a-year• DeepSeek: https://www.deepseek.com• Cowork: https://support.claude.com/en/articles/13345190-getting-started-with-cowork• Definite vs. indefinite thinking: Notes from Zero to One by Peter Thiel: https://boxkitemachine.net/posts/zero-to-one-peter-thiel-definite-vs-indefinite-thinking• Henry Ford: https://www.thehenryford.org/explore/stories-of-innovation/visionaries/henry-ford• Lex Fridman Podcast: https://lexfridman.com/podcast• $46B of hard truths from Ben Horowitz: Why founders fail and why you need to run toward fear (a16z co-founder): https://www.lennysnewsletter.com/p/46b-of-hard-truths-from-ben-horowitz• Eddington: https://www.imdb.com/title/tt31176520• Joaquin Phoenix: https://en.wikipedia.org/wiki/Joaquin_Phoenix• Pedro Pascal: https://en.wikipedia.org/wiki/Pedro_Pascal• George Floyd: https://en.wikipedia.org/wiki/George_Floyd• Replit: https://replit.com• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• Grok Bad Rudi: https://grok.com/badrudi• Wispr Flow: https://wisprflow.ai• Star Trek: The Next Generation: https://www.imdb.com/title/tt0092455• Star Trek: Starfleet Academy: https://www.imdb.com/title/tt8622160• a16z: The Power Brokers: https://www.notboring.co/p/a16z-the-power-brokers—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

If you're a chiropractor trying to improve your website traffic and get more patients through Google, this episode is for you. Discover where most clinics go wrong with SEO — and the four key areas you should be focusing on instead. Episode Webpage & Show Notes: https://propelyourcompany.com/seo-for-chiropractors-what-works/Send in your questions. ❤ We'd love to hear from you!NEW Webinar: How to dominate Google Search, Google Maps, AI-driven search results, and get more new patients.>> Save your spot

In this episode, we're talking about preventive care marketing, how to attract patients who want to stay ahead of problems, not just react when something hurts.If your marketing mostly speaks to pain and urgent symptoms, you can end up in a cycle of one-time visits and inconsistent momentum. Preventive care content helps you reach the “I feel fine, but…” crowd, the desk workers, active adults, busy parents, and anyone noticing early warning signs who wants a clear plan before things spiral.You'll learn a simple framework for what to publish, how to talk about prevention without sounding pushy, and how to guide someone from awareness to taking action. I'll also share an easy monthly content strategy you can repeat without posting every day, plus the language that helps this kind of content convert.If you want to build a steady stream of patients who value consistency and long-term progress, this is for you.

When you hear the words “data privacy,” what do you first imagine?Maybe you picture going into your social media apps and setting your profile and posts to private. Maybe you think about who you've shared your location with and deciding to revoke some of that access. Maybe you want to remove a few apps entirely from your smartphone, maybe you want to try a new web browser, maybe you even want to skirt the type of street-level surveillance provided by Automated License Plate Readers, which can record your car model, license plate number, and location on your morning drive to work.Importantly, all of these are “data privacy,” but trying to do all of these things at once can feel impossible.That's why, this year, for Data Privacy Day, Malwarebytes Senior Privacy Advocate (and Lock and Code host) David Ruiz is sharing the one thing he's doing different to improve his privacy. And it's this: He's given up Google Search entirely.When Ruiz requested the data that Google had collected about him last year, he saw that the company had recorded an eye-popping 8,000 searches in just the span of 18 months. And those 8,000 searches didn't just reveal what he was thinking about on any given day—including his shopping interests, his home improvement projects, and his late-night medical concerns—they also revealed when he clicked on an ad based on the words he searched. This type of data, which connects a person's searches to the likelihood of engaging with an online ad, is vital to Google's revenue, and it's the type of thing that Ruiz is seeking to finally cut off.So, for 2026, he has switched to a new search engine, Brave Search.Today, on the Lock and Code podcast, Ruiz explains why he made the switch, what he values about Brave Search, and why he also refused to switch to any of the major AI platforms in replacing Google.Tune in today.You can also find us on Apple Podcasts, Spotify, and whatever preferred podcast platform you use.For all our cybersecurity coverage, visit Malwarebytes Labs at malwarebytes.com/blog.Show notes and credits:Intro Music: “Spellbound” by Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/Outro Music: “Good God” by Wowa (unminus.com)Listen up—Malwarebytes doesn't just talk cybersecurity, we provide it.Protect yourself from online attacks that threaten your identity, your files, your system, and your financial well-being with our exclusive offer for Malwarebytes Premium for Lock and Code listeners.

This week, we covered the doubly heated Google Search ranking volatility, but nothing was confirmed by Google. OpenAI will soon test ads in ChatGPT responses and they will charge on an impression basis...

Most leaders assume AI and search already see the whole internet. In reality, they all operate on the same tiny slice of the web.In this episode of IT Visionaries, host Chris Brandt sits down with Sudheesh Nair, Co-Founder and CEO of TinyFish and former CEO of ThoughtSpot, to unpack why only a small percentage of the web is indexable and how that cripples enterprise AI.Sudheesh explains why the next breakthrough won't come from bigger models or better search, but from agents that can operate the web at scale, logging in, filling forms, running workflows, and surfacing the long tail of opportunities that never appear on page one. He also shares why human craft, taste, and presence will matter more than ever in an agent-driven world. Key Moments:00:00 - The Deep Web Problem02:48 - The Amazon Search Trap04:26 - Why Search is Broken07:01 - Internet is No Longer a Library08:29 - AI Answers vs Blue Links13:05 - Introducing Tiny Fish's Mission16:00 - Search as a Poor Experience18:29 - The Deep Web: APIs, Workflows & Logins22:11 - Tackling the 93% Problem25:47 - The Eight-Room Hotel Success Story29:04 - Operating the Web vs Skimming It32:42 - Real-Time Personalized Workflows38:31 - Enterprise B2B Strategy40:18 - Taste Over Tools43:08 - AI Freeing Human Experience46:36 - Travel Experiences & Local Discovery50:00 - Democratizing the Internet56:39 - The Waving Guide in China1:01:12 - Optimism for AI's Future -- 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.