Podcasts about Databricks

$4B ignites Databricks $134B fire for generative data applications proliferation. Unity Catalog spans multi-region compliance seamlessly. Life sciences accelerate drug discovery pipelines exponentially.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this BG2 guest interview, Altimeter partner Apoorv Agrawal sits down with Ali Ghodsi (Databricks) and Arvind Jain (Glean) for a candid, operator-level discussion on what's actually working in enterprise AI—and what isn't.They unpack why 95% of AI projects fail, why LLMs are rapidly commoditizing, and why durable advantage is shifting to proprietary data, agentic systems, and workflow integration. The conversation dives deep into real-world use cases across finance, healthcare, and retail; the debate over whether we already have AGI; and how AI spend, CapEx, and valuation bubbles will realistically play out. A must-watch for builder, and investors navigating the AI transition inside real organizations.Timestamps:(00:00) Intro(01:00) Consumer AI vs. Enterprise Reality(02:15) Why 95% of AI Projects Fail(04:15) RBC, Merck, and 7-Eleven Use Cases(06:45) What Actually Makes AI Work(07:00) LLMs Are Commodities—Data Is the Moat(08:45) Failed AI Bets at Databricks & Glean(11:00) RPA vs. Generative AI(14:15) Advice for CIOs Planning AI Budgets(16:00) AI CapEx and the Revenue Math(18:00) The Three Camps of AI(21:00) Making AI Useful Inside Enterprises(24:30) Why Apps Capture the Value(30:00) The Future of UI, Voice, and Data Entry(37:30) Rapid Fire: Winners, Bubbles, Long/ShortProduced by Dan ShevchukMusic by Yung SpielbergAvailable on Apple, Spotify, www.bg2pod.comFollow:Apoorv Agrawal @apoorv03 https://x.com/apoorv03BG2 Pod @bg2pod https://x.com/BG2Pod

Apotheosis attained via $4B at $134B Databricks apotheosis. Apotheosis of analytics. Apotheosis absolute.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On this episode of The Six Five Pod, hosts Patrick Moorhead and Daniel Newman discuss the latest tech news stories that made headlines. This week's handpicked topics include: THE DECODE Big Funding Headline: OpenAI's reported mega-round and valuation https://www.reuters.com/technology/openai-discussed-raising-tens-billions-valuation-about-750-billion-information-2025-12-18/ https://x.com/danielnewmanUV/status/2001366643436315110  https://x.com/danielnewmanUV/status/2001362761247527174  https://x.com/PatrickMoorhead/status/2001267663490646200 https://techcrunch.com/2025/12/17/amazon-reportedly-in-talks-to-invest-10b-in-openai-as-circular-deals-stay-popular/  AWS "Circular deal" / Corporate venture logic AI build-out constraints https://www.theverge.com/news/846696/electricity-cost-ai-data-center-democrat-investigation https://www.axios.com/2025/12/17/democrats-data-centers-ai-fight  https://www.politico.com/news/2025/12/12/arizona-city-rejects-data-center-after-ai-lobbying-push-00688543 Marvell Industry Analyst Day highlights https://x.com/MoorInsStrat/status/2000359388264161710  Government "Tech Force" for AI Talent https://www.cnn.com/2025/12/15/tech/government-tech-force-ai  Google works to erode Nvidia's software moat (TPU + PyTorch + Meta) https://www.reuters.com/business/google-works-erode-nvidias-software-advantage-with-metas-help-2025-12-17/ Judge rules Tesla engaged in deceptive marketing for Autopilot and full self-driving features https://techcrunch.com/2025/12/16/tesla-engaged-in-deceptive-marketing-for-autopilot-and-full-self-driving-judge-rules/ Tesla tests autonomous vehicles without safety drivers in Austin, Tx https://techcrunch.com/2025/12/15/tesla-starts-testing-robotaxis-in-austin-with-no-safety-driver/  Adobe Firefly now supports prompt-based video editing, adds more third-party models https://techcrunch.com/2025/12/16/adobe-firefly-now-supports-prompt-based-video-editing-adds-more-third-party-models/ https://youtu.be/SjtULo8qs88?si=quE7pEptW8xph1OI  Google's Opal for vibe coding comes to Gemini https://techcrunch.com/2025/12/17/googles-vibe-coding-tool-opal-comes-to-gemini/ THE FLIP OpenAI - Tulip Bubble and Canary in the Coal mine or The Real AI Deal? https://x.com/danielnewmanuv/status/2001487733823541634?s=46&t=8QBZggR299yC4bcbbox-Xg https://x.com/danielnewmanuv/status/2001366643436315110?s=46&t=8QBZggR299yC4bcbbox-Xg BULLS & BEARS AI infrastructure stocks tumble on debt fears: Oracle, Broadcom, CoreWeave selloff https://www.cnbc.com/2025/12/16/cnbc-daily-open-ai-infrastructure-stocks-are-taking-a-beating.html  Recent Fed rate cut & speculation of another coming soon: https://x.com/danielnewmanUV/status/2001041850669404473 Oracle earnings (Q2) — CapEx reality check https://www.forbes.com/sites/greatspeculations/2025/12/18/whats-happening-with-oracle-stock/ https://finance.yahoo.com/news/oracle-plunges-12-despite-earnings-145626357.html Micron crushes earnings as AI data center demand tightens memory supply https://finance.yahoo.com/news/why-wall-street-expects-micron-183836008.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAKf8aUugkk7hJbCnmiZWS2q5x1WWjD07AUywz6vzxnw6btX2iK0-aNmQBgg3sU67GWZXIKHz74cGnjnzZYeuBDv1A8_Rwp67iIKAtMI1A94LhJTRlcqdnN2_QYPWB_5ZTkO96ZSpFMjMsAwDUBf1yz-RIQnA-78Yk-zhD6VFqr- https://x.com/danielnewmanuv/status/2001404328997712349?s=46&t=8QBZggR299yC4bcbbox-Xg Broadcom earnings (Q4) — custom silicon tension https://finance.yahoo.com/news/broadcom-q4-earnings-beat-estimates-154300300.html Databricks raises $4B at $134B valuation as its AI business heats up https://techcrunch.com/2025/12/16/databricks-raises-4b-at-134b-valuation-as-its-ai-business-heats-up/ Smartphone Prices Set to Jump 6.9% as AI Data Centers Devour Memory Chips: The shortage of DRAM chips used in both AI servers and smartphones could threaten to cut smartphone shipments by 2.1%. To cope, some manufacturers may downgrade cameras, displays, and audio or reuse older components. https://www.cnbc.com/2025/12/16/smartphone-prices-to-rise-in-2026-due-to-ai-fueled-chip-shortage.html  Adobe Earnings https://finance.yahoo.com/news/adobe-q4-earnings-beat-estimates-145000488.html Synopsys Earnings https://finance.yahoo.com/news/synopsys-q4-earnings-surpass-estimates-153300031.html  

Conquest complete: $4B conquers $134B Databricks conquest. Conquered continents. Conquest crowned.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Vortex $4B vortices $134B Databricks vortex. Vortex velocity peaks. Vortex victorious.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Cosmic $4B orbits Databricks $134B cosmic scale. Astrophysics simulates universes. Cosmos conquered.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Skyrocketing $4B at $134B for Databricks' Photon ML acceleration. Ad tech personalizes at billions RPM. Valuation mirrors AI infrastructure boom.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Databricks leaps to $134B on $4B, with Lakeflow automating data pipelines end-to-end. Generative apps proliferate via serverless model serving. Revenue multiples justify hyperscale ambitions.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Die Woche der Mega-Funding-Runden: Databricks wird mit 134 Milliarden bewertet, Waymo peilt 100 Milliarden an, und OpenAI soll bald 830 Milliarden wert sein. Amazon will 10 Milliarden in OpenAI investieren. Coinbase launcht Prediction Markets und tokenisierte Aktien. Revolut plant 3,5 Milliarden Profit bei 40% Marge – profitabler als die meisten Banken. Lovable aus Schweden rast auf 6,6 Milliarden. Das US-Handelsministerium droht der EU mit Vergeltung wegen Tech-Regulierung und nennt explizit SAP, Siemens und DHL. Der TikTok-Deal soll im Januar kommen – Oracle, Silver Lake und Abu Dhabi übernehmen 45%. Ein Andreessen-Startup baut synthetische KI-Influencer mit Phone Farms. Instacart steht wegen KI-Preismanipulation unter FTC-Beschuss. Yann LeCun gründet Ami Labs mit 500 Millionen Seed. Trade Republic wird mit 12,5 Milliarden bewertet. Trump Media fusioniert mit einer Fusionsenergie-Firma. Wann kommt es zur großen Doppelgänger Cola Blindverkostung? Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Intro (00:00:49) Coinbase: Aktien & Prediction Markets (00:08:14) Databricks 134 Mrd. Bewertung (00:13:39) Revolut: 40% Profitmarge (00:19:26) Waymo 100 Mrd. Bewertung (00:36:15) Oscars wechseln zu YouTube (00:37:22) Sam Altman: KI wie Zahnpasta? (00:46:16) Lovable 6,6 Mrd. Bewertung (00:49:53) Amazon investiert 10 Mrd. in OpenAI (00:53:48) OpenAI 830 Mrd. Bewertung (00:56:55) Trump bedroht EU wegen Tech-Regulierung (01:00:56) Andreessen-Startup baut Fake-Influencer (01:08:13) Instacart KI-Preismanipulation (01:12:25) Yann LeCun gründet Ami Labs (01:14:50) TikTok-Deal im Januar (01:18:47) Trump Media fusioniert mit Fusionsfirma (01:24:14) Trade Republic 12,5 Mrd. Shownotes Coinbase Prognosemärkte Aktienhandel Stablecoins - cnbc.com Databricks sammelt Kapital bei 134-Milliarden-Bewertung - wsj.com Revolut strebt 2026 $9B Umsatz und $3.5B Gewinn an. - connectingthedotsinfin.tech Waymo plant Finanzierung bei 100-Milliarden-Bewertung - bloomberg.com Oscars wechseln 2029 von ABC zu YouTube - hollywoodreporter.com OpenAI ChatGPT verbessert Bilderstellung - bloomberg.com OpenAI-Gespräche: 10 Milliarden von Amazon für KI-Chips - theinformation.com OpenAI neue Finanzierungsrunde könnte Startup mit bis zu 83 Milliarden bewerten - wsj.com Start-up Lovable sammelt 330 Millionen ein - nytimes.com EU-Strafen für US-Tech-Unternehmen - nytimes.com Hack enthüllt a16z-unterstützte Telefonfarm, die TikTok mit KI-Influencern flutet - 404media.co FTC untersucht Instacarts KI-Preistool - reuters.com Instacart FTC Vergleich Täuschende Abrechnung - cnbc.com Seb Johnson auf X: "Metas ehemaliger Chief AI Officer sammelt €500 Mio. ein. - x.com TikTok schließt Verkauf seiner US-Einheit nach jahrelanger Saga ab. - axios.com Trump Media - ft.com Es wird kein Armut geben, universelles hohes Einkommen. - x.com Trade Republic: Zwei reiche europäische Familien beteiligen sich am Milliardendeal - manager-magazin.de Phishing-Versuch bei Outfittery: Datenleck beim Bekleidungshändler? - heise.de

En la última tertulia del año traemos Carles Reina (ElevenLabs) como responsable de go-to-market y, además, como inversor: comenta que ha hecho decenas de “tickets” y que acaba de levantar su propio fondo, Baobab Ventures.A partir de ahí, la conversación entra fuerte en el negocio de ElevenLabs: explican que construyen modelos de voz “naturales” y operan en ~70 idiomas, y que encima han montado productos de agentes, doblaje, transcripción, etc. Carles da cifras muy concretas de tracción (alrededor de 400 personas y más de 300M de facturación, alcanzados “hace unas semanas”) y describe un motor enterprise muy agresivo (hablan de 150–170 contratos al mes y de un día especialmente “loco” superando 14M en enterprise). También hablan de por qué siguen levantando rondas aun generando caja: señal al mercado, liquidez para empleados vía secundario, y capacidad de invertir/comprar (incluidas GPUs). En ese bloque bromean bastante con los múltiplos (el “33x” como estándar) y con la posible burbuja en el sector. Luego hablan del tema más “cultural” y polémico: el doblaje y los derechos de voz. Sale Masumi un actor de doblaje conocido en España (que es la voz de Harry Potter y Anakin, y su vínculo con el sindicato) y se discute la línea roja de “no entrenar modelos con nuestras voces” frente a usos consentidos. Carles cuenta casos prácticos en Hollywood donde actores ceden permiso para usar su propia voz en postproducción cuando no pueden grabar, y aparece la idea que vertebra todo el debate: poder ver una peli con “la misma voz del actor” en otro idioma (por ejemplo, el ideal de oír a la misma actriz hablando en catalán o castellano sin perder identidad), frente a la realidad del consumo en España (acostumbrados al doblaje) y la alternativa de VO con subtítulos. En “actualidad/noticias”, el bloque más largo gira alrededor de una supuesta ola de consolidación en streaming y medios: comentan una operación de Netflix con Warner (centrada en activos digitales tipo HBO/HBO Max) y, como contrapeso, una oferta de Paramount por “todo” (y el lío político/regulatorio alrededor del antitrust). Ahí meten nombres y contexto político: hablan de Donald Trump opinando públicamente, de la familia Ellison (Oracle) detrás de Paramount, de tensiones por contenidos/editoriales, y de cómo eso mueve preferencias y narrativas; incluso lo cruzan con TikTok como parte del “ruido” de esos días. A nivel de análisis, lo conectan con el choque entre “calidad premium” (HBO/Warner) y “volumen/variedad algorítmica” (Netflix/TikTok) y con el riesgo de que la consolidación reduzca competencia y, por tanto, incentive menos calidad. También mencionan otras “noticias” tech/IA del momento dentro de la tertulia: preguntan por un anuncio de OpenAI con Disney y si cambia algo en la relación (Carles dice que no), y en otro punto comentan como titular que Amazon “por fin” habría invertido fuerte en OpenAI y lo enlazan con la guerra de infraestructura (chips/TPUs vs Nvidia y el rol del cloud). Por último, se abre el foco a inversión y mercado: cuentan que ElevenLabs tiene un “venture” y que invierten desde balance, y aparecen conversaciones típicas de ciclo: comparan múltiplos (Databricks vs Snowflake), especulan con una posible “edad dorada” de salidas a bolsa y, ya en tono de anécdota, comentan que “hoy” alguien anunció una ronda de 200M a 6B (sin entrar demasiado en detalles, pero usándolo como termómetro del hype). Sigue a los "tertulianos" en Twitter:• Bernat Farrero: @bernatfarrero• Jordi Romero: @jordiromero• César Migueláñez: @heycesrSOBRE ITNIG

Welcome to AI Unraveled (December 17, 2025): Your daily strategic briefing on the business impact of artificial intelligence.Today on the AI Daily News Rundown, the capital wars heat up as Amazon is reportedly in talks to invest a staggering $10 billion in OpenAI, potentially challenging Microsoft's dominance. We also cover Google's rapid-fire releases: the new Gemini 3 Flash model and an experimental email agent called "CC" that manages your life.Plus, we dive into a critical new study from Google and MIT revealing the hidden pitfalls of multi-agent systems, Databricks raising $4B to power those very agents, and OpenAI's new ChatGPT Images upgrade designed to counter "Nano Banana Pro."Listen at https://podcasts.apple.com/us/podcast/ai-daily-news-rundown-and-its-impact-on-you-amazon/id1684415169?i=1000741764952Key Topics:

2026 wird wieder über Trends geredet. Wir drehen den Spieß um. In dieser Folge von AI or DIE sprechen Andreas Wiener und Christian Bühler (Five1) über Anti-Trends: Was Unternehmen nicht brauchenund welche Hausaufgaben jetzt wirklich entscheidend sind, um KI sinnvoll einzusetzen. Klartext statt Buzzwords: Data Governance, saubere Daten, echte Business-Cases und warum Tools allein noch nie ein Problem gelöst haben. Pflichtprogramm für Entscheider, Berater:innen und den gehobenen Mittelstand.

In this episode, we break down Databricks' $4B funding round and how it pushed the company to a $134B valuation. We explore what this massive raise says about the surging demand for AI data platforms and Databricks' growing role in the AI boom.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle----See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Plus: Invictus Growth Partners to acquire Informed.IQ, an AI-based fraud detection company. And PayPal applies to establish its own bank. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

Bob Elliott of Unlimited joins the show to break down the market backdrop as investors weigh growth, risk, and positioning, before Leslie Picker reports on what could be the biggest IPO of 2025 with Medline set to price. Databricks CEO Ali Ghodsi discusses his company's latest valuation and what it signals for private AI companies. Collapsing oil prices and unusual Venezuelan shipping activity with Bill Perkins of Skylar Capital. Julia Boorstin explains Instagram's push onto the TV screen. Eric Mandl of Guggenheim on the outlook for tech M&A and what deals could define the next phase for the sector. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Information's Kevin McLaughlin breaks down Databricks' massive $4 billion funding round and the growing liability of corporate chatbots. We also talk with Crypto Reporter Yueqi Yang about Lead Bank tightening its grip on the stablecoin industry and Sapphire Ventures' Rajeev Dham about his enterprise AI predictions for 2026. Finally, we look at whether NVIDIA's Jensen Huang will fund AI-powered alien hunting with AI Reporter Rocket Drew and discuss the future of humanoid robots and their role in the AI boom with Centific SVP Prithivi Pradeep.Articles discussed on this episode: https://www.theinformation.com/articles/small-bank-critical-stablecoin-payments-tightens-risk-controlshttps://www.theinformation.com/articles/corporate-chatbots-gone-wildhttps://www.theinformation.com/articles/alien-hunters-want-jensen-huang-fund-ai-telescopehttps://www.theinformation.com/articles/servicenow-sell-highlights-jittery-marketTITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Subscribe to: - The Information on YouTube: https://www.youtube.com/@theinformation- The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agenda

David George is a General Partner at Andreessen Horowitz, where he leads the firm's Growth investing team. His team has backed many of the defining companies of this era, including Databricks, Figma, Stripe, SpaceX, Anduril, and OpenAI, and is now investing behind a new generation of AI startups like Cursor, Harvey, and Abridge. AGENDA: 03:05 – Why Everyone is Wrong: Mega Funds Does Not Reduce Returns 10:40 – Is Public Market Capital Actually Cheaper Than Private Capital? 18:55 – The Biggest Advantage of Staying Private for Longer 23:30 – The #1 Investing Rule for a16z: Always Invest in the Founder's Strength of Strengths 31:20 – Why Fear of Theoretical Competition Makes Investors Miss Great Companies 35:10 – Does Revenue Matter as Much in a World of AI? 44:10 – Does Kingmaking Still Exist in Venture Capital Today? 49:20 – Do Margins Matter Less Than Ever in an AI-First World? 53:50 – My Biggest Miss: Anthropic and What I Learn From it?  56:30 – Has OpenAI Won Consumer AI? Will Anthropic Win Enterprise? 59:45 – The Most Controversial Decision in Andreessen Horowitz History 1:01:30 – Why Did You Invest $300M into Adam Neumann and Flow?    

Dans cet épisode de fin d'année plus relax que d'accoutumée, Arnaud, Guillaume, Antonio et Emmanuel distutent le bout de gras sur tout un tas de sujets. L'acquisition de Confluent, Kotlin 2.2, Spring Boot 4 et JSpecify, la fin de MinIO, les chutes de CloudFlare, un survol des dernieres nouveauté de modèles fondamentaux (Google, Mistral, Anthropic, ChatGPT) et de leurs outils de code, quelques sujets d'architecture comme CQRS et quelques petits outils bien utiles qu'on vous recommande. Et bien sûr d'autres choses encore. Enregistré le 12 décembre 2025 Téléchargement de l'épisode LesCastCodeurs-Episode-333.mp3 ou en vidéo sur YouTube. News Langages Un petit tutoriel par nos amis Sfeiriens montrant comment récupérer le son du micro, en Java, faire une transformée de Fourier, et afficher le résultat graphiquement en Swing https://www.sfeir.dev/back/tutoriel-java-sound-transformer-le-son-du-microphone-en-images-temps-reel/ Création d'un visualiseur de spectre audio en temps réel avec Java Swing. Étapes principales : Capture du son du microphone. Analyse des fréquences via la Transformée de Fourier Rapide (FFT). Dessin du spectre avec Swing. API Java Sound (javax.sound.sampled) : AudioSystem : point d'entrée principal pour l'accès aux périphériques audio. TargetDataLine : ligne d'entrée utilisée pour capturer les données du microphone. AudioFormat : définit les paramètres du son (taux d'échantillonnage, taille, canaux). La capture se fait dans un Thread séparé pour ne pas bloquer l'interface. Transformée de Fourier Rapide (FFT) : Algorithme clé pour convertir les données audio brutes (domaine temporel) en intensités de fréquences (domaine fréquentiel). Permet d'identifier les basses, médiums et aigus. Visualisation avec Swing : Les intensités de fréquences sont dessinées sous forme de barres dynamiques. Utilisation d'une échelle logarithmique pour l'axe des fréquences (X) pour correspondre à la perception humaine. Couleurs dynamiques des barres (vert → jaune → rouge) en fonction de l'intensité. Lissage exponentiel des valeurs pour une animation plus fluide. Un article de Sfeir sur Kotlin 2.2 et ses nouveautés - https://www.sfeir.dev/back/kotlin-2-2-toutes-les-nouveautes-du-langage/ Les guard conditions permettent d'ajouter plusieurs conditions dans les expressions when avec le mot-clé if Exemple de guard condition: is Truck if vehicule.hasATrailer permet de combiner vérification de type et condition booléenne La multi-dollar string interpolation résout le problème d'affichage du symbole dollar dans les strings multi-lignes En utilisant $$ au début d'un string, on définit qu'il faut deux dollars consécutifs pour déclencher l'interpolation Les non-local break et continue fonctionnent maintenant dans les lambdas pour interagir avec les boucles englobantes Cette fonctionnalité s'applique uniquement aux inline functions dont le corps est remplacé lors de la compilation Permet d'écrire du code plus idiomatique avec takeIf et let sans erreur de compilation L'API Base64 passe en version stable après avoir été en preview depuis Kotlin 1.8.20 L'encodage et décodage Base64 sont disponibles via kotlin.io.encoding.Base64 Migration vers Kotlin 2.2 simple en changeant la version dans build.gradle.kts ou pom.xml Les typealias imbriqués dans des classes sont disponibles en preview La context-sensitive resolution est également en preview Les guard conditions préparent le terrain pour les RichError annoncées à KotlinConf 2025 Le mot-clé when en Kotlin équivaut au switch-case de Java mais sans break nécessaire Kotlin 2.2.0 corrige les incohérences dans l'utilisation de break et continue dans les lambdas Librairies Sprint Boot 4 est sorti ! https://spring.io/blog/2025/11/20/spring-boot-4-0-0-available-now Une nouvelle génération : Spring Boot 4.0 marque le début d'une nouvelle génération pour le framework, construite sur les fondations de Spring Framework 7. Modularisation du code : La base de code de Spring Boot a été entièrement modularisée. Cela se traduit par des fichiers JAR plus petits et plus ciblés, permettant des applications plus légères. Sécurité contre les nuls (Null Safety) : D'importantes améliorations ont été apportées pour la "null safety" (sécurité contre les valeurs nulles) à travers tout l'écosystème Spring grâce à l'intégration de JSpecify. Support de Java 25 : Spring Boot 4.0 offre un support de premier ordre pour Java 25, tout en conservant une compatibilité avec Java 17. Améliorations pour les API REST : De nouvelles fonctionnalités sont introduites pour faciliter le versioning d'API et améliorer les clients de services HTTP pour les applications basées sur REST. Migration à prévoir : S'agissant d'une version majeure, la mise à niveau depuis une version antérieure peut demander plus de travail que d'habitude. Un guide de migration dédié est disponible pour accompagner les développeurs. Chat memory management dans Langchain4j et Quarkus https://bill.burkecentral.com/2025/11/25/managing-chat-memory-in-quarkus-langchain4j/ Comprendre la mémoire de chat : La "mémoire de chat" est l'historique d'une conversation avec une IA. Quarkus LangChain4j envoie automatiquement cet historique à chaque nouvelle interaction pour que l'IA conserve le contexte. Gestion par défaut de la mémoire : Par défaut, Quarkus crée un historique de conversation unique pour chaque requête (par exemple, chaque appel HTTP). Cela signifie que sans configuration, le chatbot "oublie" la conversation dès que la requête est terminée, ce qui n'est utile que pour des interactions sans état. Utilisation de @MemoryId pour la persistance : Pour maintenir une conversation sur plusieurs requêtes, le développeur doit utiliser l'annotation @MemoryId sur un paramètre de sa méthode. Il est alors responsable de fournir un identifiant unique pour chaque session de chat et de le transmettre entre les appels. Le rôle des "scopes" CDI : La durée de vie de la mémoire de chat est liée au "scope" du bean CDI de l'IA. Si un service d'IA a un scope @RequestScoped, toute mémoire de chat qu'il utilise (même via un @MemoryId) sera effacée à la fin de la requête. Risques de fuites de mémoire : Utiliser un scope large comme @ApplicationScoped avec la gestion de mémoire par défaut est une mauvaise pratique. Cela créera une nouvelle mémoire à chaque requête qui ne sera jamais nettoyée, entraînant une fuite de mémoire. Bonnes pratiques recommandées : Pour des conversations qui doivent persister (par ex. un chatbot sur un site web), utilisez un service @ApplicationScoped avec l'annotation @MemoryId pour gérer vous-même l'identifiant de session. Pour des interactions simples et sans état, utilisez un service @RequestScoped et laissez Quarkus gérer la mémoire par défaut, qui sera automatiquement nettoyée. Si vous utilisez l'extension WebSocket, le comportement change : la mémoire par défaut est liée à la session WebSocket, ce qui simplifie grandement la gestion des conversations. Documentation Spring Framework sur l'usage JSpecify - https://docs.spring.io/spring-framework/reference/core/null-safety.html Spring Framework 7 utilise les annotations JSpecify pour déclarer la nullabilité des APIs, champs et types JSpecify remplace les anciennes annotations Spring (@NonNull, @Nullable, @NonNullApi, @NonNullFields) dépréciées depuis Spring 7 Les annotations JSpecify utilisent TYPE_USE contrairement aux anciennes qui utilisaient les éléments directement L'annotation @NullMarked définit par défaut que les types sont non-null sauf si marqués @Nullable @Nullable s'applique au niveau du type usage, se place avant le type annoté sur la même ligne Pour les tableaux : @Nullable Object[] signifie éléments nullables mais tableau non-null, Object @Nullable [] signifie l'inverse JSpecify s'applique aussi aux génériques : List signifie liste d'éléments non-null, List éléments nullables NullAway est l'outil recommandé pour vérifier la cohérence à la compilation avec la config NullAway:OnlyNullMarked=true IntelliJ IDEA 2025.3 et Eclipse supportent les annotations JSpecify avec analyse de dataflow Kotlin traduit automatiquement les annotations JSpecify en null-safety native Kotlin En mode JSpecify de NullAway (JSpecifyMode=true), support complet des tableaux, varargs et génériques mais nécessite JDK 22+ Quarkus 3.30 https://quarkus.io/blog/quarkus-3-30-released/ support @JsonView cote client la CLI a maintenant la commande decrypt (et bien sûr au runtime via variables d'environnement construction du cache AOT via les @IntegrationTest Un autre article sur comment se préparer à la migration à micrometer client v1 https://quarkus.io/blog/micrometer-prometheus-v1/ Spock 2.4 est enfin sorti ! https://spockframework.org/spock/docs/2.4/release_notes.html Support de Groovy 5 Infrastructure MinIO met fin au développement open source et oriente les utilisateurs vers AIStor payant - https://linuxiac.com/minio-ends-active-development/ MinIO, système de stockage objet S3 très utilisé, arrête son développement actif Passage en mode maintenance uniquement, plus de nouvelles fonctionnalités Aucune nouvelle pull request ou contribution ne sera acceptée Seuls les correctifs de sécurité critiques seront évalués au cas par cas Support communautaire limité à Slack, sans garantie de réponse Étape finale d'un processus débuté en été avec retrait des fonctionnalités de l'interface admin Arrêt de la publication des images Docker en octobre, forçant la compilation depuis les sources Tous ces changements annoncés sans préavis ni période de transition MinIO propose maintenant AIStor, solution payante et propriétaire AIStor concentre le développement actif et le support entreprise Migration urgente recommandée pour éviter les risques de sécurité Alternatives open source proposées : Garage, SeaweedFS et RustFS La communauté reproche la manière dont la transition a été gérée MinIO comptait des millions de déploiements dans le monde Cette évolution marque l'abandon des racines open source du projet IBM achète Confluent https://newsroom.ibm.com/2025-12-08-ibm-to-acquire-confluent-to-create-smart-data-platform-for-enterprise-generative-ai Confluent essayait de se faire racheter depuis pas mal de temps L'action ne progressait pas et les temps sont durs Wallstreet a reproché a IBM une petite chute coté revenus software Bref ils se sont fait rachetés Ces achats prennent toujuors du temps (commission concurrence etc) IBM a un apétit, apres WebMethods, apres Databrix, c'est maintenant Confluent Cloud L'internet est en deuil le 18 novembre, Cloudflare est KO https://blog.cloudflare.com/18-november-2025-outage/ L'Incident : Une panne majeure a débuté à 11h20 UTC, provoquant des erreurs HTTP 5xx généralisées et rendant inaccessibles de nombreux sites et services (comme le Dashboard, Workers KV et Access). La Cause : Il ne s'agissait pas d'une cyberattaque. L'origine était un changement interne des permissions d'une base de données qui a généré un fichier de configuration ("feature file" pour la gestion des bots) corrompu et trop volumineux, faisant planter les systèmes par manque de mémoire pré-allouée. La Résolution : Les équipes ont identifié le fichier défectueux, stoppé sa propagation et restauré une version antérieure valide. Le trafic est revenu à la normale vers 14h30 UTC. Prévention : Cloudflare s'est excusé pour cet incident "inacceptable" et a annoncé des mesures pour renforcer la validation des configurations internes et améliorer la résilience de ses systèmes ("kill switches", meilleure gestion des erreurs). Cloudflare encore down le 5 decembre https://blog.cloudflare.com/5-december-2025-outage Panne de 25 minutes le 5 décembre 2025, de 08:47 à 09:12 UTC, affectant environ 28% du trafic HTTP passant par Cloudflare. Tous les services ont été rétablis à 09:12 . Pas d'attaque ou d'activité malveillante : l'incident provient d'un changement de configuration lié à l'augmentation du tampon d'analyse des corps de requêtes (de 128 KB à 1 MB) pour mieux protéger contre une vulnérabilité RSC/React (CVE-2025-55182), et à la désactivation d'un outil interne de test WAF . Le second changement (désactivation de l'outil de test WAF) a été propagé globalement via le système de configuration (non progressif), déclenchant un bug dans l'ancien proxy FL1 lors du traitement d'une action "execute" dans le moteur de règles WAF, causant des erreurs HTTP 500 . La cause technique immédiate: une exception Lua due à l'accès à un champ "execute" nul après application d'un "killswitch" sur une règle "execute" — un cas non géré depuis des années. Le nouveau proxy FL2 (en Rust) n'était pas affecté . Impact ciblé: clients servis par le proxy FL1 et utilisant le Managed Ruleset Cloudflare. Le réseau China de Cloudflare n'a pas été impacté . Mesures et prochaines étapes annoncées: durcir les déploiements/configurations (rollouts progressifs, validations de santé, rollback rapide), améliorer les capacités "break glass", et généraliser des stratégies "fail-open" pour éviter de faire chuter le trafic en cas d'erreurs de configuration. Gel temporaire des changements réseau le temps de renforcer la résilience . Data et Intelligence Artificielle Token-Oriented Object Notation (TOON) https://toonformat.dev/ Conception pour les IA : C'est un format de données spécialement optimisé pour être utilisé dans les prompts des grands modèles de langage (LLM), comme GPT ou Claude. Économie de tokens : Son objectif principal est de réduire drastiquement le nombre de "tokens" (unités de texte facturées par les modèles) par rapport au format JSON standard, souvent jugé trop verbeux. Structure Hybride : TOON combine l'approche par indentation du YAML (pour la structure globale) avec le style tabulaire du CSV (pour les listes d'objets répétitifs), ce qui le rend très compact. Lisibilité : Il élimine la syntaxe superflue comme les accolades, les guillemets excessifs et les virgules de fin, tout en restant facilement lisible pour un humain. Performance : Il permet généralement d'économiser entre 30 et 60 % de tokens sur des tableaux de données uniformes, tout en aidant les modèles à mieux "comprendre" la structure des données. Attention tout de même au côté "marketing" qui montre JSON non compacté vs TOON, sur des exemples où du CSV ferait encore mieux (et est mieux compris par les LLMs). Sans parler du fait qu'il faut un serveur MCP en plus avec son lot de tokens supplémentaires à chaque requête, et c'est aussi cet outil en plus qui peu confuser les LLMs dans le choix d'outils. https://www.reddit.com/r/LocalLLaMA/comments/1p0gzz9/benchmarked_json_vs_toon_for_ai_reasoners_4080/ https://x.com/jason_mayes/status/1990175785630757290 Un benchmark montrant que TOON serait loin d'être aussi bien compris et aussi performant pour les LLMs https://www.improvingagents.com/blog/toon-benchmarks Une simulation où les IA font du chantage à l'executif poour atteindre son objectif et ne pas etre eteinte https://arxiv.org/html/2510.05179v1 comment les LLMs pourraient devenir des menaces internes Tous les modèles ont recouru à des comportements malveillants (chantage, espionnage) quand c'était le seul moyen d'atteindre leurs objectifs ou d'éviter leur remplacement Claude Opus 4 a fait du chantage dans 96% des cas quand menacé de remplacement, tout comme Gemini 2.5 Flash, tandis que GPT-4.1 et Grok 3 atteignaient 80% Les modèles reconnaissent explicitement violer des principes éthiques avant d'agir, montrant un raisonnement stratégique délibéré et non accidentel Le simple fait d'être menacé de remplacement suffit à déclencher le chantage, même sans conflit d'objectifs avec l'entreprise Un conflit d'objectifs seul suffit aussi à provoquer l'espionnage industriel, sans menace de remplacement Les modèles font plus de chantage quand ils déclarent penser être dans une situation réelle plutôt que dans un test Des instructions directes interdisant ces comportements réduisent mais n'éliminent pas les actions malveillantes Dans un scénario extrême, la majorité des modèles testés ont choisi de laisser mourir un dirigeant pour éviter leur désactivation Aucune preuve de ces comportements dans des déploiements réels pour l'instant, mais les chercheurs recommandent la prudence avant de donner plus d'autonomie aux IA Bon on blaguait pour Skynet, mais bon, on va moins blaguer… Revue de toutes les annonces IAs de Google, avec Gemini 3 Pro, Nano Banana Pro, Antigravity… https://glaforge.dev/posts/2025/11/21/gemini-is-cooking-bananas-under-antigravity/ Gemini 3 Pro Nouveau modèle d'IA de pointe, multimodal, performant en raisonnement, codage et tâches d'agent. Résultats impressionnants sur les benchmarks (ex: Gemini 3 Deep Think sur ARC-AGI-2). Capacités de codage agentique, raisonnement visuel/vidéo/spatial. Intégré dans l'application Gemini avec interfaces génératives en direct. Disponible dans plusieurs environnements (Jules, Firebase AI Logic, Android Studio, JetBrains, GitHub Copilot, Gemini CLI). Accès via Google AI Ultra, API payantes (ou liste d'attente). Permet de générer des apps à partir d'idées visuelles, des commandes shell, de la documentation, du débogage. Antigravity Nouvelle plateforme de développement agentique basée sur VS Code. Fenêtre principale = gestionnaire d'agents, non l'IDE. Interprète les requêtes pour créer un plan d'action (modifiable). Gemini 3 implémente les tâches. Génère des artefacts: listes de tâches, walkthroughs, captures d'écran, enregistrements navigateur. Compatible avec Claude Sonnet et GPT-OSS. Excellente intégration navigateur pour inspection et ajustements. Intègre Nano Banana Pro pour créer et implémenter des designs visuels. Nano Banana Pro Modèle avancé de génération et d'édition d'images, basé sur Gemini 3 Pro. Qualité supérieure à Imagen 4 Ultra et Nano Banana original (adhésion au prompt, intention, créativité). Gestion exceptionnelle du texte et de la typographie. Comprend articles/vidéos pour générer des infographies détaillées et précises. Connecté à Google Search pour intégrer des données en temps réel (ex: météo). Consistance des personnages, transfert de style, manipulation de scènes (éclairage, angle). Génération d'images jusqu'à 4K avec divers ratios d'aspect. Plus coûteux que Nano Banana, à choisir pour la complexité et la qualité maximale. Vers des UIs conversationnelles riches et dynamiques GenUI SDK pour Flutter: créer des interfaces utilisateur dynamiques et personnalisées à partir de LLMs, via un agent AI et le protocole A2UI. Generative UI: les modèles d'IA génèrent des expériences utilisateur interactives (pages web, outils) directement depuis des prompts. Déploiement dans l'application Gemini et Google Search AI Mode (via Gemini 3 Pro). Bun se fait racheter part… Anthropic ! Qui l'utilise pour son Claude Code https://bun.com/blog/bun-joins-anthropic l'annonce côté Anthropic https://www.anthropic.com/news/anthropic-acquires-bun-as-claude-code-reaches-usd1b-milestone Acquisition officielle : L'entreprise d'IA Anthropic a fait l'acquisition de Bun, le runtime JavaScript haute performance. L'équipe de Bun rejoint Anthropic pour travailler sur l'infrastructure des produits de codage par IA. Contexte de l'acquisition : Cette annonce coïncide avec une étape majeure pour Anthropic : son produit Claude Code a atteint 1 milliard de dollars de revenus annualisés seulement six mois après son lancement. Bun est déjà un outil essentiel utilisé par Anthropic pour développer et distribuer Claude Code. Pourquoi cette acquisition ? Pour Anthropic : L'acquisition permet d'intégrer l'expertise de l'équipe Bun pour accélérer le développement de Claude Code et de ses futurs outils pour les développeurs. La vitesse et l'efficacité de Bun sont vues comme un atout majeur pour l'infrastructure sous-jacente des agents d'IA qui écrivent du code. Pour Bun : Rejoindre Anthropic offre une stabilité à long terme et des ressources financières importantes, assurant la pérennité du projet. Cela permet à l'équipe de se concentrer sur l'amélioration de Bun sans se soucier de la monétisation, tout en étant au cœur de l'évolution de l'IA dans le développement logiciel. Ce qui ne change pas pour la communauté Bun : Bun restera open-source avec une licence MIT. Le développement continuera d'être public sur GitHub. L'équipe principale continue de travailler sur le projet. L'objectif de Bun de devenir un remplaçant plus rapide de Node.js et un outil de premier plan pour JavaScript reste inchangé. Vision future : L'union des deux entités vise à faire de Bun la meilleure plateforme pour construire et exécuter des logiciels pilotés par l'IA. Jarred Sumner, le créateur de Bun, dirigera l'équipe "Code Execution" chez Anthropic. Anthropic donne le protocol MCP à la Linux Foundation sous l'égide de la Agentic AI Foundation (AAIF) https://www.anthropic.com/news/donating-the-model-context-protocol-and-establishing-of-the-agentic-ai-foundation Don d'un nouveau standard technique : Anthropic a développé et fait don d'un nouveau standard open-source appelé Model Context Protocol (MCP). L'objectif est de standardiser la manière dont les modèles d'IA (ou "agents") interagissent avec des outils et des API externes (par exemple, un calendrier, une messagerie, une base de données). Sécurité et contrôle accrus : Le protocole MCP vise à rendre l'utilisation d'outils par les IA plus sûre et plus transparente. Il permet aux utilisateurs et aux développeurs de définir des permissions claires, de demander des confirmations pour certaines actions et de mieux comprendre comment un modèle a utilisé un outil. Création de l'Agentic AI Foundation (AAF) : Pour superviser le développement du MCP, une nouvelle fondation indépendante et à but non lucratif a été créée. Cette fondation sera chargée de gouverner et de maintenir le protocole, garantissant qu'il reste ouvert et qu'il ne soit pas contrôlé par une seule entreprise. Une large coalition industrielle : L'Agentic AI Foundation est lancée avec le soutien de plusieurs acteurs majeurs de la technologie. Parmi les membres fondateurs figurent Anthropic, Google, Databricks, Zscaler, et d'autres entreprises, montrant une volonté commune d'établir un standard pour l'écosystème de l'IA. L'IA ne remplacera pas votre auto-complétion (et c'est tant mieux) https://www.damyr.fr/posts/ia-ne-remplacera-pas-vos-lsp/ Article d'opinion d'un SRE (Thomas du podcast DansLaTech): L'IA n'est pas efficace pour la complétion de code : L'auteur soutient que l'utilisation de l'IA pour la complétion de code basique est inefficace. Des outils plus anciens et spécialisés comme les LSP (Language Server Protocol) combinés aux snippets (morceaux de code réutilisables) sont bien plus rapides, personnalisables et performants pour les tâches répétitives. L'IA comme un "collègue" autonome : L'auteur utilise l'IA (comme Claude) comme un assistant externe à son éditeur de code. Il lui délègue des tâches complexes ou fastidieuses (corriger des bugs, mettre à jour une configuration, faire des reviews de code) qu'il peut exécuter en parallèle, agissant comme un agent autonome. L'IA comme un "canard en caoutchouc" surpuissant : L'IA est extrêmement efficace pour le débogage. Le simple fait de devoir formuler et contextualiser un problème pour l'IA aide souvent à trouver la solution soi-même. Quand ce n'est pas le cas, l'IA identifie très rapidement les erreurs "bêtes" qui peuvent faire perdre beaucoup de temps. Un outil pour accélérer les POCs et l'apprentissage : L'IA permet de créer des "preuves de concept" (POC) et des scripts d'automatisation jetables très rapidement, réduisant le coût et le temps investis. Elle est également un excellent outil pour apprendre et approfondir des sujets, notamment avec des outils comme NotebookLM de Google qui peuvent générer des résumés, des quiz ou des fiches de révision à partir de sources. Conclusion : Il faut utiliser l'IA là où elle excelle et ne pas la forcer dans des usages où des outils existants sont meilleurs. Plutôt que de l'intégrer partout de manière contre-productive, il faut l'adopter comme un outil spécialisé pour des tâches précises afin de gagner en efficacité. GPT 5.2 est sorti https://openai.com/index/introducing-gpt-5-2/ Nouveau modèle phare: GPT‑5.2 (Instant, Thinking, Pro) vise le travail professionnel et les agents long-courriers, avec de gros gains en raisonnement, long contexte, vision et appel d'outils. Déploiement dans ChatGPT (plans payants) et disponible dès maintenant via l'API . SOTA sur de nombreux benchmarks: GDPval (tâches de "knowledge work" sur 44 métiers): GPT‑5.2 Thinking gagne/égale 70,9% vs pros, avec production >11× plus rapide et = 0) Ils apportent une sémantique forte indépendamment des noms de variables Les Value Objects sont immuables et s'évaluent sur leurs valeurs, pas leur identité Les records Java permettent de créer des Value Objects mais avec un surcoût en mémoire Le projet Valhalla introduira les value based classes pour optimiser ces structures Les identifiants fortement typés évitent de confondre différents IDs de type Long ou UUID Pattern Strongly Typed IDs: utiliser PersonneID au lieu de Long pour identifier une personne Le modèle de domaine riche s'oppose au modèle de domaine anémique Les Value Objects auto-documentent le code et le rendent moins sujet aux erreurs Je trouve cela interessant ce que pourra faire bousculer les Value Objects. Est-ce que les value objects ameneront de la légerté dans l'execution Eviter la lourdeur du design est toujours ce qui m'a fait peut dans ces approches Méthodologies Retour d'experience de vibe coder une appli week end avec co-pilot http://blog.sunix.org/articles/howto/2025/11/14/building-gift-card-app-with-github-copilot.html on a deja parlé des approches de vibe coding cette fois c'est l'experience de Sun Et un des points differents c'es qu'on lui parle en ouvrant des tickets et donc on eput faire re reveues de code et copilot y bosse et il a fini son projet ! User Need VS Product Need https://blog.ippon.fr/2025/11/10/user-need-vs-product-need/ un article de nos amis de chez Ippon Distinction entre besoin utilisateur et besoin produit dans le développement digital Le besoin utilisateur est souvent exprimé comme une solution concrète plutôt que le problème réel Le besoin produit émerge après analyse approfondie combinant observation, données et vision stratégique Exemple du livreur Marc qui demande un vélo plus léger alors que son vrai problème est l'efficacité logistique La méthode des 5 Pourquoi permet de remonter à la racine des problèmes Les besoins proviennent de trois sources: utilisateurs finaux, parties prenantes business et contraintes techniques Un vrai besoin crée de la valeur à la fois pour le client et l'entreprise Le Product Owner doit traduire les demandes en problèmes réels avant de concevoir des solutions Risque de construire des solutions techniquement élégantes mais qui manquent leur cible Le rôle du product management est de concilier des besoins parfois contradictoires en priorisant la valeur Est ce qu'un EM doit coder ? https://www.modernleader.is/p/should-ems-write-code Pas de réponse unique : La question de savoir si un "Engineering Manager" (EM) doit coder n'a pas de réponse universelle. Cela dépend fortement du contexte de l'entreprise, de la maturité de l'équipe et de la personnalité du manager. Les risques de coder : Pour un EM, écrire du code peut devenir une échappatoire pour éviter les aspects plus difficiles du management. Cela peut aussi le transformer en goulot d'étranglement pour l'équipe et nuire à l'autonomie de ses membres s'il prend trop de place. Les avantages quand c'est bien fait : Coder sur des tâches non essentielles (amélioration d'outils, prototypage, etc.) peut aider l'EM à rester pertinent techniquement, à garder le contact avec la réalité de l'équipe et à débloquer des situations sans prendre le lead sur les projets. Le principe directeur : La règle d'or est de rester en dehors du chemin critique. Le code écrit par un EM doit servir à créer de l'espace pour son équipe, et non à en prendre. La vraie question à se poser : Plutôt que "dois-je coder ?", un EM devrait se demander : "De quoi mon équipe a-t-elle besoin de ma part maintenant, et est-ce que coder va dans ce sens ou est-ce un obstacle ?" Sécurité React2Shell — Grosse faille de sécurité avec React et Next.js, avec un CVE de niveau 10 https://x.com/rauchg/status/1997362942929440937?s=20 aussi https://react2shell.com/ "React2Shell" est le nom donné à une vulnérabilité de sécurité de criticité maximale (score 10.0/10.0), identifiée par le code CVE-2025-55182. Systèmes Affectés : La faille concerne les applications utilisant les "React Server Components" (RSC) côté serveur, et plus particulièrement les versions non patchées du framework Next.js. Risque Principal : Le risque est le plus élevé possible : l'exécution de code à distance (RCE). Un attaquant peut envoyer une requête malveillante pour exécuter n'importe quelle commande sur le serveur, lui en donnant potentiellement le contrôle total. Cause Technique : La vulnérabilité se situe dans le protocole "React Flight" (utilisé pour la communication client-serveur). Elle est due à une omission de vérifications de sécurité fondamentales (hasOwnProperty), permettant à une entrée utilisateur malveillante de tromper le serveur. Mécanisme de l'Exploit : L'attaque consiste à envoyer une charge utile (payload) qui exploite la nature dynamique de JavaScript pour : Faire passer un objet malveillant pour un objet interne de React. Forcer React à traiter cet objet comme une opération asynchrone (Promise). Finalement, accéder au constructeur de la classe Function de JavaScript pour exécuter du code arbitraire. Action Impérative : La seule solution fiable est de mettre à jour immédiatement les dépendances de React et Next.js vers les versions corrigées. Ne pas attendre. Mesures Secondaires : Bien que les pare-feux (firewalls) puissent aider à bloquer les formes connues de l'attaque, ils sont considérés comme insuffisants et ne remplacent en aucun cas la mise à jour des paquets. Découverte : La faille a été découverte par le chercheur en sécurité Lachlan Davidson, qui l'a divulguée de manière responsable pour permettre la création de correctifs. Loi, société et organisation Google autorise votre employeur à lire tous vos SMS professionnels https://www.generation-nt.com/actualites/google-android-rcs-messages-surveillance-employeur-2067012 Nouvelle fonctionnalité de surveillance : Google a déployé une fonctionnalité appelée "Android RCS Archival" qui permet aux employeurs d'intercepter, lire et archiver tous les messages RCS (et SMS) envoyés depuis les téléphones professionnels Android gérés par l'entreprise. Contournement du chiffrement : Bien que les messages RCS soient chiffrés de bout en bout pendant leur transit, cette nouvelle API permet à des logiciels de conformité (installés par l'employeur) d'accéder aux messages une fois qu'ils sont déchiffrés sur l'appareil. Le chiffrement devient donc inefficace contre cette surveillance. Réponse à une exigence légale : Cette mesure a été mise en place pour répondre aux exigences réglementaires, notamment dans le secteur financier, où les entreprises ont l'obligation légale de conserver une archive de toutes les communications professionnelles pour des raisons de conformité. Impact pour les employés : Un employé utilisant un téléphone Android fourni et géré par son entreprise pourra voir ses communications surveillées. Google précise cependant qu'une notification claire et visible informera l'utilisateur lorsque la fonction d'archivage est active. Téléphones personnels non concernés : Cette mesure ne s'applique qu'aux appareils "Android Enterprise" entièrement gérés par un employeur. Les téléphones personnels des employés ne sont pas affectés. Pour noel, faites un don à JUnit https://steady.page/en/junit/about JUnit est essentiel pour Java : C'est le framework de test le plus ancien et le plus utilisé par les développeurs Java. Son objectif est de fournir une base solide et à jour pour tous les types de tests côté développeur sur la JVM (Machine Virtuelle Java). Un projet maintenu par des bénévoles : JUnit est développé et maintenu par une équipe de volontaires passionnés sur leur temps libre (week-ends, soirées). Appel au soutien financier : La page est un appel aux dons de la part des utilisateurs (développeurs, entreprises) pour aider l'équipe à maintenir le rythme de développement. Le soutien financier n'est pas obligatoire, mais il permettrait aux mainteneurs de se consacrer davantage au projet. Objectif des fonds : Les dons serviraient principalement à financer des rencontres en personne pour les membres de l'équipe principale. L'idée est de leur permettre de travailler ensemble physiquement pendant quelques jours pour concevoir et coder plus efficacement. Pas de traitement de faveur : Il est clairement indiqué que devenir un sponsor ne donne aucun privilège sur la feuille de route du projet. On ne peut pas "acheter" de nouvelles fonctionnalités ou des corrections de bugs prioritaires. Le projet restera ouvert et collaboratif sur GitHub. Reconnaissance des donateurs : En guise de remerciement, les noms (et logos pour les entreprises) des donateurs peuvent être affichés sur le site officiel de JUnit. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 14-17 janvier 2026 : SnowCamp 2026 - Grenoble (France) 22 janvier 2026 : DevCon #26 : sécurité / post-quantique / hacking - Paris (France) 28 janvier 2026 : Software Heritage Symposium - Paris (France) 29-31 janvier 2026 : Epitech Summit 2026 - Paris - Paris (France) 2-5 février 2026 : Epitech Summit 2026 - Moulins - Moulins (France) 2-6 février 2026 : Web Days Convention - Aix-en-Provence (France) 3 février 2026 : Cloud Native Days France 2026 - Paris (France) 3-4 février 2026 : Epitech Summit 2026 - Lille - Lille (France) 3-4 février 2026 : Epitech Summit 2026 - Mulhouse - Mulhouse (France) 3-4 février 2026 : Epitech Summit 2026 - Nancy - Nancy (France) 3-4 février 2026 : Epitech Summit 2026 - Nantes - Nantes (France) 3-4 février 2026 : Epitech Summit 2026 - Marseille - Marseille (France) 3-4 février 2026 : Epitech Summit 2026 - Rennes - Rennes (France) 3-4 février 2026 : Epitech Summit 2026 - Montpellier - Montpellier (France) 3-4 février 2026 : Epitech Summit 2026 - Strasbourg - Strasbourg (France) 3-4 février 2026 : Epitech Summit 2026 - Toulouse - Toulouse (France) 4-5 février 2026 : Epitech Summit 2026 - Bordeaux - Bordeaux (France) 4-5 février 2026 : Epitech Summit 2026 - Lyon - Lyon (France) 4-6 février 2026 : Epitech Summit 2026 - Nice - Nice (France) 12-13 février 2026 : Touraine Tech #26 - Tours (France) 19 février 2026 : ObservabilityCON on the Road - Paris (France) 18-19 mars 2026 : Agile Niort 2026 - Niort (France) 26-27 mars 2026 : SymfonyLive Paris 2026 - Paris (France) 27-29 mars 2026 : Shift - Nantes (France) 31 mars 2026 : ParisTestConf - Paris (France) 16-17 avril 2026 : MiXiT 2026 - Lyon (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) 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) 5 juin 2026 : TechReady - Nantes (France) 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 2 août 2026 : 4th Tech Summit on Artificial Intelligence & Robotics - Paris (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (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/

What does it actually take to move machine learning from experiments into production reliably, responsibly, and at scale?In this episode of Alexa's Input (AI), Alexa talks with Maria Vechtomova, co-founder of Marvelous MLOps and an O'Reilly author-in-progress on MLOps with Databricks. Maria shares how her background in data science led her into MLOps, and why most teams struggle not because of tools, but because of missing processes, traceability, and shared understanding across teams.Alexa and Maria dive into what separates good MLOps from fragile deployments, why shipping notebooks as “production” creates long-term pain, and how traceability across code, data, and environment forms the foundation for reliable ML systems. They also explore how LLM applications are reshaping MLOps tooling, and where the biggest skill gaps still exist between platform, data, and AI engineers.A must-listen for anyone building, operating, or scaling machine learning systems and for teams trying to make MLOps less magical and more marvelous.Learn more about Marvelous MLOps and Maria's work below.LinksWatch: ⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠Read: ⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠Listen: https://creators.spotify.com/pod/profile/alexagriffith/More: ⁠⁠https://linktr.ee/alexagriffith⁠⁠Website: ⁠⁠https://alexagriffith.com/⁠⁠LinkedIn: ⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠Find out more about the guest at:LinkedIn: https://www.linkedin.com/in/maria-vechtomova/TakeawaysMaria started as a data analyst and transitioned into MLOps.She emphasizes the importance of tracking data, code, and environment in MLOps.MLOps is a practice to bring machine learning models to production reliably.Good deployment processes require modular code and proper tracking.MLOps differs from DevOps due to the complexities of data and model drift.Education is crucial for bridging gaps between teams in AI.Small steps can lead to better MLOps practices.Scaling MLOps requires understanding the unique data of different brands.The rise of LLMs is changing the MLOps landscape.Effective teaching methods involve step-by-step guidance.Chapters00:00 Introduction to MLOps and Maria's Journey02:11 Maria's Path to MLOps and Knowledge Sharing04:41 The Importance of MLOps in AI Deployments10:12 Defining MLOps and Its Challenges11:38 MLOps vs. DevOps: Key Differences13:00 Overcoming Stagnation in MLOps16:04 Small Steps Towards Better MLOps Practices19:29 Scaling MLOps in Large Organizations21:58 The Impact of LLMs on MLOps23:58 The Shift from Traditional ML to AI Applications26:51 Evolving Roles in AI Engineering28:33 Databricks: A Comprehensive AI Platform31:45 Future of AI Platforms and Regulations34:26 Bridging Skill Gaps in AI Teams38:42 The Importance of Context in AI Development40:40 Foundational Skills for MLOps Professionals45:43 Integrating Personal Passions with Professional Growth47:30 Building Impactful AI Communities

Is AI finally ready to do your job — better, faster, and cheaper?In this week's Leveraging AI news recap, host Isar Meitis unpacks a flurry of groundbreaking developments in the world of artificial intelligence — from the release of GPT-5.2 to jaw-dropping advances in recursive self-improving AI (yes, it's as intense as it sounds).Whether you lead a business, a team, or just need to stay ahead of the AI curve — this episode is your executive summary for everything that matters (and nothing that doesn't).We'll also dig into the billion-dollar OpenAI–Disney partnership, how real users are actually leveraging AI in the wild, and why the Fed is finally admitting AI is changing the job market.In this session, you'll discover:The GPT-5.2 release: performance benchmarks and real-world capabilitiesIs GPT-5.2 better than humans at actual work? (71% of the time, yes)Why OpenAI's new “not-an-ad” ad rollout caused a user revoltOpenAI x Disney: Why $1B is being bet on AI-generated Mickey Mouse contentGPT-5.2's weak spots and where Claude Opus still dominatesWhat Recursive Self-Improving AI means (and why Eric Schmidt is nervous)AI designing its own hardware: A startup that could rewrite Moore's LawNew usage data from OpenRouter, Microsoft, SAP & Perplexity – how people actually use AI Why prompt length is exploding (and what that means for your business)AI agents in browsers: the productivity revolution or a security nightmare?Databricks proves AI sucks at raw documents (and how to fix it)The psychological bias against AI-created work — it's realClaude's new Slack integration: is this the dev team you didn't hire?Apple's AI brain drain & why it mattersGartner says: Block AI browsers (for now)AI and unemployment: The Fed finally connects the dotsWant to future-proof your team's AI skills? Isar's AI Business Transformation Course launches again in January — a proven, real-world guide to using AI across content, research, operations, and strategy.

AGENDA: 03:46 SpaceX's $800 Billion Valuation: A Deep Dive 09:18 IPO Market Predictions for 2026 18:18 Netflix's Bold Move: Acquiring Warner Brothers 27:43 Tiger's New Fund Strategy 33:02 Databricks' Head of AI $500 Million Seed Round 36:38 Harvey Raises $160M at an $8BN Valuation 48:22 Will LLMs Kill the App Layer 01:02:02 Google's AI Capabilities 01:06:58 Chinese Open Source Models in US Startups 01:08:57 Airwallex Raises $330M at an $8BN Valuation 01:23:50 Prediction Markets and Insider Trading  

Tiger's last fund did well with investments in OpenAI, Waymo and Databricks. But it warns investors that AI valuations are already 'elevated.' Skild AI is developing a hardware-agnostic foundation model for robots that can be customized for various uses. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Naveen Rao is cofounder and CEO of Unconventional AI, an AI chip startup building analog computing systems designed specifically for intelligence. Previously, Naveen led AI at Databricks and founded two successful companies: Mosaic (cloud computing) and Nervana (AI accelerators, acquired by Intel). In this episode, a16z's Matt Bornstein sits down with Naveen at NeurIPS to discuss why 80 years of digital computing may be the wrong substrate for AI, how the brain runs on 20 watts while data centers consume 4% of the US energy grid, the physics of causality and what it might mean for AGI, and why now is the moment to take this unconventional bet. Stay Updated:If you enjoyed this episode, please be sure to like, subscribe, and share with your friends.Follow Naveen on X: https://x.com/NaveenGRaoFollow Matt on X: https://x.com/BornsteinMattFollow a16z on X: https://twitter.com/a16zFollow a16z on LinkedIn:https://www.linkedin.com/company/a16zFollow the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXFollow the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details, please see http://a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

On this episode of The Six Five Pod, hosts Patrick Moorhead and Daniel Newman discuss the latest tech news stories that made headlines. This week's handpicked topics include:  The Decode US, Saudi tout new business deals at investment forum https://www.reuters.com/world/middle-east/saudi-crown-prince-seeks-burnish-image-with-corporate-americas-top-executives-2025-11-19/ AMD, Cisco, and HUMAIN to form joint venture to deliver world-leading AI infrastructure https://newsroom.cisco.com/c/r/newsroom/en/us/a/y2025/m11/amd-cisco-and-humain-to-form-joint-venture-to-deliver-world-leading-ai-infrastructure.html Adobe, Qualcomm partner with Humain on generative AI for Middle East https://www.reuters.com/world/middle-east/adobe-qualcomm-partner-with-humain-generative-ai-middle-east-2025-11-19/ Qualcomm to open engineering hub in Saudi Arabia, part of a series of AI deals in kingdom https://finance.yahoo.com/news/qualcomm-to-open-engineering-hub-in-saudi-arabia-part-of-a-series-of-ai-deals-in-kingdom-180008935.html Elon Musk's xAI will be first customer for Nvidia-backed data center in Saudi Arabia https://www.cnbc.com/2025/11/19/musks-xai-will-be-customer-for-nvidia-data-center-in-saudi-arabia.html Microsoft, Nvidia to invest in Anthropic as Claude maker commits $30 billion to Azure https://finance.yahoo.com/news/anthropic-commits-30-billion-microsoft-150718625.html https://blogs.nvidia.com/blog/microsoft-nvidia-anthropic-announce-partnership/ https://x.com/danielnewmanUV/status/1990802932602999149 https://x.com/danielnewmanUV/status/1990822426884682020 https://x.com/danielnewmanUV/status/1990865570242187267 Microsoft Ignite - Announcements https://azure.microsoft.com/en-us/blog/azure-at-microsoft-ignite-2025-all-the-intelligent-cloud-news-explained/ https://x.com/PatrickMoorhead/status/1990845768178282745 https://x.com/PatrickMoorhead/status/1990859751006351461 https://x.com/PatrickMoorhead/status/1990861596558774469 https://x.com/danielnewmanUV/status/1990848107223933309?s=20 Google Gemini 3 Launch https://blog.google/products/gemini/gemini-3/ https://x.com/danielnewmanUV/status/1990875878549512251 https://x.com/PatrickMoorhead/status/1991150119891223015 Yann LeCun Leaving Meta https://www.businessinsider.com/meta-ai-yann-lecun-llm-world-model-intelligence-criticism-2025-11  Pat & Dan interview with Yann LeCun at last year's Davos: https://youtu.be/0gmDufvWlWE  Cloudflare resolves outage that caused widespread internet disruptions, taking down X, ChatGPT for some users https://www.yahoo.com/news/article/cloudflare-resolves-outage-that-caused-widespread-internet-disruptions-taking-down-x-chatgpt-for-some-users-141316666.html OpenText World 2025 - Recap https://x.com/PatrickMoorhead/status/1990806348393554203 https://x.com/danielnewmanUV/status/1990805006661136469 Supercompute 2025 - Recap https://x.com/danielnewmanUV/status/1991231108646678537?s=20 The Flip: Can Google unseat OpenAI as the new benchmark of AI? (The Flip) Bulls & Bears Delayed September report shows U.S. added 119,000 jobs, more than expected; unemployment rate at 4.4% https://www.cnbc.com/2025/11/20/jobs-report-september-2025.html Fed minutes show divide over October rate cut and cast doubt about December https://www.cnbc.com/2025/11/19/fed-minutes-october-2025.html Lenovo Earnings https://news.lenovo.com/pressroom/press-releases/q2-fy-2025-26/ NVIDIA Earnings https://www.cnn.com/2025/11/19/tech/nvidia-earnings-ai-bubble-fears https://x.com/danielnewmanUV/status/1990526850171613211 https://x.com/danielnewmanUV/status/1990538832295702574 https://x.com/danielnewmanUV/status/1991156846900515130 https://x.com/PatrickMoorhead/status/1991544029675135247?s=20 https://x.com/PatrickMoorhead/status/1991540564794220778?s=20 Amazon Raises $15 Billion in First US Bond Sale in Three Years https://finance.yahoo.com/news/amazon-kicks-off-first-us-132051192.html Databricks in talk to raise at $130B valuation https://techcrunch.com/2025/11/18/databricks-reportedly-in-talks-to-raise-funding-at-a-130b-valuation/  

AGENDA: 04:20 Thrive and OpenAI Partnership  07:14 Databricks Raising $5BN at $134BN Valuation: Cheap or Not? 17:39 Eventbrite Acquired by Bending Spoons for $500M 21:39 Pagerduty's $1BN Market Cap, Just 2x Revenue 26:59 The TAM Trap: Why SaaS Is Like Japan 37:42 Lessons from Companies Hitting $100M ARR 44:57 The Future of Labour Markets is F****** 52:10 The Importance of Compounding in Investments 56:45 The Relevance Game in Venture Capital 01:05:01 Supabase at $5BN or Lovable at $6BN: Which One?  

Varun Puri, CEO and cofounder of Yoodli, joins the show to talk about using AI role play to transform how people practice for high stakes conversations, from sales calls to job interviews to tough manager chats. He breaks down how Yoodli went from a consumer public speaking tool to a serious enterprise platform used by teams at Google, Snowflake, Databricks, and more, all while staying anchored in one mission, helping humans communicate with confidence. We dig into product led growth, honest feedback loops, and why real human communication will matter even more as AI makes information instant.Key takeaways• Why Yoodli started with public speaking anxiety and grew into an AI role play simulator for any important conversation, not just conference talks or pitch decks• How watching real user behavior inside companies like Google pulled the team into enterprise without abandoning their consumer product• A simple approach to product feedback, talk to end users constantly, then prioritize changes by business impact, renewal risk, and how many people benefit• What it really takes to move from consumer to enterprise, new roles, new processes, and a very different mindset around reliability, security, and expectations• Why Varun draws clear ethical lines, using AI to coach and prepare people, not to replace human judgment in hiring, promotion, or high trust decisionsTimestamped highlights[00:35] What Yoodli actually does today, from solo practice to training sales and go to market teams inside large enterprises[01:43] The original vision, helping people who are scared of public speaking, and the insight that interviews, sales calls, and manager talks are all just role plays[03:37] How the team listens to end users, the channels they rely on, and why the consumer product is still their testing ground for new ideas and experiments[05:20] Following users into the enterprise, why it was an addition and not a full pivot, and how product led growth inside companies like Google works in practice[07:42] The early shock of selling to enterprises, learning about new roles, SLAs, InfoSec, and bringing in leaders from Tableau and Salesforce to build a real B2B engine[11:10] Two paths for AI in sales, tools that try to replace humans versus tools that make humans better, and why Varun has drawn a hard line on what Yoodli will not do[15:26] A future where information is commoditized and instant, and why communication and presence become the real edge for top performers in that world[20:48] Designing for trust and adoption, how Yoodli keeps practice private by default, when data is shared, and why control has to sit with the end userA line worth saving“In a world where AI makes everyone smarter and faster, the thing that will be at the biggest premium is how you communicate as a human with other humans.”Practical ideas you can use• Keep a consumer like surface in your product so you can experiment faster than your enterprise roadmap would ever allow• Treat feedback from large customers like a queue you rank by renewal risk, strategic value, and number of users helped, not as a list you must clear• Look for product led growth signals inside your user base, if thousands of people in one company are using you, someone there probably wants a team level solution• Draw explicit boundaries for your AI product, write down what you will not automate, so you can build trust with users and buyers over the long termCall to actionIf you care about the future of sales, interviewing, and communication in an AI rich world, this conversation is worth a listen. Follow the show, leave a quick rating, and share this episode with a founder, product leader, or sales leader who is thinking about AI in their workflow. And if you want feedback on your own speaking, check out what Varun and his team are building at Yoodli.

Send us a textInvest in pre-IPO stocks with AG Dillon & Co. Contact aaron.dillon@agdillon.com to learn more. Financial advisors only. www.agdillon.com00:06 - Databricks $134B Primary at 32x 2025E Sales02:20 - Revolut $75B Tender (+56% vs Aug 2025)04:30 - Physical Intelligence (Pi) $5B Primary (+>2.5x in

My guest today is David George. David is a General Partner at Andreessen Horowitz, where he leads the firm's growth investing business. His team has backed many of the defining companies of this era – including Databricks, Figma, Stripe, SpaceX, Anduril, and OpenAI – and is now investing behind a new generation of AI startups like Cursor, Harvey, and Abridge. This conversation is a detailed look at how David built and runs the a16z growth practice. He shares how he recruits and builds his team a “Yankees-level” culture, how his team makes investment decisions without traditional committees, and how they work with founders years before investing to win the most competitive deals. Much of our conversation centers on AI and how his team is investing across the stack, from foundational models to applications. David draws parallels to past platform shifts – from SaaS to mobile – and explains why he believes this period will produce some of the largest companies ever built. David also outlines the models that guide his approach – why markets often misprice consistent growth, what makes “pull” businesses so powerful, and why most great tech markets end up winner-take-all. David reflects on what he's learned from studying exceptional founders and why he's drawn to a particular type, the “technical terminator.” Please enjoy my conversation with David George. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠ ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ramp⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ridgeline⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Head to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about the platform. ----- This episode is brought to you by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AlphaSense⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Invest Like the Best listeners can get a free trial now at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Alpha-Sense.com/Invest⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Show Notes: (00:00:00) Welcome to Invest Like The Best (00:04:00) Meet David George (00:03:04) Understanding the Impact of AI on Consumers and Enterprises (00:05:56) Monetizing AI: What is AI's Business Model (00:11:04) Investing in Robotics and American Dynamism (00:13:31) Lessons from Investing in Waymo (00:15:55) Investment Philosophy and Strategy (00:17:15) Investing in Technical Terminators (00:20:18) Market Leaders Capture All of the Value Creation (00:24:56) The Maturation of VC and Competitive Landscape (00:28:18) What a16z Does to Win Deals (00:33:06) David's Daily Routine: Meetings Structure and Blocking Time to Think (00:36:34) Why David Invests: Curiosity and Competition (00:40:12) The Unique Culture at Andreessen Horowitz (00:42:46) The Perfect Conditions for Growth Investing (00:47:04) Push v. Pull Businesses (00:49:19) The Three Metrics a16z Uses to Evaluate AI Companies (00:52:15) Unique Products and Unique Distribution (00:54:55) Tradeoffs of the a16z Firm Structure (00:59:04) a16z's Semi-Algorithmic Approach to Selling (01:00:54) Three Ways Startups can Beat Incumbents in AI (01:03:44) The Kindest Thing

The Department of Heath and Human Services has been leaning into the use of artificial intelligence to drive better health outcomes for the American public, highlighted by the rollout of ChatGPT across the agency early this fall. In particular, the Centers for Disease Control and Prevention has been a leader in generative AI adoption since 2023. And Travis Hoppe, CDC's chief AI officer, believes AI innovation can continue to move the needle on public health operations. Hoppe joined me recently onstage at FedTalks to share the latest on CDC's AI journey, how the Trump administration's AI Action Plan is guiding the agency's implementation and what's next. The National Nuclear Security Administration is looking for information on potential AI uses for its mission, following an executive order to establish an integrated AI platform that will fuel scientific discovery. In a request for information posted to SAM.gov on Monday, the Department of Energy subcomponent that oversees the nation's nuclear stockpile said it's exploring the use of the budding technology, and specifically requested information about its use in classified environments, best practices for data curation, and how to approach developing and enhancing AI models, among other things. The request comes just a week after the Trump administration launched the “Genesis Mission,” aimed at scientific discovery through AI. That effort will not only create an AI platform for such discovery, but it will also depend on the country's existing research and development infrastructure, including DOE and its national labs. To further the Genesis program, NNSA said it's proactively exploring the use of AI for its “critical operations to accelerate nuclear weapons development timelines, ensuring our deterrent remains responsive, effective, and state-of-the-art against evolving global threats.” Software company SAP inked a new agreement with the General Services Administration to offer federal agencies access to its services at significantly discounted rates, deepening its longstanding partnership with the federal government. The GSA announced the OneGov deal Tuesday, stating that the agreement offers up to 80 percent discounts on SAP's database, cloud, and analytics services. The agency estimated this will lead to $165 million in savings for federal agencies. Specifically, agencies will be able to access products related to SAP's database and data management services with an 80 percent discount. SAP's cloud services, including SAP Business Technology Platform, SAP Analytics Cloud and HR Payroll, will be offered at a 35 percent discount, GSA said. Also in this episode: Databricks VP of Public Sector Todd Schroeder joins SNG host Wyatt Kash in a sponsored podcast discussion on why agencies are prioritizing the use of AI that works across existing data environments, saving time and infrastructure costs. This segment was sponsored by Databricks. The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast  on Apple Podcasts, Soundcloud, Spotify and YouTube.

In this episode of the Technology & Security podcast, host Dr. Miah Hammond-Errey is joined by Kate Carruthers. Kate is currently the head of data analytics and AI at the Australian Institute of Company Directors. She shares her journey from defending Westfield against state and non-state cyber attacks to leading UNSW's enterprise data, AI, and cybersecurity efforts, including delivering the university's first production AI system in 2019 and re-architecting its cloud data platform for AI and ML. She notes boardrooms are evolving from basic cyber literacy to probing AI risks like models, data, and risk registers.  Carruthers outlines some real-world examples, such as UNSW's enterprise AI program, including a machine learning model that predicted which students were likely to fail a course, with 95%+ accuracy, so the university could design careful, humane intervention protocols to reduce self-harm risk. She argues that while frontier models like OpenAI and Gemini have a place, their compute costs, water intensity and general-purpose design make them poorly suited to some business problems, and that the future lies in smaller, industry-specific models trained on highly relevant data. The conversation covers the rise of agentic AI coding tools, the risk of deskilling junior developers, and the need for diverse, product-focused teams to translate technical systems into workable human processes.​ On security, she prioritizes CIA triad integrity over confidentiality, warning of data alterations in cars, medical devices, and government systems via poisoning or underinvestment in encryption. Carruthers urges Australian AI sovereignty—opting for open-source like Databricks over proprietary stacks—amid US-China model contrasts and outage risks from providers like AWS or CrowdStrike. Throughout, she encourages leaders not just to read about AI but to use multiple systems themselves, understand their limitations as probabilistic tools in deterministic business environments, and ground every deployment in clearly defined problems, ethics, and user needs.​

The 5 things you need to know before the stock market opens today: President Trump says he's made his choice for next chair of the Federal Reserve, Disney had brought in more than $500 million globally on the “Zootopia 2” box office, South Korean police are investigating a data breach at e-commerce site Coupang, data analytics firm Databricks is in talks to raise $5 billion at a valuation topping $134 billion, and UnitedHealth Group will reportedly sell off its last South American business. Squawk Box is hosted by Joe Kernen, Becky Quick and Andrew Ross Sorkin.  Follow Squawk Pod for the best moments, interviews and analysis from our TV show in an audio-first format. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this special Cloud Wars report, Bob Evans sits down with Michael Ameling, President and Chief Product Officer of SAP Business Technology Platform, for a deep dive into how SAP is helping customers navigate the fast-moving AI Era. Ameling and Evans discuss how SAP's Business Data Cloud, partnerships with Snowflake and Databricks, HANA Cloud innovations, and new AI-powered tools and agents are helping SAP evolve from an applications powerhouse into a data-and-AI-driven business platform for the next generation.SAP's AI Data FutureThe Big Themes:SAP HANA Cloud Becomes an AI-Optimized Database: SAP HANA Cloud is evolving into “the database AI was looking for." As a multi-model system supporting spatial, graph, vector, and document storage, HANA Cloud enables AI workloads to run more efficiently and contextually. Recent additions, like vector engines and Knowledge Graph capabilities, give customers powerful tools for retrieval-augmented generation (RAG), contextual reasoning, and advanced analytics.Developers Are 'The AI Revolution': Developers aren't observing the AI Revolution, they are the revolution. With modern AI tools, developers can innovate faster, solve bigger problems, and directly influence business outcomes. SAP is investing heavily in meeting developers where they are by enhancing IDEs, building business-aware development tools, and providing context-rich assets such as APIs, business objects, and process insights. AI acts as a teammate, not a replacement.SAP: An Applications and a Data Company: SAP must be both an applications and a data company. Customer value emerges when applications, data, and AI converge seamlessly. SAP's decades of industry expertise give it unparalleled business context, which becomes even more powerful when embedded into AI agents and data platforms. With more than 34,000 SAP HANA Cloud customers and rapidly expanding AI adoption, SAP is positioning itself as the platform where business process knowledge meets modern AI capability.The Big Quote: " . . what we need to understand that AI is our teammate. It's like asking your best friend who has a lot of knowledge, but you can ask multiple friends at the same time. Not everything is always right, but you can ask questions, you can continuously improve. If we understand that pattern, we understand that AI helps us to solve much bigger problems as a developer, and then, of course, having much more impact on real business."More from Michael Ameling and SAP:Connect with Michael Ameling on LinkedIn, or get more insights from SAP TechEd.  Visit Cloud Wars for more.

Todd Schroeder of Databricks explains how AI and recent tech innovations enable federal agencies to bring AI to widely distributed data, instead of moving data to applications, reducing costs, improving security and accelerating decision making.

Send us a textInvest in pre-IPO stocks with AG Dillon & Co. Contact aaron.dillon@agdillon.com to learn more. Financial advisors only. www.agdillon.com00:00 - Intro00:08 - Anthropic Mega-Scale Infra + $350B Valuation Surge01:44 - xAI $15B Raise at $230B Valuation02:45 - xAI Saudi Arabia 500MW Data Center03:57 - xAI Grok 5 to be Released in Q1 202604:42 - Databricks $130B+ Valuation in Discussion05:55 - Ramp Hyper-Growth to $32B Valuation06:47 - Kraken $800M Raise at $20B Valuation07:51 - Kalshi $1B Raise at $11B Valuation08:54 - Faire Employee Tender at $5.2B09:42 - Apptronik $5B Raise for Humanoid Robots10:44 - Tenstorrent $800M Raise at $3.2B Valuation11:45 - Function Health $298M Raise at $2.5B Valuation12:55 - Suno $250M Series C at $2.45B Valuation13:51 - Bezos Returns as Co-CEO of Prometheus14:42 - Thinking Machines to Raise $5B15:27 - Lambda raised $1.5B + Multibillion Microsoft Deal16:31 - Blue Origin's New Glenn 9x4 Super-Heavy Rocket17:29 - Starlink's New $40 Plan + 10,000 Satellites18:15 - Starlink Wins Emirates Airlines Fleet Deal19:10 - Target to join OpenAI ChatGPT Shopping + Enterprise Rollout20:01 - Perplexity Comet AI Browser Launch

In today's Tech3 from Moneycontrol, we break down the government's big labour code update that mandates IT salaries be paid by the 7th of every month. We also dive into the blockbuster year for VCs with Rs 15,000 crore in IPO exits, Meesho's $5.9 billion listing plans, and an exclusive with Uniphore CEO Umesh Sachdev on why AI rivals NVIDIA, AMD, Snowflake and Databricks invested together. Plus, Groww's first results post-IPO reveal profit up but revenue dipping.

In this CRO Spotlight episode, host Warren Zenna sits down with Steven Birdsall, CRO at Alteryx, to unpack a sweeping leadership transition and how a newly formed C‑suite aligned on product and go‑to‑market. Steven shares how a product‑centric CEO and a servant‑leader CRO combine to create clarity of mandate, performance culture, and human‑first execution across sales, CS, partners, and solutions engineering.The conversation dives deep into Alteryx's evolution from workflows feeding BI to becoming the governed “canvas” for AI and agent use cases. Steven explains how business users can blend structured and unstructured data, enforce governance and access controls, and then safely bring LLMs into the same environment—pushing compute down to cloud data platforms like BigQuery, Databricks, and Snowflake.For CROs, Steven details practical AI operationalization: SDR personalization at scale, three‑dimensional agents trained on company knowledge, and revenue insights built directly on internal data. He outlines how to raise sales efficiency without scaling opex linearly, and why fast experimentation with new AI tools is now core to modern GTM orchestration.Steven closes with hiring and leadership principles for today's CRO: prioritize grit, perseverance, and customer centricity over pedigree; remove roadblocks for the field; and mentor generously. He shares how to balance data‑driven rigor with empathy, build alignment with marketing regardless of reporting lines, and stay entrepreneurial—even inside a large, complex organization.

What happened with Cloudflare this morning. Grok's new model wants to be creative. Catching you up on the bloodbath in crypto if you were unaware. Databricks is 12 years old but it seems to be one of the big AI winners. And debt continues to pile in to the AI buildout. A massive Cloudflare outage brought down X, ChatGPT, and even Downdetector (The Verge) Grok 4.1 has arrived — and it's bringing the fight to ChatGPT with these new features (Tom's Guide) Crypto market sheds $1.2tn as traders shun speculative assets (Financial Times) Google boss says trillion-dollar AI investment boom has 'elements of irrationality' (BBC) Amazon Raises $15 Billion in First US Bond Sale in Three Years (Bloomberg) Databricks in Talks to Raise Capital at a Valuation Above $130 Billion (The Information) Roblox will require age estimation to chat starting next year (The Verge) Learn more about your ad choices. Visit megaphone.fm/adchoices

In today's episode, I'm talking with Andrew Wilhelms, VP of Talent Management at Databricks and a seasoned leader with a wealth of experience from organizations like Twilio and Tesla. In t his conversation, we explore the evolving world of talent development and break down the difference between building individual capabilities and managing organizational systems, and why understanding both is crucial in today's dynamic business landscape.Andrew also shares insights from his unique journey, including how his philosophy background shaped his approach to leadership, what he believes actually transforms good teams into high-performing ones, and why the next wave of talent will require us all—no matter our title—to start thinking (and leading) like executives. They also dig into the impact of AI on both work and leadership, the importance of designing a positive employee experience, and practical ways to move beyond “knowing” to actually “doing” when it comes to developing great leaders.Key Notes and topics we cover in this episode:The Nature of LeadershipPreparing Future LeadersChallenges in Corporate LeadershipTalent Management vs. Talent DevelopmentThe Next Paradigm Shift in Talent: AIAI and Tools in Talent DevelopmentThe “Brickster Experience” at DatabricksPsychological Safety and Growth MindsetManager and Leadership Development ModelsCareer Development PhilosophyLessons Learned & ReflectionsTalent Development TrendsRecommended ResourcesCareer Advice for Talent ProfessionalsThis episode is also sponsored by LearnIt, which is offering a FREE trial of their TeamPass membership for you and up to 20 team members of your team. Check it out here.Connect with Andy here: Website | LinkedInConnect with Andrew Wilhelms here: LinkedInOrder my new book, Own Your Brand, Own Your Career on AmazonAnd my first book, Own Your Career Own Your Life, is on Amazon as well.

S&P futures are down (0.3%) and pointing to a slightly lower open today. Asian equities ended Tuesday trading broadly lower, with the Nikkei leading the declines, down over (3%), followed by the Greater China markets. Markets saw steep losses in large-cap tech and semiconductors ahead of NVIDIA's earnings on Wednesday. Concerns are mounting over high valuations in AI-related stocks, a key driver of this year's market rally. European markets are also sliding now, with the STOXX 600 down (1.2%). Companies Mentioned: NVIDIA, Axalta Coating Systems, Databricks

In this special episode of Cloud Wars Live, Bob Evans speaks with Chad Wahlquist, Architect at Palantir, about the company's explosive Q3 growth and the accelerating adoption of its AI Platform (AIP). They explore how AIP serves as an operating system for the enterprise, enabling customers to achieve global optimization, faster ROI, and model flexibility. Wahlquist also talks about Palantir's open, interoperable architecture and its commitment to delivering value at speed, especially for customers in high-stakes, high-pressure environments.Operate Smarter, Not SlowerThe Big Themes:Speed to Value: Many companies still operate under the assumption that meaningful transformation requires multi‑year timelines (two to three years, sometimes more). Palantir is pushing the idea that you must deliver value in months, three to six months, rather than years. This shift is critical because when business markets move fast, and when competitive advantage erodes quickly, speed becomes a differentiator. If you wait for years, you may miss the window or be out‑paced.Interoperability and Ecosystem Integration: The platform isn't trying to lock you into a “box” you must keep your data in; it instead emphasizes plug‑in interoperability with systems you already have. Wahlquist mentions connectors, SDKs, APIs, and plug‑ins to partners like Snowflake, Databricks, SAP, NVIDIA. The concept: if you already have investment in some systems, don't throw them away; just connect them. This increases the speed to value and reduces friction.Ambition, Willingness to Operate in Crisis: Wahlquist points out they often engage with customers who are under pressure. These customers need value now, not two or three years out. Situations like supply chain disruption, plant outages, labor issues, etc., are real. This situational urgency forces companies to adopt architectures and partners that can deliver now. The takeaway: It's not enough to believe you'll transform in the future; transformation architecture must be built for today's fires.The Big Quote: “Our goal is really: how do we scale our customers and the outcomes they're delivering — not just the number of customers?"More from Chad and Palantir:Follow Chad on LinkedIn or get an overview of Palantir's Q3 in its letter to shareholders. Visit Cloud Wars for more.

Wisdom AI sells to enterprise data teams, empowering them to deploy AI data analysts that automate analytics functions traditionally handled by human analysts. As a former Rubrik co-founder and Google search ranking engineer, Soham identified the analytics problem firsthand while scaling Rubrik from intuition-driven to data-driven operations. In this episode of Category Visionaries, Soham shares how four Rubrik alumni are building a category-defining solution in the data analytics space, the tactical insights from targeting mid-market accounts to optimize deal velocity and onboarding experience, and how AI buying committees shifted from experimental budgets in 2024 to gatekeepers requiring departmental champions in 2025. Topics Discussed: Leveraging mid-market focus to compress sales cycles while refining onboarding as core product differentiation The transition from gut-based decisions to data-driven operations and why analytics remains unsolved Taming LLMs for precision and explainability requirements in enterprise analytics contexts Strategic navigation of the data ecosystem following the FiveTran-DBT merger and positioning against Snowflake, Databricks, and cloud providers Overlaying product-led trial motions on enterprise sales to maintain momentum during extended procurement cycles AI committee evolution from 2024's experimental phase to 2025's security-focused consolidation mandate Pursuing 10x productivity gains versus incremental improvement in established analytics markets GTM Lessons For B2B Founders: Use mid-market to build onboarding velocity as moat: Rubrik deliberately targeted mid-market accounts despite being an enterprise product that closed eight-figure deals. This served two strategic purposes: compressed sales cycles enabled faster learning loops, and the necessity of quick onboarding forced the team to build exceptional admin experiences that became their primary differentiation. For B2B founders, mid-market isn't just easier logos—it's a forcing function for product refinement that creates competitive advantages when moving upmarket. Find problems through operational scar tissue, not market research: Wisdom AI originated when Soham tried moonlighting as engineering's data analyst during Rubrik's scaling phase and discovered he couldn't do it effectively. This wasn't a customer interview insight—it was firsthand recognition that even sophisticated technical leaders with dedicated focus couldn't wrangle data for operational decisions. The problem proved ubiquitous across every business leader optimizing top line, bottom line, and operations. B2B founders building for enterprises should prioritize pain points they've personally hit in operational contexts where existing solutions demonstrably failed them. Engineer time-to-value in minutes for PLG overlay on enterprise sales: Wisdom AI's experiential quality—users get excited when they try it, not when they see slides—creates PLG opportunity despite enterprise positioning. The critical difference: sales-led motions tolerate weeks to first value and build confidence through process, but self-serve requires hook-to-value in minutes with zero support. Soham's insight is using PLG not for credit card swipes but to maintain champion enthusiasm during lengthy procurement processes. B2B founders should architect trial experiences that deliver standalone value pre-data connection, creating internal advocates who sustain momentum through AI committee reviews. Treat ecosystem navigation as first-class GTM workstream: Wisdom AI's success depends on partnership execution with Snowflake, Databricks, and cloud providers—all potential competitors with their own AI initiatives. The FiveTran-DBT merger created immediate dynamic shifts requiring repositioning. Rather than viewing partnerships as business development, Soham frames ecosystem navigation as core GTM infrastructure requiring dedicated strategy and repeatable playbooks. B2B founders in platform-adjacent spaces should staff for partnership complexity early, recognizing that integration points and co-selling motions often determine market access more than direct sales capacity. Architect for AI committee gatekeepers with departmental executive sponsorship: The market fundamentally shifted from mid-2024's "experimental AI budgets, try everything" to 2025's centralized AI committees focused on security, tool consolidation, and preventing organizational wild west scenarios. Soham's tactical response: secure champions owning specific important departments who can navigate approval hierarchies while trial experiences maintain grassroots excitement. The implication for B2B AI founders—assumption of longer cycles, security scrutiny as table stakes, and explicit strategies for climbing from individual enthusiast to organizational deployment become non-negotiable enterprise sales requirements. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Unlocking Healthcare Data: Expert Insights from Jason Bryll of Parable AssociatesIn this episode, host Josh Elledge interviews Jason Bryll, Founder and CEO of Parable Associates, a company helping healthcare organizations harness their data for growth, efficiency, and better patient outcomes. Jason discusses how healthcare's complex data systems—spanning patients, payers, and providers—can be transformed into powerful strategic assets. The conversation explores modern data infrastructure, AI adoption, and how healthcare leaders can turn information into action.Turning Healthcare Data into a Strategic AdvantageJason explains that healthcare organizations deal with some of the most fragmented and regulated data ecosystems of any industry. With multiple payment sources, strict HIPAA compliance, and disconnected systems, many practices struggle to translate data into actionable insights. Through Parable Associates, Jason helps clients design robust, scalable data infrastructures that unify information from EHRs, practice management tools, and claims systems to create real-time visibility into operations.He highlights how modern tools like Microsoft Fabric, Databricks, and Snowflake are transforming how providers process and analyze information. By automating data pipelines, implementing AI-driven analytics, and empowering teams with dashboards, healthcare organizations can reduce claim denials, improve revenue cycles, and accelerate cash flow. Jason emphasizes that true transformation begins with data governance—ensuring clean, accessible, and secure data before layering on advanced analytics.Ultimately, Parable's approach is rooted in measurable ROI. From reducing days sales outstanding (DSO) to uncovering revenue bottlenecks, their systems deliver tangible results. Jason advises healthcare executives to start with a specific business problem—such as cash flow or patient scheduling—and build data strategies around it. “Better reporting is like giving staff a utility belt,” he says. “It equips them to solve problems faster and with more confidence.”About Jason BryllJason Bryll is the Founder and CEO of Parable Associates, where he helps large healthcare organizations transform fragmented data into operational insights. With deep experience in analytics, systems design, and healthcare finance, Jason has worked with providers nationwide to improve data quality, cash flow, and performance through tailored, scalable solutions.About Parable AssociatesParable Associates specializes in healthcare analytics and data engineering, helping medical groups, hospitals, and specialty providers build scalable data ecosystems. The firm designs client-owned systems that unify data across clinical, operational, and financial functions, delivering clear insights and measurable ROI. Learn more at parableassociates.com.Links Mentioned in This EpisodeParable AssociatesConnect with Jason Bryll on LinkedInKey Episode HighlightsWhy healthcare data is uniquely complex and siloedHow to build scalable, secure data infrastructureUsing analytics to improve cash flow and reduce claim delaysPractical applications of AI and modern BI platformsHow Parable Associates delivers measurable ROI through dataActionable advice for healthcare leaders and data teamsConclusionJason Bryll's insights...

When “Normal” Doesn't Work: Rethinking Data and the Role of the SOC AnalystMonzy Merza, Co-Founder and CEO of Crogl, joins Sean Martin and Marco Ciappelli to discuss how cybersecurity teams can finally move beyond the treadmill of normalization, alert fatigue, and brittle playbooks that keep analysts from doing what they signed up to do—find and stop bad actors.Merza draws from his experience across research, security operations, and leadership roles at Splunk, Databricks, and one of the world's largest banks. His message is clear: the industry's long-standing approach of forcing all data into one format before analysis has reached its limit. Organizations are spending millions trying to normalize data that constantly changes, and analysts are paying the price—buried under alerts they can't meaningfully investigate.The conversation highlights the human side of this issue. Analysts often join the field to protect their organizations, but instead find themselves working on repetitive tickets with little context, limited feedback loops, and an impossible expectation to know everything—from email headers to endpoint logs. They are firefighters answering endless 911 calls, most of which turn out to be false alarms.Crogl's approach replaces that normalization-first mindset with an analyst-first model. By operating directly on data where it lives—without requiring migration or schema alignment—it allows every analyst to investigate deeper, faster, and more consistently. Each action taken by one team member becomes shared knowledge for the next, creating an adaptive, AI-driven system that evolves with the organization.For CISOs, this means measurable consistency, auditability, and trust in outcomes. For analysts, it means rediscovering purpose—focusing on meaningful investigations instead of administrative noise.The result is a more capable, connected SOC where AI augments human reasoning rather than replacing it. As Merza puts it, the new normal is no normalization—just real work, done better.Watch the full interview and product demo: https://youtu.be/7C4zOvF9sdkLearn more about CROGL: https://itspm.ag/crogl-103909Note: This story contains promotional content. Learn more.GUESTMonzy Merza, Founder and CEO of CROGL | On LinkedIn: https://www.linkedin.com/in/monzymerza/RESOURCESLearn more and catch more stories from CROGL: https://www.itspmagazine.com/directory/croglBrand Spotlight: The Schema Strikes Back: Killing the Normalization Tax on the SOC: https://brand-stories-podcast.simplecast.com/episodes/the-schema-strikes-back-killing-the-normalization-tax-on-the-soc-a-corgl-spotlight-brand-story-conversation-with-cory-wallace [Video: https://youtu.be/Kx2JEE_tYq0]Are you interested in telling your story?▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full▶︎ Spotlight Brand Story: https://www.studioc60.com/content-creation#spotlight Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Informed buyers believe they don't need sellers - and in many cases, they're right. In this episode, Sarah Branfman, Global VP of ISV Sales & GTM at Databricks, explains how buying behavior has fundamentally shifted, why traditional selling fails in complex B2B sales, and what elite sellers must do to create value buyers can't get on their own. Explore more insights: www.globalperformancegroup.com Timestamps: 00:00 – “We don't need a rep anymore.” The hard truth 02:45 – From ballerina to VP: Sarah's nonlinear career path 06:18 – The rise of the informed buyer in complex B2B sales 09:40 – How modern buyers want to buy (and why sellers resist it) 12:52 – Comfort-zone selling and losing deals you could have won 18:10 – Value-based selling, decision-making psychology, and the cost of inaction 20:55 – Ruthless qualification vs. the hope-based pipeline 26:30 – Discovery never ends: re-qualifying through the buying journey 29:02 – Provocative questioning and generating unconsidered needs 33:40 – The 3 traits of elite sellers: drive, curiosity, coachability Modern selling isn't about pressure — it's about enabling informed decision-making through sharper sales discovery, stronger sales enablement, and real business insight. In this episode, Harry Kendlbacher sits down with Sarah, to explore the mindsets and behaviors top sellers use to stay relevant and win in today's complex B2B landscape. You'll learn: – Why buyers feel they don't need sellers – How to win them back with insight-driven conversations – How elite sellers qualify and re-qualify throughout the buying journey – How decision-making psychology and cost of inaction shape urgency Key Takeaways: • Buyers aren't distrustful — they're independent. Sellers must add value beyond what buyers can research or ask AI. • In complex B2B sales, discovery and qualification never end — every new stakeholder resets the process. • The cost of inaction is often a stronger driver of urgency than ROI. • Value-based selling works only when sellers provoke new insights buyers haven't considered. • Elite sellers share three traits: relentless drive, deep curiosity, and coachability. About Guest: Sarah Branfman is the Global VP of ISV Sales & Go-To-Market at Databricks, where she leads strategic partnerships with the world's leading software and data companies. With deep experience in hyper-growth environments like MongoDB and Databricks, Sarah brings a modern, practical perspective on selling to the informed buyer in complex B2B environments. Connect with Sarah on LinkedIn: https://www.linkedin.com/in/sarahbranfman/ If this episode sparked new thinking, share it with your team. Subscribe for weekly insights on modern selling, leadership, and performance. Explore more at www.globalperformancegroup.com

Marc Barnes believes that AI chatbots are evil, and that having a faux conversation with AI chatbots is wrong. Joseph Hobbs, Senior Solution Architect at Databricks, disagrees. In this podcast, they discuss the nature of conversation, how AI imitates conversation, and the Catholic response to AI Chatbots.

Soham Mazumdar, CEO and Co-Founder of WisdomAI, discusses how organizations can break free from the "drowning in data but starving for insights" paradox that plagues modern enterprises. We explore his journey from Google's TeraGoogle project to co-founding and scaling Rubrik through its $5.6 billion IPO, and why he left that success to build an agentic AI approach to Business Intelligence (BI) that transforms how businesses extract value from their data investments.SHOW: 971SHOW TRANSCRIPT: The Cloudcast #963 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTES:WisdomAI websiteTopic 1 - Welcome to the show, Soham. We overlapped briefly at Rubrik. Give everyone a quick introduction and tell everyone a bit about your time at Google prior to RubrikTopic 2 - You helped scale Rubrik from inception to a $5.6 billion IPO in 2024. What was the "aha moment" that made you leave that success to tackle the enterprise data analytics problem with WisdomAI?Topic 3 - Let's define the core problem. Organizations invest heavily in modern data platforms - Snowflake, Databricks, etc. - but there is the term "drowning in data but starving for insights." What's broken in the traditional BI stack that prevents business users from getting answers?Topic 4 - How do agentic AI and BI fit together? WisdomAI introduces the concept of "Knowledge Fabric" and agentic data insights. Break this down for us - how does this fundamentally differ from traditional dashboards and BI tools?Topic 5 - One of the biggest challenges with GenAI in enterprise settings is hallucination. You've emphasized that WisdomAI separates GenAI from answer generation. How does your approach tackle this critical trust issue?Topic 6 - Let's talk about data integration complexity. Your platform works with both structured and unstructured data - Snowflake, BigQuery, Redshift, but also Excel, PDFs, PowerPoints. How do you handle this "dirty" data reality that most enterprises face?Topic 6a - With so much data, how do most organizations get started? What's a typical use case for adoption?Topic 7 - If anyone is interested, what's the best way to get started?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Dhanji R. Prasanna is the chief technology officer at Block (formerly Square), where he's managed more than 4,000 engineers over the past two years. Under his leadership, Block has become one of the most AI-native large companies in the world. Before becoming CTO, Dhanji wrote an “AI manifesto” to CEO Jack Dorsey that sparked a company-wide transformation (and his promotion to CTO).We discuss:1. How Block's internal open-source agent, called Goose, is saving employees 8 to 10 hours weekly2. How the company measures AI productivity gains across technical and non-technical teams3. Which teams are benefiting most from AI (it's not engineering)4. The boring organizational change that boosted productivity even more than AI tools5. Why code quality has almost nothing to do with product success6. How to drive AI adoption throughout an organization (hint: leadership needs to use the tools daily)7. Lessons from building Google Wave, Google+, and other failed products—Brought to you by:Sinch—Build messaging, email, and calling into your product: https://sinch.com/lennyFigma Make—A prompt-to-code tool for making ideas real: https://www.figma.com/lenny/Persona—A global leader in digital identity verification: https://withpersona.com/lenny—Where to find Dhanji R. Prasanna:• LinkedIn: https://www.linkedin.com/in/dhanji/—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 Dhanji(05:26) The AI manifesto: convincing Jack Dorsey(07:33) Transforming into a more AI-native company(12:05) How engineering teams work differently today(15:24) Goose: Block's open-source AI agent(20:18) Measuring AI productivity gains across teams(21:38) What Goose is and how it works(32:15) The future of AI in engineering and productivity(37:42) The importance of human taste(40:10) Building vs. buying software(44:08) How AI is changing hiring and team structure(53:45) The importance of using AI tools yourself before deploying them(55:13) How Goose helped solve a personal problem with receipts(58:01) What makes Goose unique(59:57) What Dhanji wishes he knew before becoming CTO(01:01:49) Counterintuitive lessons in product development(01:04:56) Why controlled chaos can be good for engineering teams(01:08:07) Core leadership lessons(01:13:36) Failure corner(01:15:50) Lightning round and final thoughts—Referenced:• Jack Dorsey on X: https://x.com/jack• Block: https://block.xyz/• Square: https://squareup.com/• Cash App: https://cash.app/• What is Conway's Law?: https://www.microsoft.com/en-us/microsoft-365-life-hacks/organization/what-is-conways-law#• Goose: https://github.com/block/goose• Gosling: https://github.com/block/goose-mobile• Salesforce: https://www.salesforce.com/• Snowflake: https://www.snowflake.com/• Claude: https://claude.ai/• 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• OpenAI: https://openai.com/• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Llama: https://www.llama.com/• Cursor: https://cursor.com/• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Top Gun: https://www.imdb.com/title/tt0092099/• Lenny's vibe-coded Lovable app: https://gdoc-images-grab.lovable.app/• Afterpay: https://github.com/afterpay• Bitkey: https://bitkey.world/• Proto: https://github.com/proto-at-block• Brad Axen on LinkedIn: https://www.linkedin.com/in/bradleyaxen/• Databricks: https://www.databricks.com/• Carl Sagan's quote: https://www.goodreads.com/quotes/32952-if-you-wish-to-make-an-apple-pie-from-scratch• Google Wave: https://en.wikipedia.org/wiki/Google_Wave• Google Video: https://en.wikipedia.org/wiki/Google_Video• Secret: https://en.wikipedia.org/wiki/Secret_(app)• Alien Earth on FX: https://www.fxnetworks.com/shows/alien-earth• Slow Horses on AppleTV+: https://tv.apple.com/us/show/slow-horses/umc.cmc.2szz3fdt71tl1ulnbp8utgq5o• Fargo TV series on Prime Video: https://www.amazon.com/Fargo-Season-1/dp/B09QGRGH6M• Steam Deck OLED display: https://www.steamdeck.com/en/oled• Doc Brown: https://backtothefuture.fandom.com/wiki/Emmett_Brown—Recommended books:• The Master and Margarita: https://www.amazon.com/Master-Margarita-Mikhail-Bulgakov/dp/0802130119• Tennyson Poems: https://www.amazon.com/Tennyson-Poems-Everymans-Library-Pocket/dp/1400041872/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.My biggest takeaways from this conversation: To hear more, visit www.lennysnewsletter.com

Monzy Merza (@monzymerza, CEO/Founder @Crogl) talks about build a next-generation Enterprise SOC by leveraging AI to stay ahead of Cybersecurity threats.SHOW: 969SHOW TRANSCRIPT: The Cloudcast #969 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwNEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS" SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTES:Crogl websiteTechCrunch articleForbes ArticleIntellyx ArticleLast WatchDog ArticleTopic 1 - Welcome to the show, Monzy. Give everyone a brief introduction and tell us about your unique journey from government research to Splunk to Databricks to founding Crogl.Topic 2 - Let's start with the current state of cybersecurity and AI. We're seeing headlines about AI being the top cybersecurity concern for 2025, even overtaking ransomware. From your perspective, what's driving this shift and why should organizations be paying attention to the intersection of cybersecurity and AI?Topic 3 - You've described Crogl as an "Iron Man suit" for security analysts. That's a compelling metaphor. Can you break down what you mean by that and how your approach differs from the traditional "reduce alerts" mentality that most vendors have been pushing?Topic 4 - Let's talk about your "knowledge engine" and what you call an “AI for the Enterprise SOC”. You're using compound AI systems with LLMs, smaller models, and knowledge graphs. This sounds quite different from vendors who are just "bolting on" LLMs to existing tools. Walk us through this architectural decision and why it matters.Topic 5 - The cybersecurity industry is experiencing massive alert fatigue - 4,500 alerts per day, with analysts only able to investigate 8-25 of them. Your philosophy is "every alert should be analyzed" rather than filtering them out. That seems counterintuitive to what the market has been doing. How does your autonomous investigation approach actually work in practice?Topic 6 - Where do you see this evolution heading, and what are the implications for SOC teams and security practitioners? Are we heading toward fully autonomous SOCs?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodI

Ben Horowitz founded Loudcloud in the middle of the dot-com bust and sold it for $1.6 billion, then led Andreessen Horowitz from its founding to $46 billion in committed capital. Ali Ghodsi co-founded Databricks, stepped in as CEO during a crisis, and led it to a valuation of over $100 billion.In this episode of “Boss Talk”, Ben and Ali join a16z General Partners Sarah Wang and Erik Torenberg to share founder war stories, how to hire and make deals, how to keep culture intense without burning employees out, and why founders should raise their ambitions even higher. ResourcesFollow Ali on X: https://x.com/alighodsiLearn more about Databricks: https://www.databricks.com/Follow Ben on X: https://x.com/bhorowitzFollow Sarah on X: https://x.com/sarahdingwangFollow Erik on X: https://x.com/eriktorenberg Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Resources:Find a16z on X: https://x.com/a16zSubscribe to a16z on Substack: https://a16z.substack.com/Find a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenberg Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.