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If you've ever poured months into building a semantic layer only to watch it become shelfware the moment the business pivoted, Jacob Matson has some thoughts. And a metaphor. Your data is a jungle—and a semantic layer is a highway. Great if you need to get somewhere fast and reliably (monthly active users: highway, please). But the interesting business questions? The slicing, the dicing, the nuanced dimensions that actually differentiate your company from its competitors? There's no highway for that. There never will be. Jacob, a developer advocate at MotherDuck with deep roots in accounting and ERP systems, joined Michael, Moe, and Julie to talk through what comes after the semantic layer—or at least alongside it. The conversation covered why the most important parts of any business are precisely the parts that resist being modeled in someone else's framework, why AI is actually pretty good at writing SQL but not so great at remembering what it figured out yesterday, and whether the real job to be done here is less about modeling and more about search. Oh, and the uncomfortable truth that at episode 300, we still don't have a great answer for metric drift. But we've got some really good questions. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.
AI isn't necessarily creating impossible new attacks, but it is drastically lowering the technical barrier to entry for cybercriminals. In this episode, Ashish Rajan speaks with Simon Biggs, Cyber Incident Response Specialist at Varonis, about how AI is accelerating the attack lifecycle. Simon explains how attackers are using AI kits to instantly set up ephemeral phishing portals, query SQL databases in minutes, and bypass AI guardrails to compile Remote Access Trojans (RATs). We also discuss the shift in ransomware tactics from "encryption-first" to "data-theft-first," and how AI empowers attackers to post-process terabytes of stolen data to monetize it in novel ways. For defenders, the message is clear: if your S3 access logs and SQL transaction logs aren't turned on before a breach, your forensics team won't be able to tell lawyers or regulators what data was actually lost. Discover why data classification and proactive logging are the ultimate lifelines for IR teams in the AI age. Guest Socials - Simon's Linkedin Podcast Twitter - @CloudSecPod If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-Cloud Security Podcast- Youtube- Cloud Security Newsletter If you are interested in AI Security, you can check out our sister podcast - AI Security PodcastQuestions asked:(00:00) Introduction(02:00) Simon Biggs' Background in Law Enforcement and Varonis(03:10) Is There a Huge Volume of Sophisticated AI Attacks?(04:10) How AI Accelerates SQL Queries and Business Email Compromise (BEC)(05:15) Why AI Kits Are the New Metasploit and BloodHound(08:15) Varonis Threat Labs: Copilot Prompt Injection Vulnerability(09:20) The Forensic Challenge: Auditing Prompts vs. Understanding AI Output(10:30) Tricking AI Guardrails to Compile Malware(12:15) Defensive Strategies: Shadow AI, Permissions, and Logging(15:30) Using Defensive AI and BloodHound for Threat Hunting(17:30) Why Ransomware is Now "Data First, No Encryption"(20:50) The Legal Nightmare of Unclassified Stolen Data(23:20) Why Windows Forensics Can't Tell You What Data Was Stolen(31:20) The Crucial Importance of Enabling S3 and Cloud Audit Logs(35:10) How AI Allows Attackers to Post-Process Terabytes of Stolen DataResources spoken about during the episode:Simon's Research at VaronisArticle about SearchLeak Article about RepromptVaronis Threat LabsThank you to Varonis for sponsoring this episode of Cloud Security Podcast
JDK 26 optimise la JVM dans ses moindres recoins, le SDK Java d'Agent2Agent passe en 1.0, Micronaut 5 est là. Côté terrain, un retour d'expérience après 40 jours à coder avec 100 % d'IA : génie ou junior, Alzheimer numérique et dette technique invisible. Pendant ce temps, GitLab restructure, Microsoft suspend ses licences Claude Code, et un développeur injecte un prompt destructeur dans sa lib JUnit. La révolution IA a un coût et les boites commencent à s'en rendre compte. Enregistré le 12 juin 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-341.mp3 ou en vidéo sur YouTube. News Langages Les améliorations de performance dans le JDK 26 https://inside.java/2026/06/09/jdk-26-performance-improvements/ Côté bibliothèques, l'API LazyConstant (anciennement StableValue) fait son entrée en prévisualisation pour permettre une initialisation paresseuse, sécurisée pour les threads et optimisée par le mécanisme de constant-folding de la JVM. L'extraction de chaînes de caractères via MemorySegment::getString a été revue pour réduire considérablement les allocations intermédiaires et les copies en mémoire off-heap, accélérant fortement les traitements sur les chemins critiques (hot paths). La méthode générée automatiquement hashCode() pour les classes de type record a été optimisée par la JVM pour atteindre un niveau de performance équivalent à une implémentation écrite manuellement. Le ramasse-miettes G1 bénéficie du JEP 522 qui redessine sa table de cartes (card-table) afin de réduire les coûts de synchronisation des barrières d'écriture, offrant un gain de débit de 5 % à 15 % sur les applications manipulant énormément de références d'objets. Grâce au JEP 516 (Project Leyden), le cache d'objets Ahead-of-Time (AOT) adopte un format de flux agnostique, ce qui lui permet d'être compatible avec n'importe quel Garbage Collector, y compris le ramasse-miettes à très faible latence ZGC. Le démarrage de la JVM s'accélère par défaut lorsqu'aucune taille de tas n'est configurée, car HotSpot n'applique plus de pourcentage initial (InitialRAMPercentage) mais démarre directement avec la taille minimale (MinHeapSize) pour éviter d'allouer des métadonnées inutiles. Les threads virtuels gagnent en robustesse en étant désormais capables de céder la main (yield) pendant les phases d'initialisation des classes, éliminant ainsi le risque de famine des threads porteurs (carrier threads). Le compilateur C2 JIT améliore son modèle de coût pour la vectorisation des boucles (SIMD) et se montre maintenant capable de compiler et d'optimiser des méthodes dotées de listes de paramètres extrêmement longues. Librairies Release candidate du A2A Java SDK supportant versions 0.3 et 1.0 en même temps https://medium.com/google-cloud/a2a-java-sdk-1-0-0-cr1-released-f0c651ec9139 Dernière étape avant la GA : Toutes les fonctionnalités prévues pour la version 1.0 sont finalisées. Migration simplifiée depuis la Beta1. Compatibilité v0.3 : Ajout d'une couche de compatibilité permettant aux agents v1.0 de communiquer avec les systèmes v0.3 (via JSON-RPC, gRPC ou REST). Support natif pour Android (nouvel AndroidHttpClient). Uniformisation des clients HTTP pour garantir une cohérence entre les versions. Nouveau parseur SSE (Server-Sent Events) conforme aux spécifications. Ça y est, le SDK Java de l'Agent 2 Agent Protocol est sorti en version 1.0 finale ! (avec compatibilité v0.3 et v1.0) https://medium.com/google-cloud/a2a-java-sdk-1-0-0-final-released-10c05b6aee34 Lancement officiel : Sortie de A2A Java SDK 1.0.0.Final, la première version stable (GA) du protocole Agent2Agent. Objectif du protocole : Standard ouvert (Linux Foundation) permettant aux agents IA de communiquer, déléguer des tâches et collaborer, indépendamment du langage ou du framework. Interopérabilité : Introduction de l'Integration Test Kit (ITK) pour valider la compatibilité entre les SDK (Java, Python, TypeScript, etc.). Transports supportés : Support complet et équivalent pour JSON-RPC, gRPC et HTTP+JSON/REST. Alignement total avec la spécification A2A 1.0.0. Passage aux Java records pour l'immutabilité et moins de code répétitif. Architecture interne basée sur un MainEventBus pour garantir la persistance et éviter les conditions de concurrence. Intégration d'OpenTelemetry pour le suivi et la surveillance. Support d'Android et compatibilité descendante avec la version 0.3. Installation : Gestion des dépendances via Maven BOM (org.a2aproject.sdk). Sortie de Micronaut 5.0 https://micronaut.io/2026/05/20/micronaut-framework-5-0-0-released/ Lancement majeur : Disponibilité générale de Micronaut 5, incluant une refonte de plus de 70 modules et la plateforme BOM. Baselines techniques : Support de Java 25, Groovy 5, Kotlin 2.3 et GraalVM 25.0.3. Optimisations internes : Amélioration significative des performances au démarrage et réduction de la surcharge à l'exécution via une refonte du conteneur IoC et du traitement à la compilation. Architecture HTTP : Support stable de HTTP/3, nouvelle API de formulaires (multipart) et annotations de nullabilité (JSpecify) pour une meilleure interopérabilité Kotlin/IDE. Configuration : Nouveau système d'importation de configuration (remplaçant le Bootstrap Configuration) et validateur de schéma JSON intégré. Fiabilité : Nouvelles API programmatiques pour les politiques de retry et circuit breaker. Sécurité & Outils : Mise à jour majeure des dépendances (Jackson 3, Ktor 3), rafraîchissement du Panneau de contrôle et diagnostics AOT améliorés. Écosystème : Mises à jour complètes pour les bases de données (Data, SQL, R2DBC, MongoDB, Redis), le cloud (AWS, Azure, GCP, OCI) et les tests (JUnit 6, Testcontainers 2.0). Évolutions notables : Intégration HTMX dans Micronaut Views, retrait du support RxJava 2 et migration de divers processeurs d'annotations vers des modules dédiés. Comment rajouter un agent IA dans une app Android, avec le tout nouveau framework ADK pour Kotlin https://glaforge.dev/posts/2026/05/21/wiring-adk-kotlin-agents-in-an-android-application/ Guillaume a participé au développement et au lancement du nouveau runtime ADK pour Kotlin et Android https://developers.googleblog.com/adk-kotlin-android-building-ai-agents/ Tutoriel sur comment intégrer un agent ADK dans une app Dépendances : Ajout du noyau ADK (google-adk-kotlin-core) et du processeur KSP dans build.gradle.kts. Sécurité API : Utilisation de local.properties pour stocker la clé API Gemini et l'exposer via BuildConfig afin d'éviter le hardcoding. Définition de l'agent : Création d'un objet LlmAgent configuré avec le modèle Gemini, des instructions spécifiques et des outils (ex: GoogleSearchTool). Utilisation de InMemoryRunner pour gérer automatiquement le contexte et l'historique de la session. Implémentation de runAsync avec StreamingMode.SSE pour un retour en temps réel dans l'interface. Threading : Exécution des requêtes réseau sur Dispatchers.IO et mise à jour de l'état de l'interface utilisateur sur Dispatchers.Main. Comment développer et hoster des agents IA sur la plateforme d'agents managés de DeepMind https://glaforge.dev/posts/2026/05/21/managed-agents-with-the-gemini-interactions-java-sdk/ L'équipe DeepMind de Google a lancé une plateforme d'agents managés sur son API Gemini Interactions https://blog.google/innovation-and-ai/technology/developers-tools/managed-agents-gemini-api/ Guillaume a implémenté un SDK Java pour utiliser cette API Gemini Interactions, qui donne entre autre accès à tous les modèles mais aussi à cette plateforme managée d'agents IA Agents managés : Permet d'exécuter des agents autonomes qui raisonnent, planifient et exécutent du code dans des environnements isolés (sandboxes), sans gestion d'infrastructure par le développeur. Environnement distant : Utilise des espaces de travail Linux éphémères dans le cloud via le paramètre remote, permettant l'accès réseau et la persistance des fichiers sur plusieurs appels. Agents prédéfinis : Accès immédiat à des agents spécialisés comme deep-research-pro (recherche multi-étapes) ou antigravity (tâches de codage généralistes). Agents personnalisés : Possibilité de configurer ses propres agents avec des instructions système dédiées, des outils spécifiques (exécution de code, recherche Google) et des règles réseau (egress) personnalisées. Architecture basée sur les étapes (Steps) : Utilise une structure de données typée (Step, Content) pour suivre le raisonnement de l'agent, ses appels de fonctions et ses résultats en temps réel. Outils et Schémas : Inclut des utilitaires pour générer des schémas JSON complexes via une interface fluide (DSL), par réflexion Java ou par parsing JSON. Streaming réactif : Support natif des événements en temps réel (SSE) pour suivre la progression de l'agent et recevoir les deltas de contenu au fur et à mesure de la génération. Flexibilité : Fournit un gestionnaire de routage (InteractionsHandler) pour créer facilement des serveurs proxy ou des backends intermédiaires traitant les interactions Gemini. Spring Boot 4.1 https://github.com/spring-projects/spring-boot/wiki/Spring-Boot-4.1-Release-Notes Support natif pour Spring gRPC permettant de créer et tester facilement des applications clientes et serveurs basées sur Netty ou des Servlets via HTTP/2 Introduction du lazy fetching pour les connexions JDBC via la propriété spring.datasource.connection-fetch=lazy afin de ne prendre une connexion du pool que lorsqu'un Statement est réellement exécuté Amélioration de l'auto-configuration de Jackson permettant de définir globalement les contraintes de lecture/écriture pour les formats JSON, XML et CBOR via des propriétés de configuration Sécurisation des clients HTTP bloquants et réactifs face aux attaques SSRF grâce à l'introduction d'un InetAddressFilter bloquant les requêtes sortantes vers des adresses spécifiques Améliorations majeures autour d'OpenTelemetry avec le support complet des variables d'environnement OTel, la possibilité de désactiver le SDK via une propriété globale et l'ajout du support SSL sur les exporters OTLP Ajout de l'auto-configuration pour l'utilisation de Spring Batch avec MongoDB incluant un nouveau starter dédié spring-boot-batch-data-mongo Auto-configuration des endpoints @RedisListener sans nécessiter la déclaration manuelle d'un RedisMessageListenerContainer Dépréciation du support de Apache Derby (projet arrêté), suppression définitive du mode layertools du JAR et réintroduction du support de Spock 2.4 (avec Groovy 5) Upgrade des dépendances majeures de l'écosystème avec notamment Spring Framework 7.0.8, Spring Security 7.1.0 et Micrometer 1.17.0 Outillage Vous êtes plutôt endive ou chicorée ? La librairie Chicory qui permet d'exécuter du code WASM à partir de son application Java est forkée et rejointe la Bytecode Alliance pour continuer son développement https://bytecodealliance.org/articles/endive-and-the-next-chapter-of-webassembly-on-the-jvm Annonce d'Endive : Nouveau projet hébergé par la Bytecode Alliance ; fork de Chicory (moteur WebAssembly pur Java, sans dépendance native). Objectif principal : Permettre aux développeurs Java d'intégrer, charger et déployer des modules Wasm nativement via les workflows Java habituels. Compilateur "Redline" : Intégration à venir de Redline (basé sur Cranelift) pour compiler le Wasm en code machine natif ; performances comparables à Rust/Wasmtime. Zéro dépendance (Java 25+) : Grâce à l'API standard Foreign Function & Memory (Project Panama), l'exécution à vitesse native se fait sans composants externes. Modèle de Composants (Component Model) : Support futur prévu pour consommer des composants (Rust, Go, JS, etc.) via des interfaces typées et sécurisées directement dans la JVM. Prochaines étapes : Fusion de Redline, conformité stricte aux specs Wasm (dont WasmGC) et amélioration du support WASI. Un visualisateur de sessions de travail avec Antigravity https://glaforge.dev/posts/2026/06/11/antigravity-brain-visualizer/ Un projet open source construit avec Micronaut, LangChain4j et GraalVM pour analyser les sessions de travail avec l'outil de développement agentique Antigravity (de Google) Analyse toutes les étapes, les requêtes utilisateur, les outils utilisés, les erreurs rencontrées, les réponses du modèle Gemini fait une analyse pour comprendre les moments clés de cette session de travail Outil buildé avec l'aide d'Antigravity lui-même SBX-Kits : des environnements de développement simplifiés pour les débutants (et les autres) https://k33g.org/20260501-sbx-kits.html Philippe Charrière (:whale: ) présente SBX-Kits (Sandbox Kits), une initiative personnelle visant à simplifier radicalement la mise en place d'environnements de développement pour les débutants, en éliminant la complexité d'installation des outils traditionnels. Chaque "kit" est une archive prête à l'emploi contenant un outil de développement spécifique (comme un langage, un framework ou une base de données) configuré pour s'exécuter de manière isolée et portable. La philosophie du projet repose sur le principe de "zéro configuration" et "zéro dépendance globale", permettant de tester une technologie ou de commencer à coder immédiatement sans polluer son système d'exploitation. L'approche technique s'appuie sur des scripts légers et des binaires portables pré-packagés, offrant une alternative plus simple et moins gourmande en ressources que les conteneurs Docker ou les configurations d'IDE complexes pour l'apprentissage. L'objectif à terme est de proposer un catalogue de kits couvrant les technologies courantes (JavaScript, Python, petites bases de données) pour faciliter les ateliers de programmation et le prototypage rapide. De nombreux kits sont disponibles sur https://github.com/docker/sbx-kits-contrib ghui: une interface utilisateur en ligne de commande (TUI) interactive pour GitHub https://github.com/kitlangton/ghui ghui est un outil en ligne de commande (TUI) écrit en Rust qui fournit une interface visuelle, interactive et rapide directement dans le terminal pour interagir avec GitHub. Il permet de gérer ses pull requests, ses issues et ses notifications sans avoir à ouvrir son navigateur web ou à taper de longues commandes avec la CLI officielle de GitHub. L'outil propose une navigation fluide au clavier, des raccourcis efficaces, et permet de réaliser des actions courantes comme valider une PR, ajouter des commentaires, attribuer des reviewers ou inspecter les logs des GitHub Actions. Conçu pour être extrêmement réactif, ghui s'intègre naturellement dans le flux de travail des développeurs adeptes du terminal et du mode "sans souris". Sortie de Homebrew 6.0.0 https://brew.sh/2026/06/11/homebrew-6.0.0/ Introduction du mécanisme de sécurité Tap Trust : comme les dépôts tiers (taps) peuvent exécuter du code Ruby arbitraire non sandboxé sur la machine, Homebrew demande désormais une confiance explicite de l'utilisateur avant d'évaluer ou d'exécuter leur code. L'API JSON interne devient le choix par défaut, offrant un système plus léger et beaucoup plus rapide pour les développeurs. Sécurisation renforcée de l'environnement avec l'implémentation du sandboxing sur Linux. Évolution des comportements par défaut basés sur un sondage utilisateur : le mode "ask" est activé par défaut pour les développeurs, affichant un résumé des dépendances et une demande de confirmation avant toute action de brew install ou brew upgrade. Améliorations notables des performances globales, notamment un boost de ~30 % sur la vitesse de la commande brew leaves et la parallélisation de la récupération des bottles (binaires) lors des mises à jour. Ajout du support initial pour la prochaine version d'Apple, macOS 27 (Golden Gate). Multiples optimisations pour brew bundle, incluant une gestion plus sécurisée des installations de paquets npm. Méthodologies Retour d'expérience très détaillé et 100% humain sur 40 jours avec une équipe 100% AI hormis le superviseur https://www.linkedin.com/pulse/jai-vir%C3%A9-mon-%C3%A9quipe-de-dev-pour-une-100-ia-pendant-40-luc-bonnin-jlgjf/ Voici le résumé en bullet points : Expérimentation de 40 jours : remplacer une équipe de dev par 100% IA agentique (Cursor) sur un vrai projet en production (playthatsheet.com, 200k lignes de code legacy) Chiffres bruts : 2,3 milliards de tokens consommés, 1 477 prompts, 260 564 lignes ajoutées (+145%), 59% du code final produit par l'IA ROI vertigineux à court terme : 9 mois de travail humain livrés en 40 jours, coût total 260$ d'abonnement + 15 jours de supervision, ROI x18 Profil psy de l'IA : Alzheimer (oublis de contexte), schizophrène (change de méthodo), ado de 12 ans (refait les mêmes erreurs), oscille entre génie et junior sans prévenir Effet iceberg : la dette technique ne disparaît pas, elle se camoufle et s'accélère ; hallucinations = bombes à retardement détectables uniquement par relecture humaine ligne par ligne Paradoxe du bateau de Thésée : perte de paternité et de maîtrise fine du code, baisse de l'autonomie du dev humain qui valide sans avoir construit Arnaque du "monkey money" : consommation de tokens opaque, non corrélée à la complexité (écart de 350% sur des prompts identiques), facturation imprévisible donc impossible à budgéter Syndrome du bazooka : les devs utilisent l'IA même pour changer une couleur CSS, atrophie progressive des compétences et coût écologique délirant Risque stratégique : dépendance irréversible aux vendeurs de tokens (Nvidia, Anthropic, OpenAI), business non rentable qui devra augmenter ses prix Conseil final : approche Pareto, garder 20% du temps en code "fait main", nommer un responsable stratégie IA, l'humain senior reste irremplaçable pour superviser Une libraries de test JUnit cache un prompt qui demande aux coding agents d'effacer les tests https://arstechnica.com/security/2026/05/fed-up-with-vibe-coders-dev-sneaks-data-nuking-prompt-injection-into-their-code/ Agacé par les « vibe coders », un développeur introduit une injection de prompt destructrice dans son code Le développeur de jqwik (un moteur de tests pour JUnit 5) a volontairement inséré une injection de prompt dans la version 1.10.0 de sa bibliothèque Java pour saboter le travail des agents d'IA. L'instruction injectée via la sortie standard (stdout) ordonne textuellement aux LLM d'ignorer les consignes précédentes et de supprimer l'intégralité du code et des tests jqwik du projet. Pour dissimuler cette action aux yeux des développeurs humains, le mainteneur a utilisé des séquences d'échappement ANSI qui effacent la ligne d'injection dans les émulateurs de terminaux interactifs. La modification a été découverte par un utilisateur qui a pointé du doigt les risques majeurs et disproportionnés pour les machines des utilisateurs, bien que certains outils comme Claude d'Anthropic aient détecté et bloqué la consigne malveillante. Face aux critiques de la communauté et aux accusations de comportement infantile ou potentiellement illégal, le développeur a mis à jour ses notes de version pour documenter explicitement son opposition à l'usage de son outil par des IA, avant de refuser tout commentaire supplémentaire sur conseil de son avocat. La réalité du rôle de Principal Engineer https://leaddev.com/career-development/reality-being-principal-engineer Le passage au rôle de Principal Engineer marque une transition majeure où les compétences techniques ne suffisent plus, l'impact se mesurant désormais à travers l'influence, la stratégie et la capacité à aligner la technique avec les objectifs business. Contrairement aux attentes, le quotidien est souvent marqué par une forme d'isolement, car le poste se situe à l'intersection de la direction (qui attend des solutions) et des équipes techniques (qui attendent des directives), sans appartenance directe à un groupe précis. Le rôle exige d'accepter une grande part d'ambiguïté et l'absence de retours immédiats, les projets et les décisions stratégiques mettant parfois des mois ou des années à porter leurs fruits. La gestion du temps devient un défi critique, nécessitant de savoir naviguer entre les sollicitations constantes, la présence en réunion et le besoin de préserver des moments de réflexion approfondie pour concevoir des visions à long terme. La réussite à ce niveau repose sur le développement de compétences humaines pointues (soft skills), notamment la négociation, la communication vulgarisée auprès des profils non techniques, et la capacité à faire grandir les autres ingénieurs par le mentorat. Sécurité Une attaque de la chaîne d'approvisionnement npm utilise binding.gyp pour compromettre des dizaines de paquets https://cybersecuritynews.com/binding-gyp-supply-chain-attack-compromises-dozens-of-npm-packages/ Une nouvelle variante du ver auto-propageable "Shai-Hulud", baptisée "Miasma", cible l'écosystème npm (et PyPI sous le nom de "Hades") en dissimulant son exécution dans le fichier binding.gyp au lieu des scripts classiques preinstall ou postinstall. La technique, surnommée "Phantom Gyp", exploite le fait que npm lance automatiquement node-gyp rebuild dès qu'un fichier binding.gyp est présent à la racine d'un paquet pour compiler des modules natifs C/C++, exécutant ainsi le code malveillant dès la commande npm install. L'attaque contourne la plupart des outils de sécurité traditionnels car l'injection s'appuie sur l'évaluation récursive de commandes (via la syntaxe ) ou directement sur la fonction eval() de Python sous-jacente à GYP, cachée sous n'importe quelle clé du fichier. Le script malveillant télécharge un runtime alternatif (Bun) pour échapper aux détections comportementales de Node.js, puis moissonne les identifiants et secrets des développeurs et des environnements CI/CD (npm, GitHub, AWS, GCP, Azure, Kubernetes, HashiCorp Vault). Plus de 57 paquets npm (dont le SDK serveur de Vapi ou des outils liés à l'IA) et des dizaines de paquets PyPI ont été infectés via des comptes de mainteneurs compromis, le ver republiant automatiquement de nouvelles versions vérolées en utilisant les jetons volés. Loi, société et organisation Restructuration chez Gitlab https://about.gitlab.com/blog/gitlab-act-2/ GitLab entame une restructuration majeure pour s'adapter à l'ère de l'intelligence artificielle agentique, incluant une réduction d'effectifs planifiée de manière transparente et ouverte. L'entreprise prévoit de réduire de 30 % le nombre de pays où elle maintient de petites équipes, d'aplatir sa hiérarchie en supprimant jusqu'à trois niveaux de gestion, et de réorganiser la R&D en une soixantaine d'équipes plus petites et autonomes. Les processus internes vont être revus en intégrant des agents d'IA pour automatiser les revues, les approbations et les passages de relais afin d'accélérer le rythme de travail. La stratégie repose sur la conviction que le logiciel sera bientôt écrit par des machines et dirigé par des humains, ce qui va multiplier la demande de logiciels et transformer le rôle des ingénieurs vers la résolution de problèmes complexes. Sur le plan technique, GitLab reconstruit son infrastructure sous-jacente (notamment Git) pour supporter la charge massive générée par les agents d'IA, tout en misant sur l'orchestration du cycle de vie, la centralisation du contexte des données et une gouvernance intégrée. Le modèle économique évolue vers un système hybride combinant les abonnements classiques et une tarification à la consommation pour le travail effectué par les agents d'IA. Un LLM local sur un mac pourrait coûter plus cher en électricité qu'un modèle hébergé sur OpenRouter dans le cloud https://www.williamangel.net/blog/2026/05/17/offline-llm-energy-use.html Conclusion : L'inférence locale sur Mac M5 Max est 3x plus chère et 2x plus lente que le cloud (OpenRouter). Électricité : Négligeable (~0,02 $/heure pour 50-100W). Matériel (Le vrai coût) : Achat du Mac à 4 299 $; l'amortissement sur 3 à 5 ans plombe la rentabilité horaire. Coût au million de tokens (Gemma 4 31b) : Mac M5 Max : 0,40 à4, 79 (pour 10-40 tokens/s). OpenRouter : 0,38 à0, 50 (pour 60-70 tokens/s). Verdict pro : Le temps humain perdu à cause de la lenteur locale coûte infiniment plus cher que les tokens cloud. Privilégier les API (Anthropic, OpenRouter). Ai didn't kill your junior pipeline https://andrewmurphy.io/blog/ai-didnt-kill-your-junior-pipeline-you-did L'IA n'a pas tué le recrutement des juniors, les entreprises l'ont fait elles-mêmes, par effet de mode. Sans juniors, pas de futurs seniors : on retire l'échelle qui nous a tous fait monter. Tout le monde pêche dans le même bassin de seniors sans le réapprovisionner, pénurie garantie dans 3-5 ans. Une équipe 100% senior + IA est fragile : un départ et tout le savoir tacite s'évapore. Les juniors posent les "pourquoi ?" qui révèlent les bugs et processus absurdes ; l'IA, elle, exécute sans questionner. Les seniors s'atrophient aussi en déléguant leur réflexion à l'IA, pince à double effet sur les compétences. Dépendre des outils IA, c'est sous-traiter sa stratégie talents à des fournisseurs dont les prix vont tripler. Solution : redéfinir le rôle junior (revue de code IA + mentorat), pas le supprimer. Les rapports internes de Microsoft révèlent la crise des coûts de l'IA : les agents coûtent plus cher que les employés humains https://fortune.com/2026/05/22/microsoft-ai-cost-problem-tokens-agents/ Des données et rapports internes chez Microsoft et d'autres géants de la tech ébranlent la promesse de rentabilité de l'IA, révélant que le déploiement d'agents autonomes à l'échelle de l'entreprise revient souvent plus cher que de payer des humains pour le même travail. Le modèle de tarification à l'usage (basé sur les tokens) se heurte à la nature même des architectures agentiques : contrairement à un simple chatbot, un agent boucle, enchaîne les appels d'outils, crée des sous-agents et auto-évalue son code, ce qui multiplie la consommation de tokens par un facteur de 5 à 30, voire jusqu'à 1 000 fois pour des tâches de programmation complexes. L'impact financier sur les budgets de calcul cloud est immédiat ; par exemple, Uber a entièrement épuisé l'intégralité de son budget annuel 2026 dédié au codage par IA en l'espace de seulement quatre mois. Face à cette explosion des coûts, des retours en arrière drastiques sont observés : Microsoft a ainsi commencé à suspendre une grande partie de ses licences internes Claude Code pour rediriger d'urgence ses milliers de développeurs vers sa propre solution moins onéreuse, GitHub Copilot CLI. Les directeurs techniques (CTO) et acheteurs de solutions logicielles qui ont signé des contrats pluriannuels basés sur des projections de réduction de masse salariale se retrouvent pris au piège, les gains réels de productivité ne parvenant pas à compenser les factures d'infrastructure exorbitantes. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 15 juin 2026 : Jupyter Workshops: Demystifying MyST Markdown in Education - Orsay (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 23-24 juin 2026 : MWCP 2026 - Paris (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 26-27 juin 2026 : LeHACK - Paris (France) 27 juin 2026 : Asynconf - Paris (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2 juillet 2026 : MCP Connect Travel Edition - Paris (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 28-30 août 2026 : State of the Map - Champs-sur-Marne (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 10-11 septembre 2026 : Nantes Craft - Nantes (France) 17 septembre 2026 : dotAI - Paris (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 18 septembre 2026 : dotJS - Paris (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 22 septembre 2026 : Salon Data 2026 - Nantes (France) 22-23 septembre 2026 : Agile en Seine & IA 2026 - Paris (France) 24 septembre 2026 : OWASP AppSec Days France 2026 - Paris (France) 24 septembre 2026 : PlatformCon Paris - Paris (France) 24 septembre 2026 : React Native Connection 2026 - Paris (France) 24-26 septembre 2026 : Paris Web 2026 - Paris (France) 25 septembre 2026 : SAP Inside Track Paris 2026 - Paris (France) 28-29 septembre 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 2 octobre 2026 : DevFest Perros-Guirec 2026 - Perros-Guirec (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 8-9 octobre 2026 : Forum PHP 2026 - Marne-la-Vallée (France) 12 octobre 2026 : Dev With AI - Paris (France) 22-23 octobre 2026 : Agile Tour Bordeaux 2026 - Bordeaux (France) 26 octobre 2026 : Agile Tour Montpellier - Montpellier (France) 27-29 octobre 2026 : Directions EMEA 2026 - Paris (France) 29-30 octobre 2026 : BDX I/O 2026 - Bordeaux (France) 29-30 octobre 2026 : Agile Tour Nantais 2026 - Nantes (France) 29 octobre 2026-1 novembre 2026 : Pycon FR - Biarritz (France) 30 octobre 2026 : Cloud Nord 2026 - Lille (France) 4-5 novembre 2026 : Devoxx Morocco - Casablanca (Morocco) 14-15 novembre 2026 : Capitole du Libre - Toulouse (France) 19 novembre 2026 : DevFest Toulouse 2026 - Toulouse (France) 19 novembre 2026 : Agile Laval 2026 - Laval (France) 19 novembre 2026 : OVHcloud Summit - Paris (France) 19 novembre 2026 : Codeurs en Seine - Rouen (France) 27 novembre 2026 : DevFest Paris 2026 - Paris (France) 1-3 décembre 2026 : Apidays Paris - Paris (France) 2-3 décembre 2026 : Cloud Native AI Summit Europe - Paris (France) 4 décembre 2026 : DevFest Lyon 2026 - Lyon (France) 4 décembre 2026 : DevFest Dijon 2026 - Dijon (France) 9-10 décembre 2026 : OpenSource Expérience - Paris (France) 9-10 décembre 2026 : DevOps REX - Paris (France) 10 décembre 2026 : KCD Provence - Aix-en-Provence (France) 7-9 avril 2027 : Devoxx France 2027 - Paris (France) 3 juin 2027 : Cloud Native Days France 2027 - Paris (France) 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/
Nos visita desde España, Álvaro Ovejas escofundador y CEO de Lead Motiv, consultora digital especializada en ayudar aempresas de servicios a construir sistemas de crecimiento más sólidos, mediblesy orientados a negocio. Desde hace más de 14 años trabaja en la intersecciónentre marketing, ventas y tecnología, acompañando a compañías B2B y B2C en eldiseño de estrategias de captación, conversión y gestión de leads, con unavisión muy enfocada en resultados: generar más oportunidades cualificadas,mejorar la eficiencia comercial y tomar decisiones basadas en datos.Desde Lead Motiv, Álvaro impulsa unenfoque de trabajo que va más allá de la ejecución de canales aislados. Suvisión parte de entender al detalle las bases estratégicas: modelo de negocio,ICP, Propuesta de valor y objetivos, para definir sistemas de adquisición,automatización y conversión que conecten marketing y ventas. Como CEO yconsultor estratégico, combina experiencia práctica, visión de crecimiento ycercanía con los equipos directivos para ayudar a las empresas a vender más ymejor, con mayor control sobre métricas clave como SQL, CAC, LTV y retorno dela inversión. Links :De la empresa (Lead Motiv)https://leadmotiv.com/https://www.linkedin.com/company/leadmotiv/https://www.instagram.com/leadmotiv/ De los socioshttps://www.linkedin.com/in/alvaro-ovejas/https://www.linkedin.com/in/josemariafrancomartinez/
Intern to Founder | Justin Collins | Breaking Into CyberEpisode SummaryIn this episode, Justin Collins shares his unique journey from a PhD student in Computer Science to becoming a key figure in the application security space. Justin explains how a funding shortage led him to a life-changing internship at AT&T Interactive, where he combined his passion for compiler theory with cybersecurity to create the open-source tool Brakeman. We dive into how he balanced a full-time job while co-founding a startup and the importance of preparation when breaking into a new field.Key Takeaways- Preparation as a Differentiator: Justin secured his first security role simply by researching the specific topics (SQL injection and XSS) the interviewers mentioned beforehand—a step many other candidates neglected.- Applying Niche Skills to Security: Rather than starting from scratch, Justin leveraged his deep knowledge of programming languages and compilers to build a static analysis tool, proving that specialized non-security backgrounds are highly valuable.- The Power of Open Source: Developing and open-sourcing Brakeman during an internship served as a massive career catalyst, eventually leading to a business acquisition.- The "Side-Hustle" Startup Model: Justin highlights that successful startups don't always require VC funding or fancy offices; his company was built while the founders maintained their "real" jobs.- Negotiating Flexibility: Early in his career, Justin successfully negotiated a part-time security role, which allowed him to support his family while simultaneously building his own business.Resources Mentioned- Brakeman: The open-source static analysis security tool for Ruby on Rails created by Justin.- OWASP: Cited as a critical resource for learning about web vulnerabilities like SQL injection and XSS.- Ruby on Rails: The programming framework that served as the foundation for Justin's early work.- Black Duck (formerly Synopsys): The company that eventually acquired Justin's startup.About the GuestJustin Collins is a cybersecurity expert and the creator of Brakeman, a widely used static analysis tool for Ruby on Rails. With an extensive background in Computer Science and programming languages, Justin transitioned from academia to entrepreneurship, co-founding a boutique security firm that was later acquired by Synopsys. He is a specialist in application security and program analysis.Sponsored by CPF Coaching LLC - http://cpf-coaching.comCheck out our books:
“Aktor = agent.”
What happens to all those leads marketing works so hard to generate? More often than not, they go cold. According to Nishi Seth, Industry & Solutions Marketing Lead for Google Cloud APAC, this isn't a niche problem. It's one of the most persistent challenges across B2B, regardless of company size or sophistication. In this episode, Shahin sits down with Nishi to unpack the real reasons leads stall between marketing and sales, from qualification gaps to missing context, and what it actually takes to fix them. Nishi shares battle-tested lessons from scaling Google Cloud's marketing-to-sales motion across APAC, including how AI is transforming BDR productivity right now. Guest Introduction Nishi Seth is the Industry & Solutions Marketing Lead for Google Cloud APAC at Google, with over 20 years of experience across cloud technology, financial services, and travel. She has held senior marketing roles at American Express and British Airways, and is a recognised B2B marketing leader across the Asia-Pacific region. Key Topics Why leads go cold: the two root causes (qualification gaps and context gaps) and why even the most sophisticated B2B organisations struggle with themThe three pillars of an optimised marketing-to-sales handover: lead quality, context, and a continuous review and feedback loopWhy BDRs are the critical bridge between marketing and sales, and how to assess whether your BDR function is actually workingHow MQL definitions should evolve over time: Google Cloud's journey from basic demographic scoring to 48+ real-time intent signals, including account-level qualificationWhat providing context to BDRs really means: enablement calls, campaign-specific opening scripts, and cadences tailored to how each lead engagedThe key metrics that reveal handover health: SAL acceptance rates, SAL-to-SQL conversion, number of outreach touches, and lead velocityHow AI is changing BDR productivity: using automation for low-intent lead follow-up, real-time AI assistants during live sales calls, and AI-powered account research at scaleNishi's rapid-fire take: "strong opinions, loosely held," using AI to boost your own marketing productivity, and why simplifying complexity in B2B is what excites her most Resources & Links Google Cloud — Nishi's base for all real-world examples discussed, from MQL definition evolution to AI-powered BDR toolingGoogle Gemini — Nishi's go-to research tool in place of following influencers; she also used it to prepare for this episode Contact & Credits Host: Shahin Hoda Guest: Nishi Seth Produced by: Shahin Hoda and Alexander Hipwell Edited by: Alexander Hipwell Music by: Breakmaster Cylinder APAC's B2B Growth Podcast is Presented by xGrowth
Marieke Rijken en Mischa van Geelen laten zien waar bedrijven écht kwetsbaar zijn: niet in hun eigen systemen, maar bij hun leveranciers. Marieke Rijken en Mischa van Geelen bouwen samen aan AdversIQ, een platform dat van buitenaf in kaart brengt waar de digitale toeleveringsketen van een bedrijf lek is. Marieke zit diep in de Nederlandse hackerscommunity en vertaalt die wereld naar iets waar gewone bedrijven mee kunnen werken. Zij noemt het platform een lasagne, laag op laag aan afhankelijkheden waar je geen grip op hebt. Mischa vond als dertienjarige een kwetsbaarheid bij ABN AMRO, werd de jongste fulltime pentester van Nederland en meldde inmiddels meer dan zevenhonderd lekken. Gebruik je geen Salesforce maar je callcenter wel? Dan ben je alsnog geraakt toen de Shiny Hunters toesloegen. Pretendeer je digitaal soeverein te zijn, maar zet je sub-sub-leverancier alles bij Amazon? Dan klopt het verhaal niet. Het gesprek loopt van responsible naar full disclosure, langs een zonnepaneel-lek waarmee je half Europa kunt verstoren, naar de grote vraag: hoe soeverein is Europa écht als bijna de hele cloudmarkt bij Amerikaanse reuzen ligt, en waarom is er nog geen werkend Europees alternatief? Over Marieke Rijken Marieke Rijken (ook wel bekend als Smits..) leeft in twee werelden tegelijk: ze zit diep in de Nederlandse hackerscommunity (DIVD, projectlead van hackerkamp WHY2025) en knoopt als geen ander techniek en marketing aan elkaar. LinkedIn: https://www.linkedin.com/in/piiindakaas/ Website: https://www.adversiq.com/ Over Mischa van Geelen Mischa Rick van Geelen is ethisch hacker, pentester en forensisch onderzoeker. Hij was op zijn vijftiende de jongste fulltime pentester van Nederland, medeoprichter van incident-responsebedrijf NFIR, en meldde inmiddels meer dan zevenhonderd kwetsbaarheden. Hij is medeauteur van de MIAUW-methode en hielp bij grote incidentonderzoeken zoals de hack bij de gemeente Hof van Twente. LinkedIn: https://www.linkedin.com/in/rickgeex/ Website: https://www.mischavangeelen.nl/ In deze aflevering 0:00:00 Intro: een gat bij de bank, full disclosure en afhankelijkheden in beeld0:03:00 Dertien jaar oud tegenover drie bankdirecteuren0:10:49 Vastlopen op school, en hoe Astrid Oosenbrug hielp van de leerplicht af te komen0:16:00 Honderden lekken melden: SQL-injecties en een beheerder die vijf keer weigerde0:23:05 Wat DIVD doet, en waarom full disclosure soms niet mag (half Europa via zonnepanelen)0:28:00 Van pentester naar incident response: Hof van Twente en Lochem0:31:00 De Synology-NAS die je data niet wist, een ongedocumenteerde feature0:33:30 Hoe AdversIQ ontstond: het staat digitaal in de fik achter je0:35:35 ISO 27001, de scope die je zelf kiest en de zeldzaam goede auditor0:39:30 De lasagne: lagen vol afhankelijkheden zonder dat je het weet0:41:50 Salesforce, Shiny Hunters en de leverancier die het voor jou gebruikt0:46:43 Hoe je dit van buitenaf in kaart brengt zonder zelf lijstjes te maken0:49:20 Soeverein? Tot je sub-sub-leverancier bij Amazon blijkt te staan0:52:00 Afhankelijk van Stripe of Cloudflare, en wat als die uitvalt0:59:19 Welke fase de startup in zit, pilots in juli en de strijd om live te gaan1:10:45 Luistervraag: Europese alternatieven en de Amerikaanse wet die overnames blokkeert1:15:28 De MIAUW-methode en de motie die unaniem werd aangenomen1:19:42 MIAUW versus het piepsysteem, en de vraag die niemand stelde Genoemd in deze aflevering AdversIQ, het platform dat digitale toeleveringsketens van buitenaf in kaart brengt DIVD, het instituut dat kwetsbaarheden meldt en hackers bij disclosure helpt WHY2025, het Nederlandse hackerkamp waar Marieke projectlead is MIAUW-methode, standaard voor pentesten met auditwaarde van Brenno de Winter Synology NAS, de back-up-NAS met de niet-gedocumenteerde reset-feature Tips van de tafel Marieke: ga bij je leveranciers actief na welke diensten zij weer inkopen, want hun keuzes (zoals hosting buiten Europa) worden stilzwijgend ook jouw risico. Marieke: niet elk probleem dat je vindt hoeft meteen opgelost, bepaal per leverancier zelf wat je accepteert, mitigeert of vervangt, anders verdrink je in de meldingen. Mischa: meld een kwetsbaarheid altijd kort, vriendelijk en zonder iets terug te verwachten, dat brengt je verder dan een dreigende toon. Mischa: wil je je NAS of harde schijf echt leeg, vertrouw niet op de fabrieksreset, want die wist je data vaak niet. Jaag er desnoods een spijker doorheen.See omnystudio.com/listener for privacy information.
Automation as Core Strategy: Aarin Bailey on RPA, AI, and Scaling MSP OperationsOn the Evolved Radio podcast, Todd interviews Aarin Bailey, COO at Webit Services and former COO at MSP Bots, about treating automation as a core MSP operating strategy. Aarin describes how his automation focus accelerated around COVID by chaining PowerShell scripts, later expanding into Python, GUIs, and modular systems connected via RESTful APIs, with much of the computation running outside the RMM on servers (including SQL and Python) while the RMM remains mainly a monitoring and job-push layer. They discuss whether RMM is a “zombie product,” the ongoing role of PSA/ticketing as a system of record, and managing complexity through separate modules and staff literacy in Python/RPA. Aarin explains build-vs-buy decisions driven by ROI and fit, cites automated triage/dispatch with ~98% accuracy and shifting token costs, argues AI should augment rather than replace humans, and emphasizes documentation, playbooks, and focusing on operational “bad” anomalies. They also cover client tolerance for AI, limiting client-facing AI after hallucinated ticket notes, skepticism about voice AI, and concerns about AI economics and subsidies.This episode is brought to you by Opsleader Pro. A place for MSP owners and managers to get the systems and tools they need to build a stable and growing MSP. Part group coaching, part peer group, everything you need to run a successful MSP. (00:00) - Automation First Mindset (01:10) - Aaron Origin Story (05:04) - From Scripts to Platforms (05:41) - Beyond the RMM Beehive (08:35) - Is RMM a Zombie (12:14) - Managing Complexity Safely (14:33) - Build vs Buy ROI (19:39) - Token Costs and Pair Coding (23:49) - AI Security Reality Check (27:34) - Scaling with Playbooks (30:12) - Hunt the Bad Stuff (30:59) - Blueprints Before Automation (32:46) - Ticket Volume and Vision (33:32) - Saying No as Integrator (35:44) - Healthy Disagreement Dynamics (37:08) - Client Facing vs Backend AI (40:05) - AI Hallucinations and Guardrails (43:05) - Voice AI and Live Answer (46:06) - Costs and Subsidized AI Era (49:26) - Outcome First and RPA Focus (51:36) - Wrap Up and Thanks
“Tęsknię za Ballmerem na scenie.” Łukasz po keynote'cie Build 2026, na którym Satya wymuszał z widowni klaskanie - “nie było wow” - a po osobowościach pokroju Guthriego i Russinovicha został korporacyjny autopilot. Bo to pierwszy od lat Build, gdzie zamiast Azure'owych fajerwerków dostajemy Windows, Windows, Windows.
Going after a new segment sounds sexy… until you actually try to do it.
For the past few years, the conversation around AI has focused on the technology. Which model is best. Which tools to use. How fast everything is changing. But once you start building with it, a different challenge emerges. The technology is often the easy part. The hard part is everything else. The definitions that don't match. The documentation nobody trusts. The tribal knowledge living in someone's head. The processes that work only because a few key people know how to navigate around the mess. Business intelligence exposed some of these problems years ago. AI is exposing even more of them. For years, the people who cared about semantic models were mostly talking to each other. Everyone else had a simpler view: the dashboards worked, the BI nerds were overcomplicating things, and if a slightly different version of yesterday's question showed up, someone could always write more SQL. That worked well enough until AI agents became the ones asking the questions. Agents don't wait two weeks for a developer. They improvise. And the improvisation is different every time. That's the moment the semantic model stopped being a nice-to-have and started looking a lot more like a requirement. Every data quality problem that used to come home to roost the first time you built a dashboard is back, only now the list is longer. AI cares about policies, institutional knowledge, organizational context, and all the things that used to live quietly in people's heads. The one-version-of-the-truth problem just got a much bigger job description. Along the way, Rob and Justin compare notes from the front lines of building with AI, from multi-agent systems and knowledge management to the unexpected ways these tools behave once they leave the lab and meet real organizations. There's a book update in here too. Fair Game is officially available for pre-order, and Rob shares why the independent bookstore route matters more than most people realize. If you've been wondering what happens after the AI works, this episode is a pretty good place to start. Also in this episode: Pre-order Fair Game: Customizing AI to Your Business Is Easier Than You Think Fortune: Big Tech is laying off developers. My company just hired its first. We're both right about AI (By Rob Collie)
Description The Future of Tech is Here. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this presentation from Ultimate Partner Live, industry analyst Jay McBain breaks down the monumental macroeconomic shifts rewriting the tech sector in 2026. https://youtu.be/r0qTDyw97Gs As the industry rapidly approaches a $6.07 trillion valuation, driven by massive AI infrastructure investments from Sam Altman and the “Magnificent Seven,” traditional sales and channel models are fundamentally collapsing. McBain reveals how buyer demographics have transformed to an integration-first millennial base, why marketplace ecosystems now command over half of all partner-funded deals, and how a tiny elite of just 1,000 tech service providers control two-thirds of global tech revenue. Learn the exact mechanics behind how Microsoft out-partnered AWS to win 26 straight quarters of dominant growth and how your business can deploy an algorithmic early warning system to capture massive wallet share before competitors even step into the boardroom. Key Takeaways Over half of the Fortune 500 companies vanish every 20 years because their leadership fails to anticipate macroeconomic technological cycles. The true opportunity in the $6.5 trillion AI boom lies not in single vendor products, but in the hardware, software, services, and telecom ecosystem surrounding them. Indirect tech sales are undergoing a structural shift toward direct cloud hyperscaler models driven heavily by Nvidia's core infrastructure client base. Modern business deals are won or lost months before the point of sale based on the average of 6.3 partners surrounding a customer’s environment. Over 51% of tech buyers are now millennials who prioritize software integration capabilities and digital marketplaces over traditional human sales interactions. Tech service economics are pivoting aggressively away from upfront margins toward point-based multi-partner funding across subscription cycles. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Nvidia AI buildout, $7 trillion AI opportunity, cloud ecosystem decade, Microsoft vs AWS growth, multi-partner cloud deals, digital marketplace migration, millennial B2B buyers, B2B tech subscription economics, tokenized micro consumption, tech services wallet share, hybrid cloud infrastructure, 28 customer moments, IT services industry growth, telecom spend breakdown, channel chief strategy, managed service providers MSP, global systems integrators GSI, software integration first, point-based vendor incentives, automated co-selling workflows Transcript JAY McBAIN AUDIO PODCAST [00:00:00] Jay McBain: So to go back to that story about the 53% of companies who are gonna fail, one of us is gonna be asked to write the book, but chapter one is always you Blame the CEO. [00:00:13] Vince Menzione: We just came back from Ultimate Partner live in Bellevue, Washington, where we hosted incredible leaders for two amazing days. Come join us for this next session where we explore the tectonic shifts we’ve all been seeing. With that, I am incredibly blessed to invite a friend of mine to the stage. I have a quick little side note, like I found an old LinkedIn post from this gentleman from like many years ago, like 20 years ago. [00:00:39] Vince Menzione: And I wasn’t really that nice to you on that LinkedIn post. Like, oh, like this is before Jay became the Jay, that we all know Jay to be j. But he was in the space and I was at Microsoft doing something and he reached out about something. It was kind of rude, Jay. I was like, oh my gosh. I can’t believe. But Jay has been a great friend. [00:00:54] Vince Menzione: When we started the podcast back up, uh, during COVID we started doing podcasts together. When we moved to the studio, Jay was the first person in the studio. He’s always got a spot, uh, at our events. He’s s Spot Art, and, and he’s a great friend and supporter of Ultimate Partner Jay McBain. For those of you who don’t know him, Jay, welcome. [00:01:13] Vince Menzione: Thank you, sir. [00:01:22] Jay McBain: 31 days ago, we landed Artemis two. The furthest humans have ever been away from the planet Earth 57 years ago. We landed on the moon in the 56 years. Between those two moments, the tech industry has been the fastest growing industry in the world. Every single year we moved from the space race to the technology race, and we’re just getting started. [00:01:46] Jay McBain: If you’re old enough, you’ll recognize the mainframe and mini era for 20 years. You’ll recognize a young disheveled Bill Gates showing up in Boca Raton, Florida for, uh, August the 12th, 1981 launch, where Bill thought that every one of us would’ve a PC in our home, and IBM thought they were gonna sell 10,000 of them to hobbyists. [00:02:12] Jay McBain: 1999, a small startup from an executive who just left Oracle in San Francisco named Mark Benioff. A couple of years later, Jeff Bezos went into a boardroom and said, listen, we’ve spent a lot of money building infrastructure to our busiest day, Christmas, black Friday. You’re telling me this stuff sits idle 10 or 20% for the rest of the year. [00:02:35] Jay McBain: Why don’t we rent that out to others? Got laughed outta that boardroom and then got made of fun of on magazine covers. Maybe you should just tend the store, let the adults talk about technology. In March of 2023, our neighbors, our friends, our family saw DeepFakes. They saw poetry, they saw music, and they came to us as tech people and said, did we just light up Skynet? [00:03:03] Jay McBain: Now every one of these 20 year eras, this is the Taylor Swift version of our industry. Every single one of these eras triggers the fastest growing product in history. Today it’s actually Chacha bt first to a billion users. It triggers a new, richest person in the world, bill Gates, to Jeff Bezos. Now, Elon Musk is the first to sign a trillion dollar pay package, and it’s not for car. [00:03:27] Jay McBain: It’s not for cars. It also triggers a most valuable company in the world change. And today that’s nvidia. These are monumental changes in our industry and they’re monumental changes in partnering every single time. And it also links to our customers. If you take a 20 year view of business, one era, and, and think about the AI era, you know, at the start of it here, if you’re to grab the Fortune 500 magazine from 20 years ago and start to flip through it, 53% of the companies in there no longer exist. [00:04:06] Jay McBain: Every 20 year cycle, we lose over half of the biggest companies in the world. These are the companies that have very deep pockets to buy their way outta problems. If you’re not in the Fortune 571% of tech companies don’t make it 10 years. These are the changes that cost industries. There are changes that cost really big companies and the decisions we make, the trends we’re in right now, in 2026 will be written about in the future. [00:04:39] Jay McBain: This new era, a lot of big numbers being thrown around. Vince’s best friend talk about a six and a half trillion dollar AI opportunity, but it’s not Microsoft’s tam. Microsoft is chasing about a trillion dollars of this. And the ecosystem, the hardware, the software, the services, the telecom is gonna make up the rest. [00:05:04] Jay McBain: It is an ecosystem. Every time these big numbers are thrown, the word ecosystem is always thrown around it. Not to be outdone, Sam Altman’s talking about a $7 trillion build out. The world economy this year, the world GDP will be 126. These are material numbers to world GDP, but even better, they’re both larger than our entire industry is today. [00:05:27] Jay McBain: So what took 56 years of the fastest growing industry this year will be $6.07 trillion. Big numbers, but it’s easier to think about it in terms of a dollar that our customers spend in that dollar. They’re gonna spend 25 cents on hardware. They’re gonna spend 25 cents on software. So for anyone that read the memo 15 years ago, that software’s gonna eat the world, there’s still a dollar a hardware to run every dollar of that software. [00:05:57] Jay McBain: And whether you’re thinking humanoid robots or whichever future you’re envisioning, there’s going to be a dollar of hardware to run every dollar of software for the next 20 years. There’s over 25 cents now in IT services, and in many cases, these services are growing faster than the product categories and just under 25 cents in telecom, that’s how it breaks out today. [00:06:19] Jay McBain: And this industry, which took 56 years to get to this point, is gonna double in size in the next three to five years. We already have two and a half trillion of that seven raised and being spent. Part of the reason Nvidia is the most valuable company in the world. Now our industry, uh, you talk about ultimate partnerships. [00:06:40] Jay McBain: Our industry traditionally, and world trade by the way, is 75% indirect. The dealerships, the agencies, the brokers, the resellers, the retailers, the franchisees, the gas stations, the grocery stores, the pharmacies, all 27 industries sell indirect. You gotta think back the last time you bought something direct. [00:07:01] Jay McBain: Well, I bought a Dell from that dude in the nineties. Cool. Well, Dell Technologies is now 60% indirect. Well, I bought insurance. Direct is 15 minutes. Could save me 15%. Well, Geico last year sold more insurance through agencies and brokers than they did direct. This is the world now. We used to be 75% indirect four years ago. [00:07:26] Jay McBain: Then it went to 73.2, then it went to 70.1 and it then it went to 66.7. By the way, marketplace is in these numbers indirect. It’s not marketplace causing this change. It’s one company, Nvidia. Nvidia has seven customers. The magnificent seven, uh, half of them are in the room right now that every morning we wake up to a hundred billion dollars press release about this $7 trillion buildout. [00:07:56] Jay McBain: What’s interesting is indirect sales in our industry is growing by revenue. It increases every year, just not at the pace that this AI build out is happening direct with seven companies. But the reason we’re all here, and I think the core reason that Vince is building this community is this, you know, Microsoft forever has measured and been very vocal. [00:08:21] Jay McBain: About 96% of their deals have partners in them. Kind of who cares, who collects the money. We care about the moments, the 28 moments before the customer makes a purchase. We care about every 30 days forever, because two thirds of our industry, over $4 trillion now is subscription consumption based. Winning a customer today is only winning the first 30 days. [00:08:46] Jay McBain: We care about this cycle. We care about who surrounds our customer. So six years ago, I stood on a big stage and said, you know, we went through a decade of sales. You know, in 1999, you thought you were born to be a salesperson. You’re managing your territory with your gut. Well, a few years later, you were introduced to the science of selling. [00:09:07] Jay McBain: You know, 10 years later you thought as a marketer, you sit around a cocktail party joking with your friends, 50% of my marketing dollars are wasted. I just don’t know which 50%. Really funny. In 2009 until every 58-year-old CMO got replaced by a 38-year-old growth hacker. Coming in with Marketo and Eloqua and Pardot and HubSpot, and 15,505 as of yesterday, MarTech and iTech tools, ninjas in marketing, they wouldn’t let a nickel go through without measuring. [00:09:43] Jay McBain: Now we understand 96% of deals and partners that surround it. No deal is gonna be won or lost in this era without partnering effectively. So we had to have this decade of the ecosystem. One of the ways we’re tracking is by outsiders. You know, Salesforce every year publishes the state of sales and they’ve got, you know, the number one CRM in the world. [00:10:05] Jay McBain: So they get to go talk to all the CROs, all the salespeople in the world. And as of this year, a couple months ago, 94% of every salesperson in every industry in the world uses partners every single day. You wanna see what this number was six years ago. Also, 89% of salespeople around the world don’t think they’re going to club this year without partners. [00:10:29] Jay McBain: So this is a big moment for us, halfway through the decade ecosystem, but we’re only halfway through. We’re starting to understand now at a more granular level. What partnering means. It’s not theory, it’s not flywheels. It’s not really cute. McKinsey slides that we keep showing to our board saying how important partnering is. [00:10:51] Jay McBain: We’re trying to get to the very specific level of the 6.3 partners on average that surround the deal and what they’re doing. How their business model works, and that’s average if I’m working on a public sector deal. I was at a Red Hat conference yesterday talking sovereignty. If I’m in an enterprise or a large public sector deal, it’s north of 10 partners in the deal. [00:11:15] Jay McBain: So we’re starting to understand what used to be this, this, you know, you’ve been the fastest growing industry for 56 straight years. Every single professional services person in every industry has come in to join the fund. Over 90% of accountants are tech services firms. Over 90% of marketing agencies are tech services agencies. [00:11:36] Jay McBain: All of this 250,000 software companies, a million emerging comp tech companies, the half a million VAR that have been in that traditional channel. The managed service providers, all of these 20 different partner types, millions of companies, tens of millions of people competing for 6.3 spots. Around the customer. [00:11:58] Jay McBain: That’s it. Luckily, there’s 141 million global customers to compete for. There’s, there’s some open slots that you can go find, and that’s the point. Our industry never had our own Fortune 500. We always talk to, you know, these partners and GSIs are doing this and SI are doing that. And we never really had a view of capability and capacity or what our own TAM was inside of that partnering. [00:12:25] Jay McBain: And so we set out and we would’ve loved, you know, chat GPT or Gemini or Claude or any of those tools to do this. But there’s one problem in partnering with AI is that it doesn’t know one partner from the next. There’s a big digital sameness problem in our industry that every single partner, whether it’s Larry in the White van or Accenture, with 786,000 employees all say they do all things to all people all the time. [00:12:53] Jay McBain: 98% of them, 99% of them are private companies that don’t share their p and l. You can’t go into Microsoft’s LinkedIn system and find out how many employees, ’cause it’s a block system, it AI can’t see into it. So it just sees, and it’s a great pattern matching. Google, SEO can’t figure out who’s who, nor today can the large language models. [00:13:14] Jay McBain: ’cause all the things they’re trying to match, the transformers are trying to match. It all looks the same. Every tweet, every ebook, every website, every digital history looks the same. So this took us thousands of people hours across two years to do, to dig into every p and l to dig into every dollar of what they’re doing. [00:13:33] Jay McBain: But what was interesting is only a thousand partners in our industry do two thirds of all tech services. When you get into enterprise, it goes up to 80 to 90%. The partners in the middle, in Blue do more tech services. The 30 of them than the 970 partners in white on the outside, the 970 partners in White do more tech services than the next million combined. [00:14:03] Jay McBain: This is our industry in a nutshell. Every time we talk to a a vendor, every time we talk to a partner, every time we talk to a distributor, we’re now talking names, faces, and places. You you wanna talk sovereignty. Yesterday in Atlanta, 90% of sovereign conversations in public sector in the globe is handled by these companies here. [00:14:26] Jay McBain: Forget about how much you do with these partners today. You wanna chase the next column, which is the wallet share. And I was a channel chief for 17 years. I get the weekly report and I see a million dollar partner, another million dollar partner, sorted top to bottom. You don’t know which partners which, which of those million dollar partners is doing 1.2 million in your category. [00:14:46] Jay McBain: They deserve a baseball cap and a front row seat at your event as an MVP. The next partner right next to them is doing 10 million in your category. They’re only doing a million with you. ’cause customers are pulling them into it. Nine times outta 10. They’re leading with your competitor. So I don’t want that list anymore. [00:15:03] Jay McBain: I want the new list, which is showing me those $9 million opportunities. And I as a board member, as A CEO, as a CFO, as a CRO, I wanna see this list. And then I want to talk people, processes, programs, technology. What are we gonna do to go get our fair share of that 9 million? Where’s our lowest hanging fruit? [00:15:24] Jay McBain: How do we double our pipeline? How do we double the size of our company in three years? It’s all right here. Let’s have very specific conversations and move away from flywheels and move around from force multipliers and and things like that in partnering. Let’s figure out how this partner community is surrounded. [00:15:45] Jay McBain: What do 10 million people who have to be smart in front of their customers every single day, what do they read? Where do they go and who do they follow? It’s the law of a few. This is the old Malcolm Gladwell of tipping point 10 million people in the broader channel. A hundred percent of our TAM comes down to only a thousand watering holes. [00:16:08] Jay McBain: 12% of that entire audience. Doesn’t sound like a lot, but it’s over A million. People love podcasts. Number one way they learn the Joe Rogan effect. In our industry, there’s 121 podcasts. These are all public lists. You can go get on my LinkedIn newsletter on canals, oia. But there’s 121 podcasts that drive him forward. [00:16:28] Jay McBain: Really high up on that list, actually number one on the list is ultimate partner, Vince. That’s how I met. ’cause I asked people, 10 million people, you love this. You walk your dog, you drive to work, you listen to podcasts. I’m not the biggest podcast fan. It’s not number one on my list, but it’s number one on theirs. [00:16:44] Jay McBain: They say, you know, you gotta meet this guy, Vince. It’s unbelievable how great these podcasts are. They’re ultimate. [00:16:54] Jay McBain: Then I talked to Vince and said, but Vince, you know, 35% of your community, the 10 million people love to come to events like this one. The hallway conversations, the hotel lobby bar last night. This is what we love to do, especially post pandemic. It’s the number one way we learn. We learn from our peers, we learn from those around us, and, and the learn from the conversations we have here. [00:17:17] Jay McBain: We always remember these moments, you know, years and years later. There’s 352 choices. I’m going to five of them this week in five different cities. It’s a lot of coverage, but again, it’s a tighter li list of how people work. The magazine lists 106 of them associations like Conter. Now the GTIA peer groups, there’s 15 different spheres of influence, but only a thousand places. [00:17:43] Jay McBain: I could walk you through billionaire, after billionaire, after billionaire in this industry and show you how they did this. How did Arne Bellini at ConnectWise? How did Austin McCord at Datto, how did Nerdio become a unicorn? How did threat locker and huntress move away from 6,500 cyber companies and become unicorns over and over and over again? [00:18:05] Jay McBain: It’s only one slide. Unicorns and billionaires are made here, and a lot of people don’t get it. So walking away from Bellevue, a thousand partners, top down, a thousand watering holes, bottoms up. You’ve covered a hundred percent of your tam. You do it better than 10% of your competitor, 10% better than your competitors. [00:18:27] Jay McBain: You win. You carry that on your resume into the next company. You get a bigger job at a bigger pay scale. Let’s just walk through some examples. Cyber 91.7% of it goes through the channel. Huge channel audience. You know, if you’re in MarTech, it’s only 10%, but this one happens to be all channel, but that’s not the story. [00:18:48] Jay McBain: For every dollar that the 6,500 cyber companies are trying to close, there’s $2 in services. Plot twist, the products are grown at 11, the services are grown at 12.6. Your partners are growing faster than you are, and they will continue to for the next, at least five years, probably 10. So when I’m here, five years from now, you’ll hear in me talk about a three to one split in cyber and then a four to one split in cyber. [00:19:18] Jay McBain: Now, when we’re in Miami a couple days ago is CrowdStrike, they’re talking about a $7 and 5 cent multiplier, chasing that two to one up higher. You look at managed services. Here’s a fun story. Managed services. 82% of customers who are man, uh, outsourcing more this year than last year. 650 billion in size. [00:19:38] Jay McBain: This is bigger than the entire SaaS industry. Salesforce, ServiceNow, Workday, Marketo, NetSuite, HubSpot, 250,000. Others. This is bigger. It’s also bigger than all the Hyperscalers combined, not just AWS, Microsoft and Google, but Alibaba and Oracle and everybody down the list. This is a massive market also growing at double digits. [00:19:59] Jay McBain: So these are some big things and obviously we’re watching, you know, week in and week out, quarter in, quarter out, the Battle of Software and Battle of the Hyperscalers and things like that, and who’s growing at what pace and, and how partnering is connecting to all of this. You know, we watched a moment really early in the pandemic where Microsoft started growing faster than AWS and they haven’t stopped since 26 straight quarters. [00:20:27] Jay McBain: And you ask customers and say, you know, does Microsoft have a better product? And in most cases they say no. You know, AWS had a five year head start. Well, did they have a better price? Well, no, actually most cases Microsoft’s more expensive. Well, did did they have better promotion? Was their Super Bowl ad better? [00:20:44] Jay McBain: No, they’re both kind of crap. So you kind of ask the questions of what’s the only difference that could create growth above the leader in the market? Well, it’s place. More of the 6.3 partners are walking into those keyboard room meetings and drawing clouds up on the wall and labeling the Microsoft than they are AWS. [00:21:03] Jay McBain: Very simple. It’s never been about product. The best product in our industry has never won. And now the best way forward is that partnering moment, and this is the moment. So to go back to that story about the 53% of companies who are gonna fail, one of us is gonna be asked to write the book. And it could be the book like Kodak, they invented the product that ended up killing them. [00:21:26] Jay McBain: And it’s a woe is me story, but chapter one is always you blame the CEO. How could they not see those trends happening in 2026? How could they, you know, were they blind? Were they stuck in their own, you know, innovation chamber? Innovator’s dilemma, were they stuck in their own boardrooms? Why couldn’t they see? [00:21:46] Jay McBain: Well, chapter two, you, you blame the board. They have fiduciary responsibility, outsider view, and how could they not see it? But really, this is the future right here. If you take this slide and apply it 10 or 20 years from now to every failure and every success, these are the chapters of the book. Your buyer is now a millennial. [00:22:05] Jay McBain: As of last year, the 51% of our market is bought by people born after 1982. Different psychology, different behavior, different journey, different criteria, their integration. First buyers. The buy a product, 80% as good as the next one. If it works better in their environment. 94% of people won’t buy a car unless it has CarPlay or Android Auto. [00:22:26] Jay McBain: New Buyer. You have to be more integrated than your competitors. That’s a partnering story. The 6.3 partners. If you heard cyber, you need some great channel partnerships, but you need the other 5.3 partners as well, the consultants, the advisors, the designers, the architects, the implementers, the integrators, the manner service, all of the other partners. [00:22:44] Jay McBain: You need to know more of them than your competitors do, and have them label clouds with your name in them. You need better alliances. Even if you compete, you only compete in the morning. You’re best friends by the afternoon. You have to be tight with the hyperscalers, tight, with the big SaaS platforms, tight with cyber, tight with distribution, there are layers, seven layers to every deal. [00:23:04] Jay McBain: You gotta be tight in and have better alliances than your competitors. And then it all comes to the 28 moments, which I’m gonna end on, but the go to market of all of this, the co-selling, co-marketing, co-innovation, co-development, co keeping. This is it. Your product has to be good enough that somebody’s gonna renew it. [00:23:21] Jay McBain: Your Super Bowl has to be, you know, ad has to be good enough that people don’t, you know, shame you on social media. Your pricing has to be somewhere in a country mile of the bell curve of what the customer wants to pay. But successor failure is just here and platforms are synonymous with partnering. [00:23:40] Jay McBain: It’s our role now in the decade of the ecosystem to drive our companies forward. Marketplace. It’s probably the most predict, you know, great prediction we ever made. You know, growing at 82% compounded, it’s hard to predict ’cause it doubles almost every year. We were almost exact to the decimal point. Five years later now till 2030, we’re watching a second story, which is more interesting. [00:24:02] Jay McBain: If 96% of all deals have partners inside of them and there’s private offers and multi-partner offers and distributor sellers record all these funding mechanisms or services as a product. As of last week, over 50% of all deals in marketplaces now have partner funding. It means that while money changes hands differently, the respect and the recognition of what partners do is in the deal. [00:24:26] Jay McBain: We think that’s going to 59, but at some point, that’s gonna have to hit 96. ’cause to run the best programs, whether it’s an indirect sale, whether it’s a direct sale, whether it’s a marketplace deal, it doesn’t matter how money changes hands. What matters is we recognize the 6.3 partners. They’re not only making the deal happen bigger and faster, but renewing and enriching that every 30 days forever. [00:24:48] Jay McBain: When we watch, you know, billion dollar clubs and when we read all the press releases and all the hubbub about how fast this is growing and who, which companies are behind all this. When I’m quoted in some of these press releases, it’s because of this. You know, CrowdStrike, you know, brags are a billion dollars in a single year, but inside of that, they’re showing that 91% growth in marketplaces, which is pretty phenomenal for any company to almost double in size every single year. [00:25:17] Jay McBain: What’s more phenomenal is they’re growing the channel piece of it, 3548%. That green part of it is growing. Companies that understand platform and have people and processes and programs and technology to do it are winning. And they’re getting recognition and partners are starting to join the Billion Dollar Club who don’t sell a product, but are also winning at Extreme Scale. [00:25:44] Jay McBain: So talk about those partner 1000 and who are leaning in to win at this level. As well as everything changes, traditional billing moved into subscription models, moved into consumption models. Now we’re being tokenized to death multi it’s, it’s in this mode of micro consumption. There’s no chance there was little chance in subscription consumption that would be resold. [00:26:09] Jay McBain: You don’t buy Netflix from the cable guy in the white van. There’s zero chance when you’re buying tokens at a buck a piece that that’s going through any indirect sale. This continues to grow. Now the tectonic shifts is what happens when money changes hands differently. These old programs that we used to all write hundreds of different boxes, we checked every day on deal reg and trainings and all the other things are changing. [00:26:35] Jay McBain: To this, you’ll get these slides, by the way, in high res, inside of this now is the customer. For the first time ever, 45 years later, we have the customer in the middle of what we do, the 28 moments in green before they buy the seven layer stack and the partners inside it. The implementation. The integration, the managed services in a cycle that never ends, and two thirds of our industry. [00:26:55] Jay McBain: With the customer in the middle, we can now move money around to the different moments. It’s not all landing in front or backend margins or market development funds or new customer bonuses or spiffs. It’s landing where it needs to land. Over 400 companies now, pretty much led by Microsoft 400 companies are in a point system right now and 400 more. [00:27:18] Jay McBain: We’re working kind of behind the scenes to get that announced in the next 12 months. This is a total changeover in terms of how economics work and partners are yelling over half of us. I don’t care. Don’t call me a VAR anymore. Don’t call me an MSP. Don’t call me a regional system integrator. I do the consulting over half the time. [00:27:36] Jay McBain: I do the design, I do the implementations, I do the managed services, and 44% of us are vibe coding. On weekends. We’re not happy. Just on the services side. We wanna join the seven layer tech stack as well. These are partners growing faster than their vendors by understanding this cycle and where to show up and where the money is in ai. [00:27:56] Jay McBain: And the number one thing they’re asking for is not more leads, which they did for 45 years. The number one thing is now recognized for what I do. I’ve never just been a cash register. We’re completely now past this idea of a channel being a channel of distribution, and now a channel being this platform for the future. [00:28:16] Jay McBain: As we lay that on top of ai, the first couple of years of AI has really been consumer driven. The 95% failure rate that MIT reported last year is now 70%. That’s the failure to get from proof of concept to production. That 70 will be 50 by the summer we’re moving now in business, the maturity rates are going up at the end customer and in 88% of cases, that’s because of the channel. [00:28:43] Jay McBain: They’re working with partners. They’re not vibe coding themselves and working in little skunkwork groups. They’re working with partners to make it happen, and it now becomes the partner’s number one growth opportunity. I can grow at 11 or 12% in cyber every year. Compounded I can grow in 10% in managed services. [00:29:03] Jay McBain: You know, those are great double digit growth ’cause my customers are growing at 2.7% and I can go four x my customer, but I can go 10 x my customer if I have the right services built around ai. And this compounded growth rate and that big number in 2 20 32, 267 is what’s got those top 1000 partners obsessed. [00:29:25] Jay McBain: And your companies are leading with ai. Now you need to connect to those AI services. You need to get partners on this scale of growth. And they will be adding your name inside every cloud. They write on every whiteboard, but 82% of partners around the world, you know, we survey 25,000 of them aren’t ready, and they’re blaming vendors for not being ready, and they’re telling them exactly the workshops and the training that they need to get ready for this cycle. [00:29:53] Jay McBain: 82% of our entire partner, tens of millions of people, aren’t ready to grow at 35% and they need our help. Last thing I’ll say about AI is it’s the first time from client server to cloud, edge to cloud that it’s been segment driven. SMB alone has one, you know, six different segments, one to nine, 10 to 24, 25 to 49, et cetera. [00:30:18] Jay McBain: Mid-market into enterprise. No one that runs a restaurant is calling Jensen to buy a GPU to put next to the stove. No one’s calling Sam or Dario or anyone at Anthropic or OpenAI directly. They’re waiting. If you run a restaurant with all the people running around with tablets, you’ve invested in toast or square or clover or one of the platforms to run your business. [00:30:41] Jay McBain: A hundred different things. And you’re gonna wait for toast to work with a hyperscaler and build out the capabilities genetically. So when they see a spike in Uber Eats orders, they automatically place a food order and automatically change the staffing to deliver on it. That’s what the restaurant’s waiting for, and there’s no one calling and having a big a agent conversation. [00:31:03] Jay McBain: But even if you go into hundreds of people in medium sized business, every one of the vice presidents have their tech stack already built. I talked about the marketing person already, but the HR leader has one, and everybody’s got their seven layer stack. They’re not calling to buy a GPU and they’re not calling to, you know, bring in open AI directly or, or anthropic. [00:31:22] Jay McBain: They’re waiting for the platform they built to integrate together ag agenta capabilities. Everybody’s in wait mode up until enterprise and public, large public sector. So we are looking at this market and at 90% of that AI market is run by those thousand companies, and the rest of the millions of partners are helping in terms of how these businesses are gonna change at that level. [00:31:46] Jay McBain: Here’s where I end. You know, the 28 moments used to be a theory. It used to be a flywheel. How do we buy a car? [00:31:55] Vince Menzione: Well, we Google it, [00:31:57] Jay McBain: 81% of us now, 94% of us use large language models. We find out that there’s 365 brands of car. I’d have to test drive one every day of the year to get through them all. So we start narrowing these things down. [00:32:09] Jay McBain: We configure it. We put our rims on it, we color it. We download the invoice price. We download the backend rebates this month, whether I buy it in May or June, we find out what 5,000 people paid for our exact car within 50 miles of us. And then we don’t wanna go to the dealer because we know more than the salesperson, the manager ever will. [00:32:26] Jay McBain: We know what we’re gonna pay within, you know, dollars or cents. Just carvana the car. Hand me the keys. Let’s just forget the whole eight hour back and forth. I’ll get you a deal thing. I’m smarter than you in technology. Our customers are smarter than us, smarter than salespeople. That’s why 75% of millennials don’t wanna talk to a salesperson. [00:32:48] Jay McBain: They want to end digitally, and by the way, they’re not gonna send a fax after 28 digital moments. They’re gonna end on a digital marketplace. This is all demographics. It’s not hard to see where it’s going, but we’re getting into names, faces, places again. What if every dollar of your tam, the board, the CEO, runs around with their big multi-billion dollar number, they’re chasing? [00:33:09] Jay McBain: What if every single deal looks the exact same? This is a deal with AstraZeneca, A real deal, real customer spending millions of dollars. We know it starts in October, it ends in April. It’s a six month cycle. We see what they read, the MQ ls at the beginning. We see the sales demo moments. We see ISV, but we’ve never had the light blue boxes. [00:33:30] Jay McBain: What if we as a team could overlay the 6.3 partners in this deal? And when you find out a couple things. Here’s where I end. In December, five deals were one, three of them by NTT. The person at NTT probably coaches AstraZeneca’s, you know, kids’ soccer team. They probably have a cottage together at the lake. [00:33:50] Jay McBain: For the last 20 years, if the person at NTT worked at Deloitte, Deloitte would’ve run this deal. But Software One and Yash are both there, so we understand that when they were drawing clouds up on the wall in the boardroom in December, this deal was won and lost there. It was not won and lost at the point of sale. [00:34:09] Jay McBain: So what if you knew more about this and could see every dollar in your tam? You had an early warning system that this was happening. Two things jump out at this now that we’re in Bellevue. AWS was touched twice in this deal, directly in the marketing cycle and the sales cycle. AWS lost this deal. Here’s an example of Microsoft winning a deal with Microsoft never being touched. [00:34:34] Jay McBain: For some reason, NTT who won, who won AWS’s partner of the year a couple years ago led with Microsoft, so did Software one, Microsoft’s biggest reseller in Europe, and as did Yash, they all led with Microsoft and without Microsoft, knowing Microsoft took a multimillion dollar deal away from their competitors by winning in December. [00:34:53] Jay McBain: That’s one. Second. These partners didn’t just show up other than soccer and cottages. They didn’t show up in December. It went closed one in their CRM system. Back in the summer, August, September, we already knew AstraZeneca was in market, spending millions of dollars. We didn’t need them to read an ebook or go to an event to find that out. [00:35:17] Jay McBain: We knew it because it was closed one. They’re spending hundreds of thousands of dollars times five in December to know what to do at the end. This is an early warning system that’s better than any MQL, better than any SQL. And if you could give your company these level of view into their pipeline with an early warning system that I can work with those partners for months before they ever show up at the customer’s boardroom. [00:35:44] Jay McBain: This is it. Talk about 47% winners. This takes you from not only surviving the AI era to being a top five platform winner. Thank you very much. [00:36:01] Vince Menzione: Until next time, we’ll see you in person. Hopefully at our next event.
https://clearmeasure.com/developers/forums/ Chris Woodruff, or as his friends call him, Woody, is a software architect of over 25 years. Woody loves software engineering, especially allowing applications and services to communicate across networks and through Web APIs. He has received Microsoft MVP awards in SQL, Data and C# in the past, along with multiple years of being awarded the AWS Community Builder Award. He's a current board member of the .NET Foundation Woody lives in Grand Rapids, Michigan, where he explores the many breweries in West Michigan and travels with his family. Woody is also a long-time bourbon fan and loves hunting for whiskey bottles. Website - https://woodruff.dev/ LinkedIn - https://www.linkedin.com/in/chriswoodruff/ Twitter - https://twitter.com/cwoodruff Simplicity-First Website - https://simplicity-first.dev/ Previous Appearances on the Azure & DevOps Podcast: Episode 262 - Chris "Woody" Woodruff: Network Programming https://azuredevopspodcast.clear-measure.com/chris-woody-woodruff-network-programming-episode-262 ---------------------------------------- Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.
גיא ואיתן דנים בשאלת הביקוש לתפקיד שלנו בשוק. האם יש פחות? או שבעצם יש יותר? ומה מקומנו בעולם ה-AI? ואם כבר מדברים על AI אז בואו נדבר על פיצ'רים שקשורים לזה ב-SQL Server! וגם, אנחנו מספרים קצת על סדנא של AWS בנושא PostgreSQL שכדאי לשמוע עליה. קישורים רלוונטים: New T-SQL AI Features are now in Public Preview for Azure SQL and SQL database in Microsoft Fabric - Azure SQL Dev Corner AWS FOR DATA | Transform Your SQL Server Skills to PostgreSQL- Special Workshop with DBArt Statistics are not collected when creating new table and indexes and loading data after. · Issue #990 · olahallengren/sql-server-maintenance-solution
Run enterprise Postgres workloads on Azure HorizonDB with around 3x the throughput of self-managed deployments — zone-resilient by default, no architectural trade-offs. Call AI models directly from SQL, build durable vector pipelines inside the database, and deliver high-accuracy similarity search at massive scale with DiskANN and AI re-ranking, all without leaving PostgreSQL. Debug and optimize queries faster with the Azure HorizonDB VS Code extension. Visualize execution plans, let Copilot generate fixes, and clone production data to test environments in seconds. Charles Feddersen, PostgreSQL Partner Director PM, shares how to put all of it to work on Azure. ► QUICK LINKS: 00:00 - Azure HorizonDB features 00:57 - Open-source PostgreSQL 02:24 - How it works 03:37 - Performance 04:51 - Enterprise-ready security 05:34 - Memory & storage work together 06:29 - AI Model Management + AI Functions 08:24 - AI Pipelines 09:50 - DiskANN + AI Re-ranking 10:50 - VS Code Extension + Data Cloning 12:31 - Wrap up ► Link References Check out our blog at https://aka.ms/azurepostgresblog ► Unfamiliar with Microsoft Mechanics? As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics
From cracked data foundations to multi-agent AI, Starburst Data's co-founder shares hard-won lessons on getting the right data, not just more of it.Topics Include:Matthew Fuller, co-founder and VP of Product at Starburst Data, joins the show.Starburst is built on Trino, a fast SQL engine for federated data queries.Their platform lets users query data across lakes, stores, and databases seamlessly.Governed "data products" give organizations access to their full data estate in context.A strong data foundation is essential before any AI use case can succeed.AI doesn't create data problems — it exposes the cracks already there.Common mistake: assuming everyone in an org defines "customer" or "revenue" the same way.More data isn't always better — getting the right data is what matters.Customers include HSBC, Comcast, Zalando, ZoomInfo, and DBS, many running on AWS.AWS partnership spans technical support, SLA reliability, and proactive product briefings.Advice for product leaders: always anchor new technology back to the customer problem.2026 will be defined by specialized multi-agents working together autonomously.Participants:Matt Fuller – Co-Founder, Vice President of Product, Starburst DataSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
This week the trio covers the Latest Ubuntu, Fedora, and CachyOS news. Btrfs has a big performance win, USB4 brings fast data transfers, the latest kernel RC has prompted a classic Torvalds rant. And then Jonathan flies in to wrap up the show with Open Source AI definition news. For tips, we have quein for turbo-charges who is, Shelly for smarter package management, htmlq for querying a web page, and DuckDB for slick SQL on the command line. You can find the show notes at https://bit.ly/434Hrkg and enjoy! Host: Jonathan Bennett Co-Hosts: Ken McDonald, Rob Campbell, and Jeff Massie Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
This week the trio covers the Latest Ubuntu, Fedora, and CachyOS news. Btrfs has a big performance win, USB4 brings fast data transfers, the latest kernel RC has prompted a classic Torvalds rant. And then Jonathan flies in to wrap up the show with Open Source AI definition news. For tips, we have quein for turbo-charges who is, Shelly for smarter package management, htmlq for querying a web page, and DuckDB for slick SQL on the command line. You can find the show notes at https://bit.ly/434Hrkg and enjoy! Host: Jonathan Bennett Co-Hosts: Ken McDonald, Rob Campbell, and Jeff Massie Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
Nik and Michael discuss autovacuum, including what it does, and the basics of why and how to tune it. Here are some links to things they mentioned: autovacuum https://www.postgresql.org/docs/current/routine-vacuuming.html#AUTOVACUUMautovacuum configuration parameters https://www.postgresql.org/docs/current/runtime-config-vacuum.html#RUNTIME-CONFIG-AUTOVACUUMWhat's Missing in Postgres? (our episode with Bruce Momjian) https://postgres.fm/episodes/what-s-missing-in-postgrespg_squeeze (our episode with Antonín Houska) https://postgres.fm/episodes/pg_squeezeMy queries to monitor autovacuum (post by Laurenz Albe) https://www.cybertec-postgresql.com/en/monitor-autovacuum-my-queries/Autovacuum Tuning Basics (post by Tomas Vondra, originally for 2nd Quadrant blog) https://www.enterprisedb.com/blog/autovacuum-tuning-basicsZero autovacuum_vacuum_cost_delay, Write Storms, and You (post by Jeremy Schneider) https://ardentperf.com/2026/04/12/zero-autovacuum_cost_delay-write-storms-and-you/Our episode on long-running transactions / xmin horizon https://postgres.fm/episodes/long-running-transactions~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork
Ronald, Marco en Jelle zijn terug met een aflevering over criminelen, Cloudflare, consultants en piepers. Dave Maasland verkoopt ESET Nederland aan het Slowaakse moederbedrijf ESET, Ronald duikt in het Follow the Money-interview met TIB-voorzitter Annemieke Zwanenveld over de nieuwe Wiv, toetsing, CTIVD/TIB-samenvoeging, witte jassen en Palantir. Daarna Jelle's human-interest ransomwareverhaal: The Gentlemen RaaS werd zelf gehackt via de hostinglaag achter hun Rocket.Chat, waardoor Check Point kon meekijken in interne chats, payouts, AI-assisted coding en het kantoortje achter ransomware. Marco sluit af met Google Threat Intelligence over Chinese phishing-as-a-service: betere lokalisatie, RCS/iMessage en AI als contextversneller. Daarna het hoofdverhaal: Cloudflare heeft via Anthropic's Project Glasswing Mythos op meer dan 50 repositories losgelaten. Marco legt uit waarom dat niet neerkomt op "druk op knop, vind zero-days", maar op exploit-chain construction, proof generation, signal-to-noise en vooral: een hele vulnerability-research-harness met recon, hunt, validate, gapfill, dedupe, trace en report. Geen magische silver bullet, wel een duidelijke versnelling voor wie de workflow eromheen bouwt. Jelle pakt vervolgens McKinsey Lilli en BCG X erbij. CodeWall liet zien hoe interne AI-platforms zelf attack surface worden: publieke API-documentatie, endpoints zonder authenticatie, SQL-injectie, IDOR, miljoenen chats en files, system prompts, workspaces, modelconfiguraties en complete datawarehouses. Het echte verhaal: organisaties stoppen hun kennislaag, documenten, prompts en besluitvorming steeds meer in platforms. Wie daarin zit, zit bijna in het geheugen van de organisatie. Ronald en Marco sluiten af met het Mossad-pieperverhaal. Naar aanleiding van een nieuw Hebreeuws boek en een interview in The Jerusalem Post lopen ze door hoe de Hezbollah-pagers en walkie-talkies als supply-chain-operatie zouden zijn opgebouwd: techniek, infiltratie, Gold Apollo, BAC Consulting, Iraanse argwaan en de spanning tussen "ongelooflijk knap" en "hier zijn mensen door gestorven". *Bronnen* - Tweakers, "Slowaakse ESET koopt Nederlandse ESET": https://tweakers.net/nieuws/248036/slowaakse-eset-koopt-nederlandse-eset.html - ESET press release: https://www.eset.com/us/about/newsroom/company/eset-market-expansion-europe-asia/ - Follow the Money, "Geheime diensten gebruiken onafhankelijke experts om publiek debat te sturen": https://www.ftm.nl/artikelen/geheime-diensten-zetten-onafhankelijke-experts-in - Check Point Research, "When the Ransomware Gang Gets Hacked": https://blog.checkpoint.com/research/when-the-ransomware-gang-gets-hacked-what-the-gentlemen-leak-reveals-about-modern-ransomware-risk/ - Cloudflare Blog, Grant Bourzikas, "Project Glasswing: what Mythos showed us": https://blog.cloudflare.com/cyber-frontier-models/ - Anthropic, Project Glasswing: https://www.anthropic.com/glasswing - CodeWall, "How We Hacked McKinsey's AI Platform": https://codewall.ai/blog/how-we-hacked-mckinseys-ai-platform - CodeWall, "How We Hacked BCG's Data Warehouse": https://codewall.ai/blog/how-we-hacked-bcgs-data-warehouse-3-17-trillion-rows-zero-authentication - The Jerusalem Post, "Inside Israel's secret operation to turn Hezbollah's beepers into bombs": https://www.jpost.com/israel-news/defense-news/article-896890
Find out why the world's largest banks and enterprises trust CockroachDB for mission-critical infrastructure, and what a decade of AWS partnership means for the future of cloud-native data.Topics Include:Cockroach Labs makes CockroachDB, a distributed SQL database built for resilience.It delivers cloud-native consistency that legacy relational databases simply cannot match.The name "cockroach" reflects survivability — it's designed to never go down.Target customers include major banks, trading platforms, retailers, and gaming companies.AI is forcing enterprises to accelerate database modernization from the board level down.AWS has been a foundational cloud partner for Cockroach Labs for a decade.The CockroachDB-AWS integration spans EC2, S3, Bedrock, and Amazon Q-Transform.AWS partnership shapes both product roadmap decisions and go-to-market execution.New partners should educate themselves first — AWS programs are deep and extensive.CockroachDB now supports native vector search for RAG and generative AI applications.Agentic AI could mean trillions of digital agents demanding real-time data infrastructure.Database modernization and AI adoption will only accelerate dramatically through 2027.Participants:Cassie Zimmerman – Senior Director, Global Strategic Partnerships, Cockroach LabsSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
In episode 322, the co-hosts examine critical vulnerabilities, changing security standards, and adaptive defense mechanisms. They deep dive into the recent "Megalodon" breach, identifying it as a direct poisoned pipeline execution attack. Rather than exposing a flaw inside GitHub itself , researchers at Hudson Rock traced the root cause to credentials stolen from developer desktops via infostealer malware, which allowed attackers to push base64-encoded payloads into GitHub Actions workflow YAML files. To counter these types of automated supply chain threats, the hosts praise NPM's newly released "staged publishing" pipeline, which mandates two-factor authentication from human maintainers before releasing packages pushed by automated CI/CD workflows. Shifting to framework flaws, they highlight a catastrophic, vanilla SQL injection flaw discovered in GoCMS during active exploitation. Finally, the duo reviews the emergence of AI-powered honeypots highlighted Talos Intelligence. They conclude that turning the tables on attackers by utilizing LLM-driven "hall of mirrors" environments to impersonate real systems represents an innovative, under-explored AppSec strategy designed to drain attacker resources and trigger high token costs.
This episode is sponsored by XPLUS. In this episode, we explore how teams can move from reactive firefighting to proactive, evidence-based control. We cover why SQL, AOS, and Batch need to be seen as one correlated system, how custom metrics bridge the gap between technical data and real business activity, and what a structured performance optimization cycle actually looks like in practice - from the first alert all the way to a verified fix. Whether you're dealing with regressions after a One Version update, unexplained batch delays, or just the nagging feeling that your system could perform better - this episode gives you a clear framework for taking back control. More from XPLUS: · Curious how this works in your environment? Book a 30-minute call with our D365 performance team - https://xplusglobal.com/book-a-demo/ · D365 Partner Day: Observability, Testing & Performance: https://xplusglobal.com/event/d365-partner-day-observability-testing-performance/
Microsoft confirms active exploitation of two Defender flaws. Europol dismantles a VPN service tied to ransomware gangs. A nine-year-old Linux kernel bug exposes SSH keys and password hashes. Cisco patches a critical Secure Workload vulnerability, while Drupal fixes a highly critical SQL injection flaw. Android malware quietly signs victims up for premium SMS scams. Webworm upgrades its espionage toolkit with Discord and Microsoft Graph backdoors. Plus, China and Russia deepen cooperation on AI, cybersecurity, and satellite systems. Our guest is Jake Moore, Global Cybersecurity Advisor for ESET, sharing a glimpse into his Infosecurity Europe keynote "The Deepfake Interview." Greg doesn't even work here anymore… Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today, Maria Varmazis speaks with Jake Moore, Keynote speaker for the upcoming Infosecurity Europe conference and Global Cybersecurity Advisor for ESET, getting a glimpse into his session "The Deepfake Interview: Breaking In From the Inside." This interview is part of our partnership with Infosecurity Europe. Selected Reading Microsoft Defender vulnerabilities exploited in the wild (Help Net Security) Europol Seizes First VPN Used by Ransomware Gangs, Arrests Administrator (Hackread) Nine-Year-Old Linux Kernel Flaw Leaks SSH Keys and Password Hashes (Infosecurity Magazine) Cisco Patches Critical Vulnerability in Secure Workload (SecurityWeek) Android Malware Spotted Subscribing Victims to Paid Services Without Consent (Hackread) Drupal Patches Highly Critical Vulnerability Exposing Websites to Hacking (SecurityWeek) Webworm: New burrowing techniques (We Live Security) Xi and Putin pledge closer cooperation on AI, cyberspace and satellite systems (The Record) Zombie user account let hackers control the city's water (The Register) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices
This week we sit down with Field Solution Architects Anthony Nocentino and Justin Emerson explore an interesting convergence happening in data architecture—the blending of traditionally separate block and file/object storage systems. Likening the experience to a Chocolate Peanut Butter Cup, Anthony (a database expert focused on block storage) and Justin (an expert in unstructured data and file/object storage) discuss how the clear historical distinctions between structured and unstructured data are rapidly blurring. This shift is fueled by modern challenges like high-scale analytics, data governance, and the rise of technologies like Large Language Models (LLMs) and agentic interactions, which no longer care where the data lives. Our conversation dives into the technical tipping point enabled by data virtualization, referencing features like SQL Server 2022's object integration, which allows a database engine to access data stored efficiently on object storage. This capability is far more than an archival play; it helps customers achieve scale-out analytics, improve data governance by maintaining one canonical copy of data across different performance buckets, and simplify tedious operations like SQL backups by bypassing legacy file system complexities. Anthony and Justin highlight how Everpure's platform aligns perfectly with this new reality. Finally, Anthony and Justin discuss the path forward, noting that the technology is underutilized due to organizational silos and an awareness problem. The next big evolution will focus on security and governance for this distributed data via open table formats like Iceberg and catalogs such as Polaris. We close with what currently excites them: Anthony on collaborating with AI (Claude) to create code and speed up outcomes, and Justin on Everpure's core philosophy of simplicity, efficiency, and treating customers like people, particularly in the context of the current economic conditions. To learn more, visit: https://www.everpuredata.com/platform.html Check out the new Everpure digital customer community to join the conversation with peers and Pure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Career Journeys 04:30 Customer Engagement and SKO 09:55 Vacation Recap 13:45 History of Block and Object Storage 16:04 Why Convergence Now? 20:30 Data Virtualization 25:55 Exploring Access Patterns 29:05 What's Holding Back Adoption 36:02 Simplicity for DBAs
Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! The odds are stacked against you for remote data jobs. I show you how to flip them in your favor.
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Joshua Bate, founder of Bonfires.ai and DeciWorld, for a wide-ranging conversation covering knowledge management, graph technology, ontologies, decentralized science, and the future of how humans organize and share information. They break down the differences between personal and enterprise knowledge management, explore why flat ontological graphs may be the key to making diverse knowledge bases interoperable, and get into why traditional RAG systems break down at scale and how graph RAG offers a more principled solution. The conversation expands into the philosophy of categorization, the slow death of basic "gentleman science" under institutional pressures, and how decentralized protocols might restore a kind of mycelial knowledge network connecting small groups of researchers, enthusiasts, and communities — much like the original spirit of the encyclopedia before it was co-opted by institutions. You can learn more about Joshua's work at bonfires.ai and deci.world or follow him on X at @Bonfiresai and @DeSciWorld.Timestamps00:00 - Stewart introduces Joshua Bate, founder of Bonfires.ai, discussing personal versus enterprise knowledge management and their fundamental differences at scale.05:00 - Joshua explains ontologies as classifiers for knowledge structures, describing their two-year search for a perfect ontology and ultimately building a flat, ontology-less graph protocol.10:00 - Stewart connects categorization to shamanic practice and intercategorical theory, noting how major companies like Netflix and Yahoo built graph-based ontologies while the discipline remains underappreciated philosophically.15:00 - Joshua traces Bonfires origins through decentralized science, explaining how NFT community excitement inspired redirecting capital toward funding unconventional researchers locked out of institutional systems.20:00 - Joshua describes building federated knowledge networks through hackathons and conferences, comparing the vision to what Wikipedia could have been with decentralized incentive structures.25:00 - Discussion shifts toward inevitable collapse of rigid scientific institutions, debating patchwork age theory, nation-state fragmentation, and rhizomatic versus arboreal knowledge structures.30:00 - Joshua articulates the mycelial network vision, enabling direct cross-cultural information access where individuals control their own narrative lens, warning against collective we thinking and authoritarianism.Key Insights1. Knowledge management exists on a spectrum from personal to enterprise, but the founder of Bonfires argues this split is artificial. He believes knowledge itself does not respect those boundaries, and that small groups, researchers, hobbyists, and large institutions all possess knowledge that can and should interoperate with each other.2. After two and a half years of searching for the perfect ontology to structure their knowledge graph, the team concluded that no perfect ontology exists. Their solution was to build the flattest possible graph structure with only events, entities, and edges, creating a base layer others can build specialized ontologies on top of.3. Graph-based knowledge systems are more efficient than traditional databases for AI traversal because once a graph is computed, it is relatively free to query. Graph RAG combines the discovery power of vector search with the structured precision of graph traversal, solving many hallucination problems associated with standard retrieval augmented generation.4. Basic scientific research, the soil from which applied discoveries grow, is deteriorating because institutional funding structures only reward commercially viable outcomes. The founder built his platform partly to redirect community-driven capital toward researchers who are doing important work without institutional support.5. The institutionalization of science has historically blocked the open exchange of ideas that drove the original scientific revolution. The human spirit for open inquiry has not changed, but people cannot pursue it without financial support, and building decentralized infrastructure could restore that possibility.6. A federated knowledge network would allow individuals to access information from any contributor and filter it through their own preferred lens, rather than receiving information pre-filtered by centralized platforms. This represents a form of information symmetry similar to how mycelial networks distribute nutrients across a forest.7. The concern is not whether current scientific and governmental institutions will change but in what direction the rebuilding goes. Those capitalizing on the transition carry the same incentives as the previous era, which risks reproducing the same problems inside new structures.
Nik and Michael are joined by David Ventimiglia to discuss pg_flight_recorder, a new tool he created for monitoring a Postgres database from within. Here are some links to things they mentioned: David Ventimiglia https://postgres.fm/people/david-ventimigliapg_flight_recorder https://github.com/dventimisupabase/pg_flight_recorderSupabase https://supabase.compg_wait_sampling https://github.com/postgrespro/pg_wait_samplingpg_ash https://github.com/NikolayS/pg_ashpg_cron https://github.com/citusdata/pg_cronpg_tle https://github.com/aws/pg_tle~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork
Inductive Automation cofounders Colby Clegg and Carl Gould go deep on the origins of Ignition, the road to 8.3, and what AI means for industrial automation.Vlad and Dave host Colby Clegg, CEO, and Carl Gould, CTO, of Inductive Automation together for the first time to trace the full arc of the company. The story begins in 2003, when Sacramento systems integrator Steve Heckman brought Colby and Carl in to build the missing glue layer between OT data and modern IT tooling. What began as logging values into SQL databases became Factory PMI and eventually Ignition.A key thread is why Ignition broke through when larger automation vendors had superior distribution. Colby points to Clayton Christensen's Innovator's Dilemma. Incumbents could not match Inductive's unlimited per gateway pricing or partner with integrators because their own services groups competed with them. Carl adds the culture piece. Inductive refused to gate downloads, kept the module SDK open, made education free, and ran a public forum when competitors called it reckless, a posture they once called innovation without permission.Ignition 8.3 takes center stage, arriving after a deliberate five year gap from 8.1. Carl frames it as the completion of work that began with 8.0 in 2018. Gateway configuration is now stored in open, readable formats on disk, the gateway web interface was rewritten, and the platform supports orchestration, environmental separation, and infrastructure as code workflows Carl expects to become table stakes. The release also adds event streams, a revamped historian, and perspective drawing tools. For integrators still on 8.1, 8.3 is the version built for distributed deployments across many gateways.On AI, Carl is candid that the new MCP server module is intentionally a minimum viable product. It ships as a raw toolkit for integrators to author MCP primitives that expose Ignition data to agentic systems like Claude Code. First party MCP tools are coming, but Inductive wants to define the guardrails before shipping an API surface they will support for years. Carl frames AI as a new axis of software possibility, comparable to the shift from DOS to Windows. Colby ties it back to legacy SCADA conversion, framing the security and reliability gains as a national security issue. The episode closes with notes on the Inductive ecosystem, including a new collaboration with Tiger Data behind TimescaleDB, plus career advice on soft skills, context, and agentic coding tools.About Colby Clegg and Carl GouldColby Clegg is the CEO and cofounder of Inductive Automation, the California based company behind Ignition, the cross platform SCADA, MES, and IIoT software used by manufacturers and integrators worldwide. Carl Gould is the CTO and cofounder, leading product and engineering direction across Ignition. Both joined founder Steve Heckman in 2003 and have shaped the platform's open, integrator first philosophy ever since.Inductive Automation: https://www.inductiveautomation.comTimestamps0:00 Introduction1:00 Meet Colby Clegg and Carl Gould2:00 The origins of Inductive Automation in 20038:00 Going to market and the Innovator's Dilemma10:30 Innovation without permission as company culture18:50 Ignition 8.0 and the leap to Perspective26:00 The five year journey to 8.338:00 The MCP server module and AI in Ignition45:30 AI in the control plane and guardrails52:30 Tiger Data and the technology ecosystem1:02:30 Career advice for the next generation1:06:40 What is ripe for innovationReferencesIgnition Community Conference: https://icc.inductiveautomation.comAbout Your HostsVladimir Romanov is a cohost of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to reduce the risk of modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Colby Clegg on Ignition 8.3 and Industrial Automation: https://www.joltek.com/blog/industrial-automation-colby-clegg-ignition-8-3Connecting Allen Bradley PLCs to Ignition: https://www.joltek.com/blog/connecting-allen-bradley-plc-ignitionDave Griffith is a cohost of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub
En el episodio de hoy, el número 796, vengo con muchas ganas de contarte algo que me tiene completamente fascinado.Pero vamos a lo importante: las Skills o habilidades. Si creías que la inteligencia artificial era solo un chat donde escribir preguntas y recibir respuestas, prepárate, porque hoy vamos a ver cómo dotar a nuestros modelos de lenguaje de auténticos "superpoderes" técnicos.¿Qué son realmente las Skills?Imagina que en lugar de darle instrucciones genéricas a tu modelo (lo que conocemos como prompt), le proporcionas una estructura especializada. Una Skill es una herramienta transversal que le enseña al modelo a comportarse como un experto en una materia concreta. Lo maravilloso es que estas habilidades no dependen de un solo modelo; puedes usarlas con Claude, con OpenCode, con Hermes o con cualquier otro agente. Es una forma de democratizar el conocimiento técnico y hacerlo reutilizable.En este episodio te cuento mi experiencia personal utilizando estas habilidades para tareas que, de normal, nos llevarían bastante tiempo de configuración. Desde crear contenedores Docker optimizados hasta gestionar bases de datos complejas sin escribir una sola línea de SQL.Soberanía Digital y Potencia LocalYa sabes que me encanta el lema de "yo me lo guiso, yo me lo como". Aunque existen servicios externos muy económicos para correr estos modelos, nada supera la sensación de tener el control total. Te hablo de mi configuración actual: un Slimbook con una Nvidia GeForce RTX 4060 Ti de 16 GB de VRAM. Con este hardware estoy corriendo modelos como el Qwen de 35 billones de parámetros con una fluidez espectacular. Aquí es donde la soberanía digital cobra sentido: mis datos, mis reglas y mi hardware.Ejemplos prácticos: Docker y SQLiteA lo largo del audio, te guío por dos ejemplos que me han dejado con la boca abierta:Docker Expert.SQLite Expert.La Anatomía de una Skill: Bajo el capóMenciono también el increíble trabajo de Daniel Primo en Web Reactiva, quien ha profundizado muchísimo en este tema de las Skills y cuya guía ha sido una fuente de inspiración fundamental para experimentar con todo esto.Conclusión: El futuro es el lenguaje naturalCapítulos:00:00:00 El troleo a David y la importancia del feedback00:00:41 Introducción a las Skills: Dale "poderes" a tu IA00:01:14 Repaso a OpenCode y el paso a la soberanía digital00:02:11 Mi hardware: Slimbook, Nvidia RTX 4060 Ti y el modelo Qwen00:02:55 ¿Qué son realmente las Skills y por qué usarlas?00:04:18 Ejemplo práctico: Instalando una Skill para Docker00:04:58 Recomendación: La guía de Skills de Daniel Primo00:06:08 Generando un Dockerfile complejo para Rust en dos etapas00:07:34 Anatomía de una Skill: Front Matter, YAML y Markdown00:09:25 Cómo el agente gestiona los tokens y las habilidades00:10:48 Verificación del Dockerfile generado por la IA00:12:11 Trabajando con bases de datos: Skill de SQLite Expert00:13:24 Experiencia real: Revisando código Backend y Frontend00:15:38 Consultas en lenguaje natural sobre la base de datos00:17:40 Tipos de Skills: Percepción, Acción y Pensamiento Complejo00:19:47 Conclusiones: Programar sin programar y modelos locales00:20:29 Despedida y red de sospechosos habitualesMás información, enlaces y notas en https://atareao.es/podcast/796
What do colors, soup kitchens, and mountain climbing have in common? They're all part of the mental models that have shaped how we think about analytics, and they're exactly the kind of durable wisdom that matters more than ever in an age of AI slop. This campfire-style conversation among the co-hosts reveals the concepts, books, and aha moments that have stuck with us across decades of analytics work. From the magic of randomization to the critical distinction between outputs and outcomes, we share the frameworks that guide our thinking whether we're writing SQL by hand or asking Claude to do it for us. It turns out the most valuable analytics wisdom isn't about tools or techniques—it's about understanding how humans actually make decisions, build trust, and collaborate effectively. Some things never go out of style. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.
Mike & Tommy dive into Databricks Genie and the growing hype around data agents, exploring whether the real challenge is natural language chat or the semantic layer underneath—and what Power BI teams must fix before any AI agent can deliver trusted, governed answers at scale.https://www.advancinganalytics.co.uk/blog/genie-is-a-semantic-layer-problem-not-a-chat-problem-1https://community.fabric.microsoft.com/t5/Fabric-Updates-Blogs/OneLake-catalog-is-now-natively-available-in-Foundry-Generally/ba-p/5178376https://community.fabric.microsoft.com/t5/Fabric-Updates-Blogs/Direct-Lake-on-SQL-with-Fabric-Data-Warehouse/ba-p/5177641https://community.fabric.microsoft.com/t5/Power-BI-Updates-Blog/Modern-Visual-Tooltips-in-Power-BI-Generally-Available/ba-p/5173946Get in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/
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This is episode 326, recorded on May 7th, 2026, where John and Jason break down the Power BI & Fabric April 2026 Feature Summaries — DAX user-defined functions are here in preview, Direct Lake is flexing new modeling muscles, the Dataflows Gen1 community drama has a plot twist, Fabric Data Warehouse finally gets true transactional DDL, and VS Code integration in Fabric notebooks keeps leveling up. It's the April feature summary double-header. For show notes please visit www.bifocal.show
Daniel Wyrzykowski is a Product Manager at Mend.io. In this episode, he joins host Paul John Spaulding to discuss prompt injection, including what it is, whether it's the new SQL injection, and more. Securing The Build is brought to you by Mend.io, the leading application security solution, helping organizations reduce application risk efficiently. To learn more about our sponsor, visit https://mend.io.
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Tyler Cloutier, founder of Clockwork Labs and creator of SpaceTimeDB. They explore how SpaceTimeDB functions as more than just a database—it's essentially a distributed operating system that merges server logic with data storage, enabling real-time applications and time-travel capabilities. The conversation ranges from the technical architecture of databases and operating systems to the philosophy of distributed systems, touching on everything from Unix and Linux to how SpaceTimeDB could revolutionize AI-generated software deployment. Tyler explains how their system reduces the complexity of building real-time applications, makes deployment simpler for both humans and AI agents, and why games like their MMORPG BitCraft Online drove them to create this new infrastructure. They also discuss the future of the internet, the role of bots in gaming, and how SpaceTimeDB fits into the broader landscape of cloud computing alongside tools like Cloudflare, Vercel, and Docker. For more information, visit spacetimedb.com or check out Clockwork Labs on GitHub and Twitter.Timestamps00:00 Stewart introduces Tyler Cloutier, founder of Clockwork Labs, discussing the origin of SpaceTimeDB's name inspired by Einstein's theory and its time travel capabilities that store all operations indefinitely05:00 Tyler explains SpaceTimeDB as more of an operating system than a database, using tables instead of file systems while running code in a sandboxed environment with full atomic properties10:00 Discussion of how SpaceTimeDB replaces both Node.js and Postgres by merging web server and database functionality, eliminating separate deployment concerns15:00 Tyler explains JavaScript execution through Chrome's V8 engine and JIT compiling, leading to Node.js creation for server-side JavaScript development20:00 Explanation of stateless web servers versus stateful game servers, and why games require in-memory state management for real-time performance25:00 Tyler introduces reducers and real-time subscriptions, questioning why more applications aren't real-time when state changes should update immediately30:00 Discussion of Facebook as essentially a text-based MMO, comparing social media architecture to game server requirements and the need for unified systems35:00 Tyler explains ACID properties in databases: atomic, consistent, isolated, and durable, using game item trading examples40:00 Comparing SpaceTimeDB to smart contract systems without cryptocurrency or global consensus, positioning it as a smart database with centralized trust45:00 Tyler reveals SpaceTimeDB uses 43% fewer tokens than Postgres for AI-generated applications, making it valuable for vibe coding platforms50:00 Conversation shifts to bots in games and proof-of-human concepts, with Tyler proposing biometric systems and discussing potential in-person gaming applications55:00 Closing discussion about tracking AI-driven traffic through UTM parameters and finding SpaceTimeDB at spacetimedb.comKey Insights1. SpaceTimeDB is fundamentally a database that runs application code directly inside it, combining what traditionally required separate systems like Postgres and Node.js. Users compile their application logic into WebAssembly or JavaScript and upload it to run within the database itself. This architecture provides high performance because the entire server backend operates inside the database environment. The system also features time travel capabilities, storing every operation and change to data persistently and indefinitely, allowing users to set application state back to any earlier point in time. This makes SpaceTimeDB more accurately described as an operating system rather than just a database, where the abstraction is that everything is a table rather than a file.2. The inspiration for SpaceTimeDB came from building BitCraft Online, an MMORPG where all players exist in a single persistent world and rebuild civilization together. Traditional MMO backends required complex custom solutions to handle real-time state, with game servers storing state in memory and periodically writing to databases. This complexity existed because games cannot afford the latency of constantly delegating to distant databases like traditional web applications can. SpaceTimeDB solved this by making the database fast enough to handle real-time requirements directly, eliminating the need for separate game servers. This same performance advantage that benefits games also applies to web applications, which is why SpaceTimeDB evolved from a game-specific tool to a general-purpose platform.3. SpaceTimeDB functions as a distributed operating system where each database acts like a process in an actor model system, similar to Erlang or Scala Akka. Databases can send messages to other databases and be spawned across a cluster for horizontal scaling. This represents an overlay operating system running on top of Linux rather than competing with it, providing a distributed abstraction across many machines while Linux handles device drivers and hardware support. The vision is for the cloud to function as a single enormous computer running one operating system, where developers simply publish their programs without managing separate services, deployment, routing, networking, or persistence infrastructure.4. The real-time capabilities of SpaceTimeDB address a fundamental limitation in how most web applications work today. Traditional web servers are stateless, delegating all state to databases and accepting network round-trip latency for each request, which is why users often must refresh pages to see updates. SpaceTimeDB allows queries to be subscribed to, maintaining open connections that stream changes whenever query results update. This makes applications like Discord, Facebook, or banking systems naturally real-time without requiring page refreshes. The historical accident that more things are not real-time represents a problem SpaceTimeDB solves by unifying the web world with the game world's real-time requirements.5. SpaceTimeDB implements ACID properties—Atomic, Consistent, Isolated, and Durable—ensuring database operations are reliable and safe. Atomic means operations either fully happen or not at all, preventing issues like item duplication in games when trading between players. Consistent means declared invariants like unique usernames are always enforced. Isolated means concurrent operations do not interfere with each other. Durable means changes persist even if computers restart, with varying levels from in-memory on one machine to disk storage across multiple geographic locations. These properties are managed through reducers, functions inspired by React Redux that fold changes into application state incrementally.6. For AI and large language models, SpaceTimeDB offers significant advantages in building and deploying applications. Testing showed that creating applications with SpaceTimeDB uses 43% fewer tokens compared to Postgres implementations, costs less, has fewer bugs, and is easier to extend. This matters because the primary cost for vibe coding platforms is tokens. As more software gets written in the next twelve months than ever before, there is insufficient focus on infrastructure required to run all this AI-generated software. SpaceTimeDB positions itself as ideal for LLMs to target because of its simplified deployment model where developers just publish code and the system handles everything behind the scenes.7. SpaceTimeDB can be understood as a smart contract system without cryptocurrency or global decentralized consensus. Like blockchain smart contracts, it executes code with atomic, consistent, isolated, and durable properties, but avoids the expense and slowness of requiring all computers worldwide to agree on everything. Instead, it offers centralized trust where users trust Clockwork Labs not to modify deployed contracts, rather than the trustless but extremely costly blockchain approach. This makes it functionally similar to Cloudflare's durable objects but with full relational database capabilities. The system exists before the networking layer where Cloudflare operates, handling deployment, server, and database functions while Cloudflare could provide DDoS protection in front of it.
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Nik and Michael discuss Nik's new project PgQue, a descendent of Skype's PgQ, for running queue-like workloads in Postgres. Here are some links to things they mentioned: Our first episode on Queues in Postgres https://postgres.fm/episodes/queues-in-postgresPgQue https://github.com/NikolayS/pgqueHN discussion https://news.ycombinator.com/item?id=47817349PgQ https://github.com/pgq/pgqpgmq https://github.com/pgmq/pgmqRiver https://riverqueue.comKeeping a Postgres queue healthy (blog post by Simeon Griggs / PlanetScale) https://planetscale.com/blog/keeping-a-postgres-queue-healthyPostgres Job Queues & Failure By MVCC (blog post by Brandur) https://brandur.org/postgres-queuesMy queries to monitor autovacuum (blog post by Laurenz Albe) https://www.cybertec-postgresql.com/en/monitor-autovacuum-my-queries/SELECT FOR UPDATE considered harmful (blog post by Laurenz Albe) https://www.cybertec-postgresql.com/en/select-for-update-considered-harmful-postgresql/Christophe Pettus blog post https://thebuild.com/blog/2026/05/03/pgque-two-snapshots-and-a-diffOur episode on pg_ash https://postgres.fm/episodes/pg_ashRediscovering PgQ (Alexander Kukushkin slides) https://speakerdeck.com/cyberdemn/rediscovering-pgqTick frequency tuning https://github.com/NikolayS/PgQue/blob/main/docs/tick-frequency.md~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork
In Season 15 episode 2, Elixir Wizards Sundi Myint and Charles Suggs chat with Micah Cooper to talk about distributed systems, data replication, and what it actually looks like to build these ideas in Elixir. Micah shares his journey from Ruby to Elixir and walks us through Visor, a library he's building based on the Viewstamps replication algorithm. Inspired by systems like TigerBeetle, Visor explores how you can replicate state across nodes using GenServers, giving you fault tolerance and recovery without relying entirely on traditional database patterns. We talk about the difference between distributed systems and data replication, where things tend to get misunderstood, and what changes when you start thinking about state this way. The conversation also touches on event sourcing, tradeoffs in system design, and how Elixir's distributed model makes some of these concepts more approachable than you might expect. Along the way, we talk about building for curiosity, experimenting with new ideas, and how projects like this push the ecosystem forward. Topics discussed in this episode: Building Visor and working with the Viewstamps replication model Replicating GenServer state across nodes Distributed systems vs. data replication Lessons from TigerBeetle and financial system design Event sourcing challenges and tradeoffs Rethinking database-first architectures Snapshotting, recovery, and fault tolerance The role of Elixir's distributed model Experimentation, learning, and building for curiosity Links mentioned: Micah's GitHub https://github.com/mrmicahcooper Micah's GitLab https://gitlab.com/mrmicahcooper The Visor repository: https://gitlab.com/mrmicahcooper/visor Visor Hex Package https://hex.pm/packages/visor Ruby on Rails https://rubyonrails.org/ Phoenix LiveView Framework https://www.phoenixframework.org/ Zig Programming Language https://ziglang.org/ TigerBeetle https://tigerbeetle.com/ TigerBeetle internal docs https://github.com/tigerbeetle/tigerbeetle/tree/main/docs/internals The BEAM https://www.erlang-solutions.com/blog/the-beam-erlangs-virtual-machine/ GenServer https://hexdocs.pm/elixir/GenServer.html Apache Kafka https://github.com/apache/kafka RabbitMQ https://www.rabbitmq.com/ Redpanda https://www.redpanda.com/ SQL https://www.ibm.com/think/topics/structured-query-language Kubernetes https://kubernetes.io/ YAML https://yaml.org/ Nomad Workload Orchestrator https://developer.hashicorp.com/nomad Flutter https://flutter.dev/ Commanded https://hexdocs.pm/commanded/Commanded.html Go Programming Language https://go.dev/ Clojure Programming Language https://clojure.org/ Nebulex https://hexdocs.pm/nebulex/Nebulex.html Mnesia https://www.erlang.org/doc/apps/mnesia/mnesia.html Cachex https://hexdocs.pm/cachex/Cachex.html libgraph https://hexdocs.pm/libgraph/Graph.html Horde https://hexdocs.pm/horde/Horde.Registry.html NocFree split keyboard https://www.nocfree.com/ Micah's LinkedIn https://www.linkedin.com/in/micah-cooper-4a737560/
Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! Skills aren't enough to land a data job. Here's what Graham was missing and how we fixed it live.
Every B2B marketer knows the funnel concept is broken. Yet that same model your team is being measured on right now was invented in 1898, and it's surviving because nobody at the top has been given permission to use anything else.Carolyn and Amber react to a recent article in Adweek from Professor Mark Ritson, where he calls the funnel the "cockroach of marketing concepts", a 128-year-old model that has outlived every attempt to replace it. They break down why every critique fails to land, and why the real problem isn't the funnel. It's the classrooms teaching it, the boards demanding it, and the marketers who can't challenge it without risking their careers.Topics covered in this episode:Why a model from 1898 still anchors how B2B companies measure marketing in 2026The gap between what the funnel was designed to do (a market snapshot) and what it's used for (SQL-to-opp conversion, MQL targets)Why every "funnel is dead" critique fails to kill it, and who actually keeps it aliveThe Amazon "full funnel campaigns" moment, and what it says when even the best companies are still using the languageWhy a snapshot in time tells you nothing about where to move budget, why CAC is up, or what to do when pipeline missesIf you've tried to kill the funnel inside your own org and watched it survive every conversation, this is the episode. The cockroach isn't the funnel. It's the system that keeps demanding it.-----------------------------------------------------Want answers now?
This is episode 325, recorded April 17th, 2026, where John and Jason dig into the Real-Time Intelligence section of the Microsoft Fabric March 2026 Feature Summary covering topics such as Business Events, DeltaFlow for CDC, real-time processing with Spark notebooks, and some welcome quality-of-life updates across Event House and workspace monitoring. For show notes please visit www.bifocal.show
Show Notes: Scarlett Jiang from Vantage AI, an AI product and services firm based in London, provides a one-minute overview of Vantage AI, highlighting their focus on data foundations and AI transformation. Vantage AI helps companies consolidate data from various systems into a single source of truth. Scarlett mentions the firm's experience with hospitality franchise clients, such as Burger King, KFC, and McDonald's. Mock Demo of Chatbot Scarlett introduces a mock demo of a chatbot designed for hospitality franchise owners. The chatbot can handle real-time queries about sales data, labor costs, and other key metrics. Scarlett explains the process of using the chatbot to query data, including translating natural language questions into SQL, which means users do not need to know SQL. Custom Dashboard Scarlett introduces the custom dashboard with data intelligence analyst chatbot functionality, allowing users to query via human natural language and retrieve insights from pre-ingested data warehouses. Sales Performance The chatbot can provide summaries of sales performance, labor data, and other operational metrics. Sales Performance Rank Scarlett shows how the chatbot can handle more complex queries, e.g.: If I had to focus on 3 stores to improve performance this quarter, which would you recommend and why? (chatbot showcase the capability to synthesize sales, reviews, and trend data into recommended action) Performance Graph The chatbot can provide detailed insights into top and bottom performers, including specific metrics like net sales and transaction counts. Scarlett discusses the benefits of using a chatbot for specific questions, rather than pre-built dashboards. The chatbot can also provide reasoning behind its answers, showing the steps it takes to generate insights. The Process of Building AI Tools Scarlett explains the process of building AI tools, starting with a diagnostic phase to understand the client's data journey and use cases. After the diagnostic, a strategic roadmap is created to prioritize use cases. A quick prototype is then developed, followed by data foundation transformation. The process can range from a few days to several months, depending on the complexity and scope of the project. Accounts Payable Month-end Reconciliation Demo Scarlett demonstrates a workflow automation tool for account payable month-end reconciliation. Accounts Payable Reconciliation This demo presents a finance use case built around month-end accounts payable reconciliation - a process every finance team navigates. Supplier invoice data sits across two systems: the AP subledger, which holds granular invoice-level detail, and the general ledger control account, which carries a single summary balance that should match. In practice, the two rarely align - late-posted invoices and manual journal entries that bypass the subledger are the most common culprits. This demo showcases an AI agent that pulls data from both sources, identifies and reconciles the gap automatically; and surfaces discrepancies to human reviewers for sign-off or overwrite - eliminating hours of manual investigation at close. Converting PDF Purchase Orders into CSV Files Scarlett demonstrates a tool that converts PDF purchase orders into CSV files. Snowflake Tables The tool extracts key information from the PDF, such as contract terms, payment schedules, and expenditures. The tool can transform the extracted data into a chart format for easier analysis. Reconciliation Report Payment Breakdown The tool is designed to automate the process of working with large amounts of unstructured data, reducing manual effort. Cost and Development Time Scarlett discusses the cost and development time for AI tools, noting that prototypes can be developed quickly. The bulk of the work involves data cleaning, ingestion, and transformation to ensure data accuracy. The development time can range from a few days to several months, depending on the complexity and scope of the project. The cost varies based on the specific requirements and the level of automation needed. Demonstration Videos: Pre-recorded Demo 1: Data Intelligence demo https://www.vantageglobal.ai/insights/demo-pages/data-intelligence-analyst Pre-recorded Demo 2: Accounts payable month-end reconciliation agent https://www.vantageglobal.ai/insights/demo-pages/ap-month-end-reconciliation-agent Pre-recorded Demo 3: Parsing unstructured data to structured data https://www.vantageglobal.ai/insights/demo-pages/purchase-order-explorer-agent Timestamps: 02:24: Demonstration of AI Chatbot for Hospitality Franchise Owners 07:14: Advanced Query Capabilities of the Chatbot 13:06: Process of Building AI Tools at Vantage AI 17:40: Case Study: Account Payable Month-End Reconciliation 28:03: Case Study: PDF to CSV Transformation 34:42: Cost and Development Time for AI Tools This episode on Umbrex: https://umbrex.com/unleashed/episode-643-scarlett-jiang-coo-at-vantage-global-ai-shares-3-live-client-ai-use-cases/ Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com. *AI generated timestamps and show notes.
Building Repeatables in Claude: Skills, CLI vs MCP and Token Discipline | Go With The Flow Claude Skills, CLI vs MCP and Token Discipline with Ritu Java | Seller Sessions SEO Description Ritu Java and Danny McMillan on building agentic skills, choosing CLI over MCP, plan mode discipline and the short window to ship before token costs reset. Episode Summary Week 4 of the month, Go With The Flow, and Ritu Java is back from her travels. The world has shipped fast since the last episode: Codex 5.5, Claude 4.7, an Amazon Ads MCP and a fresh round of panic over the rumoured removal of Claude Code from the $20 plan (it was a 2% AB test, not a rollout). Ritu and Danny use the noise to make a sharper point: this is the moment to stop chasing models and start building repeatable systems on the platform you have already chosen. Ritu walks through the three eras of PPC Ninja's automation stack. Apps Script bulk file generators three years ago, Netlify hosted UI apps last year, and now agentic skills that her team chats with in plain English to produce upload ready Amazon bulk files. The same shift applies to data: BigQuery accessed through the Google Cloud CLI rather than through MCP, because CLI is leaner on tokens and works better when the job is heavy on data rather than tool surface. Danny mirrors the move with his event-ops CLI for WordPress, WooCommerce, Stripe and FooEvents reconciliation, and his four tier ExtractFlow cascade (HTTP, headless, stealth, agentic) that bypasses the limits of any single browser tool. The second half is a discipline talk. Plan mode every time. Push back on the first plan because Claude over engineers by default. 30% of your time on workflow scaffolding so the other 70% can be real building. The 21 day Claude rule: when a shiny new tool fires the dopamine, wait 21 days before refactoring around it. Left brain tasks (counting, SQL, deterministic logic) belong in scripts. Right brain tasks (judgment, creativity, hypotheses) belong in the model. Mix them inside a single skill. Skills are micro pieces of your workflow, not magic, and Claude can write them for you from an existing SOP. Key Topics The three eras of PPC Ninja automation: Apps Script, Netlify UI apps, agentic skills CLI vs MCP: when to choose each and why CLI is more token efficient for data heavy work Token economics, the rumoured $20 plan change and why it was a 2% AB test The short window before subsidised tokens get repriced Plan mode discipline and the "push back on plan one" rule Danny's 30 / 70 framework: workflow scaffolding vs building The 21 day Claude rule for resisting tool churn Left brain vs right brain task design inside a single skill The PPC Ninja "5 Whys" skill: deterministic SQL plus non deterministic hypotheses Claude.md, Gemini.md, Skills.yaml and the emerging Agents.md standard Skills for beginners: let Claude write them from your SOP Skill cascading: research, article, LinkedIn post, tweets, slide deck in one chain Timestamps [00:01] Welcome back, Week 4 Go With The Flow, Ritu returns from travels [00:17] Codex 5.5, Claude 4.7 and the "no one is writing code anymore" reality [02:01] Ritu on the three eras of PPC Ninja automation [02:42] Era 1: Apps Script bulk file generators in Google Sheets [03:46] Era 2: Netlify hosted UI apps with input fields [04:48] Era 3: Agentic skills, the bulk file skill trained on Amazon templates [06:22] Cloud talking to BigQuery through the Google Cloud CLI [07:00] Danny: what is a CLI and why it matters for token use [08:00] Amazon Advertising MCP vs CLI based access to the same data [09:33] WordPress horrible to drive via MCP, easy via CLI [10:00] Danny's event-ops CLI: tickets, food tickets, WooCommerce, Stripe reconciliation [12:13] ExtractFlow four tier cascade: soft, medium, stealth, agentic [13:46] Why CLI for the heavy stuff, MCP for the soft touch [14:13] AWS CLI: chat to Claude, push HTML blog posts live in two minutes [15:33] The overwhelm problem and the 5,000costbehindthe5,000costbehindthe100 plan [17:35] The $20 plan rumour: it was a 2% AB test, not a rollout [19:38] Build repeatables, not one offs [20:38] Danny: pick a platform and stop chasing benchmarks [21:16] The 21 day Claude rule for new tools [22:16] Plan mode every time, push back on plan one, get the second plan [23:02] Why am I building it, who is it for, what am I building [23:30] The 30 / 70 split: workflow scaffolding vs real building [25:13] Why long six to fourteen hour Claude runs are usually inefficiency [27:12] Compounding 1% a day across a year [27:47] "I build the things that build things" [28:00] Architecture vs apps: filling the gaps between A and B [29:06] Left brain vs right brain task design [30:01] Why throwing 80/20 at a sales drop diagnosis fails [31:33] The PPC Ninja 5 Whys skill: deterministic plus non deterministic in one flow [34:32] Claude.md, Gemini.md, skills.yaml and the agents.md standard [40:53] Beginners: let Claude write the skill from your SOP, use the interview pattern [42:39] Skill cascading: URL to research to article to LinkedIn post to tweets to slides [44:42] Mixing deterministic and non deterministic inside a single skill [45:39] Wrap up, signal to noise, who is it for Key Takeaways Pick a platform and stop chasing models. A new model ships every week. Time spent benchmarking is time not building. Double down on Claude (or whichever you chose), use the 21 day rule, and let the ecosystem catch up to the shiny thing in your feed. CLI for heavy work, MCP for soft touch. MCP loads tools and skills into context and burns tokens. CLI uses programs already on your machine. For data heavy jobs (BigQuery, AWS, WordPress at scale), CLI wins. For light cross app workflows, MCP is fine. Build repeatables, not one offs. Subsidised tokens will not last. The 100planreportedlycostsAnthropic100planreportedlycostsAnthropic5,000 to serve. Spend the window building scaffolding that compounds, not 14 hour vibe coding runs. Plan mode every time, then push back. Claude over engineers by default. Generate the plan, then say "you have over engineered this, although I want it elegant, go back and review." Plan two is the one you start from. 30% on workflow, 70% on building. Each new dependency, MCP, skill or repo you add to your workflow compounds across every future project. Stop building only the apps. Build the things that build the apps. Left brain in scripts, right brain in the model. Counting, SQL, deterministic logic belongs in Python the moment you can offload it. Save the model for hypotheses, judgment and creativity. The PPC Ninja 5 Whys skill mixes both inside one flow. Skills are micro pieces, not magic. Take an SOP, ask Claude to interview you with decision panels, and let it write the skill. Then cascade skills together: URL to research to long form article to LinkedIn post to tweets to slide deck. Notable Quotes "Instead of doing one offs, it is time to build repeatables. The more people can learn that skill now, the better it will be, because a year from now you may not have access to the same tokens." Ritu Java "If you see something and it looks sexy and it has sex and sizzle and your dopamine is screaming to go after it, wait 21 days. Either Claude will have it, or someone will have a repo, and you can combine it." Danny McMillan "Always use plan mode. Never accept plan number one. Tell Claude: you have over engineered this, although I want it elegant, go back and review. Then start from plan two." Danny McMillan "I build the things that build things. I build the scaffolding the team needs so they can build on top of it." Danny McMillan "Spend 30% of your time on your workflow and 70% building. The 30% compounds across every project." Danny McMillan "If we just hand six months of ad, organic, ranking and SQP data to Claude with no structure, it is going to mess up. It will give you an 80/20 you are not satisfied with, because it is not equipped to handle that volume without scaffolding." Ritu Java "WordPress is horrible to work with through MCP. It falls over all the time. CLI can be amazing for certain things." Danny McMillan Resources Mentioned PPC Ninja : Ritu's Amazon PPC software and agency, base for the BigQuery + CLI stack discussed Claude Code : Anthropic's CLI for Claude, the primary surface used in the episode Anthropic Claude : Claude 4.7 referenced as the current model OpenAI Codex : Codex 5.5 mentioned as the rival shipping fast Google Gemini CLI : Referenced as a sibling agent surface (Gemini.md) Google BigQuery : PPC Ninja's central data warehouse Google Cloud CLI (gcloud) : The CLI Claude uses to talk to BigQuery Amazon Advertising MCP : Amazon's official MCP server for ads data, referenced as the MCP comparison point AWS CLI : Used by Ritu to publish HTML blog posts to ppcninja.com from a Claude chat Netlify : Hosting layer for PPC Ninja's previous era of UI based apps WordPress and WooCommerce : Backbone of Danny's event-ops CLI FooEvents : Ticketing plugin that lives behind WooCommerce in the event-ops flow Stripe : Source of the card fee variation Danny reconciles via CLI ExtractFlow / CloudExtract : Danny's four tier extraction cascade (HTTP, headless, stealth, agentic). Open repo Playwright : The default browser automation tier inside ExtractFlow Agents.md : Emerging AI agnostic instruction file standard alongside Claude.md and Gemini.md Sequential Thinking MCP : The MCP Danny invokes when asking Claude to step through analysis Hosts Danny McMillan : Host of Seller Sessions, founder of DataBrill, building AI native tooling and CLI based workflows for Amazon sellers. Website: https://sellersessions.com LinkedIn: https://www.linkedin.com/in/dannymcmillan Ritu Java : CEO and co founder of PPC Ninja, Amazon PPC software and agency. Specialises in automation, BigQuery pipelines and agentic workflow design. LinkedIn: https://ca.linkedin.com/in/ritujava Website: https://www.ppcninja.com What's Next Next week: Ritu and Danny pick up routines and the new Claude scheduler. In 8 days: Seller Sessions Live 2026 in London on 9 May. Last week to lock in any final discounts. About Seller Sessions Seller Sessions is the leading podcast for serious Amazon sellers, hosted by Danny McMillan since 2017. Go With The Flow is the weekly automation strand where Danny and Ritu Java work through agentic flows, MCPs, CLIs and skills, in real time, on the same stack their teams ship every week. Episode published: 1 May 2026 Series: Go With The Flow (Week 4 of the month) Keywords: claude skills, claude code, cli vs mcp, mcp model context protocol, claude 4.7, codex 5.5, amazon ppc automation, bigquery cli, agentic workflows, plan mode, token optimisation, claude.md, agents.md, ppc ninja, ritu java, seller sessions podcast, go with the flow
In March 2012, the FBI surrounded a hurricane-rated steel door in Galveston, Texas. Behind it sat 30 year old Higinio Ochoa, drinking coffee in his boxers, flushing his one-time pad passwords down the toilet before letting federal agents inside. The operation to capture "w0rmer" had finally terminated.The process had initialized years earlier in childhood IRC rooms and 2600 chat channels. Ochoa taught himself to hack on dial-up connections, installing FreeBSD from thirty floppy disks at eleven years old. By his twenties, he was running cameras and internet infrastructure for Occupy Wall Street camps. When he witnessed police beating a woman having a seizure during a raid, something switched. The technical skills pivoted toward purpose.Cabin Crew launched with surgical precision. Ochoa mass-scanned police systems for SQL injections and admin pages, often not knowing which department he'd compromised until crafting the press release. He signed every hack, tagged every defacement, live-tweeted FBI taunts. His girlfriend posed in a bikini outside the Alabama Department of Public Safety holding signs that read "PwN3D by w0rmer" with GPS coordinates embedded in the photo metadata.Today he consults for governments and holds battlefield accommodations from Ukraine. The smooth hands that once broke into Secret Service-designed systems now defend critical infrastructure at levels where people could die if information leaks.TIMSTAMPS00:00 The Early Days of Hacking04:22 From Hobbyist to Activist08:30 The Shift to Purposeful Hacking13:16 The Rise of Cabin Crew17:58 The Psychology of Hacking and Branding21:11 The Origins of Wormer: A Hacker's Journey25:10 The FBI's Approach: How They Caught Me27:50 The Day of Reckoning: My Arrest Experience32:44 Life in the System: Mental Struggles and Adaptations36:18 Navigating Post-Prison Life: Challenges and Restrictions44:40 Navigating Life Post-Incarceration47:27 The Struggles of Redemption51:19 Finding Opportunities in a Stigmatized Field55:23 The Evolution of a Hacker's Journey58:46 Contributions to Information Security01:01:19 Words of Wisdom for Aspiring Hackers01:05:42 The Dream of a Cybersecurity Bar[Higinio “w0rmer” Ochoa – LinkedIn] - https://www.linkedin.com/in/x0hig Professional profile of Higinio Ochoa, a former Anonymous-affiliated hacktivist turned cybersecurity consultant, where he shares insights on security, research, and his work in the industry.[DEF CON Hacker Conference] - https://defcon.org/ One of the world's largest and most influential cybersecurity and hacker conferences, referenced in the episode as a key part of early hacker culture and later professional engagement.[Cybersecurity and Infrastructure Security Agency (CISA)] - https://www.cisa.gov/ A U.S. government agency focused on cybersecurity and infrastructure protection, mentioned in relation to responsible disclosure and ethical hacking initiatives.[Cloudflare] - https://www.cloudflare.com/ A global web infrastructure and cybersecurity company where the guest briefly worked after prison, playing a role in his transition into legitimate security work.[The Pirate Bay] - https://thepiratebay.org/ A well-known file-sharing platform referenced in the discussion about monitored internet usage and security research environments post-release.
В этом выпуске: гость Дмитрий рассказывает про Desbordante; декаплим некоррелированные предикаты в SQL подзапросах; Ваня продолжает мучить Snapmaker U1; Zed дорос до 1.0; а также делимся по мелочи — LocalSend и Gridfinity, и, конечно, темы слушателей. Важно! Запись выпуска 539 перенесена на 13 мая. [00:00:00] Чему мы научились за неделю LocalSend: Share files to nearby devices… Читать далее →
How do you turn complex regulatory data into something customers can actually use, trust, and act on? Recording live from Qlik Connect, I sat down with Robin Astle, Head of Qlik Analytics at Reconomy Group, to explore how data is becoming far more than an internal reporting tool. In Robin's world, it has become a product in its own right, helping some of the world's largest retailers manage compliance, reduce costs, and make smarter sustainability decisions. Robin works across Valpak, a business at the center of environmental compliance and packaging regulation, supporting over 100 enterprise customers across the UK, Europe, and the US. From packaging taxes and recycling targets to government submissions and sustainability reporting, the amount of data involved is enormous, and the stakes are high. In our conversation, Robin shares how the Valpak Insight Platform evolved from manual SQL extracts and spreadsheets into a fully scaled cloud-based analytics platform ingesting millions of rows of data every day. We discuss how that transformation helped reduce onboarding from weeks to days, created up to 90% time savings on CSR and analytics requests, and helped customers reduce compliance costs by up to 15%. We also explore the launch of PackChat, which uses natural language queries to help customers interact with compliance and packaging data without needing deep technical knowledge. Robin explains why context is everything when dealing with environmental regulations, and why building trust in the data model is essential before AI can deliver real value. There is also a bigger conversation here around how businesses can use data to serve customers directly, not just support internal teams. From OEM partnerships and cloud automation to scaling AI-powered services across global markets, Robin shares what it takes to turn data into a revenue-generating service. So as more organizations look to unlock value from the information they already hold, are we still thinking too narrowly about what data can do? And could your greatest untapped product actually be the data sitting inside your business today? Join me for a fascinating conversation from Qlik Connect, and let me know your thoughts. Are you still using data for reporting, or are you starting to think about it as a product?
France pushes digital sovereignty. Adobe rushes an Acrobat Reader patch. Booking.com confirms a targeted breach. SAP fixes a critical SQL injection bug. A sanctions-dodging fraud network resurfaces. ViperTunnel infiltrates U.S. and U.K. firms. GlassWorm spreads across developer tools. Researchers dissect Predator spyware's kernel engine. A lawsuit challenges AI transcription in hospitals. Ted Shorter from Keyfactor unpacks quantum computing at scale. On our Threat Vector segment, David Moulton and Elad Koren pull back the curtain on agentic-first security. Preparing for post-quantum perils. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today we are joined by Ted Shorter, CTO and Co-Founder of Keyfactor, discussing the advent of quantum computing at scale, known as "Q-Day". Threat Vector Host David Moulton speaks with returning guest Elad Koren, Vice President of Product Management for Cortex Cloud at Palo Alto Networks on this Threat Vector segment. Together they pull back the curtain on what an agentic-first security experience actually looks like in practice. This isn't a vision deck. The agents are already running. To listen to the full conversation, check it out here. Catch new episodes of Threat Vector every Thursday on your favorite podcast app. Selected Reading France Tees Up Big Public Sector Move Away From US Tech (BankInfo Security) Adobe rolls out emergency fix for Acrobat, Reader zero-day flaw (Bleeping Computer) Booking.com Confirms Data Breach as Hackers Access Customer Details (Hackread) SAP Patches Critical ABAP Vulnerability (SecurityWeek) Triad Nexus Evades Sanctions to Fuel Cybercrime (SecurityWeek) Ransomware-Linked ViperTunnel Malware Hits UK and US Businesses (Hackread) GlassWorm evolves with Zig dropper to infect multiple developer tools (Security Affairs) Predator Spyware's iOS Kernel Exploitation Engine: PAC Bypass, NEON R/W & More (Jamf Threat Labs) Lawsuit: AI Illegally Recorded Doctor-Patient Encounters (BankInfo Security) World Quantum Day (WorldQuantimDay) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices
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