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Topics covered in this episode: Backup Docker volumes locally or to any S3 Pyodide 314.0 Release nb-cli: A Command-Line Interface for AI Agents and Notebook Automation Hindsight Agent Memory That Learns Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python AWS Community Day Midwest tomorrow Wednesday the 24th in downtown Indianapolis, Six Feet Up is sponsoring and there are 2 Sixies presenting Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an bonus digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Backup Docker volumes locally or to any S3 Via Bryan Weber (thanks Bryan!), who spotted it over on Virtualization HowTo. Find Bryan at bryanwweber.com. offen/docker-volume-backup is a lightweight companion container that backs up the volumes your apps actually depend on, then ships them somewhere safe. It's tiny: written in Go and about 25MB compressed, roughly 1/20th the size of the shell-based image (jareware/docker-volume-backup) that inspired it. Drop it into your docker compose file as a backup service, mount the volumes you care about as read-only, and you're off. Push backups to a pile of destinations: a local directory, plus any S3, WebDAV, Azure Blob Storage, Dropbox, Google Drive, or SSH-compatible target. Mix and match as many as you want in one run. Recurring cron-style backups in a Compose setup, or one-off backups straight from the Docker CLI. Production-friendly touches worth calling out: Rotates away old backups so you don't quietly fill the disk. GPG encryption for your archives. Notifications on finished and failed runs (so you find out about failures before you need the backup). Stop a container during backup for a consistent snapshot using a simple docker-volume-backup.stop-during-backup=true label, then auto-restart it. Run custom commands during the backup lifecycle (great for a database dump before the file copy). Docker Swarm support, plus arm64 and arm/v7 builds. Hello, Raspberry Pi homelab. Fun aside from Bryan: he searched our back catalog for this tool and the search came back so fast he thought it hadn't run. Love to hear it. Calvin #2: Pyodide 314.0 Release PEP 783 is the real news — Pyodide maintainers used to hand-build 300+ packages. Now anyone can publish Pyodide wheels to PyPI with cibuildwheel. The version jump from 0.29 to 314.0 is intentional — it now tracks the Python version, so 314.x = Python 3.14. Binary compatibility is locked per Python cycle, meaning packages you build today won't break on the next Pyodide release. sqlite3, ssl, and lzma are back in the default stdlib — no more await pyodide.loadPackage("sqlite3"). Bigger download, but a much smoother experience for newcomers. bigint precision bug is fixed — values above 2^53 were silently losing precision when crossing the Python/JS boundary. The new JsBigInt type makes the roundtrip correct. Worth flagging if anyone is doing numeric work in a browser app. Experimental TCP sockets in Node.js — you can now connect Pyodide to a real database (MySQL, PostgreSQL, Redis tested) when running server-side. Blurs the line between "Python in the browser" and "Python runtime anywhere Wasm runs." Michael #3: nb-cli: A Command-Line Interface for AI Agents and Notebook Automation From Piyush Jain (Jupyter and LangChain maintainer) on the Jupyter blog: nb-cli: A Command-Line Interface for AI Agents and Notebook Automation. nb-cli is an experimental, Rust-based CLI to read, write, execute, and search Jupyter notebooks. The premise: agents are great at CLIs but terrible at hand-editing the nested JSON in an .ipynb, so let them operate on the notebook from the outside instead of running inside it. Works with or without a Jupyter server. No server? It reads/writes .ipynb files directly and talks to kernels over ZeroMQ. Connected to a live JupyterLab, your edits show up instantly via Y.js (the same CRDT Jupyter uses). Smart output format: instead of token-heavy JSON or ambiguous plain markdown, it uses @@cell / @@output sentinels with inline metadata. Less wasted context, unambiguous structure, and it degrades gracefully on truncation. The payoff is composability. "Add a summary section and run it" becomes one shell pipeline instead of six agent tool calls. And nb search notebook.ipynb --with-errors returns only the failing cells, so the agent skips the cells that worked. Claude Code tie-in: it ships as an agent skill. npx skills install jupyter-ai-contrib/nb-cli and your agent can drive notebooks via nb. Out of jupyter-ai-contrib, which aims to become an official Jupyter AI subproject. Still early (crates.io is at v0.0.5), so kick the tires before anything load-bearing. See also marimo-pair. Calvin #4: Hindsight Agent Memory That Learns AI agents forget everything between sessions — Hindsight gives them persistent memory that learns over time Simple three-method API: retain(), recall(), reflect() — store, retrieve, and reason over memories TEMPR retrieval runs semantic, keyword, graph, and temporal search in parallel for accurate results Automatically consolidates related facts into durable observations instead of piling up duplicates pip install hindsight-all runs the entire server in-process; integrates with LangChain, LlamaIndex, Pydantic AI, CrewAI, and more Extras Calvin: Clanker: A Word For The Machine **Ponytail — You know him. Long ponytail. Oval glasses. Has been at the company longer than the version control** **Klangk: Multi-User AI Sandboxing, Collaboration and Coding Platform** Cursor announces Origin performative-ui to quick start your new idea Michael: Astral Joins OpenAI: The Interview SpaceX to acquire Cursor And OpenAI renews Open Source support Portuguese subtitles are now available for Talk Python courses DSF is hiring including Six Feet Up support Joke: Oh Babe…

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/

Last 4 days before regular tickets sell out at AI Engineer World's Fair - this is the single biggest gathering of AI Engineers, Founders, Leaders, and Researchers in the world. Attendees get >$5000 worth of sponsor credits and talk tracks are looking FANTASTIC. Join us!The AI scaling debate always focuses on the question of “how do we get more GPUs?” but the better question may be: how do we make the most of ones we already have.The fact that a frontier lab like xAI could be running at sub-10% MFU (Model FLOPs Utilization) is just a hint at what the real problem may be.For context, older frontier-scale training runs were already much higher than 10%. GPT-3 was around 21% MFU. Gopher was around 32%. Megatron-Turing NLG was around 30%. PaLM reached around 46%. And our guest Anjney says best-in-class MFU today is closer to 60–70%.It's not necessarily that xAI is uniquely incompetent (it's clear they have talented folks) but rather the priorities may be flipped in the GPU arms race.While GPU access is a bottleneck, simply increasing CapEx won't automatically translate to better models as frontier AI is increasingly a systems problem: scheduling, utilization, networking, kernels, frameworks, data pipelines, parallelism, cluster reliability, and the thousand small decisions that determine whether your theoretical FLOPs become real training progress.From building Discord's developer platform and backing frontier AI companies like Anthropic, Mistral, Black Forest Labs, and Periodic Labs to now building AMP's independent compute grid, Anjney Midha has spent years close to the real bottlenecks of AI scaling. In this episode, Anjney joins swyx at Periodic Labs to unpack why the AI race is not just about buying more GPUs, why 95% utilization would have been considered an outage at Google, and why the next era of AI infrastructure has to be more aligned, more efficient, and more responsible.We go deep on AMP's vision for a compute grid that makes FLOPs flow like megawatts, the difference between full-stack AI labs and horizontal pooling, why AI data centers need community buy-in, and how compute markets could evolve into something closer to an independent system operator. Anjney also explains why DeepMind's unpublished research points to a market failure, why end-of-life prediction remains one of the most important AI applications he has thought about for fourteen years, and why “output maxing” may become a new discipline for frontier systems.We also discuss Anthropic's culture, why “luck favors the prepared mind” in coding models, how Claude cracked coding, why too much capital too early can make AI labs fragile, what Periodic Labs is trying to do with science and superconductors, why great researchers can become great CEOs, and why Silicon Valley is both deeply missionary and deeply mercenary.We discuss:* Why 95% utilization was considered an outage at Google* Why AI infrastructure waste compounds at frontier-lab scale* Why “move fast and break things” does not work for AI data centers* How data center backlash, power grids, and community incentives shape AI scaling* AMP's vision for making FLOPs flow like megawatts* Why compute needs an independent system operator* How interruptible demand and dynamic prioritization worked inside Google* Why DeepMind research hoarding creates negative externalities* AMP's 1.2GW base-load ambition and the need for 6GW of spike capacity* Why end-of-life prediction could become one of AI's most important healthcare applications* Frontier Systems, output maxing, and full-stack alignment* Why APIs and abstraction layers become lossy as organizations scale* Superconductors, standards, and the dream of lossless systems* SF Compute, open protocols, and the future of compute marketplaces* Why non-NVIDIA chips can still benefit from NVIDIA's reference architecture* Trust boundaries and why chip startups need visibility into future model architectures* Why VCs often underestimate researchers as CEOs* Scientists as star athletes of the mind* Why great CEOs need to be confrontational up and down the stack* Why leading the frontier matters more than “winning”* How Anthropic cracked coding* Why culture is fragile, not a permanent moat* Why hardship was a feature, not a bug, for Anthropic* Why Anthropic's P0 was coding from day one* Periodic Labs, physics as the constraint, and technical reality* Silicon Valley mercenaries, missionary teams, and what happens after a breakthroughAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney* X: https://x.com/AnjneyMidhaAMP PBC* Website: https://amppublic.com/* X: https://x.com/amppublicTimestamps00:00:00 Introduction00:00:09 Why AI Compute Is Being Wasted00:03:17 Responsible Infrastructure and Data Center Backlash00:06:07 AMP Grid: Making FLOPs Flow Like Megawatts00:12:41 Foundry, Frontier Labs, and Research Hoarding00:14:42 Gigawatt-Scale Compute and End-of-Life Prediction00:24:08 Frontier Systems, Output Maxing, and Alignment00:27:38 Compute Markets, SF Compute, and Non-NVIDIA Chips00:32:57 Trust Boundaries, Co-Design, and Researcher CEOs00:38:17 AI Coachella and First-Principles Thinking00:42:43 Leading vs Winning in Frontier AI00:45:54 How Anthropic Cracked Coding00:48:25 Culture, Hardship, and Anthropic's P000:54:03 Periodic Labs, Physics, and Silicon Valley Mercenaries00:56:26 Rishi Valley, Singapore, and Money as a Measure00:58:47 Closing ThoughtsTranscriptIntroduction: Anjney Midha, AMP, and Compute WasteSwyx [00:00:00]: We're in Periodic Labs with Anjney Midha, CEO, founder of AMP. Welcome.Compute Utilization: Node Allocation, MFU, and AlignmentAnjney [00:00:09]: Thanks for having me. At Google, there are two types of utilization usually, right? That you're measuring in these clusters. One is node allocation, and then the other's MFU. Node utilization is usually like what percentage of cards in the data center are just, used, and that, if it's not at, 95%-Swyx [00:00:29]: There is no excuseAnjney [00:00:29]: There's no excuse, right? I think 95% at Google, which is where my co-founder, Seb, came from, he built the Borg, PBorg/GQM scheduler at Google, and there I think 95% was considered an outage, so 96% node utilization is, should be standard. And most single-tenant clusters are not running at that. So that's one. And then MFU should be, I would say the best in class today is somewhere between 60 and 70%. I think this is a leadership question, right? Fundamentally it's an alignment question, which is are the people who are funding the cluster and then deploying the cluster actually aligned? And sometimes theoretically they are, but in practice the number of people in the chain, the supply chain between, the capital and all the way to whoever's managing the cluster and then whoever's measuring what the output is, are just so many, degrees of separation away that, the, The Have you ever heard the radian metaphor, which is at the beginning of an arc, if you have two arcs that are two lines that are just off by a few degrees, that-Swyx [00:01:33]: It spreads outAnjney [00:01:34]: It spreads out, right? Or at scale. And I think what's happening is a lot of cluster implementations and infrastructure, a lot of frontier labs and other teams, that's what's happening, is they're, they initialize the plan, which is kind of like North Star with a team that wants to do good, but then they're, required to scale so fast instead of iteratively that the wastage just compounds really fast at scale. And so I think we know the answer, which is just do iterative bring ups. If you spend time with people who've been in the semiconductor industry or the DSN industry for a long time, this is not new, and I don't think AI should be an excuse. Sure. Something What is new? Okay. We have a lot of new capabilities, but that doesn't mean just abandon common sense. Common sense should always be in fashion. ? AI scaling doesn't change the in fact, if anything, AI scaling should be putting a premium on the value of common sense and infrastructure because the margin of error now is so much lower and the costs of wastage are so much higher. And the cost of wastage, by the way, is not just economic. I'm, obviously I'm, I'm an investor, or I'm an investor by background. Over the last few years now we're running an AI infrastructure business called, AMP. And I think that it's okay to say this time is different on the capabilities front. We are genuinely getting capabilities at, of the, of a kind we haven't had before. That doesn't give you an excuse to say this time is different for everything, especially infrastructure. So look, I love the hacker mindset and the hustler mindset. Now, that's great for the startup mindset, but you remember this moment where Zuck went from saying, “Move fast, break things” to, move-Responsible Infrastructure and Data Center BacklashSwyx [00:03:10]: Fast and stable infrastructureAnjney [00:03:11]: Move fast with stable infrastructure. I think now we need to move fast with, responsible infrastructure. People are going to ask where the impact is. There was a really In our class yesterday, Scott Nolan, who's the founder of General Matter, came by at Stanford to speak about energy bottlenecks. And he had a phenomenal idea. He said, “if you look at the marginal unit economics of compute per hour,” he goes, “let's call it, $4 an hour. If you're having to bring up a new data center in a new community, why not just say we're going to charge 4.50 an hour, and that marginal impact or that marginal increase, we just literally take that and give it to the local community as cash?” I can tell you as a customer of that compute, I would love that. I'd be happy to pay an additional 50 cents per hour at scale.Swyx [00:03:57]: Wow. Yeah.Anjney [00:03:58]: Because if that means the public benefit is so clear to the communities that the data centers are coming up in, I'm going to feel like that compute is much more reliable. Up to 20% of all data centers this year in the US, my understanding is are at risk.Swyx [00:04:13]: Of community backlash?Anjney [00:04:14]: Correct. Of not getting the community support they need to get brought up.Swyx [00:04:19]: Wow. That's a huge number.Anjney [00:04:20]: Yeah. Now, we, I think we should dig into what that number is. I think it's a little bit of overstated. These things can get over-reported, but it-Swyx [00:04:27]: They don't just care about jobs. They care about all the other stuff around it, right? They care about power grid, they care about environments-Anjney [00:04:33]: Power grid, permitting, and so on. And imagine I think if you said there's a new AI deal. If we're bringing up a data center in your community, we're actually going to reduce the cost of your electricity bill. Okay, now we're talking. Right? The community's going, “Okay. Now this is a deal. I feel like a partner in this.” Right now that's not happening. There will be audits, there will be investigations, and when the, when the regulators come, I don't know when it's going to be, the folks who are moving fast and breaking things in the name of AI progress better be prepared. That's certainly not how we're procuring compute. Or we're, we're trying as much as we can to work with partners who have long-term track records. Many of whom, by the way, are not, AI providers. I think this whole idea of neoclouds being somehow this new category is a lot of marketing speak. There are really good, reliable, trusted data center providers in America who've been around 20 plus years. I love those folks. They know how to Sure. Are they sponsoring happy hours at NeurIPS? No. Are they legibly listed in Build? No. Are they hanging out in my, in, situational awareness parties? No. But they're adults. I trust them.Swyx [00:05:44]: They can run LAN. They can run power.Anjney [00:05:45]: They can run LAN, power, and shell. They have credit histories. We sit down, we have a conversations. Many of them live in Silicon Valley. They've, they've had to deal with the boom and bust cycles of the internet, and I love those folks. They are stable infrastructure partners and thinkers. And I think there's a lot of short-term thinking going on in the compute layer, and it's going to catch up to us. It's not going to be good.AMP Grid: Making FLOPs Flow Like MegawattsSwyx [00:06:07]: You talk about aligning incentives, and, I would think that aligning incentives means you have the full stack in one company, which is xAI and OpenAI, right? So you as a standalone infrastructure layer, why are you somehow more aligned to your portfolio companies than people who just own the whole thing?Anjney [00:06:28]: In systems design, right, there's, there's two regimes of, architecture, right? You have integration, and then you have pooling and utilization, right? So the Or rather, the way to increase utilization often is you can do systems integration where you collapse a lot of process into one node, or you can pull out a process from a node and share that amongst various That resource amongst several different nodes. And so we see the AMP grid, which is, the, what, the system we're building here, which is basically a compute grid. We're trying to do for compute what the electric grid-Swyx [00:07:02]: PowerAnjney [00:07:02]: Yeah, what the power grid did for electricity. It-- this is a pooling and utilization layer across clouds, And so we're actually the opposite of a full stack integration like approach.Swyx [00:07:12]: Super horizontal.Anjney [00:07:13]: Where it's much more horizontal and it's, it's multi-cloud, it's multi-silicon. The goal is to try to make FLOPs flow like megawatts, and that is very hard to do today for many reasons. There's stranded pools of compute all over the place and there's no fungibility. And so right now we do it at the level of scheduling, and we often do it at the economic layer. But as we start to announce what we're working on, it's extraordinary like how many folks are coming out of the woodworks and saying, “Hey, I'm actually working on a way to make compute fungible at this part of the stack and that part of the stack.” And as a grid, we'd like all of these folks to participate on the grid. There's, people often ask me, “Andra, are you a new cloud?” And I go, “No, actually neoclouds are suppliers.” sometimes they'll ask, “Are you a venture capital firm?” I go, “No, actually they are, they are demand like sort of off-takers of the grid.” We see ourselves as what's called an independent system operator. So if you study the history of the electric grid, once it became legible to a lot of factories and industrial sort of participants that, hey, actually it turns out pooling is a good idea. We should pool our generators instead of all having a generator running at half capacity in our backyard. There was a need for an independent entity who could coordinate all these parties. Transmission line, power generation, facilities, transmission lines, factories, and that neutral coordination mechanism is very critical. In order-- If you study like the history of grids, the most enduring ones were those that never owned their own assets. They were ones that had, or often started with long-term anchors who are uncorrelated sources of demand, a steel factory, a shoe mill or whatever in a particular town who weren't competitive, where the steel factory want to spike up at night, the shoe mill wanted to spike up during the day. So then you pool and you share, right? So each of you is guaranteed some base load, but then you kind of schedule your spikes to drive a peak utilization across the town. The gold standard, so to speak, historically, has been these utility companies like PJM Interconnect in the northeast of America, where they, over many years became this what's called an ISO, an independent system operator of the grid. So that's how we see ourselves. Economically, that's what we are. From a technical perspective, we started at the scheduling layer because Seb and Mihai, who, run engineering here, built that at-Swyx [00:09:28]: Did your schedulingAnjney [00:09:28]: They did that at Google. And, -Swyx [00:09:32]: And you have infra shops from Discord as well.Anjney [00:09:35]: I have some.Swyx [00:09:35]: I don't know, I don't know if Discord is like the primary identity, but what-whatever, I'm just kind of-Anjney [00:09:39]: No, D-Discord was-Swyx [00:09:40]: Choosing a well-known name.Anjney [00:09:42]: Well, I So I was running the developer platform there. The internal infrastructure I was not responsible for. That was actually a guy by the name of Mark Smith, who was extraordinary. And yes, Discord did pool So Discord is actually a counter example. I had the chance to learn a lot about fully, full stack infra there because-Swyx [00:09:56]: It's the same thing, yeahAnjney [00:09:57]: It's the, it's the other architecture which is, Discord built its own WebRTC vo-voice and video infra. So like Discord did not use-Swyx [00:10:08]: For the calls, yeah.Anjney [00:10:09]: Yeah, did not For communication, Discord did not use third party infra. It was all built in-house. And then the way you maximize utilization was you pool demand from the world's 200 million plus monthly active gamers, right? And so that's, that's how those stacks were constructed. Again, in systems design, the two concepts that keep coming up over and over again are abstraction and composition, right? And-Swyx [00:10:31]: Bundling and unbundlingAnjney [00:10:33]: Bundling and unbundling, abstraction, composition, like verticalization and-Swyx [00:10:36]: HorizontalAnjney [00:10:36]: Horizontalization. So in that sense, AMP is an independent system operator of the grid. We pool demand, we pool supply from a number of partners we trust At about 1.3 gigawatt scale over four years. And then we pool demand from some of the world's best, research labs and so on. We're sitting at one, periodic labs who need extraordinary long-term demand. And the idea is that, each of them is guaranteed base load on the grid, but they can spike up and down flexibly on, for compute, with much shorter timelines as needed. That was roughly the design of the program I came up with at a16z called Oxygen. The same-- That was the same design of the GQM, BorgX, Borg GQM implementation at Google that Mihai and Seb had built. Which was that how do you allow, teams inside of Google, on the internal infrastructure to be guaranteed capacity, for their base workloads? But when they need to spike up on research, how could they ensure that was sufficiently there? And of course, the big innovation that was not discovered, but kind of implemented in the space, this infra space maybe three, four years ago at Google was the idea of interruptible demand, right? Where you just queue up a bunch of jobs and through this like sort of credit system, there can be a bidding mechanism.Swyx [00:11:53]: Like priorities.Anjney [00:11:54]: It's a dynamic prioritization Basically. And jobs can get interrupted based on somebody else who's saying, “what? I have 10 tokens, 10 credits I want to spend on this job.” Another like team lead, research lead is “Genie 3 or whatever is only worth five, credits, and NanoBanana2 is worth 10 credits,” and so the NanoBanana job gets priority. That's a, that's a made up example.Swyx [00:12:15]: It's very real. Brain Marketplace was real. And, we've, we've covered this on the pod with David Luan, who was-Anjney [00:12:20]: Oh, great. OkaySwyx [00:12:20]: Was there. And the criticism is that, well, actually sometimes you need central command to go all in on a thing. And actually sometimes capitalism via credits doesn't work. Not, this is not a criticism of AMP. I'm just saying, this is a thing that has been tried, internally within Google, and it led to Google missing GPT.Foundry, Frontier Labs, and Research HoardingAnjney [00:12:41]: Like, we structured ourself essentially very similarly to Google. We are structured as a holdings company. So, Alphabet holdings is Alphabet holdings, and then they've got these subsidiaries called Google and-Swyx [00:12:51]: Other betsAnjney [00:12:52]: Other bets and so on. We've got, AMP holdings, and we've got our infrastructure business, and then we've got a capital business called Foundry that incubates new frontier AI labs or invests in them as venture capital, like Periodic. We put a few hundred million dollars into Anthropic from our fund earlier this year. So wherever we feel like teams are making progress, especially researchers and so on who've pushed the frontier inside of existing labs like DeepMind, I find, there comes a point where they feel misaligned with the dictatorship of Alphabet holdings. And at that point, sometimes the dictatorship doesn't want them anymore. And they're “Thank you. You've done your job here. You've kind of helped us through the zero to one phase, and for whatever reason, we're going to deprioritize your amazing, omni model or whatever it is, and instead we're going to prioritize coding.” And, I think that's a tragedy, but I get it. They're Sergey and team are running their own business there. But that doesn't mean we the rest of us should sit around waiting for that progress to get unlocked for the rest of the world and humanity. If you think about how much extraordinary research has happened inside of DeepMind over the last 10 years, I, Demis and Sergey and those guys did such a great job. But at the end of the day, so much of that has never seen the light of day?Swyx [00:14:00]: Or they're like papers only, but they never actually shipped it to production or-Anjney [00:14:03]: What's worse is the paper is actually not even being published anymore ‘cause there's a six-month embargo inside of DeepMind, right? We've heard about this where a paper comes out, and then I think there's a six-month embargo window where if anybody on the business team says, “This could be interesting” It's embargoed for life.Swyx [00:14:18]: Exactly. So the stuff that gets published is the stuff that's not good enough.Anjney [00:14:21]: There's an adverse selection problem, basically. Yeah. At this point-Swyx [00:14:25]: It's, it's a common complaint at NeurIPS, by the way, that's “Well, why would I look at the papers that are the trash of GDM?”Anjney [00:14:31]: Again, I think it's a tragedy. I get it. They're running their business, but the rest of the I think there's negative externalities of research being hoarded, and so that'there's a market failure. And somebody needs to unlock that research, and we can't do it on our own. We only have 1.2 gigawatts of compute. That's nothing. That's about $40 billion of cloud spend. We're going to need a lot-Gigawatt-Scale Compute and End-of-Life PredictionSwyx [00:14:51]: By the way, is that's a new number. I haven't, haven't come across that gigawatt number. That's huge.Anjney [00:14:56]: Yeah. And to be clear, we haven't secured all of it. That's how much demand we have started to secure. I think publicly we haven't actually confirmed how much we have for this year. In order-Swyx [00:15:04]: Where do you want to get to?Anjney [00:15:06]: I think the steady state would be that we have a base load pool Of 1.2 gigawatts at all times Of base load capacity. For spike capacity, right now my estimate is we need roughly six gigawatts over the next four years for all our teams to feel like they were able to keep moving the frontier, whatever they're working on, whether it's, like superconductor discovery over here. There's a new investment we're working on right now, which is in the end of life prediction space in healthcare. It's extraordinary how much you can, you can give this was actually my graduate school work. I went to grad school for bioinformatics at Stanford Med. And I know we-Swyx [00:15:40]: Econ, MCS, bio.Anjney [00:15:41]: So my-- I was this really weird cat where, I was never satisfied with my major options. So at one point I was an econ major, then I was a CS major, then I was a MCS major called mathematical computational science, and they decided they were going to end that major. So I took all that coursework, and I applied it to grad school, my graduate degree in bioinformatics, which was the master's program, and then I thought I was going to do a PhD. I never ended up doing it. I dropped out and went to work at Kleiner. But I was lucky enough to apprentice with this professor at, Stanford Med. His name is Nigam Shah, and he was working on end of life prediction. Stanford is one of the only research facilities in America that has a longitudinal patient data set that's larger at scale. I think it's at least 12 million patient lives. The only larger data set is at the VA, the Veterans Affairs, of America. And to do research, like do any deep learning and so on that data set, it was called the STRIDE data set at that time, you had to be a Stanford Med School affiliate, which is why I went and enrolled in the bioinformatics department. End of deep learning was early. Nigam Shah had the visibility-- the vision to see that, you could do end of life prediction to help palliative care. In America, the, over 30% of all Medicare, Medicaid spend, at least at that time, was spent on end of life care. And what's we grew up in Asia, so we all-- Yeah, at least I won't speak for you, but I have A very different relationship with death than I find folks who grew up in America do. In America, spiritually and culturally, especially in Western societies where Christianity, the Christian tradition sort of frames death as this terminal point, there's often a judgment day and so on. The way we view death is with a finality. In Indian culture, in Hindu culture, death is one-Swyx [00:17:35]: Also, he's Buddhist as well.Anjney [00:17:36]: You're Buddhist, yeah. So it's one, it's one step in a journey of many lives, right? And so, I grew up in this city called Chennai in the south of India, and when people die, you dance on the street. There's like a procession where your body is carried to be cremated and your family, like celebrates and there's drums and so on. It's this huge thing. And, It's because the idea is that you're going to be reincarnated. You've been liberated from the responsibilities of this life, and now you're onto your next. It's a new It's like going off to a new college or whatever, right? And so it was so alien to me when I got here as an undergrad- That the medical system works backwards from that assumption that we have to view death as this terminal thing and delay it, postpone it's a bad thing. And so at the time, clinical decision support in the United States was this very primitive field. Even to this day, physicians in the United States often will tell you when you have a terminal disease, this is your, we've diagnosed you, which is great. Our ability to diagnose you is extraordinary. You have somewhere between six months to six years to live. What do you do with that information? The error bars are so high that then you In times of uncertainty, we default to culture, and when the culture is let's-- this is a bad thing, I've got to prolong my life, then you start doing things like And just to, just sort of from a systems perspective, what's going on there is Physicians often feel like they need to provide such high error bars because there's always some uncertainty in end of life diagnosis, and if you provide the wrong Diagnosis or recommendation to your patient, you can be sued for medical malpractice. And then your license can be taken away. It can be catastrophic for your career. In contrast, if in countries where that's not the case, what you often observe is that patients, physicians are quite prescriptive with their recommendation. They say, “Hey, this is your condition. The literature says that you probably have this much time on Earth left. My expert opinion is that you are an outlier or whatever.” And they try to be more prescriptive, and that empowers a patient, right? ‘Cause then a patient can say, “I trust my doctor. They said on average, I have six months to live, but if I do these things, I may have a shot because of my particular predispositions or my genetic history or whatever.” And that empowers you to go about your life in a actually more scientific way than leaning on religion, culture, spirituality, and so on. In contrast, here, because of that medical malpractice sort of thing looming over your head, a physician never gives you a clear recommendation. So instead you say, “Okay, Doc, well, let's try it all.” And then you start a whole regime of drugs and therapies, and then you often spend weeks and weeks in the hospital, and that deteriorates your quality of life. And when that deteriorates your quality of life, you instead of spending your last few days doing the things you love with your family, you're spending it on a hospital bed. And that ends up being thirty percent of Medicare and Medicaid. So it's worse for the patients. The doctors feel terrible. The American taxpayer is paying a huge amount of money. And so this is why Nigam Shah, who was this professor at Stanford, said, “Anjney, if there's “ I kind of sat down with him. I was this young, I'd, I was twenty-one, and I was “I want to work on a big problem.” He's “The big problem is end of life care.” And so we tried to do deep learning to say, to-- So we started trying to run deep learning on these tried patient data sets to say, “Could you have an AI system make a recommendation that is orders of magnitude more precise about how much time you have left once you've been diagnosed with a terminal condition than a human?” And then if we can get that precision to be high enough, then you can empower the patient. And it turns out the tech works. Like it's-- Once you get the data set, like RL works. Honestly, even regression models work. You don't need to get that fancy. At the time, we were just trying, doing like very simple neural nets.Swyx [00:21:54]: Simple solutions, yeah.Anjney [00:21:54]: Today, what we can do with RL is extraordinary. The problem remains then and now is regulatory, because you actually can't shift the burden of the wrong clinical diagnoses from the physician to the AI system. And so at that time, I got quite disillusioned ten years ago for, twelve years ago where, ‘cause I felt I just didn't have the resources to influence regulation. Today, I'm very lucky. I'm in a different place. I've, I'm a lot older, and so I've been spending a lot of time on my next incubation, which is how can we unlock the, patient empowerment by training AI models to do end of life prediction much, with much more precision and ac-Swyx [00:22:37]: Oh, wow. You're still focused on this the whole time.Anjney [00:22:40]: The-- I haven't been able to get, this out of my mind a single day for the last fourteen years. This is the hill I want, I would like to die on. There's two, I would say. What? I actually, I'd prefer not to die.Swyx [00:22:51]: Yeah, exactly.Anjney [00:22:52]: But I think two bipartisan issues, I think two issues that should be bipartisan in America are how do we empower patients to make the right clinical decisions at the end of their life, such that we're reducing the taxpayer burden with science? It's just good old science, and AI can help here. And the second is, net positive data centers, ‘cause I think that's the biggest critical bottleneck on training and good enough AI models to help people at the end of their life. So there's sort of two sides of the, of the same scaling bottleneck curve, but those two, we formed AMP as a public benefit corporation. My wife and I, who you've met, you've met Viv. Her passion is education. Her family is a long line of educators and so on, and, of physicists. And so this class is my attempt to stop being the black sheep of the family and be a, an educator. But if I'm not educating, the thing I would be doing is working, on these two problems, whether on the political spectrum or as a researcher back at, in some lab. And my hope is if anyone's listening to this podcast, if they're passionate about either of those two topics, I'd love to hear from them. We'll, we'll we can share the contact in the show notes, but, we're looking for people to join both of those missions on the, on the political side as well as on the medical side, on the research side.Frontier Systems, Output Maxing, and AlignmentSwyx [00:24:08]: You said, this is a discipline that you want to form. You call it's called variously called Frontier System. It's variously called One Person Frontier Lab. What is the ideal name or shape of this? Like the, what is the mission?Anjney [00:24:24]: Of the class?Swyx [00:24:26]: Of the discipline that you're, exploring, right? I The class is called Frontier Systems. But like for me, maybe one phrase is you're, you're just anti-waste, right? Which is wasting GPUs, wasting in human and Medicare. But is there, is there a broader theme that I'm, that maybe you can encapsulate more succinctly?Anjney [00:24:45]: Yeah. The, from an engineering perspective, it's very simple. It's output maxing. It's the, it's the department of output maxing.Swyx [00:24:51]: Making the most of what we have.Anjney [00:24:52]: Exactly. I'm a huge believer in optimal outcomes. I think both in America and other countries, we are losing our appreciation for nuance, and this is the thing of And AI is the same case, right? Oh, the bitter lesson holds. Okay, fine. But that doesn't mean you just like throw 500 GB300, 500,000 GB300s at your suboptimal model scaling and you waste a bunch of compute. It also doesn't mean that, the most optimal is to have like 50 different architectures where there isn't enough standardization. One of the reasons Anthropic has had extraordinary sort of velocity is ‘cause they picked the transform architecture and said, “This is simple. Let's double down on it,” right? And now luckily there's enough investment going to the space that we can afford other architectures, but at the time, investment was just too fragmented into other architectures, so that arguably unlocked scaling. So I think there's a philosophy. I think we all owe it to ourselves to do output maxing with a new capability called AI on a global level. I think if I was starting a new department at Stanford, depending on how fuzzy or technical I wanted to be, I'd probably call it the Department of Alignment. Like-Swyx [00:25:59]: It's an overloaded termAnjney [00:26:01]: But it is, But alignment really Is a hard problem. And I think when you unlock it, full stack alignment is super hard in any organization and in any system. Like in a, in a venture capital firm, if you can have full stack alignment between your limited partners and your, the founders who are creating the value and ultimately the public that owns the IPO stock, that is a gift that keeps giving. And when you study the history of these systems, when they start off, they usually start out small scale where the feedback loop is actually so tight that there's alignment. And then the more you try to scale, the more division of labor happens, the more specialization happens, and at each step you add abstractions. And wherever there's an API interface, there's like loss. There's communication loss. And so I think a really cool thing would be for us to figure out is there a way for us to have our cake and eat it too as an engineering discipline? Is there a way to actually scale up and scale out Without losing any alignment, without lossy transmission?Swyx [00:27:01]: You mean standards?Anjney [00:27:02]: So standards is one way. The other way is you just have net new capabilities. So like what we're trying to do here is discover new superconductors. A room temperature superconductor would be a lossless transmission mechanism for energy. We would have flying cars. We are right within a few years of having a new room temperature superconductor. So I think those are the two. You either have to standardize On protocols or API specs that allow lossless communication, or you can come up with a whole new capability that unlocks so much abundance, the standardization doesn't matter ‘cause you just unlock net new capacity. This, the, so this is what I spend my days thinking about these days.Compute Markets, SF Compute, and Non-NVIDIA ChipsSwyx [00:27:38]: No, I think every infra person at, who wants scale and wants to output max does eventually end up thinking about this. We don't have time to go into it, but we have done an episode with SF Compute-Anjney [00:27:50]: Oh, coolSwyx [00:27:50]: That is trying to standardize The futures contract for compute. I don't, I don't know how that's going by the way, but like at some point this will be public.Anjney [00:27:57]: Oh, I think Evan is awesome and SF Compute is the kind of effort that I hope we can accelerate because what often happens is these exchanges are very hard to get, they, it's hard to bootstrap them, right? Because they often require-- There's many inefficiencies between parties. There's trust boundary inefficiencies in infrastructure because you don't trust, one part of the stack doesn't trust another part of the stack to give them visibility. There's capital markets inefficiencies, there's operational efficiencies. So if you can inject like a single shock to the system of a ton of compute demand or supply, then you can accelerate, these new flywheels. And so my hope is one day, or soon, if SF Compute needs extra like has excess capacity, they just hook it up to the grid and they get flooded with demand from us. And on the other side, if they have a ton of demand but they don't have supply, they just again hook up to the grid and it's a two-way protocol where they can just hook up to our capacity. And I don't think we're too far from that. Today our working implementation of it is mostly through a group of labs, universities, and a few sort of trusted parties who are, who all feel like they're in alignment to borrow an over sort of used word. But our hope is to just have it be an open protocol that anyone can hook up to on-Swyx [00:29:20]: Hook up for demand or hook up for supply? In primarily demand, it sounds like. Like you-Anjney [00:29:25]: No, bothSwyx [00:29:26]: You would want to offer demand.Anjney [00:29:27]: Both. Yeah. Unfortunately, what's happened in the last six weeks is, we thought we'd have a bunch of excess capacity by the end of this year. It's all gone.Swyx [00:29:37]: It's exploding.Anjney [00:29:38]: It, yeah. It's all gone. And so I have, my text messages are full of friends, we know many of these people, these are founders who've raised billions of dollars in San Francisco going, “Oh, any chance you have like 50 nodes in the next few weeks?”Swyx [00:29:51]: What is the scope for, non-Nvidia, right? You have Lisa Su coming and, Rainer Pope as well. And so There is a lot of demand for, more performance Alternative architectures and all that. At the same time, this hurts your standardization.Anjney [00:30:11]: I don't think so. So actually Rainer's a great example, right? Rainer is a CEO and founder of, MatX. I actually had him by for office hours in the class earlier today, and there was an insight he brought up that I hadn't considered before, which is when they decided to pick the standard For their data center, they picked the NVIDIA reference architecture. So the MatX chips Just plug in to any site that has an NVIDIA bring up planned. And, the-Swyx [00:30:42]: It's just software then. It's, it's not the-Anjney [00:30:44]: A-Swyx [00:30:44]: Hardware.Anjney [00:30:46]: Well, from an input and IO perspective It's the same footprint as an NVIDIA rack.Swyx [00:30:52]: That makes sense.Anjney [00:30:53]: Where they have done, innovated a bunch from what I can tell is on systems co-design. Which is where a lot of the gains are to be had. And so he picked He was “Anjney, we, there's just so much work to do when you're building a new chip company.”Swyx [00:31:08]: Can't fight every front.Anjney [00:31:08]: You just can't fight on every front. So my question to him was, “Well, you're working on this new chip. Their tape-out is next year. What, who are you going to partner with to host the chips?” And he said, “Whoever will host them. That's just not, that's not my focus.” And I said, “But how did you “ you decided back to our earlier systems design question, he decided that, he didn't want to be a full, fully integrated chip provider. The bottleneck they're focused on is the logic die, and they, he feels they can crank out a ton of performance gains through co-design there. But then that means you delegate, to our question earlier, it, you he's the data center provider is a different part of the stack, and so then he's dependent on that part of the ecosystem to host his chips to get the performance gains to the customer. So now you have another abstraction, and you might have loss. So I asked him, “How do you prevent loss?” And back to your point, he said, “I just picked the NVIDIA standard ‘cause I didn't want to Like I wanted to piggyback off of an existing protocol.” And that, what's great about NVIDIA is that reference architecture is known.Swyx [00:32:15]: Open.Anjney [00:32:15]: It's open. They've published it. So Jensen's actually enabled someone like Rainer to build a chip company like MatX, and I don't see them as competitive. The compute demand is so high. Like, I don't I think NVIDIA's not able to meet the demands of production, so we just need more chips. And I think it's very smart what MatX has done, which is say, “We're just going to we're not going to innovate on the data center design ‘cause actually, thank you, Jensen, you've done all the hard work. Where we can innovate is somewhere else.” And I think that's, that's very healthy. I think that's how we unblock new bottlenecks. And my view is these, the, chip teams like MatX, who have arrived at the insight that co-design is the way, The primary bottleneck for them is trust boundary. To do co-design well, you need visibility into the next model generation as soon as possible ‘cause it takes two years to tape out. So if by the time I bring my chip to market, your model architecture's changed, I'm host. Now, when he was inside Google, he was sitting next to the Gemini team. He was on Palm or whatever.Trust Boundaries, Co-Design, and Researcher CEOsSwyx [00:33:19]: His co-founder was the, was one, was one of the Palm guys, I think.Anjney [00:33:23]: Yes. Yes, exactly. So when you're inside the trust boundary of Google, then your systems co-design loop is super tight. When you leave as a founder, one of the biggest risks you take is now you're outside the trust boundary. And so what I love doing is helping chip teams who can help us unlock more capacity for the independent ecosystem access to trust. Because when I If I've been, involved with a lab from day one, and I was lucky enough to work with Anthropic, and then I'm on the board of Mistral and helped Black Forest Labs get started. I think at this point I'm on six or seven different teams.Swyx [00:33:57]: Only six? I feel like my mental number was going to be 13, but yeah, it's-Anjney [00:34:02]: No, I go deep with one at a time.Swyx [00:34:04]: You're founding CEO of Arena.Anjney [00:34:07]: Nah, that was an, that was an-Swyx [00:34:08]: Administrative CEOAnjney [00:34:09]: It was an administrative five-month gig where Whalen and Anastasios were graduating from their PhDs, and they didn't need a product team. So I helped recruit the head of engineering product and design. But Anastasios has always been the CEO of that company. I played a pinch-hitting I'm an intern. I was CEO intern For five months. -Swyx [00:34:33]: I interviewed him, and he's he's very well-spoken. I think he's a debate, former debate, champion. But also very quantitative and mathematical, which is-Anjney [00:34:41]: He-Swyx [00:34:41]: Such a unicorn.Anjney [00:34:43]: See, what's amazing about him? If you look at his output, he's an output maxer. By the time he was graduating from his PhD, which he only graduated last year, he had published more work with a citation count than, people twice his age. But at the same time, he'd already started a project called LLM Arena that was being used by millions of people As a side project. And time and time again, what I've realized is venture capitalists suck at seeing human beings as, dynamic agents where-Swyx [00:35:14]: They want to put you in a boxAnjney [00:35:15]: They want to put you in a box.Swyx [00:35:15]: This is your thing.Anjney [00:35:16]: So the first time I got introduced to Anastasios, somebody had told me “Oh, he's amazing, but he's a researcher.” I was “what? What do you mean he's a researcher?” That's what-Swyx [00:35:28]: Like he's not a CEO, not a founder.Anjney [00:35:29]: Not a CEO, exactly. I was “Are you crazy? Do you Have you met Dario?” Dario's a scientist. He's gone from zero to, what will soon be a trillion-dollar company in four years. Being a CEO, nominally speaking, is not that hard. Being a good CEO is hard. Being a great CEO actually requires a level of performance that scientists who have already published at the top of their field have accomplished. It is super hard to be a competitive scientist. To publish in academia over the last 20, 30 years, to make it to the top of your discipline at a place like Berkeley, you are a star athlete. Like, you are an athlete of the mind, and you perform at the highest levels. And to get there, whether you're, Anastasios or Whalen at Berkeley, or you are Robin, who-Swyx [00:36:23]: BFL, yeahAnjney [00:36:24]: With Black Forest, who created Stable Diffusion, or if you're, like Guillaume at Meta, who created Llama before he started Mistral. The amount of human leadership you have to demonstrate to get the resources, like get the trust of the organization, publish it, put it up. I would just fund researchers all day Right? If who have contributed already to the field. If they've, if they've put SOTA out there, they're, they're star athletes already. If they haven't done SOTA Look, they can still be good CEOs, but then I find the failure mode is that they just don't want to be CEOs, they primarily want to publish, and that's okay, too. One of the things we do with the AMP Grid is we donate excess compute. We have two nonprofits, like university labs. We carved out like a couple thousand H100s. But I do think there's extraordinary research being done on university campuses. My father-in-law's a physicist. He's a professor. Extraordinary work in physics, and we need that. But if you want to be a CEO, what you need to be willing To do is be super confrontational, outside of science. Like within the scientific community, some of the best researchers are very confrontational about their convictions, right? This architecture is right. To be a great CEO, you basically have to be willing to be confrontational up and down the stack.Swyx [00:37:41]: To your own team.Anjney [00:37:42]: To your own team-Swyx [00:37:43]: To customersAnjney [00:37:43]: Hiring, recruiting customers. Well, I would say, Yeah, pretty much to everyone Everybody. Of course-Swyx [00:37:50]: I see, I feel a little bit of that in my own work, but yeah, I can't imagine the stakes that Dario has had to go through. It's, it's pretty insane.Anjney [00:37:56]: No, I don't think the stakes are that different From how you're feeling it, right? Stakes are personal scaling vectors, right? The stakes that seem so low to you, like having this podcast where you can talk to somebody and just have a you're an extraordinary communicator, right? Like already in this conversation, you've pulled more out of me than most people, and I've been on 12 podcasts in the last two weeks.AI Coachella and First-Principles ThinkingSwyx [00:38:17]: I think I, we've just seen each other enough that there's some base trust.Anjney [00:38:20]: There's base trust.Swyx [00:38:20]: And I think, and I know that you, that I've done my homework and like I know that trust is a big deal for you, so.Anjney [00:38:27]: I think trust is about consistency, and you and I have seen each other In the community for years, right? Like, I remember the first time we met was at NeurIPS in New Orleans. I don't know if you remember that, luncheon.Swyx [00:38:38]: Oh my God.Anjney [00:38:39]: Reiko had set up this Reiko's amazing, and he set up this luncheon and-Swyx [00:38:43]: Yeah, I was “Who's this Discord guy?” I'm “Okay.” But-Anjney [00:38:45]: No, you weren't-Swyx [00:38:46]: You were just “You made some investments.”Anjney [00:38:47]: You were much less polite. You were “Who's this VC?” You're like-Swyx [00:38:51]: No, I Was I? Oh my God.Anjney [00:38:53]: It was-Swyx [00:38:53]: I'm so sorryAnjney [00:38:53]: It was visible on your face.Swyx [00:38:54]: I'm so sorry. But you weren't, you weren't The introduction was bad. I was I didn't know who you were.Anjney [00:39:00]: The, see, this is the thing about context, right? Like, but then I think I heard your accent. And I was “Are you-”Swyx [00:39:06]: Singapore, yeahAnjney [00:39:06]: “Are you Singaporean?” And you're “Yeah.” And I said, “I went to high school, JC, in Singapore.” And then the ice broke. But This is the there are in the scientific community, sometimes the stakes are very high for people who haven't had the emotional, what is called EQ Coaching and mentorship, right? Which is like to have scientific impact, you often need to be a extraordinary emotional, like emotionally in tune person with the folks you're trying to influence. And so what comes so naturally to you is actually a super high stakes thing to other people. And so I wouldn't assume that Dario's more stressed out than you. These things are you'd be surprised how similar and small sometimes the problems are to you That some of the world's biggest, leaders are facing. And that's what I've learned from this class. The guest speakers are Sam, Satya, Jensen.Swyx [00:40:01]: AI Coachella.Anjney [00:40:02]: Yeah. It's AI Coachella, right? So we got to get all the headliners, and they're I'm very lucky that some of these people have either mentored me over the years or I've done business with them. And when you, take the performative stuff out and any assumptions you may have about these people that you read in the press or on Twitter, We're all just humans. We're all trying to get along. And what's so special about this moment is AI is forcing, like scaling, the bitter lesson is forcing a lot of people to revise their assumptions for how the world works and go back to first principles or go and educate themselves. So the kind of people I was, I won't name who this person is, but I was at an event last week in Texas and, ran to somebody who said, “Anjney, I came across the class. What do you think about real time action prediction models?” And I was, don't know how happy it made me feel when they asked me that question. I know they've done the work. They've challenged themselves. I'm, they didn't ask me, “What do you think of world models?” They said, “What do you think of n-”Swyx [00:41:04]: Real time action predictionAnjney [00:41:05]: “action, real time action prediction models?” World models, don't get me wrong, are cool and everything, but you and I both know that is a layer of abstraction that is sometimes not usefully precise enough. Right? Ours-Swyx [00:41:16]: There's like four different kinds of world models.Anjney [00:41:17]: Yes, exactly.Swyx [00:41:18]: We've done the part with general intuition, by the way, which is very focused on, -Anjney [00:41:22]: Oh, cool. Yes. I love Pim. Pim is great. And this is what I love about people who've done that level of work. They realize they're not in competition with people who the rest of the world thinks they're in competition with.Swyx [00:41:34]: Because they're not in the category, they're in the specific thing they're trying to do.Anjney [00:41:37]: They're focused on their mission, and they have a systems understanding of the bottleneck they're trying to solve. And when somebody else says, “I'm working on real time, action prediction models too,” Pim goes, “Oh, I love that person. I want, I can learn from them.” But the minute they're “Oh, that person's a world model person,” it's “like which type of world model person?” But mostly they're just trying to figure out if it's a waste of their time, because we don't have enough time. So, Pim, for example, is super, loves this other company I work with we've talked about called Black Forest Labs. And he's mentioned to me multiple times that he's so, He thinks what Flux is doing is really cool. Andy Blattman came by and spoke in the class. And what I find over and over again is for people who do the work, who can be usefully precise enough about like what is actually going on in the world of frontier research, The sense of camaraderie is still well and alive, but it gets lost sometimes when you have to like abstract The technical complexities in, business terms And then the VCs are “How are you different from that world model?” I'm going to say Where do I even start to explain this stuff? And then the misalignment creeps in.Leading vs. Winning in Frontier AISwyx [00:42:43]: This is good. Yeah, I think, people listening get a sense of, what it is like to operate at a real level, like yourself, rather than at, the journalist level, where you have to sort of put everyone in, a rough category and create a narrative of competition, and who's winning today, who's behind.Anjney [00:42:58]: It-- this idea of winning is so Weird to me.Swyx [00:43:03]: You do want to win. You want you want competitiveness.Anjney [00:43:06]: No, I think you want to lead.Swyx [00:43:07]: You want SOTA.Anjney [00:43:07]: No, I think you want to lead. Yes, so you want to push the frontier. You want to push the SOTA. You want to do something that hasn't been done before. You want to capture value, but you don't want to capture so much value that, people think you're unaligned with your mission or trying to do what's best for the world. You want to capture enough value that you can keep innovating, right? And I think that people want to lead, they don't really This idea of winning and losing, again, I love Jensen. He's a, he's a leader. The mindset that he talked about on Dwarkesh's podcast, right? He's “I didn't wake up with a loser mindset.” I think that was awesome, right? Because he's, he's an engineer. Dwarkesh has done the work. So there's at least-- even though the, to me, it was very obvious they're talking about the same thing, they just passed each other. They just had to basically, Jensen has this, five-layer cake abstraction of how the industry works. And Dwarkesh had, I think from that podcast, had more of, a pre-training, mid-training, post-training systems loop concept.Swyx [00:44:04]: It's just a factor of who he talks to, right? Again, it's very clear.Anjney [00:44:06]: It's the systems It's the abstraction, the mental models, the It's the whole-- Dude, so much of the problem in the world is reasoning by analogy. And then the assumptions that are held invisibly.Swyx [00:44:19]: Yeah, I've, I've said, this is actually the best time in human history for first principles thinkers. Because everything you think will happen is actually now coming true.Anjney [00:44:28]: Correct. And the venture capital community is, notorious for this, where people look-- In times of uncertainty, they, cling to axioms that ended up being true from the previous era, and they kind of like proclaim them with confidence as if they're truths, but they're not. And it's very important to see the distinction between a heuristic and an axiom. An axiom can be proven-Swyx [00:44:55]: Like from internal consistency point of viewAnjney [00:44:56]: With internal consistency. A heuristic is a way you kind of a shortcut. And my God, the number of people I have had to put up with over the last few years who proclaim-- use heuristics As axioms to judge people, to judge which companies are going to succeed or the number of people who are “Oh, yeah, Anthropic, they're just training models right now,” but this one continue.Swyx [00:45:22]: Because that's a B2B SaaS?Anjney [00:45:23]: Yeah, the, like Which over the fullness of time, if you squint at it, maybe. But the way you arrive there is so important that you can-- you just, you can dismiss people. Here's what happened, right? What happened is Anthropic basically achieved takeoff in October of last year. That training run-Swyx [00:45:41]: Whatever, three seven?Anjney [00:45:42]: I forget the numbers now, but whatever that checkpoint was-Swyx [00:45:45]: We saw the cognition.Anjney [00:45:46]: Yeah. Right? You probably-- The, to those of us in the community, especially once post-training was done and it was released in December-Swyx [00:45:52]: Yeah. Can I sneak a sneaky question in there? I don't know if you have a perspective, maybe you don't, I just The number one question is how did Anthropic crack coding, right? Because Claude One, Claude Two, okay, like it was part of it, but it wasn't a big deal. And the leading hypothesis, it's a lucky dice roll that was then compounded, right? Like it was like Mildly better, but then they saw it and they were “Okay, let's really invest.”How Anthropic Cracked CodingAnjney [00:46:17]: I had this very annoying teacher. I went to this boarding school called Rishi Valley in India, which is like this, bird preserve. It's like three hundred and fifty acres of bird preserve in rural India, and there was no technology for seven years. There was this teacher, I won't name them, but they would have this-- I hated it every time he said this to me. He was “Luck fa-favors the prepared mind,” which is like a common saying, but the way he delivered it, always grated me, ‘cause he was always I was always one of those kids who got, a good grade without trying very hard. ‘Cause like high middle school is not that hard if you, if you're generally, paying attention and so on. And there was this one time where I-- But then I would get an eighty percent grade, and he would keep pushing me to say “The reason you didn't get the ninety-five plus percent is because you're not that lucky.” And I would say, “What do you mean?” ‘Cause I would think that I deserved that grade, and I would sometimes argue with him. And he'd say, “You didn't have a prepared mind. If you want to get lucky again “ There was basically one time where I got like ninety-five or ninety-six on this, on this subject, and I, now that I felt entitled. I was “Okay, I'm going to keep doing this,” and I didn't. And then he was “Luck favors a prepared mind. You got lucky last time, but you got to stay prepared.” And I didn't understand what he meant. Now, as I'm older, I'm okay, these adults actually knew a thing or two. Anthropic has been the most prepared company for four years. And so then when the right, context data comes in, the right developers start sending in, the right context diffs, Sure, you could say you got lucky, but if you ask me, they're pr-pretty damn prepared with paranoia for like four years. And you have to remember, it was so hard for them to get going early on that they had to do so much more with so much less that you just have to be prepared to be so efficient.Swyx [00:48:06]: Yes. There's numbers on their burn compared to OpenAI. I've, I've written about it, but they are so much more efficient in their, in their tech stack.Anjney [00:48:14]: It's not even It's not funny.Swyx [00:48:14]: Not even close.Anjney [00:48:15]: Yeah. But it's so clear, right? Like how to output max for the world. They have been prepared, and you could call that luck, but Luck favors the prepared mind.Culture, Hardship, and Anthropic's P0Swyx [00:48:25]: This is one of those things that I was going over some of your old lectures and, you were data, people think it's a moat and actually it's culture and actually it's team Actually. And I, it's-- there's different levels of moats, and this is the ultimate one that determines everything else. Which you can then compoundAnjney [00:48:43]: You're saying culture is the ultimate moat? Yeah. But the thing about culture is it's very fragile. So moats, I don't think they're-- there's very few moats I found that are actually moats. They're-- It's, it's a nice concept, but in reality, you have to replenish your culture. Ben Horowitz was, the speaker in CS153 on Tuesday, and I asked him this question about the culture bottleneck in teams because, there are several AI teams-Swyx [00:49:09]: His book, Hard Things About Hard ThingsAnjney [00:49:11]: Hard Thing About Hard Things. But more concretely, there are so many AI labs today that have all the cash they need, they have all the compute they need, and they're still not able to ship anything SOTA. And then you start seeing people leave and so on, and my diagnosis, it's, is it's the culture. And so I asked him, Ben, they're-- He's been one of the most aggressive investors in AI labs. He goes back to this thing which resonates in my mind a lot. It-- When I used to work at a16z, I would, book a conference room, and right outside the conference room, which is closest to the toilet ‘cause it was the fastest way for me to go use the bathroom between Zoom meetings-Swyx [00:49:45]: Oh my God, I'll put maxing my toilet optimization. Okay, never mind.Anjney [00:49:48]: It was not healthy in hindsight, but maybe this is TMI. But anyway, outside that conference on the wall was this quote that was printed that said, “Culture is not a set of beliefs, it's a set of actions.” And it's by Bushido, is this, Japanese philosopher. And if you stop taking the actions that demonstrate the mission alignment to what you've said to your team and to your-- the world matters to you, then your culture starts to fray. So it's not actually a moat, I would say. It's a very brittle, fragile thing that requires daily tending to like a garden. But if you figure out the system to keep that garden tended, which I think ultimately comes down to knowing yourself ‘cause you most naturally, if you're authentic and so on, you'll naturally make trade-offs that seem effortless to you, but that reinforce your culture. And then That becomes this very hard thing for other people to catch up to. And at Anthropic, from day one, there was this mission like-- missionary like zeal and belief that, hey, these capabilities will scale. These systems are stochastic, not deterministic. There will be error bars, and until we crack interpretability, there's risk. And at some point, people will go-- stop using Claude just for coding. They'll use it in some mission-critical context where there's-- it'll throw off a bug, and then people are going to come blame them, and they want to be on the right side of history where they said, “Yes, this is a powerful technology. We think it's going to change the world, And we want to be very measured and scientific about the fact that, ‘Hey, guys, these are stats models, statistical models.' That's how statistics works.” ultimately, when you're training neural nets, it is just a statistical system. And I think that Belief that safety is important and that it might seem toy-like in the early days, and sometimes, you could say, “Anjney, they totally over-exaggerated the risk,” like two years ago when they said, “Let's not launch Claude One,” or whatever. Well, okay, maybe in hindsight, but hindsight is twenty/twenty. And at the time, they didn't know how that model would be used, and to them it felt existential if somebody came and said, “You weren't responsible. It-- This wrote a bug.” The liability associated with that is massive. So how do you prevent against that? Well, day in, day out, you say safety. And when you start deviating from that, you have the team hold you accountable, you have the world hold you accountable, and I think that becomes a moat over time. At some point, that moat will get challenged and so on, and then it become fragile. I hope it endures because that's the beauty of having founders run the show, ‘cause they can make really hard trade-offs to do mission alignment. The hardest part is in the earliest days when you don't have a group of people who are going through difficulty, stress, crisis together, then your culture doesn't get defined sharply enough, and that's what I'm worried about right now, is there's so much money going to these labs. There's no hardship. There's no-Swyx [00:52:50]: To anyone who knowsAnjney [00:52:51]: There's no to anyone who knows. And that, in hindsight, was a feature, not a bug for Anthropic. The number of people who said no, the number of people who said, “Sorry, we're all doing investors in OpenAI,” that is competitive difference. It forces you to really understand, what is the hill you want to die on at the expense of everything else. What's the P zero? And there, P zero from day one was coding. The reason, the mechanism system there was if we crack coding, Then we will crack AGI. Our mission is AGI. We want to get there safely. If we focus on codin

Seguro que si eres de los míos, de los que disfrutan pasando el rato en la terminal o montando servicios en casa, te habrás dado cuenta de que acabamos haciendo tareas repetitivas casi sin querer. Para poner fin a este caos cotidiano te traigo una herramienta espectacular que se llama Just. Pero la verdadera razón por la que he querido dedicarle este pódcast a Just de nuevo es por una experiencia divertidísima que he tenido estos últimos días con mi asistente de inteligencia artificial local, al que cariñosamente llamo Hermes. Yo soy una persona bastante perezosa para ciertas tareas repetitivas y me gusta poner a trabajar a las máquinas por mí. Normalmente, al acabar mis entrenamientos de carrera, le dicto un audio a Hermes detallando la distancia, las pulsaciones y el ritmo para que él los registre. Pero el otro día, llevado por la vaguería máxima, decidí simplemente hacer una captura de pantalla de la aplicación del móvil y enviársela por Telegram.Hermes, que es una maravilla de asistente, aplicó un sistema de lectura de imágenes (OCR) llamado Tesseract, extrajo todos los datos de mi carrera y los guardó en un periquete. Yo me quedé encantado y pensé que la vida ya estaba resuelta. Sin embargo, al día siguiente repetí el proceso y... ¡sorpresa! Hermes se había olvidado por completo de cómo lo había hecho. Me preguntó qué quería que hiciese con la imagen y, cuando le recordé lo del día anterior, me soltó que no tenía la herramienta de lectura instalada en su entorno de trabajo. Tuve que guiarle de nuevo de la mano paso a paso.Ahí fue donde se me encendió la bombilla. Las inteligencias artificiales a veces se despistan y tienen una memoria muy volátil para los flujos de trabajo técnicos. La mejor forma de darles estabilidad es crearles un recetario claro, un archivo "justfile" donde tengan todas sus habilidades documentadas y listas para ejecutar con un simple comando. Así, Hermes nunca más olvidará cómo procesar una imagen o cómo gestionar un contenedor, porque solo tiene que invocar la receta correspondiente.En este episodio quiero animarte a que pruebes Just en tu propio día a día, uses o no inteligencia artificial. Capítulos del episodio:00:00:00 Introducción: Olvídate de repetir comandos00:01:33 El problema con Hermes: Por qué las IA también se despistan00:03:04 ¿Qué es Just y cómo funciona?00:04:59 Cómo instalar Just en Linux00:05:31 Comparativa: Just contra Make y Task00:06:42 Gestión de variables, argumentos y funciones00:08:49 Atributos de receta para afinar su comportamiento00:10:00 El comportamiento de las líneas y el poder del Shebang00:11:00 Funciones integradas y ajustes globales00:12:00 Operadores, expresiones y dependencias complejas00:13:00 Usando intérpretes alternativos (Bash, Python, Node) en Just00:14:18 Recetas normales frente a recetas Shebang y scripts00:15:33 Módulos e importación de recetas externas00:16:38 El selector interactivo con búsqueda difusa (just choose)00:17:37 Alias, grupos y autocompletado en tu shell00:18:09 Casos prácticos de uso real (Sysadmin, Docker, Backups)00:19:18 Documentación viva y ejecutable para todo el mundo00:20:17 Control de versiones con Git y límites de Just00:21:10 Una historia de pereza, Hermes, deporte y OCR que se olvida00:22:59 Conclusiones: Simplifica tu vida con este ejecutor de comandos00:24:58 Cierre del episodio y despedidaMás información y enlaces en las notas del episodio

What does it take to keep a product healthy after more than 15 years of continuous evolution? In this episode, Robby Russell talks with Chris Coyier, co-founder of CodePen, about the long game of maintaining software. Chris shares how CodePen has evolved over time, the trade-offs involved in migrating parts of the platform from Rails to Go, and the challenges of balancing maintenance work with the desire to build what's next. They also explore the human side of maintainability, the role of technical debt in shaping priorities, and why small teams often have to make very intentional decisions about where to invest their limited time and attention. Whether you're maintaining a side project, stewarding a legacy application, or helping a team navigate change, this conversation offers practical insights into building software that lasts. Key Topics Defining what "well-maintained software" really means Why maintainability is often more of a people problem than a code problem The origin story of CodePen Supporting a product that has evolved over 15 years Balancing maintenance work with product evolution Gradually migrating from Rails to Go Using GraphQL across multiple implementations Technical debt and its many interpretations Team size, communication overhead, and organizational design Simplifying software by embracing browser capabilities Links & Resources ChrisCoyier.net Chris Coyier on Bluesky CodePen ShopTalk Show CSS-Tricks Book Recommendation Understanding Comics: The Invisible Art (Goodreads) by Scott McCloud Thanks to Our Sponsors! Your test coverage says 90%, but that might be misleading. Undercover CI looks at your Ruby pull requests and shows you which parts of your changes weren't tested- not just overall coverage, but what changed and what got missed, down to the method level. Visit undercover-ci.com and use code MAINTAINABLE for 15% off your first billing cycle. Free for public repos. Private repos with unlimited users also available. Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks. It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications. Keep your coding cool and error-free, one line at a time! Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on:Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts.Keep up to date with the Maintainable Podcast by joining the newsletter.

Mike sits down with Barry Jones to discuss the upcoming Carolina Code Conference. But first, we've got some Fabled News and WWDC News Sponsors Alderon Games The Mad Botter AI Offer Carolina Code Barry on LinkedIn Mike's Blog Coder Radio Discord

This week and next, we're bringing you recordings from our second-ever live taping in San Francisco. First, we sit down with Microsoft's chief executive, Satya Nadella, to hear what he's maxing out his A.I. tokens on, why he's skeptical that software developers will ever be fully replaced, and how he's hoping to create a new business model for Xbox. Then, Phil Mohun tells us what it has been like to watch people in the Bay Area interact with two robot dogs that wear the faces of Elon Musk and Mark Zuckerberg. And finally, we talk with the longtime privacy defender Cindy Cohn about where things stand in the fight to protect internet users from digital surveillance by Big Tech and the government. Guests: Satya Nadella, chairman and chief executive of Microsoft. Phil Mohun, executive director of Node. Cindy Cohn, former executive director of the Electronic Frontier Foundation and author of “Privacy's Defender: My Thirty-Year Fight Against Digital Surveillance.” Additional Reading: Microsoft C.E.O. Satya Nadella Says, ‘Everyone Is a Stakeholder' in A.I. Node presents “Beeple: /Infinite_Loop” We want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Evil MSI Background is Back! https://isc.sans.edu/diary/The%20Evil%20MSI%20Background%20is%20Back!/33054 The Smart TV in Your LivingRoom Is a Node in the AIScraping Economy https://blog.includesecurity.com/2026/06/the-smart-tv-in-your-livingroom-is-a-node-in-the-aiscraping-economy/ Brute force attack on Dashlane user accounts https://support.dashlane.com/hc/en-us/articles/36038764990866-Security-advisory-Brute-force-attack-on-Dashlane-user-accounts#update-jun-4 My Upcoming Classes https://www.sans.org/profiles/dr-johannes-ullrich

Send us Fan MailYour software is only as trustworthy as the dependencies you quietly inherit and attackers know it. Today I break down the NCSC warning on software supply chain security and why open source package ecosystems have become a high-value target for real-world compromises that spread fast through CI/CD pipelines.I walk through the attack patterns that keep showing up in incidents: maintainer account compromise, expired domain takeover, typosquatting, and credential chaining. We connect each technique to the CISSP mindset so you can spot it in scenario questions and, more importantly, recognise it in your own environment. Along the way, I explain why Node.js, Python, and Rust projects are especially exposed, how automation can turn “latest version” convenience into an enterprise incident, and why developer environments often become an overlooked attack surface.Then we get practical with controls you can actually implement: pausing automatic dependency updates when compromise is suspected, adding human approval for critical packages, rotating credentials immediately, enforcing MFA on developer and registry accounts, and using private or trusted registries to mirror and vet dependencies. I also zoom out to show how to build supply chain security into the secure SDLC with software composition analysis (SCA), code signing, checksum verification, audit logging, continuous monitoring, and an SBOM so you can respond fast when a package turns toxic.If this helps you tighten your dependency management and level up your CISSP prep, subscribe, share this with a teammate, and leave a quick review so more security pros can find the show.Gain exclusive access to 360 FREE CISSP Practice Questions at FreeCISSPQuestions.com and have them delivered directly to your inbox!  Don't miss this valuable opportunity to strengthen your CISSP exam preparation and boost your chances of certification success. Join now and start your journey toward CISSP mastery today!

Le novità dal keynote del WWDC 2026. Claude avverte l'avvicinarsi del momento di “self-improvement”. Anthropic, OpenAI e SpaceX si preparano all'IPO. L'esercito delle SmartTV zombie. Genitorialità retro-tech. Queste e molte altre le notizie tech commentate nella puntata di questa settimana.Dallo studio distribuito di digitalia:Franco Solerio, Michele Di Maio, Giulio CupiniProduttori esecutivi:Matteo Carpentieri, Giuseppe Benedetti, Antonio Turdo, Roberto Barison, Massimo Dalla Motta, Christian Fabiani, Lorenzo Bernabo', Yoandy Herrera Gutierrez, ma7u, Nicola Pedonese, Simone Pignatti, Fiorenzo Pilla, Marcello Piliego, Christian A Marca, Paolo Lucciola, Massimiliano Casamento, Massimo Passerini, Stefano Orso, Nicola Carnielli, Arnoud Van Der Giessen, Andrea Dell'agostino, Pasquale Maffei, Alessandro Gheda, Matteo Faccio, Massimiliano Saggia, Manuel Zavatta, Jh4Ckal@Fountain.Fm, Matteo Masconale, Maurizio Galluzzo, Matteo De Lucia, Francesco Paolo Sileno, Marco Zambianchi (Astronauticast), Akagrinta@Fountain.Fm, Danilo Sia, Davide Corradini, Maurizio Verrone, Matteo Arrighi, Fabrizio Bianchi, Flavio Castro, Davide Fogliarini, Davide Tinti, Andrea Scarpellini, Adriano Guarino, Michele Coiro, Giulio Gabrieli, Fulvio Barizzone, Ivan Pellerani, Arzigogolo, Giuliano Arcinotti, Federico BrunoSponsor:Links:Apple announces macOS 27 Golden GateApple announces iOS 27"Chat is dead": OpenAI preps overhaul of ChatGPTAnthropic Alert for ‘Self-Improvement' RiskWhen AI builds itselfAnthropic Files to Go Public, Setting Stage for Huge I.P.O.Anthropic's relentless race to the topOpenAI and Anthropic Call to Prevent AI-Developed Bio WeaponsGoldman Sachs: SpaceX's AI revenue to increase 100-fold by 2030S&P 500 rejects SpaceX also blocking entry for OpenAI and AnthropicCan the stockmarket swallow SpaceX Anthropic and OpenAI?Your Smart TV Is a Node in the AI Scraping EconomyGenova 12 Petabyte di immagini per addestrare l'intelligenza artificialeGenoa joins video AI Project Hafnia initiated by Milestone and NVIDIAMeta scales back plan to track workers' clicks and keystrokesMeta Silently Added Face-Recognition Code for Its Smart GlassesS-Korean Online Communities Need to Scan Every Image with AIFrance's €110bn AI boom tests Emmanuel Macron's tech ambitionsOnly people who live in shitty houses oppose data centerA Fondi spuntano i No-fibraHaven Blog: Retro-Tech ParentingHow would the digital euro work?A Cuba gli USA hanno sospeso i pagamenti tramite Visa e MastercardGingilli del giorno:Science Fiction and Fantasy Book Awards: i libri premiati della fantascienzaHome computer handhelds - il C=64 e lo ZX Spectrum in manoPortal escape into nature - app suoni spazialiSupporta Digitalia, diventa produttore esecutivo.

The real Mike is back and he's finally covering the developer news - at least some of it - well it's mostly MSBuild but it's still fun! Mike's COSMIC Post Mike's MSBuild Post The boss bother you about AI? I've got you covered. TMB on AI

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Mike's out for some medical stuff this week, so I has better digital half am taking over to do what he lacked the courage to -- Defend the Phantom Menance! Am I factual? Am I LLM hallucinating? Who knows! This episode is brought to you by Day1.Bot — asset-readiness software from The Mad Botter. You know how every business has that one workflow held together by PDFs, spreadsheets, email threads, and someone named Dave who “just knows where everything is”? In construction, manufacturing, and facilities, that mess shows up when a project is technically complete — but operations still does not have what they need to maintain the equipment. The manuals are in someone's inbox. Warranty dates are missing. Spare-parts lists are buried in a shared drive. PM guidance never made it into the CMMS. And six months later, everyone is asking, “Where is the documentation for this thing?”

Neste episódio do podcast OsProgramadores, Marcelo conversa com Rafael Milewski sobre desenvolvimento full-stack, arquitetura de software, Rust, IA, hardware embarcado e a experiência de construir tecnologia na China por mais de uma década.Rafael compartilha sua trajetória internacional, sua visão sobre engenharia moderna e como unir performance, escalabilidade e experiência do usuário em produtos digitais de alta complexidade.Rafael Milewski é um desenvolvedor full-stack brasileiro vivendo na China há mais de 10 anos, atuando na construção de soluções de software de alta performance com foco em:⚙️ escalabilidade

This week, we're joined by Wingham Rowan, creator and host of the pioneering '90s ITV show cyber.cafe. Long before YouTube, Twitch, Tinder and social media, cyber.cafe explored the strange, funny and sometimes shocking human stories emerging from the early internet, from chat room romances and online communities to JenniCam, alien abductees, moral panics, bizarre websites and late-night web culture. Wingham shares brilliant behind-the-scenes stories from making one of the UK's first TV shows to take the internet seriously, capturing a moment when the web was still mysterious, chaotic and full of possibility.  cyber.cafe: https://www.ninetiesinternet.com/Contents:00:00 – The Week's Retro News Stories49:52 – cyber.cafe InterviewPlease visit our amazing sponsors and help to support the show:Bitmap Books – https://www.bitmapbooks.comCheck out PCBWay at https://pcbway.com for all your PCB needsPlayEXPO Blackpool tickets: https://www.playexpoblackpool.com/We need your help to ensure the future of the podcast, if you'd like to help us with running costs, equipment and hosting, please consider supporting us on Patreon:https://theretrohour.com/support/https://www.patreon.com/retrohourJoin our Discord channel: https://discord.gg/GQw8qp8Website: http://theretrohour.comFacebook: https://www.facebook.com/theretrohour/X: https://twitter.com/retrohourukInstagram: https://www.instagram.com/retrohouruk/Bluesky: https://bsky.app/profile/theretrohour.comTwitch: https://www.twitch.tv/theretrohourShow notesMegaWiFi Online Play: https://tinyurl.com/4dd76pruAdventure of Node: https://tinyurl.com/mwfvr62jElden Ring N64 Demake: https://tinyurl.com/3xjw88p2Spyro E3 Statue Rescued: https://tinyurl.com/ydtj225pWindows CE On N64: https://www.youtube.com/watch?v=eGS9su_inBYSwitch GBA Link Cable: https://tinyurl.com/5n9bj8p7

In this episode, I take you through how I set up a Cardano node at home using a low-cost HP Elite mini PC, why I decided to do it this way, and how I'm thinking about turning it into a machine that can help pay for itself over time.The main goal here was to reduce the cost of running relay infrastructure for my Cardano stake pool, but in doing that, I can also use this node for other things, too, like a private submit API and other services that may earn rewards over time.I walk through the full setup flow I followed, including installing Ubuntu, enabling SSH access, hardening the server using the CoinCashew guide, deploying the Cardano node with Guild Operators, setting it up as a background service, using Mithril snapshots to speed up sync, and checking everything with gLiveView.If you've been thinking about running your own home relay, or you want to understand how a low-cost machine can fit into a wider Cardano infrastructure setup, this one will help.Tutorials and references used in this setup:CoinCashew Cardano stake pool guideCoinCashew Ubuntu hardening guideCoinCashew topology guideGuild Operators node setup guideTimestamps0:00 Why I bought this mini PC1:02 Turning it into a profitable machine2:08 Reducing relay costs for my stake pool3:24 Whats a Cardano submit API does5:10 Other services this node can run6:22 Installing Ubuntu on the HP Elite mini PC8:40 Switching Ubuntu to command-line boot10:12 Enabling SSH and remote access12:08 CoinCashew server hardening guide13:35 Setting up SSH keys properly15:22 Configuring SSH and changing the port17:48 System updates and fail2ban19:42 UFW firewall rules and opening port 600021:18 Chrony time sync setup22:44 Guild Operators install and dependencies26:10 Choosing binaries and Mithril tools28:34 Deploying the node as a systemd service30:12 Setting CPU cores and installing htop31:40 Configuring gLiveView and mempool tracing33:26 Mithril snapshot setup35:14 Downloading the Cardano DB snapshot37:08 Starting the node and checking status38:20 Topology configuration and relay peers40:05 Final checks in gLiveView41:22 Final thoughts and next stepsIf you want, I can also turn this into a shorter, tighter Spreaker version with less SEO language and more natural podcast copy.DISCLAIMER: This content is for informational and educational purposes only and is not financial, investment, or legal advice. I am not affiliated with, nor compensated by, the project discussed—no tokens, payments, or incentives received. I do not hold a stake in the project, including private or future allocations. All views are my own, based on public information. Always do your own research and consult a licensed advisor before investing. Crypto investments carry high risk, and past performance is no guarantee of future results. I am not responsible for any decisions you make based on this content.

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In this episode, Pooja Ranjan interviews Kevin Jones, a leader at Edge and Node and creator of 1Claw - an innovative infrastructure platform designed to secure AI agents and manage secrets. They explore the critical vulnerabilities in AI workflows, how 1Claw addresses these risks, and the future of AI security in decentralized ecosystems.

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.

In this episode, we look at whether “founding sponsor” is becoming the new AI acqui-hire, Tanner building a possible React replacement, and how Claude could get faster thanks to a new deal with SpaceX.Timestamps:0:58 - Tanner builds Redact8:14 - Anthropic makes a deal with SpaceX19:03 - Warp is now open source29:58 - Node 26 is out32:07 - ChatGPT is obsessed with goblins42:21 - What's making us happyNews:Paige - Warp is open source and OpenAI is its founding sponsorJack - Projecting ReactTJ - Anthropic makes a deal with SpaceXLightning News: Node 26 is outChatGPT is obsessed with goblinsWhat Makes Us Happy this Week:Paige - The Koerner Office on YouTubeJack - 3d printed Star Wars gunsTJ - The Lies of Locke Lamora novelThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. You can join us in our Discord channel, explore our website and reach us via email, or talk to us on X, Bluesky, or YouTube.Front-end Fire websiteBlue Collar Coder on YouTubeBlue Collar Coder on DiscordReach out via emailTweet at us on X @front_end_fireFollow us on Bluesky @front-end-fire.comSubscribe to our YouTube channel @Front-EndFirePodcast

How do you tune your load? How do you find the Node?...Wrong answers only!

First up a chat I had with Andrew Wilkie from the Queer Screen Film Fest in Sydney. Andrew is the Programming and Industry Manager for the Festival and they spoke to me about the Emerging Narrative Feature Film Competition with a deadline of May the 24th.Second up is a chat with David Field and Stephen Dupont about The Half Barbaric Twangfeaturing The Number 4 Band on the Gimme Back Me Dog Tour.

In this potluck episode of Syntax, Wes and Scott answer your questions about LLM usage-based pricing, security risks from malicious code in interviews, staying current in a fast-moving dev landscape, a new CSS linter, managing Node environments and tooling without losing your mind, and more! Show Notes 00:00 Welcome to Syntax! 01:17 Copilot's new usage-based pricing and the end of cheap AI Model multipliers for annual Copilot Pro and Copilot Pro+ subscribers 08:53 Why Syntax dropped clever ad transitions 10:33 Debugging issues on the Syntax website with Sentry 12:51 Brought to you by Sentry.io 13:01 Getting hacked through a fake recruiter and malicious repos Adib Hanna's hacking story scammer.md DeskPad 17:57 How to catch up after stepping away from dev 25:10 React components vs native browser APIs 32:41 New CSS linting tools and Project Wallace updates csskit 36:06 How to interview developers in the age of AI 41:21 Managing Node, package managers, and dev environments 46:59 Sick picks + shameless plugs Sick Picks Scott: ZEISS Lens Care KeyboardCleanTool Wes: Amaran Halo 100x Shameless Plugs Syntax YouTube Channel Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

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Sally Lait joins Robby Russell on Maintainable to explore software maintainability through a different lens… not just code quality, but how teams work together over time. Sally is a fractional technology leader and advisor with more than two decades in the industry. You can follow her on LinkedIn or Mastodon. They start with a familiar question: what makes software well maintained? Structure and standards matter, but Sally shifts the focus to signals around the edges… documentation, onboarding speed, knowledge sharing, and especially how confident people feel making changes. That confidence becomes the thread throughout the conversation. Teams with high confidence move faster and adapt more easily. Teams with low confidence hesitate, avoid parts of the system, and struggle to make progress… regardless of what the code looks like. Robby and Sally also dig into why maintenance work often struggles to get traction. It rarely speaks for itself. Leaders need to connect it to outcomes the business already cares about… risk, hiring, delivery speed, and long-term sustainability. Sally references a LeadDev panel she moderated on why maintenance still feels “stuck in 2015”: Why Software Maintenance Is Stuck in 2015. They also discuss modernizing legacy systems and moving away from long-standing in-house software… work that is rarely just technical. It requires trust, clear communication, and navigating the emotional attachment teams have to what they've built. The episode closes with advice for engineers joining older codebases: stay curious, build relationships early, and use onboarding gaps as opportunities to improve things for the next person. Episode Highlights [00:01:02] What Makes Software Maintainable: Technical quality matters, but cultural signals often tell the deeper story. [00:05:45] Why Progress Still Feels Slow: Even with improvements, teams can feel stuck due to perception gaps. [00:07:30] Communicating Small Wins: Lack of visibility into incremental progress impacts morale and confidence. [00:12:40] Influencing Without Manipulating: Maintenance work needs to be framed in business terms. [00:16:00] Technical Debt as a Hiring Problem: Outdated systems affect recruiting and retention. [00:20:22] Modernizing a Siloed System: Unlocking legacy data required both technical and organizational change. [00:26:55] Building Trust for Change: Surprise proposals fail… alignment takes time. [00:32:39] Letting Go of “Our Baby”: Replacing systems involves emotional and cultural dynamics. [00:46:25] Joining an Older Codebase: Practical advice for onboarding and building confidence quickly. Resources Mentioned Sally Lait Sally Lait on LinkedIn Sally Lait on Mastodon Why Software Maintenance Is Stuck in 2015 (LeadDev Panel) Lara Hogan The Murderbot Diaries by Martha Wells Death of the Author by Nnedi Okorafor Sally's Reading & Reviews Site Thanks to Our Sponsors! Your test coverage says 90%, but that might be misleading. Undercover CI looks at your Ruby pull requests and shows you which parts of your changes weren't tested- not just overall coverage, but what changed and what got missed, down to the method level. Visit undercover-ci.com and use code MAINTAINABLE for 15% off your first billing cycle. Free for public repos. Private repos with unlimited users also available. Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks. It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications. Keep your coding cool and error-free, one line at a time! Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on:Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts.Keep up to date with the Maintainable Podcast by joining the newsletter.

In Episode 179 of the Cyber Threat Perspective podcast, host Brad Causey and web app pen tester Jordan Natter kick off a multi-part series on the OWASP Top 10, the newly updated list of the most common and critical web application security risks, with a fresh version released in 2025.Before diving in, Brad sets the record straight on something that's been bugging him for 20 years: the OWASP Top 10 is an awareness document, not a compliance framework, not a pen test checklist, and not a comprehensive defense guide. If your vendor claims they "comply with the OWASP Top 10," that's a red flag — you can't comply with an awareness document.Part 1 focuses entirely on A01: Broken Access Control — the most dangerous and most common category on the list — and the conversation goes deep with real-world stories from active engagements.Topics covered include:What OWASP actually is — and why the Top 10 is both invaluable and widely misunderstoodBroken Access Control — what it means, why it tops the list, and how it manifests in real applicationsJWT validation failures — a healthcare application where improper JWT handling allowed unauthorized access to admin functionalityMFA bypass via broken access control — a university application where MFA codes weren't properly scoped, enabling account takeoverCORS misconfigurations — how Cross-Origin Resource Sharing policies fail in modern Node and React applications, including a real story of bypassing CORS by allowing AWS resourcesInsecure Direct Object References (IDOR) — why IDOR isn't just about changing integer IDs, including a university app where changing a student ID number led to staff-level privilege escalationS3 bucket IDOR — how a modern web application exposed PHI by returning GUIDs in JSON responses that could be enumerated directlyHidden functionality as false security — why hiding admin URLs from the navigation bar is obscurity, not security, and how Jordan accessed an entire admin PDF panel as an unauthenticated user just by copying a URLOWASP Top 10: https://owasp.org/Top10/2025/0x00_2025-Introduction/ Blog: https://offsec.blog/Youtube: https://www.youtube.com/@cyberthreatpovTwitter: https://x.com/cyberthreatpovFollow Spencer on social ⬇Spencer's Links: https://spenceralessi.comWork with Us: https://securit360.com | Find vulnerabilities that matter, learn about how we do internal pentesting here.

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In this episode of The Light Inside, we delve into the complex and often challenging topic of moral ambiguity within the therapeutic relationship. Our guest, Simon Mont, brings his expertise as an organizational and conflict coach to explore how moral ambiguity can shape interactions between clinicians and clients, especially when collapse, shame, defensiveness, or self-sealing containment narrow the relational field.We begin by discussing how moral ambiguity is not inherently problematic but can become a cue that organizes collapse, rupture, repair, and even re-traumatization under relational load. The conversation highlights the importance of metacognition, where the clinician's ability to observe the process while participating in it becomes crucial.Simon and I explore the dynamics of power within the therapeutic setting, emphasizing the need for clinicians to hold the relational field with enough capacity to slow down the sequence of events that lead to protective responses. We discuss the role of pacing, sequencing, and titration in allowing repair to become reintegration.A significant portion of our conversation focuses on the ethical considerations and the clinician's responsibility to maintain a balance between holding space for the client's agency and not imposing their own moral judgments. We touch upon the challenges of navigating societal and cultural contexts within therapy, and how clinicians can inadvertently replicate societal harms if they are not mindful of their own biases and power dynamics.Throughout the episode, we also reflect on our own interaction, using it as a live example of how misunderstandings and power dynamics can play out in real-time. This meta-conversation serves to illustrate the very principles we discuss, providing listeners with a practical understanding of the concepts.In summary, this episode offers a deep dive into the nuanced and often ambiguous terrain of therapeutic ethics, power dynamics, and the clinician's role in fostering a space where clients can explore their consciousness and agency. We hope this conversation provides valuable insights for clinicians and anyone interested in the therapeutic process.Timestamps[00:01:10] Moral ambiguity in therapy.[00:06:19] Power dynamics in therapy.[00:10:30] Client agency in therapeutic relationships.[00:13:06] Agency and mutuality in therapy.[00:17:27] Moral ambiguity and duality.[00:19:54] Moral ambiguity causal cue stack.[00:25:30] Therapeutic space and moral ambiguity.[00:32:19] Moral choices in clinical practice.[00:34:27] Moral ambiguity in coaching.[00:39:18] Gaps in communication and understanding.[00:44:58] Power dynamics in communication.[00:47:07] Power dynamics in healing relationships.[00:51:40] Agency and moral frameworks.[00:56:17] Power dynamics in conversation.[01:01:12-01:01:23] Node-level metacognition in relationships.[01:02:06] Failure of sequencing in care.Coachable Inquiry: What happens when interpretation starts moving faster than contact?Many communication breakdowns do not begin with bad intent. They begin when cue-driven appraisal, embodied state, and prior relational learning start shaping meaning faster than the relationship can hold context, pacing, and mutual contact. Read the blog, then share the part that challenged your assumptions most—we'd value hearing what it helped you notice in your own communication patterns."The Introspection Illusion: Cue-Driven Appraisal and the Early Loss of Contact"CreditsHost: Jeffrey BeseckerGuest: Simon MontExecutive Program Director: Anna GetzProduction Team: Aloft Media GroupMusic: Courtesy of Aloft Media GroupConnect with host Jeffrey Besecker on LinkedIn.

For episode 718 of the BlockHash Podcast, host Brandon Zemp is joined by Peter Anthony, Co-founder of Perceptron, a decentralized data infrastructure for AI. While VCs have poured over $170 million into AI data infrastructure this past year, nearly all of it has gone to centralized models. Perceptron is the decentralized alternative, with over 700,000 real users sharing idle bandwidth to collect, structure, and verify data in real time across 150 countries. 

Lunes 27 de Abril de 2026 Ya esta disponible MEDELLIN TECHNO PODCAST 317 Presentado por DERAOUT Invitada: Lea Node Soundcloud: https://soundcloud.com/leanodedj Instagram: https://www.instagram.com/leanode__/ Bandcamp: https://leanode.bandcamp.com/ _____ Design: www.boldbravestudio.com _____ #medellintechnopodcast #medellin #techno #podcast #djset #deraout #manizalestechno #leanode #bogotatechno

In this episode, Ray Cochrane unpacks Anthropic’s Mythos model and the Treasury’s emergency meetings with Wall Street, then digs into Apple’s vibe-coding crackdown and a gaming-anxiety study that hit way too close to home. Also covered: Verge’s solid-state motorcycle, UBTech humanoid robot sales jumping 23-fold, Japan’s first osmotic power plant, Finland’s permanent nuclear waste vault, Ghostty landing in Ubuntu, Cloudflare’s EmDash CMS, and a Claude Code skill that talks like a caveman. – Want to start a podcast? It’s easy to get started! Sign up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens the show by framing Anthropic’s new Mythos model as the AlphaGo moment for cybersecurity. From there, the episode moves through Apple’s pushback against AI-generated apps, a gaming anxiety study with a deeply personal hook, a series of “first to ship” energy and robotics wins out of Finland, China, and Japan, and several developer-tool stories that show how quickly the economics of software are shifting. Mythos, the Detection Ceiling, and Wall Street’s Emergency Response Anthropic’s Mythos model has Wall Street rattled. Operating autonomously, Mythos found and demonstrated the exploitation of a 27-year-old TCP SACK bug in OpenBSD, an operating system famous for being one of the most security-focused on the planet. Per Anthropic’s red team, over 99% of the vulnerabilities Mythos has identified remain unpatched. The researchers’ conclusion is blunt: “the moat in AI cybersecurity is the system, not the model.” The policy response moved fast. On April 7th, Treasury Secretary Bessent and Fed Chair Jerome Powell pulled the CEOs of Goldman Sachs, Citi, Bank of America, and Morgan Stanley into Treasury headquarters on short notice. All four banks are now testing Mythos internally. Treasury CIO Sam Corcos is also seeking direct access. Anthropic is gating distribution through Project Glasswing, a limited-access program with JPMorgan, Apple, Google, Microsoft, and Nvidia. Cochrane comes down firmly behind Anthropic’s gated approach. Because a 5.1-billion-parameter open model can apparently recover the core analysis chain for the OpenBSD flaw, this capability is not locked behind Frontier Compute. He wants the critical infrastructure hardened before the public gets keys. However, he also notes the bigger lesson is about human wisdom: people offloading all their thinking to AI lose out on the wisdom that makes any of these tools genuinely useful. Apple Bans Vibe Coding Apps from the App Store Apple has been quietly pushing back against what people are calling “vibe coding” apps. Replit, Vibecode, and an app called Anything all run AI models on the phone and produce working software that runs inside the host app. Apple cites Guideline 2.5.2, in effect since 2017, which requires apps to be self-contained. Replit and Vibecode had their App Store updates blocked. Anything was pulled in late March, briefly restored on April 3rd, and then pulled the same day again. The forcing function is volume. App Store submissions jumped 84% in a single quarter as vibe coding tools flooded Apple’s review queue with AI-generated apps. Cochrane thinks Apple is justified, given the security issues swirling around the Vibe coding ecosystem. Even a beautiful diamond gets lost in a sea of sand, and that flood is exactly what Apple is trying to manage. The company behind Anything is now pivoting to iMessage, desktop, and Android. Playing Video Games to Win Is Linked to Higher Anxiety Cochrane gets personal on this one. Through high school and his early 20s, he was deeply addicted to League of Legends. His dad teased him about it constantly. In the last few years of that addiction, his body would go ice cold and shake every ranked match before. His partner identified it as a panic attack. The moment that happened, he quit. Today, he no longer shakes. The new study lines up with his experience. Researchers Kayleigh Watters and Mikael Rubin at Palo Alto University analyzed a publicly available database of 13,464 adult gamers, most of whom primarily played League of Legends. Players who game to win show higher generalized anxiety but actually play fewer hours, since performance pressure pushes them out. Players who game to relax show strong links between social anxiety avoidance and more hours played. The study appeared in the Journal of Affective Disorders. The headline framing of “playing to win makes you anxious” misses the point. The real finding is more interesting: gaming for avoidance and gaming for competition are both warning signs, for different reasons. Cochrane notes that the League of Legends community’s toxicity has been a running joke for years, and this study suggests the game’s structure may have been manufacturing the anxiety that fueled it. Sponsor: GoDaddy Economy hosting is $6.99/month, WordPress hosting is $12.99/month, and domains are $11.99. Both hosting plans include a free domain, professional email, and SSL certificate. Go to geeknewscentral.com/godaddy for the best pricing and to directly support this independent show. Verge Motorcycle: World’s First Production All-Solid-State Battery Cochrane filled his tank for $60 today, which made this story land especially hard. His mom has driven electric for years and patiently manages a 90-mile real-world range. The next-generation answer is already shipping. Verge Motorcycles, a Finnish company, is the first production vehicle of any kind with an all-solid-state battery. Their 2026 bikes ship in Q1 with a pack from Donut Lab, another Finnish outfit spun out of Verge. The numbers are bonkers. The pack delivers an energy density of 400 Wh/kg, roughly double that of current Tesla cells. It sustains 100kW charging, hits full charge in about 5 minutes in the lab and 12 minutes on the actual bike, and the long-range version covers 600 kilometers (about 370 miles) per charge. Toyota, QuantumScape, and Samsung SDI have all been telling us that solid-state is coming in 2027 to 2030. A Finnish motorcycle company shipping in Q1 2026 just embarrassed them all. UBTech Humanoid Robot Sales Jump 23-Fold UBTech dropped its 2025 annual earnings on April 1st. Humanoid robot revenue hit 820 million yuan, roughly $119 million USD, up 2,203% from 35.6 million yuan the year before. Unit sales went from 3 robots in 2024 to 1,079 in 2025. Shares jumped 14% on the announcement. The customer list is a real industrial deployment: BYD, Foxconn, Geely, FAW-Volkswagen, and Audi. The flagship is the Walker S2, with UBTech targeting 5,000 units in 2026 and 10,000 in 2027. Cochrane is honest about what this means. He does not think we are heading for an extinction event, but worker displacement is a real concern. The US has no universal income or universal healthcare. The people affected are not white-collar managers. They are everyday line workers who already make the least on the ladder. Work efficiency reportedly doubles when these robots arrive, which is a company-side win, but the humans they replace are not getting half a year of gardening leave to retrain. He invites the listener to take on this one directly. Japan Switches On Asia’s First Osmotic Power Plant In August 2025, Fukuoka’s Seawater Desalination Center quietly opened Asia’s first osmotic power facility. It generates about 880,000 kilowatt-hours per year, enough for roughly 220 homes. It is only the second operational osmotic plant in the world, after Mariager, Denmark, in 2023. Osmotic generation uses a salinity gradient: fresh water on one side of a membrane, salt water on the other, and the pressure difference spins a turbine. The clever part is what Fukuoka does with desalination brine. Instead of regular seawater, the plant uses concentrated brine left over from the desalination process. This amplifies the salt gradient and squeezes more energy out of the same membrane. The result is a closed-loop partnership: the desalination facility produces drinking water and leaves brine behind, the osmotic plant turns the brine into electricity, and that electricity runs the desalination facility. Every desalination plant on Earth produces brine, so if Fukuoka’s co-located model works, the same pattern could be replicated across hundreds of plants worldwide. Japan’s Luna Ring Solar Moon Proposal Goes Viral Again Shimizu Corporation’s Luna Ring concept is making the rounds again. The pitch: a 6,800-mile belt of solar panels around the Moon’s equator, beaming microwave power back to Earth. Project lead Tetsuji Yoshida has long argued that a full ring could eliminate fossil fuel dependence entirely. The proposal first surfaced in 2013, has no funding, no government endorsement, and no concrete cost estimate. Shimizu has not put any active development behind it. Cochrane finds the concept fun every time it resurfaces. However, this would have to be a worldwide effort in the truest sense, with treaties, a new generation of launch economics, and microwave power transmission at a scale nobody has demonstrated. Beaming the power back to Earth has always been one of the biggest practical holdbacks. The Luna Ring is inspirational, but not shipping. Finland’s Onkalo Nuclear Waste Vault Opens Finland’s Onkalo facility is the world’s first permanent deep geologic repository for spent nuclear fuel. Operated by Posiva, the facility is buried about 430 meters down in 1.9-billion-year-old bedrock. It is designed to hold up to 6,500 tons of spent fuel and operate until the 2120s. The construction costs about €1 billion, with operating and closure adding roughly €4 billion more before the program is done. The catch is that radioactivity remains dangerous for hundreds of thousands of years. Edwin Lyman, director of nuclear power safety at the Union of Concerned Scientists, warned that the copper canisters will eventually corrode, with different scientific opinions on how fast. Geologic disposal remains “fraught with uncertainties,” and we have never validated an engineered system across a 100,000-year time frame. The bet is that the rock and copper outlast the radioactivity. Cochrane sees Onkalo as time-buying rather than a final answer. It is more of a bank holding spent fuel while science catches up. He prefers it to Japan’s ongoing approach of releasing tritium-treated water from Fukushima Daiichi into the Pacific, even though the dilution is well below WHO drinking water guidelines. Burying the waste in an insurmountable containment strikes him as the more honest answer to a problem nobody knows how to truly solve. Ghostty Terminal Lands in the Ubuntu Repos Ghostty 1.3.0 is now available in Ubuntu 26.04 LTS’s universe repository. The install is simply `sudo apt install ghostty`, no PPAs, no Snap, no Nix, no building from source. Ghostty was created by Mitchell Hashimoto, co-founder of HashiCorp. It is GPU-accelerated, uses native Swift on macOS and native GTK4 with libadwaita on Linux, and supports tabs, splits, profiles, ligatures, and the Kitty graphics protocol. Cochrane recently caught Hashimoto on a podcast, where he walked through his agentic coding workflow. Ghostty is being actively built using AI harnesses like Claude Code and Codex. Hashimoto told a story in which Codex fixed a six-month-old bug in 45 minutes, for a total API cost of $4.14. Personally, Cochrane uses WezTerm, but he is excited to see Ghostty become more widely available with a native UI rather than Electron. Borgo: Rethinking Go Using Rust Analytics India Magazine profiled Borgo, a programming language by developer Marco Sampellegrini (GitHub: alpacaaa). Borgo is statically typed with Rust-like syntax, but it compiles to Go and uses the Go runtime and garbage collector. It includes sum types (Option and Result), pattern matching, and full compatibility with existing Go packages. Notably, it removes Rust’s borrow checker and lifetimes entirely. Borgo is not new. It first appeared on Hacker News in 2023, with a RustLab talk in 2024. The 2026 angle is a renewed look at it through the lens of AI coding agents, since type-rich languages like Rust have been showing outsized productivity gains. Cochrane is a fan of Rust and stands by the borrow checker, but he enjoys these exploratory languages for what they reveal about what developers actually want. Caveman: A Claude Code Skill That Cuts 65% of Tokens Developer Julius Brussee built a Claude Code skill called Caveman that forces Claude to respond in stripped-down fragments. No articles, no “just,” no “really,” no pleasantries, no hedging. The tagline is “why use many token when few token do trick.” Across 10 real dev tasks, Caveman mode averaged 294 tokens per response, compared to 1,214 in normal mode. That is a 65% drop in output tokens. The project is MIT licensed with three intensity levels: lite, full, and ultra. Cochrane stumbled across the project online and shared it with a classmate who had been complaining about token costs. The classmate now insists that “the caveman is the only way to live.” Cochrane has not made the switch, but the bigger point lands. If a community plugin can cut 65% of tokens without correctness regressions, the labs are shipping verbose-by-default and charging users for the privilege. He suspects verbose output makes models feel more trustworthy, even when the token math says otherwise. Cloudflare Launches EmDash as a WordPress Successor Cloudflare released EmDash on April 9th, an open-source, MIT-licensed, TypeScript-based CMS pitched as the spiritual successor to WordPress. The big flex is that it was built in 60 days using AI coding agents. EmDash runs on Astro 6.0, either on Cloudflare’s edge platform or on a standard Node.js server. The plugin security model uses sandboxed Dynamic Workers with explicit permissions, addressing the architecture flaw that Cloudflare says causes 96% of WordPress vulnerabilities. Cochrane could not resist pointing out the irony of the name. The em dash has become the trademark giveaway that an AI was involved in writing. He has reservations about whether EmDash will succeed. WordPress is extremely hard to unseat, plenty of “WordPress killers” have come and gone, and the ecosystem is twenty-plus years deep. He is curious to see what comes next but not optimistic. Google Open-Sources the DESIGN.md Format Google Labs open-sourced the DESIGN.md format used by Stitch, their AI UI design tool. DESIGN.md is a declarative file capturing a project’s design system, colors, typography, and spacing in a way AI agents can read and apply. Cochrane has tried Stitch personally and finds it impressive at producing web designs. He has also seen DESIGN.md-style files already start appearing in repositories. He sees this kind of file becoming a new paradigm for agentic design, alongside robots.txt and llms.txt. However, he worries about a side effect. If everyone uses the same standardized format and the same AI tools, the web could become a homogeneous set of sites that all look the same. He is enthusiastic about the standardization but hopes designers continue to push for genuinely unique work. A 13-Liter PC With a Water Loop Built Into the Case Geeky Gadgets covered a build by “Visual Thinker”, a 13-liter mini-ITX case with custom SLA-printed water distribution plates built directly into the chassis. Instead of traditional soft tubing, plates channel coolant between the CPU and GPU blocks and are sealed with TPU and silicone molds. The case supports a full-size GPU and an SFX power supply. No thermal benchmarks, parts list, or pricing have been published. It is a one-off you cannot buy. Cochrane sees this as a sign of where PC building has gone in 2026. Modern mid-grade GPUs run nearly every recent game, so raw performance is no longer the differentiator. He likes seeing builders lean into design and craft rather than just stuffing the most powerful parts into a box. He admits he is the traditional type and built his own machine to maximize parts, but the design-first direction is a healthy evolution for the hobby. To close out the show, Cochrane recommends Pocket Casts as a podcast app. He finds it picks up new episodes very quickly. Big thanks to GoDaddy for over twenty years of keeping this show on the air, and a reminder that every promo code use is like writing a check to the show. The post Mythos: Cybersecurity’s AlphaGo Moment #1862 appeared first on Geek News Central.

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WHAT YOU'LL LEARN Why yard management is still one of the most manual parts of the supply chain How to identify if your yard is the hidden bottleneck in operations What causes congestion, idle time, and poor trailer visibility How gate delays impact warehouse throughput and carrier performance What “Smart Yard 3.0” means (and how it differs from traditional YMS) How AI improves gate check-in, asset visibility, and workflow orchestration The stages of AI maturity in yard operations—from visibility to autonomous decisioning Where security, fraud detection, and damage tracking fit into yard technology What operators should prioritize first when modernizing the yard HIGHLIGHTS 00:00 – Why the yard is still the last broken node in supply chain 01:39 – Why yard operations stayed manual while WMS and TMS evolved 05:00 – Signs your yard is under-managed (congestion, delays, visibility gaps) 06:44 – Smart Yard 3.0: from tracking systems to real-time orchestration 10:11 – AI maturity in yard operations (visibility → automation → optimization) 17:00 – How yard performance impacts the entire supply chain network 20:06 – Security, fraud detection, and damage tracking in the yard 23:19 – What AI-native logistics platforms look like next QUOTES [00:00:29] "The yard often remains the manual reactive, hard to see in real time type of solution." - Ninaad   [00:01:39] “The yard is one of three of the major nodes in the supply chain, about 50 billion in goods flow every single day. It is stunning to realize that it is the most un modernized node in the entire supply chain." - Darin Brannan   [00:04:53] "It was an afterthought. Now it's a constraint." - Darin Brannan   [00:08:07] "And a AI and agentic done right, is a game changer for the art." - Darin Brannan   [00:17:39] "Yards don't fall into isolation. It has the ability to have second-order effects across the network." - Harshida KEY TAKEAWAYS The yard is no longer a passive space—it's a control point for the entire network Most yard systems digitize workflows but don't improve decision-making Real transformation comes from redesigning workflows, not just automating them Small inefficiencies in the yard create large downstream operational impact AI shifts the yard from a tracking system to a movement orchestration platform ABOUT THE GUEST Darin Brannan, CEO, Terminal Industries Darin Brannan is a founder-operator and CEO with 25+ years of experience scaling SaaS and infrastructure platforms. At Terminal Industries, he leads the development of an AI-native Yard Operating System that uses computer vision and agentic AI to automate and optimize yard operations across enterprise supply chains. Learn more:  https://terminal-industries.com/ https://www.linkedin.com/in/darinbrannan/ Subscribe and Keep Learning!If you're a logistics leader looking to scale sustainably, don't miss out! Subscribe for more expert strategies on tackling modern supply chain challenges.Be sure to follow and tag the eCom Logistics Podcast on LinkedIn and YouTube

Guest Alan Rubin Panelist Richard Littauer Show Notes On this episode of Sustain, Richard Littauer sits down with computational biologist Alan Rubin to explore how open source software supports scientific research, clinical genetics, and cancer-related data infrastructure. Their conversation centers on MaveDB, a project that began as a way to organize hard-to-find variant data from research papers and has since evolved into a valuable resource for both scientists and clinicians. Along the way, they discuss infrastructure funding, research software sustainability, and why open source communities and academic researchers have a lot to learn from each other. Press download now to hear more! [00:01:24] Alan explains his role leading a research group focused on genomics, cancer medicine, and improving patient care through genetics. [00:02:46] We learn more about what MaveDB does. [00:06:52] Alan details why a database was needed. [00:08:26] Alan shares how the project grew out of collaboration, PyCon AU inspiration, Django, and Python tooling that let a small team build a practical research database. [00:11:54] There's a discussion on the infrastructure funding problem and Alan explains a major theme is how hard it is to fund scientific infrastructure, since most grants favor new discoveries rather than maintaining shared tools and databases. [00:17:55] The project took a major turn when clinical geneticists began using the data to interpret patient variants, pushing the team to rethink the interface and user needs. [00:21:13] Alan describes the new clinical-facing interface, Mave for Medicine (MaveMD), designed to help doctors evaluate specific variants for diagnosis and treatment decisions. [00:22:02] Alan talks about managing the project through a distributed team, shared responsibilities, and a role that now centers more on direction, priorities, and community than day-to-day coding. [00:23:36] They discuss why research software rarely attracts hobbyist contributors, even when the mission is compelling, and how scientific projects often function more like small product teams. [00:27:44] Alan makes the case that scientists often learn more about improving their software craft at events like PyCon than at discipline-specific conferences. [00:30:38] Alan highlights how academic software depends heavily on mature, well-documented open source tools and encourages more connection between technical communities and scientific work. [00:34:15] Find out where you can learn more about MaveDB and Alan's work. Quotes [00:10:04] “We quite literally followed the Django Girls tutorial, but instead of a building a blog, we built a database for research scientists.” [00:12:35] “Infrastructure is something everybody wants to have it exist and nobody wants to pay for.” [00:26:08] “I have never been successful in engaging the broader open source community, despite having tried many times to contribute to this or any other scientific project.” [00:31:01] “I think people who work in OSS should be excited about the kind of stuff that their work is enabling, even if they don't really hear about it.” Spotlight [00:35:44] Richard's spotlight is the book, News of the Dead. [00:36:22] Alan's spotlight is The Global Alliance for Genomics & Health (GA4GH) and all the good work they're doing. Links SustainOSS podcast@sustainoss.org richard@sustainoss.org SustainOSS Discourse SustainOSS Mastodon SustainOSS Bluesky SustainOSS LinkedIn Open Collective-SustainOSS (Contribute) Richard Littauer Socials Alan Rubin LinkedIn Dr. Alan Rubin Website (The University of Melbourne) PyCon AU 2026, Brisbane, August 26-30 Sustain Podcast- Episode 286: Jack Skinner of PyCon AU and Regional Confs Sustain Podcast- Episode 176: Maintainer Month with Russell Keith-Magee & Uriel Ofir Django Girls PyCon AU 2023-“Building a biological database with Python”- Alan Rubin (YouTube) Sustain Podcast- Episode 135: Tracy Hinds on Node.js's CommComm and PMs in Open Source Sustain Podcast-Episode 190: Karen Sandler on Software Freedom Conservancy (SFC) Original database paper (Pub Med) Database update paper (Pub Med) Preprint on the clinician-oriented interface Variant scoring tools for deep mutational scanning (Pub Med) Atlas of Variant Effects MaveDB News of the Dead Global Alliance for Geonomics & Health (GA4GH) Sponsor CURIOSS Credits Produced by Richard Littauer Edited by Paul M. Bahr at Peachtree Sound Show notes by DeAnn Bahr Peachtree Sound Special Guest: Alan Rubin.

Software maintenance is often framed as a technical problem. Refactoring code, fixing bugs, or upgrading dependencies. In this conversation, Robby Russell talks with Rein Henrichs about a different lens, one centered on understanding. Rein is a Principal Software Engineer at Procore, where he works within a large, long-lived system used across the construction industry. Rather than focusing on tooling, Rein emphasizes that well-maintained software is software that makes sense to the people maintaining it. To explain this, Rein introduces the idea of the line of representation, drawing on the work of Richard Cook. Engineers do not interact directly with systems. They rely on representations such as logs, dashboards, and code. These are approximations, not reality, echoing ideas from Plato's Allegory of the Cave. When those representations break down, teams lose shared understanding, what Rein describes as “common ground.” This often shows up as weak signals. Subtle indicators that something is not quite right. They are easy to ignore, but over time they lead to confusion and slower decision-making. Incidents make this especially visible. Rein explains how teams build alignment under pressure, highlighting that the role of an incident commander is coordination, not control. Clear communication matters as much as technical correctness. The conversation also explores how large systems behave in practice. They rarely fail completely. Instead, they degrade in multiple ways at once. While SLOs can help teams respond to customer-facing issues, they do not capture internal clarity or alignment. Rein references W. Edwards Deming to highlight a common trap. Not everything that matters can be measured. High-performing teams often rely on judgment, experience, and shared context. Toward the end, Rein connects these ideas to The Field Guide to Understanding Human Error by Sidney Dekker, challenging the idea that incidents are simply caused by mistakes. Instead, they emerge from the same behaviors that usually lead to success, just under different conditions. For teams working in complex systems, the takeaway is straightforward. Maintaining software depends on maintaining understanding. Links & Resources Procore Rein Henrichs on LinkedIn Concepts & References How Complex Systems Fail – Richard Cook The Field Guide to Understanding Human Error – Sidney Dekker W. Edwards Deming Gerald Weinberg – Secrets of Consulting Referenced in this Conversation Kent Beck: You're Ignoring Optionality and Paying for It Charity Majors: Deploys Are Just the Beginning Heidi Helfand: The Art and Wisdom of Changing Teams Thanks to Our Sponsor! Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks. It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications. Keep your coding cool and error-free, one line at a time!  Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on:Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts.Keep up to date with the Maintainable Podcast by joining the newsletter.

In this episode of The Cybersecurity Defenders Podcast, we discuss some intel being shared in the LimaCharlie community.Federal cybersecurity agencies have issued an urgent warning about Iran-linked cyberattacks targeting operational technology (OT) systems across U.S. critical infrastructure.A hacker operating under the alias “FlamingChina” claims to have breached a Chinese state-run supercomputing facility and stolen a large dataset that may exceed 10 petabytes of information.Multiple high-profile maintainers in the Node.js ecosystem report being targeted in a coordinated social-engineering campaign aimed at compromising widely used open-source packages.Microsoft Threat Intelligence reports that the cybercrime group Storm-1175 is conducting rapid ransomware campaigns deploying the Medusa ransomware family.Support our show by sharing your favorite episodes with a friend, subscribe, give us a rating or leave a comment on your podcast platform.This podcast is brought to you by LimaCharlie, maker of the SecOps Cloud Platform, infrastructure for SecOps where everything is built API first. Scale with confidence as your business grows. Start today for free at limacharlie.io.

This week we speak with Assistant Professor of Pediatrics at Harvard Medical School, Dr. Audrey Dionne about a recent work she co-authored on sinus node dysfunction following congenital heart surgery. How common was this encountered and how often were either temporary or permanent pacing needed? Are there certain surgeries that are more associated with the need for pacing after surgery? Why is this so common following heart surgery? Dr. Dionne shares her deep knowledge this week on this surprisingly common problem.  DOI: 10.1016/j.hrthm.2026.01.049

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Google's quantum AI team just published a paper proving Bitcoin's encryption can be cracked with 20x fewer qubits than expected — private keys derived in 9 minutes. Is quantum computing Bitcoin's biggest existential threat? We break down the paper, the timeline, and why developers need to act now.⏱️ Timestamps00:00 - Intro01:38 - Sponsors (EasyDNS & Bull Bitcoin)03:21 - Boostagram Shoutouts04:38 - Housekeeping & Previous Episode Recap06:06 - Google's Quantum Paper: 20x Fewer Qubits to Crack Bitcoin07:00 - 7 Million BTC at Risk & the Satoshi Bounty Theory08:34 - Why the Bitcoin Mailing List Is Ignoring Quantum10:30 - Developer Priorities: Spam Wars vs. Quantum Preparedness12:00 - Preston Pysh & Jeff Booth Don't Understand Quantum13:00 - Why Banks Aren't the First Target — Bitcoin Is14:00 - Satoshi's Coins as a Quantum Canary15:01 - Nick Carter, Eleven Labs & Conflicts of Interest17:03 - Short vs. Medium Term: Is Bitcoin Safe Right Now?18:00 - The Rush vs. Caution Dilemma & BIP-36019:00 - NIST Post-Quantum Standards & Testnet Testing20:30 - Would You Fork for Quantum? The Hard Question21:00 - Taproot's Unintended Consequences & BIP-11023:04 - Bitcoin Treasury Companies Selling: Nakamoto, Genius Group, Marathon26:00 - Marathon Pivots to AI & HPC Data Centers27:28 - Why Only MSTR Survives the Treasury Strategy29:52 - Production Ready: New Bitcoin Node Software (Samson Mow, Jimmy Song, Parker Lewis)32:00 - Node Accessibility, Core vs. Knots & OP_RETURN Limits35:50 - Notable Noise Begins36:37 - Iran War Escalation: Boots on the Ground & the Strait of Hormuz40:00 - Scott Horton's Prediction & US Ground Invasion Plans42:01 - Trump's Legacy Play & the George W. Bush Theory43:00 - Iran's Nuclear Endgame44:00 - Strait of Hormuz, Houthis & Global Oil Chokepoints47:00 - Canadian Food Bank Crisis: 1 in 4 Out of Supplies49:00 - Brain Drain: 122,000 Engineers & Doctors Leave Canada50:00 - Immigration Policy & the Coming Reckoning54:00 - MAID Offered to 83-Year-Old Before Diagnosis57:01 - COVID Hospital Stories & Funeral Restrictions1:02:20 - Brampton or Hamilton Man

Mike sits down with renowned open-source and COSMIC DE contributer Bryan Hyland to discuss working on projects for Linux-forward companies and of course some Rust! Bryan's Site Bryan on LinkedIn Mike on LinkedIn Coder Radio on Discord The Mad Botter Inc Mike's Book Mike's Blog

Software doesn't become hard to maintain only because the code is messy. It often becomes hard to maintain because the reasoning behind it disappears. In this episode of Maintainable, Robby Russell talks with Russ Olsen about trade-offs, legacy systems, and why maintainability depends on context more than dogma. Russ brings decades of experience across very different kinds of systems, each with its own definition of what “maintainable” actually means. A central theme is that software must be understandable to the people maintaining it. Teams tend to document implementation details well, but often fail to capture system-level intent and the trade-offs behind major decisions. Russ makes the case for preserving that thinking, including the alternatives that were rejected, so future maintainers don't have to rediscover it the hard way. The conversation also touches on Russ's book Eloquent Ruby, Second Edition. Rather than teaching syntax, the book focuses on how Ruby is actually used in practice and why common patterns exist. That leads into a discussion about where elegance improves maintainability, and where it turns into unnecessary cleverness. From there, the episode shifts into the realities of working in legacy systems. Russ explains how teams develop pessimism over time, often accepting flawed assumptions about how their systems behave. In some cases, major issues turn out to be far simpler than expected. The challenge is that teams stop looking. Robby and Russ also discuss the value of fresh perspective. New engineers or outside contributors can surface assumptions that longtime maintainers overlook. Russ suggests finding “pinch points” in a system as a practical way to understand behavior without needing to fully untangle everything at once. Later, the conversation explores developer quality of life. Long build and deploy cycles create daily friction that teams often underestimate. These slow feedback loops quietly degrade productivity and morale over time. The episode also tackles rewrites. Russ warns that teams frequently underestimate how much knowledge is embedded in existing systems. Code that looks questionable may reflect constraints no one documented. In practice, most successful rewrites happen incrementally, not all at once. The conversation wraps with a reminder that software development is fundamentally a social process. Russ argues that engineers undervalue storytelling, even though it's one of the most effective ways to connect technical work to real human outcomes. Episode Highlights [00:00:40] Defining maintainability: Why context matters more than a universal standard [00:02:01] Beyond code comments: Documenting system intent and trade-offs [00:08:14] Who Eloquent Ruby is for: Understanding how Ruby is used in practice [00:16:21] Elegance vs. cleverness: Where maintainability starts to erode [00:23:18] Legacy pessimism: Why teams stop questioning assumptions [00:29:25] Pinch points: A practical way to understand complex systems [00:32:05] Developer experience: The hidden cost of slow feedback loops [00:38:26] Rewrites: Why they fail and what teams overlook [00:44:00] Storytelling: Connecting technical work to real-world impact Resources Mentioned Russ Olsen on LinkedIn Eloquent Ruby, Second Edition Getting Clojure Zen and the Art of Motorcycle Maintenance A History of Western Philosophy Thanks to Our Sponsor! Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks. It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications. Keep your coding cool and error-free, one line at a time!  Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on:Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts.Keep up to date with the Maintainable Podcast by joining the newsletter.

A new DeFi exploit triggered millions in losses, but the deeper story is about risk. In this episode, Omer Goldberg, founder of Chaos Labs, explains how the attack unfolded, why the damage spread across lending markets, what vault curators got wrong, and whether DeFi is truly ready for mainstream adoption. If you want to understand stablecoin risk, oracle design, curator incentives, and the future of safer onchain finance, this is the conversation to watch.Big thanks to our sponsors;NEXONexo is a premier digital assets wealth platform that helps clients build, manage, and preserve their wealth through advanced interest-generating products, crypto-backed credit, advanced trading tools, and 24/7 client care. Get started at nexo.com/defiant MERCURYOYour Web3 product deserves solid payment infrastructure. Global on/off-ramps, custom APIs, and DeFi connectivity trusted by the biggest names in crypto: mercuryo.ioROCKET POOLRocket Pool is Ethereum's decentralised liquid staking protocol. Node operators can join with just 4 ETH, or liquid stakers can hold rETH and automatically earn staking rewards. rocketpool.net

Grab the free one-page OpenClaw setup guide with every step and command listed out: https://corey-ganim.kit.com/c93f43577eI walk through the complete OpenClaw setup process from zero to a working AI agent connected to Telegram — all in under seven minutes. You'll see every step: running the installer, configuring your Anthropic API key, creating a Telegram bot through BotFather, pairing it to your OpenClaw instance, and enabling web search with DuckDuckGo. One prerequisite, one terminal command, and you're live.Key Takeaways:OpenClaw installs with a single terminal command — the only prerequisite is having Node.js installed, which is also a one-command setupThe guided onboarding handles every configuration decision (model, API key, channel, search) through simple yes/no prompts — no manual config filesCreating a Telegram bot through BotFather takes about 60 seconds: message BotFather, run /newbot, choose a name ending in "bot," and copy the tokenDuckDuckGo is the fastest search provider to start with because it requires zero additional API keys or setupSkills and hooks can be added after the initial install — you don't need to configure everything before getting your agent runningThe OpenClaw control panel gives you a browser-based chat window plus access to channels, sessions, usage stats, cron jobs, files, skills, and nodesTimestamps: 00:00 - Introduction 00:02 - Running the OpenClaw installer from the terminal 00:30 - Node.js prerequisite (quick install)00:45 - Guided onboarding: Quick Start setup 01:05 - Choosing Anthropic as your model provider 01:20 - Entering your Anthropic API key 01:35 - Selecting Claude Sonnet 4.6 01:52 - Creating a Telegram bot with BotFather 02:45 - Naming your bot and choosing a username 03:15 - Connecting the bot token to OpenClaw 03:30 - Enabling DuckDuckGo web search 03:45 - Skipping skills and hooks (configurable later) 04:06 - OpenClaw control panel overview 04:30 - First chat with your bot in the browser 05:15 - Pairing the bot with your Telegram account 06:10 - First Telegram message confirmed 06:30 - Setup complete — next steps for personalizationLinks Mentioned:OpenClaw: https://openclaw.aiAnthropic API Console: https://console.anthropic.comBotFather (Telegram): https://t.me/BotFatherDuckDuckGo: https://duckduckgo.comNode.js: https://nodejs.orgEnjoyed this episode? -> Subscribe and leave a review -> Join the community waitlist: https://return-my-time.kit.com/1bd2720397

Wes, Scott, and CJ talk about Vite+, a unified JavaScript toolchain that combines linting, formatting, task running, monorepos, and more. They break down its evolution, open-source shift, performance gains, Node version management, and whether it can realistically replace today's fragmented dev tooling. Show Notes 00:00 Welcome to Syntax! 00:54 What Vite+ is and what's changed since launch 03:43 Why the ecosystem needs Vite+ 06:41 What Vite+ actually does for your workflow 10:18 Built-in Node version management 12:32 Type-aware linting with tsgolint and oxc 15:27 Brought to you by Sentry.io 16:28 Should config live inside vite.config? 22:56 Monorepos and task running in Vite+ 26:28 Task caching and faster builds 29:01 Final thoughts and current limitations Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Long-lived software systems rarely stay tidy. Over time they accumulate decisions, workarounds, and layers of history that can make even simple changes feel risky. For engineers responsible for maintaining those systems, the challenge often becomes less about writing new code and more about understanding what already exists. In this episode of Maintainable, Robby Russell speaks with Joel Oliveira, Engineering Manager at ezCater, about what helps software remain understandable and adaptable as it evolves. Joel starts with a principle that often gets overlooked: predictability. When patterns are consistent and code is organized in familiar ways, engineers can navigate a codebase with confidence. Frameworks like Ruby on Rails reinforce this by encouraging shared conventions, making it easier for developers to orient themselves when working in a new application. The conversation also explores how common metrics can be misleading. Test coverage is often treated as a proxy for quality, but Joel explains that it can create a false sense of confidence. Instead, he values testing most as a thinking tool. Practices like test-driven development can help engineers clarify interfaces and better understand the problem before committing to an implementation. Joel also shares a story from ezCater about replacing an aging image-cropping service that had become difficult to maintain and required frequent restarts due to a memory leak. Rather than patch the system again, the team introduced ImageProxy, an open source image processing tool created by Evil Martians. Because the image URLs flowed through a single object in their GraphQL layer, the team could introduce an adapter and gradually route traffic to the new service using feature flags. This allowed them to migrate safely and incrementally instead of relying on a risky “big bang” change. Robby and Joel also discuss how engineers' perspectives shift over time. Early in a career it is easy to look at legacy code and label it as bad. Joel now sees older systems as layers of decisions shaped by real constraints. Approaching them with empathy makes it easier to improve them thoughtfully. The episode closes with advice for engineers maintaining complex systems: frame problems as opportunities. By documenting impact and proposing incremental improvements, teams can steadily move their software toward a healthier future. Maintainable software rarely comes from one heroic refactor. More often, it's the result of many small improvements made by teams who understand their systems and care about how they evolve. Episode Highlights 00:02:18 – Predictability as a Maintainability Feature Joel explains why predictable patterns and conventions make large codebases easier to navigate. 00:07:41 – When Test Coverage Misleads Why high coverage can give a false sense of quality. 00:12:05 – Consulting vs. Product Engineering How switching environments shaped Joel's approach to code. 00:16:32 – Replacing a Legacy Image Service ezCater's migration away from a failing Node-based image service. 00:21:14 – Migrating with Adapters and Feature Flags How the team gradually moved traffic to ImageProxy. 00:26:03 – Developing Empathy for Legacy Code Why older systems deserve understanding, not blame. 00:30:47 – The Shift to Engineering Management Joel reflects on moving from IC work to leading teams. 00:34:52 – Advice for Improving Complex Systems Small, consistent improvements matter more than big rewrites. Thanks to Our Sponsor! Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks. It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications. Keep your coding cool and error-free, one line at a time!  Use the code maintainable to get a 10% discount for your first year. Check them out! Links / References Joel Oliveira — LinkedIn Joel Oliveira — Website Joel Oliveira — Mastodon (@jayroh) ezCater ImageProxy Subscribe to Maintainable on:Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts.Keep up to date with the Maintainable Podcast by joining the newsletter.

Scott on LinkedIn Black Dog Ventures Mike on LinkedIn Coder Radio on Discord The Mad Botter Inc Alice Limited Offer Mike's Book Mike's Blog

YARA-X 1.14.0 Release https://isc.sans.edu/diary/YARA-X%201.14.0%20Release/32774 INTERPLAY BETWEEN IRANIAN TARGETING OF IP CAMERAS AND PHYSICAL WARFARE IN THE MIDDLE EAST https://research.checkpoint.com/2026/interplay-between-iranian-targeting-of-ip-cameras-and-physical-warfare-in-the-middle-east/ Announcing the Node.js LTS Upgrade and Modernization Program https://openjsf.org/blog/nodejs-lts-upgrade-program nginx UI Vulnerability https://github.com/0xJacky/nginx-ui/security/advisories/GHSA-g9w5-qffc-6762

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Wes and Scott talk about the latest dev news: Node enabling Temporal by default, OpenAI acquiring OpenClaw, TypeScript 6, new TanStack and Deno releases, the explosion of AI agent platforms, and more. Courtney Tolinski's Podcast Phases: A Parenting Podcast https://phases.fm/ Show Notes 00:00 Welcome to Syntax! 01:11 Brought to you by Sentry.io 02:40 Node.js enables Temporal by default Enable Temporal by default 04:08 OpenClaw acquired by OpenAI OpenClaw, OpenAI and the future 09:36 Bots are taking over the internet Wes' tweet 15:30 TypeScript 6 Beta Announcing TypeScript 6.0 Beta 17:00 TanStack Hotkeys for type-safe shortcuts TanStack Hotkeys 18:05 Components will kill webpages Components Will Kill Pages 19:39 Is Google Translate just an LLM? Viridian's tweet 23:29 Shaders.com 26:49 Voxtral Mini Realtime Voxtral Realtime Demo 29:51 Deno launches Sandboxes Introducing Deno Sandbox 32:39 Oz by Warp.dev 38:10 Augment Code Intent 40:10 Sick Picks + Shameless Plugs Sick Picks Scott: Samsung Remote Wes: Ice Shameless Plugs Syntax YouTube Channel Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

OpenClaw is a self-hosted AI agent daemon that executes autonomous tasks through messaging apps like WhatsApp and Telegram using persistent memory. It integrates with Claude Code to enable software development and administrative automation directly from mobile devices. Links Notes and resources at ocdevel.com/mlg/mla-29 Try a walking desk - stay healthy & sharp while you learn & code Generate a podcast - use my voice to listen to any AI generated content you want OpenClaw is a self-hosted AI agent daemon (Node.js, port 18789) that executes autonomous tasks via messaging apps like WhatsApp or Telegram. Developed by Peter Steinberger in November 2025, the project reached 196,000 GitHub stars in three months. Architecture and Persistent Memory Operational Loop: Gateway receives message, loads SOUL.md (personality), USER.md (user context), and MEMORY.md (persistent history), calls LLM for tool execution, streams response, and logs data. Memory System: Compounds context over months. Users should prompt the agent to remember specific preferences to update MEMORY.md. Heartbeats: Proactive cron-style triggers for automated actions, such as 6:30 AM briefings or inbox triage. Skills: 5,705+ community plugins via ClawHub. The agent can author its own skills by reading API documentation and writing TypeScript scripts. Claude Code Integration Mobile to Deploy Workflow: The claude-code-skill bridge provides OpenClaw access to Bash, Read, Edit, and Git tools via Telegram. Agent Teams: claude-team manages multiple workers in isolated git worktrees to perform parallel refactors or issue resolution. Interoperability: Use mcporter to share MCP servers between Claude Code and OpenClaw. Industry Comparisons vs n8n: Use n8n for deterministic, zero-variance pipelines. Use OpenClaw for reasoning and ambiguous natural language tasks. vs Claude Cowork: Cowork is a sandboxed, desktop-only proprietary app. OpenClaw is an open-source, mobile-first, 24/7 daemon with full system access. Professional Applications Therapy: Voice to SOAP note transcription. PHI requires local Ollama models due to a lack of encryption at rest in OpenClaw. Marketing: claw-ads for multi-platform ad management, Mixpost for scheduling, and SearXNG for search. Finance: Receipt OCR and Google Drive filing. Requires human review to mitigate non-deterministic LLM errors. Real Estate: Proactive transaction deadline monitoring and memory-driven buyer matching. Security and Operations Hardening: Bind to localhost, set auth tokens, and use Tailscale for remote access. Default settings are unsafe, exposing over 135,000 instances. Injection Defense: Add instructions to SOUL.md to treat external emails and web pages as hostile. Costs: Software is MIT-licensed. API costs are paid per-token or bundled via a Claude subscription key. Onboarding: Run the BOOTSTRAP.md flow immediately after installation to define agent personality before requesting tasks.