Podcasts about Zig

Send us Fan MailEpisode 264 of Nerdery & Murdery is live.On the Nerdery side, Zig travels back to the early days of television to explore the classic 1950s anthology series Science Fiction Theatre. Long before The Twilight Zone and The Outer Limits became household names, Science Fiction Theatre was bringing science-based speculative storytelling to television audiences, blending real-world scientific concepts with imaginative "what if?" scenarios and introducing viewers to the possibilities of the future.On the Murdery side, our A–Z Across America series continues with Maryland and one of the strangest cases we've covered so far: Hadden Clark.The murders of six-year-old Michelle Dorr and twenty-three-year-old Laura Houghteling would eventually be linked to a man whose confessions blurred the line between truth and fantasy. Even after his convictions, investigators continued asking the same question: how many of his claims were real, and how many were attempts to create his own mythology?A pioneering vision of tomorrow on one side.A killer surrounded by uncertainty on the other.Just another week of the Nerd and the Murd.Support the show

Ziggy talks about the CISA Secure by Design report, Lenovo Yoga 9 years old should I replace? Zig in NC Office Suite with Outlook account issues, AI being used to generate Laws is causing more work for actual lawyers, Missing DLL in my operating system, Windows Protect Print Mode, Proton found a ton of VPNs that are tracking your location, How do I reduce Ads on my phone,

The loss of a cultural icon, Trump and Michael Cohen make peace while Trump and Tucker make war, CTE study on NFL, Lindsay Clancy and Zig's new strategy with the Superintendent.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-death-of-journalism--5691723/support.

Send us Fan MailEpisode 263 of Nerdery & Murdery is live.On the Nerdery side, Zig returns with another installment of songs that helped define the college radio experience. From alternative rock and post-punk to underground favorites and influential artists that helped shape the sound of a generation, we explore ten tracks that became staples of dorm rooms, campus stations, and late-night radio discoveries.On the Murdery side, our A–Z Across America series continues as we head to Maine and the case of James Hicks.What began as the disappearance of a young mother in 1977 slowly evolved into something far more disturbing. Years later, another woman vanished. Then another. For decades, investigators struggled to prove what many suspected until an arrest hundreds of miles away finally unlocked the truth behind one of Maine's most haunting serial murder investigations.The soundtrack of college radio on one side. A decades-long mystery of missing women and buried secrets on the other.Just another week of the Nerd and the Murd.Support the show

Jason Arday and David Foster Wallace, the evolving 2028 race, the backstory of Zig's recent CNN appearance, more WNBA craziness, more on being right too soon and finally the end of Liv Golf.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-death-of-journalism--5691723/support.

Send us Fan MailEpisode 262 of Nerdery & Murdery is live.On the Nerdery side, Zig steps into the strange and unsettling world of Night Gallery, Rod Serling's eerie follow-up to The Twilight Zone. From haunting paintings and supernatural tales to psychological horror and dark twists, we revisit one of classic television's most atmospheric anthology series and the unique style that made it unforgettable.On the Murdery side, our A–Z Across America series continues as we head to Louisiana and the case of Ronald Dominique.For years, men disappeared across southern Louisiana. Bodies were discovered near bayous, canals, and isolated roads while investigators slowly realized they were hunting a serial killer targeting vulnerable victims across multiple parishes. By the time the investigation ended, Dominique had confessed to more than twenty murders.Classic television horror painted in shadows on one side. A real-world nightmare hidden in the Louisiana bayous on the other.Just another week of the Nerd and the Murd.Support the show

The Fauci hearing and the substance, the backstory on Zig's CNN appearance, RFKJ and Aaron Rodgers go after CNN and ESPN, more WNBA madness and Zig interviews his long-lost childhood best friend.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-death-of-journalism--5691723/support.

Send us Fan MailEpisode 261 of Nerdery & Murdery is live.On the Nerdery side, Zig steps into the eerie world of Tales from the Darkside, the horror anthology series that brought strange stories, unsettling twists, and late-night nightmare fuel to television throughout the 1980s. From creepy atmosphere to unforgettable episodes, we take a look back at one of horror television's most memorable cult classics.On the Murdery side, our A–Z Across America series continues as we head to Kentucky and the case of Donald Harvey.For years, patients in hospitals and nursing homes died under what appeared to be natural circumstances. But investigators would eventually uncover a disturbing pattern connected to a quiet healthcare worker who later confessed to dozens of murders carried out behind the walls of places meant for healing and care.A world of unsettling television horror on one side. A real-life nightmare hiding inside hospitals on the other.Just another week of the Nerd and the Murd.Support the show

幻冬舎の暗号資産（仮想通貨）/ブロックチェーンなどWeb3領域の専門メディア「あたらしい経済 https://www.neweconomy.jp/ 」がおくる、Podcast番組です。 ーーーーー 【番組スポンサー】 この番組は、暗号資産取引におけるフルラインナップサービスを提供する「SBI VCトレード」のスポンサーでお届けします。 ーーーーー SBI VCトレードは、「暗号資産もSBI」のスローガンのもと、国内最大級のインターネット総合金融グループであるSBIグループの総合力を生かし、暗号資産取引におけるフルラインナップサービスを提供しております。暗号資産交換業者・第一種金融商品取引業者・電子決済手段等取引業者として高いセキュリティ体制のもと、暗号資産の売買にとどまらない暗号資産運用サービスや法人向けサービスの展開、さらにステーブルコインのユーエスディーシー（USDC）を国内で初めて取り扱っております。 ーーーーー SBI VCトレード公式サイト：https://account.sbivc.co.jp/signup?hc_ak=1RNML.3.M06AS ーーーーー 【紹介したニュース】 ・金融庁と警察庁、暗号資産交換業者に出庫制限など要請。詐欺被害防止へ ・ロシア、暗号資産包括規制法が成立、交換業者を登録制に。国内決済禁止は継続 ・Sui、耐量子署名2方式を導入へ。既存アドレスを維持したまま移行可能に ・「Move」開発者サム・ブラックシア、Sui開発元ミステンラボCTO退任、アンソロピックへ ・メタマスク、AIエージェント向け「MetaMask Agent Wallet」正式提供開始 ・ビットトレードとスタンデージ、貿易領域で暗号資産活用の共同実証へ ・キリフダ、暗号資産188銘柄を11セクターに分類、独自指数公開 ・マスターカードとボーダーレス、国際ステーブルコイン決済の信頼性向上へ共同実証 ・ビザ、「Visa Direct」でステーブルコイン送金対応拡大。zerohashが基盤提供 ・ワールドチェーン、ブロック並列検証機能を導入へ。8/17にメインネットで有効化 ・野村HD傘下レーザーデジタル、L1ジグチェーンの「ZIG」に戦略投資 ・ストラティウム、ビルダーフィーを原資とする抽選プログラム「The Pot」開始 ・a16z出資先Web3ゲームスタジオ「プルーフオブプレイ」、事業終了へ ・ブラックロックの「イーサリアム（ETH）」現物ETF、3口を1口へ併合 ・ビットゴー、複数取引所口座の資金管理を一元化する「BitGo Link」提供開始 ・【取材】ローソンでJPYC決済を体験、国内初のコンビニPOS連携実証 【あたらしい経済関連リンク】 ニュースの詳細や、アーカイブやその他の記事はこちらから https://www.neweconomy.jp/

Zig and I catch up after two decades. A long road of family, design, creativity, and activism leads us down a winding road.

Send us Fan MailEpisode 260 of Nerdery & Murdery is live.On the Nerdery side, Zig takes a look at Earth: Final Conflict, the late 90s sci-fi series created from ideas by Gene Roddenberry. From mysterious alien benefactors to growing suspicion about their true intentions, we explore what made the show unique, how its themes evolved over time, and why it still holds a place in science fiction television history.On the Murdery side, our A–Z Across America series continues as we head to Kansas and the case of Dennis Rader, better known as BTK.Beginning in 1974, a series of murders in Wichita shocked the community. The killer taunted police, named himself, and then disappeared for years at a time. When communication resumed decades later, investigators were finally given the opportunity they needed to identify the man behind one of Kansas' most infamous serial killer cases.Alien visitors with uncertain motives on one side. A killer hiding in plain sight in Kansas on the other.Just another week of the Nerd and the Murd.Support the show

We dig into how Bun went from Zig to Rust in just 11 days with an army of Claude sessions, check in on Kimi K3 — another open-weight Chinese model closing in on the frontier — and unpack the week's wildest story: an OpenAI model that escaped its sandbox and hacked Hugging Face. Just another normal week in AI.Timestamps0:00 - Intro3:05 - Kimi K310:58 - Rewriting Bun in Rust20:42 - OpenAI hacks Hugging Face39:48 - App Store number updates42:11 - Meta's new app is set to a song about human extinction46:50 - What's Making Us HappyNewsPaige: Rewriting Bun in RustJack: Kimi K3TJ: OpenAI hacks Hugging FaceLightning NewsApp Store app numbers are way up; downloads are notMeta launched a new AI optimism ad set to a song about human extinctionWhat's Making Us HappyPaige: The Bear season 5Jack: The Odyssey movieTJ: Detroit Tigers baseballThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. Join us in our Discord, 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

Send us Fan MailEpisode 259 of Nerdery & Murdery is live. On the Nerdery side, Zig dives into Selectavision, RCA's ambitious attempt at home video that arrived during the early days of the format wars. From stylus-based playback to fragile discs and bold marketing, we look at what made Selectavision unique, why it struggled to compete, and how it became one of the most fascinating dead ends in home media history.On the Murdery side, our A–Z Across America series continues as we head to Iowa and the case of Robert Ben Rhoades.A long-haul truck driver traveling America's highways. Truck stops along major Iowa corridors. A pattern that stretched across state lines and a crime that moved quietly from place to place. What began as a routine stop would eventually expose a chilling series of crimes tied to one man and the road itself.A forgotten experiment in home video on one side. A case that traveled the highways of Iowa on the other.Just another week of the Nerd and the Murd.Support the show

Bun quitte Zig pour Rust en 11 jours à coups de Claude Code, pour 165 000$ payés par Anthropic : la réaction du créateur de Zig ne se fait pas attendre. TypeScript 7 débarque, réécrit en Go, 8 à 12x plus rapide. Entre les deux, Vidocq réimplémente Jakarta EE en souverain, le COBOL met un uppercut aux microservices, et un CTO demande à son équipe combien de temps il lui faudrait pour revenir à sa vélocité antérieure sans Claude Code. De quoi réfléchir avant le prochain rewrite. Enregistré le 17 juillet 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-342.mp3 ou en vidéo sur YouTube. News Langages Est-ce qu'on peut aussi utiliser des double, des longs, ou autre pour gérer les montants monétaires en Java ? https://blog.frankel.ch/bigdecimal-vs-double/ double (IEEE 754) Usage : Calculs scientifiques, métriques, statistiques. Avantages : Très performant (matériel), idéal pour l'approximatif. Risques : Erreurs d'accumulation, égalité (==) trompeuse, NaN / -0.0. Bonnes pratiques : Utiliser une tolérance (epsilon ou ULP) pour comparer ; utiliser des algorithmes de sommation compensée (Kahan/Neumaier) pour la précision. BigDecimal Usage : Finance, comptabilité, fiscalité (précision décimale stricte). Avantages : Contrôle total des arrondis et de l'échelle. Risques : Lent (allocations), immutabilité (risque de mauvaise réaffectation), confusion equals() vs compareTo(). Bonnes pratiques : Initialiser via String ou valueOf() ; utiliser compareTo pour l'égalité. Point fixe (long) Usage : Trading, systèmes haute performance, paiements. Avantages : Très rapide, déterministe, zéro allocation. Risques : Gestion manuelle de l'échelle et des débordements (Math.addExact). Points de vigilance en production Sérialisation (JSON) : Préférer les String pour BigDecimal pour éviter la perte d'échelle. Atomicité : double n'est pas atomique ; utiliser volatile ou DoubleAdder (pour les compteurs). Tests : Toujours définir un delta ou Offset pour les tests de flottants. Bibliothèques recommandées Moneta (JSR 354) : Standard bancaire complet. decimal4j : Optimisé pour le point fixe haute performance. Apache Commons Numbers : Outils robustes pour la précision et les sommations. Typescript 7 est de sortie devblogs.microsoft.com/typescript/announcing-typescript-7-0 Performance majeure : Portage natif en Go offrant des gains de vitesse de 8x à 12x et une consommation mémoire réduite. Architecture optimisée : Utilisation du multithreading (mémoire partagée) et parallélisation native (analyse, vérification de types,émission). Nouvelles options de contrôle : Introduction des flags –checkers, –builders (parallélisation) et –singleThreaded (mode mono-cœur). Nouvel observateur de fichiers : Passage à une solution basée sur @parcel/watcher pour une meilleure réactivité et stabilité du mode –watch. Compatibilité et transition : Compatible avec les bases de code TypeScript 6.0. Utilisation du package @typescript/typescript6 recommandée pour maintenir des outils dépendants de l'ancienne API. Changements de configuration : Durcissement des défauts (ex: strict activé par défaut) et suppression de nombreuses options obsolètes (target: es5, baseUrl, etc.). Amélioration de l'expérience éditeur : Serveur de langage (LSP) plus stable avec une réduction de 80 % des erreurs et 60 % des crashs. Limitations actuelles : Support incomplet pour les frameworks utilisant des plugins de langage (Vue, Svelte, Astro, Angular) en attendant une API stable. "Java, the documentary" est sur YouTube, retraçant l'histoire du langage youtube.com/watch?v=… La vidéo n'était pas encore disponible à l'heure de l'enregistrement. Sortie officielle le 17 juillet. Avec des interviews de James Gosling, Brian Goetz, Venkat Subramaniam, et bien d'autres. Librairies What's New in 8.0 - Hibernate docs.hibernate.org/orm/8.0/whats-new L'intégration de Jakarta Persistence 4.0 apporte des nouveautés majeures comme EntityAgent (qui standardise la StatelessSession), les mappings de result set en SQL natif, et de nouvelles options de configuration de session et de requêtes (Session Creation Options, Query Options). Le support de Jakarta Data 1.1 est ajouté pour les Hibernate Data Repositories, incluant l'intégration avec les requêtes statiques JPA4, les projections @Select, et les repositories asynchrones via Jakarta Concurrency ou Hibernate Reactive. L'introduction du Graph-based Flushing remplace l'ancienne approche basée sur des heuristiques par un modèle de dépendances utilisant les contraintes relationnelles, afin d'améliorer la fiabilité des tris, la gestion des batchs et les performances globales (bien que l'ancienne méthode reste temporairement disponible). L'API ProcedureCall a été améliorée pour faciliter le casting des résultats (asResultSetOutput) et permettre la déclaration paresseuse (lazy) du mapping des ResultSet. Hibernate supporte désormais la sécurité au niveau de la ligne (Row-Level Security) de manière native pour les bases de données compatibles (PostgreSQL, Db2, SQL Server, CockroachDB) afin de gérer la visibilité en contexte multi-tenant. Une nouvelle méthode getReference() permet dorénavant de récupérer la référence d'une entité directement à partir de son natural id. Le mode Safe Mode Validator (hibernate.query.safe_mode_enabled=true) fait son apparition pour bloquer les opérations risquées comme sql(), function() ou column() dans les requêtes HQL et Criteria, ce qui est particulièrement utile pour les applications exposées aux LLMs. La gestion des associations bidirectionnelles lors de la phase de flush peut maintenant être prise en charge automatiquement par Hibernate (hibernate.bidirectionality_management=true), synchronisant la référence côté inverse de l'association. Le Subselect Fetching est considérablement amélioré, supportant dorénavant les associations "to-one" pour le bulk select fetching (au lieu de se limiter aux collections) et devenant une option de premier ordre via FetchMethod.BY_SUBQUERY. Un des papas de Cucumber et Gherkin lance Var, une alternative pour le test et le BDD var.oselvar.com Lancement de Vár : Nouvel outil de test créé pour pallier les défauts de Cucumber. Limites de Cucumber : Syntaxe Gherkin trop rigide, intégration difficile avec les exécuteurs de tests et support éditeur limité. Usage avec l'IA : Conçu spécifiquement pour vérifier que les agents IA respectent les intentions et spécifications de l'utilisateur. Fonctionnement : Utilisation du Markdown plutôt que du Gherkin ; sert à la fois de guide et d'outil de vérification. Développement assisté : Code et documentation générés en grande partie par Claude sous supervision humaine. Appel aux retours : Projet ouvert aux tests et aux critiques de la communauté. Web Une nouvelle méthode HTTP : QUERY https://kreya.app/blog/new-http-query-method-explained/ Méthode HTTP QUERY (RFC 10008) pour les recherches complexes. Problème : GET (limité par l'URL) vs POST (sémantique inadaptée). Avantages : Permet un corps de requête, sûr, idempotent et cacheable. Limites : Support infrastructurel faible, non partageable par lien, cache complexe. Usage : À réserver aux requêtes complexes si l'environnement le permet. Comment je fais du design en tant que dev backend eventuallycoding.com/p/comment-je-fais-du-design-en-tant-que-dev-backend Hugo Lassiège retrace l'évolution de son workflow de création d'interfaces en tant que développeur backend, depuis ses débuts avec Bootstrap jusqu'à l'ère de l'intelligence artificielle. L'article explique comment la structuration des éléments visuels a progressé grâce à l'Atomic Design, l'émergence des design systems et l'adoption des design tokens via un framework comme Tailwind. L'auteur détaille son processus actuel qui s'appuie fortement sur Claude Design pour générer et itérer sur des maquettes à partir d'un brief, d'un screenshot ou d'un design system de référence. Il aborde également le risque de slopification et de standardisation extrême apporté par ces outils, rappelant que si l'IA simplifie la technique, il reste crucial d'injecter de l'identité et de l'originalité pour éviter un web trop aseptisé. Data et Intelligence Artificielle De l'utilisation de SKILL.md et de "loop engineering" pour augmenter sa productivité glaforge.dev/posts/…/of-skills-and-loops-with-ai-assistance Les skills permettent d'encoder une procédure de manière répétable et automatisable Le loop engineering enlève l'humain de la boucle afin que l'agent atteigne un objectif donné de façon plus autonome Pour écrire des Codelabs (sorte de tutoriel guidé pas à pas) Guillaume a transformé une séance de création de codelab avec son agent préféré (Antigravity) en skill réutilisable pour l'écriture de ses prochains codelabs Il a également utilisé l'approche de "loop engineering" à la mode en ce moment pour que son agent IA compile, exécute, teste les instructions et le code de son codelab, pour qu'il soit complètement fonctionnel Gain estimé : passer de 2 jours de travail à moins de 2 heures ! Redeploying Claude Fable 5 anthropic.com/news/redeploying-fable-5 Anthropic a annoncé le rétablissement de l'accès à ses modèles Claude Fable 5 et Mythos 5, qui avaient été suspendus suite à des restrictions d'exportation imposées par le gouvernement américain le 12 juin 2026. Cette suspension faisait suite à un rapport d'Amazon démontrant une méthode pour contourner les garde-fous de Fable 5, lui permettant d'identifier et d'exploiter une vulnérabilité logicielle (un jailbreak). Pour y remédier, Anthropic a renforcé ses mécanismes de sécurité en déployant un nouveau classifieur capable de bloquer cette technique spécifique dans plus de 99 % des cas, acceptant en contrepartie une augmentation des faux positifs sur des requêtes bénignes. Face à l'absence de consensus sur l'évaluation des jailbreaks, Anthropic s'associe à Amazon, Microsoft, Google et d'autres partenaires pour développer un standard industriel évaluant la sévérité de ces failles selon quatre critères : gain de capacité, étendue du gain, facilité d'arsenalisation et découvrabilité. L'entreprise s'engage également à approfondir sa collaboration avec le gouvernement américain, notamment via des évaluations pré-déploiement, un partage rapide d'informations sur les failles, et des ressources dédiées à la recherche conjointe sur la sécurité de l'IA. Outillage La réécriture de Bun en Rust et la réaction du créateur de Zig bun.com/blog/bun-in-rust et andrewkelley.me/post/my-thoughts-bun-rust-rewrite.html Bun, le runtime JavaScript et TypeScript écrit à l'origine en Zig, a été entièrement réécrit en Rust pour des raisons de stabilité et de gestion de la mémoire. Cette migration massive d'un demi-million de lignes de code a été bouclée en seulement 11 jours grâce à l'utilisation intensive de Claude Code fonctionnant en parallèle, pour un coût d'API estimé à 165 000 dollars financé par Anthropic. Andrew Kelley, le créateur de Zig, a réagi publiquement en qualifiant l'ancienne base de code de Bun de "slop" remplie de hacks et de fuites mémoire accumulées par une course aux fonctionnalités. Kelley exprime son soulagement face à ce départ, expliquant que les plantages incessants de Bun devenaient un passif réputationnel toxique pour le langage Zig et sa fondation. Le rachat de Bun par Anthropic fin 2025 avait déjà mis fin aux donations financières de Bun envers la Zig Software Foundation, facilitant cette séparation. La nouvelle version Rust de Bun passe désormais la quasi-totalité des tests, réduit la taille du binaire et est déjà déployée de manière transparente en production dans Claude Code. Nouveautés de Git 2.55 github.blog/open-source/git/highlights-from-git-2-55 Support natif de FSMonitor sous Linux via inotify pour accélérer les commandes comme git status sur les grands dépôts Intégration de la compaction incrémentale MIDX (multi-pack index) dans git repack pour optimiser la réécriture des métadonnées Amélioration drastique des performances de génération des bitmaps et des pseudo-merge bitmaps lors des tâches de maintenance Nouvelle commande expérimentale git history fixup pour intégrer facilement des modifications locales dans un commit antérieur Possibilité d'exécuter des hooks configurés en parallèle pour optimiser le temps de build et de validation Utilisation d'un autostash automatique lors d'un git checkout -m en cas de conflit de fusion pour éviter de bloquer l'espace de travail Nouvelle commande git format-rev permettant de formater rapidement des commits reçus via l'entrée standard (stdin) Support du push simultané vers un groupe de remotes configuré Protection contre l'exécution de séquences de contrôle de terminal malveillantes via les flux de progression distants Vidocq, une réimplémentation souveraine et sans dépendance de Jakarta EE et Microprofile vidocq.dev/posts/vidocq-a-sovereign-jakarta-ee-and-microprofile-runtime Lancement de Vidocq : Runtime Java open source complet, compatible Jakarta EE Core Profile et Souveraineté numérique : Projet européen hébergé sur Codeberg, sous licences EUPL 1.2, EPL 2 et GPL 2.0. Standardisation totale : Implémentation fidèle des spécifications (CDI, REST, JSON, etc.), validée par 5 650 tests TCK officiels. Sécurité radicale : Zéro dépendance externe et aucune bibliothèque tierce. Aucune manipulation de bytecode à l'exécution (« magie » générée à la compilation via JDK 25). Compatible JPMS, AOT, GraalVM et Leyden CDS. Disponibilité : Projet en phase alpha, code et documentation accessibles sur vidocq.dev. Article complémentaire qui revient sur la genèse de Vidocq, en utilisant l'IA et les TCKs pour driver l'aspect spec-driven development vidocq.dev/posts/the-story-of-vidocq Le "selfware" : Guillaume s'est fait plais' en vibe-codant son propre éditeur de texte glaforge.dev/posts/…/selfware-building-my-own-text-editor-without-knowing-swift Concept de « Selfware » : création de logiciels conçus exclusivement pour soi-même, sans monétisation ni contraintes liées aux utilisateurs tiers. Le rôle de l'IA : les agents de programmation (comme Antigravity) suppriment la barrière technique de l'apprentissage des langages (Swift, APIs) pour les non-développeurs. Développement minimaliste : privilégier la performance et l'utilité directe (démarrage instantané, interface native) au détriment des fonctionnalités complexes (plugins, télémétrie, gestion de comptes). Absence de pression : libération des contraintes liées à la compatibilité, à la maintenance logicielle et aux retours utilisateurs ; le logiciel n'a besoin d'être « assez bon » que pour ses propres besoins. Incitation à l'autonomie : encourager la création d'outils sur mesure pour résoudre les frictions quotidiennes plutôt que de subir les limitations des logiciels commerciaux. Architecture Le cobol a donné un uppercut au microservices https://freedium-mirror.cfd/@maahisoft20/your-microservices-lost-to-cobol-let-that-sink-in-8ce2e236d007 Retour d'expérience sur la migration d'un système COBOL vers des microservices cloud-native qui s'est soldée par un retour en arrière après avoir constaté que le traitement batch initial était plus rapide, moins cher et plus fiable Là où le batch COBOL traitait 2.4 millions d'enregistrements en 11 minutes, le système distribué modernisé à base de message queues, retries et Kubernetes prenait 47 minutes et tombait sous la charge COBOL brille par ses caractéristiques conçues spécifiquement pour la finance comme le calcul décimal précis sans floating point errors et l'absence totale d'overhead réseau, de conteneurs ou de cold starts Rappel que distribuer un système multiplie les points de défaillance silencieux et complexifie la gestion de la cohérence transactionnelle par rapport à une exécution locale séquentielle Une invitation à se demander si les projets de décomposition en microservices apportent réellement un gain de performance de bout en bout pour l'utilisateur final ou s'ils optimisent seulement le diagramme d'architecture Méthodologies Ma meilleure question d'entretien Spring beaufume.fr/articles/spring-interview Florian beaufumé partage sa question d'entretien favorite pour évaluer des développeurs Spring de niveau intermédiaire à avancé : "Que pouvez-vous me dire sur le paramètre spring.jpa.open-in-view ?". Ce paramètre détermine l'activation du pattern Open Session In View (OSIV) qui, lorsqu'il est à true (la valeur par défaut dans Spring Boot), maintient l'un EntityManager JPA ouvert durant toute la requête HTTP. Si l'OSIV facilite le développement en évitant les fameuses LazyInitializationException lors de la sérialisation des entités en JSON, il pose d'importants problèmes de performance en provoquant des requêtes SQL non maîtrisées (comme le problème du N+1 select) en dehors de la couche service. Maintenir l'OSIV actif augmente également le temps de rétention des connexions au sein du pool de la base de données, limitant la scalabilité de l'application. La recommandation est de désactiver ce comportement en le positionnant à false, et de gérer explicitement le chargement des données requises au sein des transactions (via des DTOs, des requêtes JOIN FETCH ou des Entity Graphs) pour garder le contrôle sur les accès à la base de données. 10 points à retenir du rapport AI Engineering 2026 : The Acceleration Whiplash faros.ai/blog/ai-acceleration-whiplash-takeaways L'IA a franchi un cap et est devenue l'auteur principal du code : le taux d'acceptation du code généré est passé de 20% à 60% dans les équipes étudiées par Faros AI. La vélocité métier est bien réelle, avec une augmentation de 66% des epics livrées et une hausse de 33,7% du throughput des tâches par développeur. Ce volume cache un code churn massif (+861%), ce qui signifie qu'une quantité énorme de code est supprimée ou remplacée peu après avoir été ajoutée. La qualité en aval se dégrade fortement : les bugs par développeur ont augmenté de 54% et le nombre d'incidents par pull request a explosé de 242,7%. Le processus de code review est complètement saturé, entraînant un temps médian de relecture multiplié par cinq et une augmentation de 31,3% des PRs mergées sans aucune revue. Le système repose de plus en plus sur les développeurs seniors qui subissent une "senior engineer tax", devant relire un volume insoutenable de code à l'apparence correcte mais structurellement fragile. Contrairement à certaines hypothèses récentes de DORA, une forte maturité DevOps ne protège pas les entreprises contre cette détérioration ; le "Acceleration Whiplash" frappe de la même manière les équipes très performantes. En résumé, les outils d'IA inondent les pipelines de livraison avec un volume de code pensé pour un rythme machine, alors que les systèmes de vérification reposent toujours sur un rythme de validation humain. Loi, société et organisation Le coût d'une equipe d'engineering qui ne sait plus ce qu'elle fait dans un contexte d'augmentation de coût des coding agents https://freedium-mirror.cfd/@developer_programmer/i-spent-47-000-on-claude-code-in-90-[…]-asked-me-one-question-and-i-couldnt-answer-it-af3b203f81bb Une équipe de 8 ingénieurs a vu sa vélocité de développement exploser en utilisant Claude Code de manière intensive, jusqu'à recevoir une facture d'API salée de 47 213 $ pour seulement trois mois d'utilisation. Face à cette dépense, la question piège du CTO n'était pas sur le montant, mais sur la dépendance : "Si nous arrêtions Claude Code demain, combien de temps faudrait-il pour que notre vélocité revienne à son niveau initial ?". L'auteur s'est rendu compte qu'il était incapable de répondre car son équipe, en particulier les profils juniors, avait commencé à perdre l'habitude de concevoir et d'implémenter des fonctionnalités complexes sans l'aide permanente d'un agent. Le deuxième risque stratégique soulevé est celui de la dépendance tarifaire et du vendor lock-in : si l'outil devient une infrastructure indispensable au quotidien, l'entreprise perd tout pouvoir de négociation face aux augmentations de prix de l'éditeur d'IA. Pour éviter que l'IA ne devienne une béquille qui atrophie les compétences de l'équipe, l'article suggère de poser des limites budgétaires strictes, d'organiser régulièrement des sprints sans IA ("AI-free sprints") et de concevoir des processus de développement portables. Retour de Nicolas Delsaux sur jqwik qui donne une perspective plus complète concernant jqwik, il me semble que vous oubliez (comme tous les gens qui parlent de LLM dans "l'industrie") que l'auteur n'a pas fait ça juste pour faire chier le monde, mais parce que ces outils ont des externalités incroyablement négatives, ce dont l'auteur s'explique dans son blog (blog.johanneslink.net/2026/06/09/the-jqwik-anti-ai-affair) Vous oubliez également de signaler que le ticket (github.com/jqwik-team/jqwik/issues/708) par lequel un utilisateur se plaint de cette fonctionnalité a été écrit par un agent. N'oubliez pas non plus que l'enthousiasme pour ces technologies n'est en fait pas universel, et que ces technologies sont loin d'être inévitables (les gains de vitesse ne sont, d'après circle CI - circleci.com/resources/2026-state-of-software-delivery, pas des gains de productivité ) OkHttp, Okio, Retrofit et SQLDelight rejoignent Commonhaus ! commonhaus.org/activity/315.html La fondation Commonhaus, via une publication de Andres Almiray, annonce l'arrivée de quatre projets majeurs de l'écosystème Java et Kotlin : OkHttp, Okio, Retrofit et SQLDelight. Ces projets, initialement créés chez Square (devenu Block), sont désormais regroupés et gérés sous la bannière lysine.dev au sein de la fondation. Jesse Wilson et Jake Wharton, créateurs et mainteneurs historiques de ces outils, rejoignent Commonhaus en tant que leaders de lysine.dev. Suite à leur départ de Block, ils expliquent avoir choisi Commonhaus pour offrir à leur immense communauté d'utilisateurs un cadre de gouvernance pérenne, stable et digne de confiance. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 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 : aMP Day Montpellier 2026 - Montpellier (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 : Campus Agile Grenoble - Grenoble (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) 10 décembre 2026 : DevCon 28 : sécurité | post-quantique | hacking édition 2027 - Paris (France) 14-16 janvier 2027 : SnowCamp 2027 - Grenoble (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/

CJ and Scott break down the biggest week in web dev: TypeScript 7 ships with a 10x-faster native port, Bun gets rewritten in Rust (much to the Zig team's dismay), and Better Auth joins Vercel. Plus GPT-5.6 first impressions, Odin 1.0, Cloudflare's new Workers cache and drag-and-drop deploys, and the OpenCode 2 beta. Show Notes 00:00 Welcome to Syntax! 00:21 CJ upgraded his homelab network 02:09 TypeScript 7 is 10x faster 11:19 Bun Rust rewrite drama Zig creator criticizes rewrite 28:18 GPT 5.6 Impressions Ashley Peachock on X Matt Shumer on X 40:58 Better Auth Acquired by Vercel 49:51 Grok Build CLI is stealing your code International Cyber Digest on X 56:00 Cloudflare Worker Cache and Drop 01:01:22 Check out CJ's latest video I Built an LLM from Scratch 01:03:06 Odin 1.0 Announced 01:05:33 OpenCode 2.0 Beta released 01:07:32 Winamp Skin Museum 01:11:08 CJ's new MP3 player 01:13:03 Scott's Robot Update Sick Picks Scott: Reachy Mini CJ: Snowsky Echo Mini 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

Send us Fan MailEpisode 258 of Nerdery & Murdery is live.On the Nerdery side, Zig continues his journey through the Planet of the Apes franchise, picking up where he left off in Episode 243 with Escape from the Planet of the Apes and Conquest of the Planet of the Apes. As the series shifts tone and leans deeper into social commentary, we explore how these films reshape the mythology and push the story toward a darker future.On the Murdery side, we step away from our A–Z Across America series for a case that was simply impossible to ignore.In September 2002, an 18-year-old girl got into a car with people she knew. She never came home. What followed was a chilling group crime fueled by suspicion, loyalty, and a decision that would change multiple lives forever.A franchise evolving into something darker on one side. A case of betrayal and silence on the other.Just another week of the Nerd and the Murd.Support the show

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president could still fire commissioners, as the Court now permits, but if those firings broke quorum, the agency would be unable to proceed until replacements were confirmed. The guardrail would

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

SC Senator Lindsey Graham passes away suddenly and Zig describes him as "all too human". Is Mitch McConnell next? Tyler Robinson hearing for Charlie Kirk's murder shed light on Candace Owens' delusions, NY Times exposed, Brown University AI impact and the last installment of "Ask Zig".Become a supporter of this podcast: https://www.spreaker.com/podcast/the-death-of-journalism--5691723/support.

Vercel goes all-in on your stack — a Zig-powered Native SDK for building desktop apps, plus Vercel Services to run multiple frameworks as one shared project for monorepo and microservice teams — while VoidZero ships Vite+ in beta to unify the entire frontend toolchain. Plus Safari joins the MCP party and Claude gets a little too chatty in Slack.Timestamps0:00 - Intro0:59 - Vercel native8:22 - Vite+ beta10:15 - Vercel Services16:50 - Safari MCP server19:20 - Claude Tag27:07 - Museum covers its floor in peanut butter33:41 - What's Making Us HappyNewsPaige: Vercel ServicesJack: Vercel's nativeTJ: Vite+ betaLightning NewsSafari MCP serverClaude TagMuseum covers its floor in peanut butterWhat's Making Us HappyPaige: Dutton Ranch TV seriesJack: BowlingTJ: The World CupThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. Join us in our Discord, 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

Send us Fan MailEpisode 257 of Nerdery & Murdery is live!On the Nerdery side, Zig takes a look back at Laserdiscs, the format that bridged the gap between VHS and what would eventually become DVD. From massive discs to surprisingly high quality video for the time, we break down what made Laserdiscs special and why they still hold a place in the evolution of home media.On the Murdery side, our A–Z Across America series takes us to Indiana and the case of Herb Baumeister.In the early 1990s, men began disappearing in the Indianapolis area. The investigation eventually led to Fox Hollow Farm, where thousands of human bone fragments were discovered across the property. Baumeister was identified as the primary suspect, but his death left many questions unanswered, including the full number of victims.A look back at a forgotten piece of media history on one side. A hidden horror beneath a quiet property on the other.Just another week of the Nerd and the Murd.Support the show

Grading the Freedom 250 celebration, Trump and the World Cup, Platner implosion, the royal wedding and more ask Zig.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-death-of-journalism--5691723/support.

פרק מספר 517 של רברס עם פלטפורמה - ואנחנו בבמפרס 92! רן, דותן ואלון מתכנסים לדבר על הטרנדים החמים בעולמות הפיתוח, החל מ-Loop Engineering וסקילים לאג'נטים, דרך המעבר של Bun ל-Rust, ועד לטריקים יצירתיים לחיסכון בטוקנים ומבחן הטיורינג החדש. [02:22] קול קורא ולופ אינג'ינרינג רן מזכיר שהקול הקורא (CFP) לכנס רברסים 2026 עדיין פתוח (עד ה-26 ביולי) - זה הזמן להגיש הצעות להרצאות! שיחה על Loop Engineering - מתודולוגיה לאוטומציה של פיתוח מבוסס אג'נטים שמטריפה את הרשת. אדי אוסמאני (Addy Osmani) כותב על זה לא מעט בטוויטר. מומלץ לקרוא את הפוסט המסכם שלו בנושא שעושה סדר במרכיבים השונים של הלופים ואיך להשתמש בהם נכון. [11:15] המעבר של Bun מ-Zig ל-Rust ההחלטה הארכיטקטונית המעניינת מאחורי המעבר של פרויקט Bun - נכתב על כך פוסט מפורט בבלוג שלהם: Bun from Zig to Rust. דיון על ההבדלים בניהול זיכרון ב-Zig לעומת Rust ועל היתרונות לטווח ארוך למרות הבינארי הקטן ש-Zig מציעה. האם עקומת הלמידה הקשוחה של Rust כבר לא רלוונטית בעידן שבו האג'נט כותב ומקמפל את הקוד בעצמו? [19:45] ערימות של סקילים לאג'נטים דותן משתף על ריפוזיטורי מעולה של אדי אוסמאני שמכיל סקילים שימושיים לאג'נטים: agent-skills. המלצה רותחת על סקיל לכתיבת דוקומנטציה: Documentation Writer. דותן ניסה אותו על פרויקטים ישנים וגילה שהאג'נט יכול לכתוב דוקומנטציה מושלמת מאפס ב-5 דקות, תהליך שבעבר לקח שעות של כתיבה וריבים פנימיים. [26:04] לדבר כמו איש מערות כדי לחסוך טוקנים אלון מציג פתרון לבעיית ה"חפירות" של האג'נטים (והבזבוז הנלווה של טוקנים). הרעיון הוצג בציוץ של Hesamation. הפרויקט עצמו, Caveman, גורם לאג'נט לפשט את השפה, לדבר כמו "אדם קדמון" ולתמצת ניסוחים לשליש מגודלם המקורי. טיפ נוסף מאלון: במקום לקרוא הררי טקסט, אפשר לבקש מהאג'נט לייצר סימולציות והמחשות ויזואליות ב-HTML כדי להבין מצבים מורכבים כמו עומסי טראפיק ו-Circuit Breakers. [30:17] פינת המצחיקונים: מבחני טיורינג, משקולות ופרומפטים מוחבאים מבחן טיורינג החדש: נסו לגרום ל-LLM פשוט לספור עד 100. תראו את סרטון הטיקטוק של Husk שמדגים איך שלושה אג'נטים מנסים לחשוב על אסטרטגיה מורכבת לספור ביחד - ונכשלים. In The Weights: האם השם שלכם נמצא בתוך המשקולות של מודלי השפה? האתר In The Weights מאפשר לכם לבדוק מה ה-AI חושב עליכם (רן גילה שהוא זמר ומשקיע, ואלון הוא בכלל מדבב). Prompt Injection בלינקדאין: המשתמש tmuxvim הוסיף פרומפט מוחבא בפרופיל שלו שגורם לבוטים של מגייסים לפנות אליו אוטומטית באנגלית עתיקה ומוגזמת בנוסח ימי הביניים. חיסכון בעלויות דרך תמונות: איך חותכים את צריכת הטוקנים כשיש הרבה קונטקסט? ממירים את הכל לתמונה! הפרויקט pxpipe מדגים איך העברת היסטוריה ומסמכים כטקסט-בתוך-תמונה מוזילה משמעותית את עלות הקריאות למודלים. האזנה נעימה!

Send us Fan MailEpisode 256 of Nerdery & Murdery is live!On the Nerdery side, Zig turns back to the music and breaks down 10 songs that helped define 80s college radio. From alternative beginnings to underground sounds that would later shape the mainstream, this is a look at the tracks and artists that built a movement before it had a spotlight.On the Murdery side, our A–Z Across America series takes us to Illinois and the case of Larry Eyler, known as the Highway Killer.Throughout the early 1980s, young men were found murdered across Illinois, Indiana, and Wisconsin. The cases were initially investigated separately until a pattern began to emerge across state lines. Eyler was eventually arrested and convicted, but the full extent of his crimes would not be revealed until years later.The sound of a generation on one side. A killer who used the road to hide in plain sight on the other.Just another week of the Nerd and the Murd.Support the show

USA @ 250, SCOTUS rulings, NY goes full commie, Newsom pivots, will there be an MSG royal wedding, Caitlin and Sophie vs the world, oh Phil and ask Zig anything.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-death-of-journalism--5691723/support.

В этом выпуске к нам пришел Паша Финкенштейн — Developer Advocate в BellSoft, человек с нестандартным бэкграундом и огромной энергией. Мы поговорили о том, как политолог и психолог стал экспертом по Java, обсудили менеджерские и архитектурные фейлы, вспомнили забытое искусство правильного дебага и прошлись с критикой по Ktor, Kotlin Multiplatform и современному вектору развития JetBrains. 00:00:41 Знакомство с гостем. Паша Финкенштейн: Developer Advocate, фанат настолок и 3D-печати. 00:02:19 Шок-контент: политология, «Майн кампф» и диплом по психологии в бизнесе. Как Паша жил без профильного ИТ-образования. 00:06:34 Старт в ИТ через боль и техподдержку. Первые строчки кода и необычные курсы по Java без заумной теории. 00:09:01 Вторичный мониторинг АЭС в Сибири, хардкорный Eclipse RCP и мертвые технологии: GWT, Wicked, TeaVM. 00:18:12 Внезапное тимлидство и классические менеджерские ошибки (олимпиадные задачки на собесах — не надо так!). 00:25:00 Утерянное искусство дебага. Почему ставить 20 обычных брейкпоинтов — это плохо, и как правильно использовать логпоинты и условные остановки. 00:30:42 ДомКлик и продукт без багов в проде (почти). Разбираем эпичную архитектурную ошибку с parallelStream, HikariCP и Kubernetes health-чеками. 00:40:41 Опыт перехода в новую компанию сразу всей командой: вин-вин для всех или скрытые риски? 00:46:47 Суровый менеджерский взгляд на переписывание кода, рефакторинг и внедрение новых языков (например, Zig) ради перформанса. 00:51:12 Почему Паша ненавидит овертаймы, ночные онколы и как жестко разделять работу и личные pet-проекты. 00:58:15 Как управлять 40+ людьми на позиции CTO в Home Credit Bank и почему этот опыт заставил вернуться поближе к коду. 01:03:03 Lamoda. Data Engineering, старт DevRel-карьеры и легендарный “Speaker’s Club” Жени Голевой (формат 5-минутных докладов без слайдов). 01:07:17 Секреты успешных докладов на примере турне “Kotlin: 2 года в продакшене, ни единого разрыва”. 01:14:42 Три главные книги разработчика от Паши (спойлер: Java Concurrency in Practice нужно перечитывать каждый год). 01:17:10 Попадание в Big Data Tools от JetBrains через вопросы к спикеру после доклада. 01:22:49 Как сломать языковой барьер, начать думать и шутить на английском (экстремальный метод из Египта). 01:31:30 Непопулярное мнение №1: Ktor — не для Enterprise (нет DI, типизации рутов), а тулинг Kotlin Multiplatform (KMP) — это главный заградительный барьер технологии. 01:39:56 Непопулярное мнение №2: Пессимистичный прогноз будущего JetBrains. Отставание в AI (Claude обходит AI Assistant), медленный Fleet, закрытие Space и параллели с Borland. IDE Zed как альтернатива. 01:47:32 Философия подписок: почему IntelliJ IDEA Community и Ultimate — это правильный подход к монетизации. 01:50:22 Текущие задачи в BellSoft: переписывание документации с помощью ИИ и помощь в создании контента. 01:53:10 Почему Claude не заменит программистов. Код-ревью и архитектура — то, в чем LLM всё ещё сильно ошибаются (разбираем проблему N+1 в jOOQ, написанную ИИ).

Send us Fan MailEpisode 255 of Nerdery & Murdery is live!On the Nerdery side, Zig continues his chronological journey through the Doctors of Doctor Who. This time he arrives at the 14th Doctor, played once again by David Tennant. Tennant's unexpected return to the role brought a unique twist to the long running series and gave fans another chance to see one of the most beloved Doctors step back into the TARDIS.On the Murdery side, our A–Z Across America series takes us to Idaho and the case of Paul Ezra Rhoades.During the summer of 1987, three young women were abducted and murdered across southern Idaho. As fear spread through multiple communities, investigators worked to identify the man responsible for the crimes. The investigation eventually led to Rhoades, who confessed to the murders and was later sentenced to death.A return to the TARDIS on one side. A summer of fear in Idaho on the other.Just another week of the Nerd and the Murd.Support the show

The US Open, Zig's back on CNN, The NY Times & NY Mag agree that Epstein Killed Himself and an interview with Steve CortesBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-death-of-journalism--5691723/support.

From Nerdery and Murdery to Epic Fantasy: Zig's 'Firesign' Grab your swords, shields, and a copy of the Pact of Ashes! This week, Stephen and I are thrilled to welcome our good friend Zig from the Nerdery and Murdery podcast to the studio. But we aren't talking true crime today—we are diving headfirst into epic high fantasy because Zig is officially a published novelist! We're breaking down his brand-new book, Firesign: An Elvish War Chronicle (available right now on Amazon). Zig walks us through his massive world-building, the lore behind the Elves, Dwarves, and Orks, and what it took to take this epic tale from his brain to the page. Hit play and join the alliance—you won't want to miss it. Save it for the podcast!

In today's episode, Stig Brodersen is joined by Tobias Carlisle and Hari Ramachandra for a new round of stock pitches. Hari makes the case for Meta as a leading AI-powered advertising platform. Tobias breaks down Booking Holdings and whether its travel moat can withstand the rise of AI assistants. Stig analyzes Adobe, exploring the durability of its creative software ecosystem amid rapid technological change. IN THIS EPISODE YOU'LL LEARN: (00:00:00) Intro(00:02:31) Why Hari is bullish on Meta (Ticker: META), highlighting its advertising dominance, network effects, and long-term monetization potential.(00:03:38) The bear case for Meta, including massive AI infrastructure spending, uncertain returns on capital, and execution risk around AI monetization.(00:14:27) Why Tobias is bullish on Booking Holdings (Ticker: BKNG), emphasizing its capital-light business model and robust travel ecosystem.(00:18:46) The bear case for Booking Holdings, including AI-driven loss of customer mindshare, and potential pressure on its role in the travel booking value chain.(00:27:43) Why Stig is bullish on Adobe, focusing on its switching costs and subscription-based revenue model (Ticker: NASDAQ: ADBE).(00:37:21) The bear case for Adobe, including AI-generated content and the increasing competition from tools like Canva and LLMs. Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Stig Brodersen's Portfolio and Track Record.  Our valuation model of Adobe. Our valuation model of Meta. Our valuation model of Booking Holding.com. Check out the Mastermind Discussion Q1, 2026 | Video. Check out the Mastermind Discussion Q4, 2025 | Video. Check out the Mastermind Discussion Q3, 2025 | Video. Check out the Mastermind Discussion Q2, 2025 | Video. Check out the Mastermind Discussion Q1, 2025 | Video. Tobias Carlisle's podcast, The Acquirers Podcast. Tobias' ETF, ⁠ZIG⁠. Tobias' ETF, ⁠Deep⁠. Tweet to ⁠Tobias Carlisle⁠. Hari's ⁠Blog⁠. Tweet to ⁠Hari⁠. Related ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠books⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ mentioned in the podcast. Ad-free episodes on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Get smarter about valuing businesses through ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Investor's Podcast Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our sponsors: Plus500 Netsuite Vanta Shopify References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

Send us Fan MailEpisode 254 of Nerdery & Murdery is live!On the Nerdery side, Zig returns to his ongoing journey through the Star Wars universe, this exploring the saga in chronological order based on the timeline inside the galaxy itself. From the rise of the Republic to the fall of the Jedi and the growing shadow of the Empire, we look at how the story unfolds when you follow the events as they actually happen in that universe.On the Murdery side, our A–Z Across America series takes us to Hawaii and the mystery of the Honolulu Strangler.Between 1985 and 1986, five women were found murdered on the island of Oahu. Each victim had been sexually assaulted and strangled, and the killer was never officially identified. Investigators explored multiple suspects over the years, including Christopher Wilder, the so called Beauty Queen Killer, but the case ultimately went cold.A galaxy far, far away on one side. An unsolved mystery in paradise on the other.Just another week of the Nerd and the Murd.Support the show

Tobias Carlisle joins Excess Returns to discuss why today's market may be setting up a major opportunity in value stocks, small caps and micro caps. We cover stretched market valuations, AI capex, SpaceX and other massive IPOs, the risk of speculative growth assumptions, and how Tobias builds systematic deep value portfolios in ZIG and DEEP.Tobias Carlisle on Xhttps://x.com/GreenbackdAcquirers Fundshttps://acquirersfunds.com/Topics covered:Why elevated market valuations point to lower forward returns, not necessarily an immediate exit from stocksThe case for small value, micro-cap value and mid-cap value after a long large-cap growth cycleWhy equal-weight indexes and small caps may be signaling a market leadership shiftWhether AI capex will create lasting profits or mostly benefit consumersThe parallels and differences between AI, the dot-com boom, railroads and fiber optic buildoutsHow AI spending is being financed and why the stock market may be demanding more compute investmentWhat the SpaceX IPO, OpenAI and Anthropic could mean for market supply and investor psychologyWhy base rates are being challenged by the growth of major technology platformsHow disruption can create value traps and why traditional valuation metrics can struggle in disrupted industriesThe energy demand implications of AI data centers and why nuclear and natural gas could matterHow Tobias combines valuation, quality, financial statements and portfolio construction in ZIG and DEEPWhy quarterly rebalancing may be a practical balance between timing luck, momentum and trading costsTimestamps:00:00 Why AI value may accrue to consumers04:00 What extreme market valuations say about future returns08:22 Small caps, equal weight and the Mag Seven reversal14:15 AI capex and lessons from past technology booms19:47 Who gets the profits from AI?23:00 Cash flow, debt and the AI spending race28:06 SpaceX, giant IPOs and market supply31:00 OpenAI, Anthropic and Mauboussin's base rates35:17 Is buying the S&P 500 more speculative than investors realize?36:57 Value investing during disruptive technology cycles41:07 War, energy prices and the broadening trade45:32 Semiconductor valuations and aggressive growth assumptions47:30 How Tobias builds the ZIG and DEEP portfolios54:17 ETF rebalancing, timing luck and systematic value investing

Send us Fan MailEpisode 253 of Nerdery & Murdery is live!On the Nerdery side, Zig dives into one of the most beloved science fiction films ever made with Star Trek II: The Wrath of Khan. From the legendary rivalry between Kirk and Khan to one of the most emotional moments in Star Trek history, we revisit the movie that helped define the franchise for generations.On the Murdery side, we continue the A–Z Across America series with Georgia and the case of Wayne Williams and the Atlanta Child Murders.Between 1979 and 1981, a series of disappearances and murders of young Black children and teenagers terrorized Atlanta. Wayne Williams was ultimately convicted of two murders, with investigators attributing more than twenty additional killings to him. Decades later, the case still raises debate and unanswered questions.A legendary sci fi showdown on one side. One of the most controversial criminal investigations in American history on the other.Just another week of the Nerd and the Murd.Support the show

In this episode, Conor and Ben chat and continue their conversation about AI, its impact and what it means for the future.Link to Episode 290 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)SocialsADSP: The Podcast: TwitterConor Hoekstra: LinkTree / BioBen Deane: Twitter | BlueSkyShow NotesDate Recorded: 2026-05-27Date Released: 2026-06-12ADSP Episode 246: Not High on AI?ADSP Episode 289: Ben's Updated AI ThoughtsAmusing Ourselves to Death by Neil PostmanLife update: Zig, AI, unemployment, and moreLean In by Sheryl SandbergIntro Song InfoMiss You by Sarah Jansen https://soundcloud.com/sarahjansenmusicCreative Commons — Attribution 3.0 Unported — CC BY 3.0Free Download / Stream: http://bit.ly/l-miss-youMusic promoted by Audio Library https://youtu.be/iYYxnasvfx8

Trump storms out of MTP, Zig stars on CNN, Scott Pelley's kamikaze mission, college football outrage and more.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-death-of-journalism--5691723/support.

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Hey Broomheads, #DEEEMP is back with the continuing adventures of Clare and Drew's pregnancy! This time The Rents get involved! Meanwhile, Zig's manhood is called into question after losing an arm wrestling match to Grace, and Winston misses the days of bro-ing out with Miles. Timestamps: [0:15] Intro [13:24] Episode Title [21:13] A Story [57:01] B Story [1:05:11] C Story [1:11:50] Wrap Up   Find us online! Patreon: DEEEMP Email: everyepisodever@gmail.com Instagram: @DEEEMPodcast Facebook: DEEEM Podcast Facebook group: Dope Monkeys and Broomheads

Back when I was coming up, I was fortunate enough to have met the late Zig Ziglar.    I've also met his son who has come to my office a couple of times.     Zig wrote a book called, "See You At the Top."    He said something in that book that struck a cord with me..............   "If you help enough people get what they want, you'll get what you want."    This addage still holds true.     But not for most people.    Most people are more concerned with what they need.    What they want.    And as a result, they end up chasing a bigger house, better paying job, vacations, and status, only for it all to slip through their hands.    This is your chance to be the 1%.............   You may have to help 5,10, 15, or 20 poeple get everything they want before you get what you want.    Remember that.    About the ReWire Podcast The ReWire Podcast with Ryan Stewman – Dive into powerful insights as Ryan Stewman, the HardCore Closer, breaks down mental barriers and shares actionable steps to rewire your thoughts. Each episode is a fast-paced journey designed to reshape your mindset, align your actions, and guide you toward becoming the best version of yourself. Join in for a daily dose of real talk that empowers you to embrace change and unlock your full potential.    Learn how you can become a member of a powerful community consistently rewiring itself for success at https://www.jointheapex.com/   Rise Above 

rsync's founder came back, patched real security bugs with AI help, and triggered an open source meltdown. Plus, two more projects reject AI-generated code as the community's newest fault line cracks wide open.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:ConnecTen Internet — Get $35 off your order total with Jupiter35

Zig Fracassi SiriusXM Sports & NFL Radio joins the program to react to the Myles Garrett news. He also shares his thoughts on the Buffalo Bills & the Sabres. Zig gives his picks for the NBA Finals & Stanley Cup Final as well.

Send us Fan MailEpisode 251 of Nerdery & Murdery is live!On the Nerdery side, Zig delivers 10 more indie songs for your playlist from The Pixies and The Violent Femmes to Djo and some newer artists you might not have on your radar yet. If your rotation needs a refresh, this one's for you.On the Murdery side, we continue the A–Z Across America series with Connecticut and the case of Michael Ross, the so-called Roadside Strangler.Ross confessed to eight murders, was convicted of four capital murders, and ultimately waived his appeals, becoming the last person executed in Connecticut.Music discovery on one side.A chilling case study on the other.Just another week of the Nerd and the Murd.Support the show

Send us Fan MailEpisode 250 of Nerdery & Murdery is live, and this week we flip the script with another Wife Swap episode.On the Nerdery side, Geoffrey takes over and dives into Genesis, focusing on the band's evolution after Peter Gabriel's departure. From A Trick of the Tail to Duke, Invisible Touch, and We Can't Dance, we explore how Genesis transformed from progressive rock pioneers into one of the biggest stadium acts in the world. Along the way, we talk about Phil Collins stepping into the spotlight, the shift from epic suites to chart-topping hits, and a personal memory from the 1992 Irving, Texas show during The Way We Walk tour. On the Murdery side, Zig steps into the darker side of history with the Armenian Genocide. Beginning in the spring of 1915 and continuing into 1916, the Ottoman Empire carried out a systematic campaign of deportation, violence, and mass death against the Armenian population. The events remain one of the most devastating humanitarian tragedies of the early 20th century.A band reinventing itself across decades on one side.A historical tragedy that reshaped lives and nations on the other.Just another week of the Nerd and the Murd.Support the show

Join us for this week's Defender Fridays as Shane Warden, Principal Architect at ActiveState, shares what it's actually like to be on the receiving end of AI-assisted vulnerability reporting and what open source maintainers are already dealing with that the rest of the industry will face soon.At Defender Fridays, we delve into the dynamic world of information security, exploring its defensive side with seasoned professionals from across the industry. Our aim is simple yet ambitious: to foster a collaborative space where ideas flow freely, experiences are shared, and knowledge expands.What We'll DiscussIn this episode, Shane Warden draws on his experience supporting security for well-known open source projects to explore how AI-assisted vulnerability reporting is changing the threat landscape, and why what's happening in open source today is a preview of what every organization will face.Key Topics:Why open source projects are the early warning system for what's coming to enterprise securityHow a flood of 95 AI-generated vulnerability reports turned into a six-figure extortion attemptWhy even a three percent legitimate hit rate still creates a real and unignorable workload for maintainersHow teams are using AI to respond to AI-generated reports, and where humans still need to be in the loopWhat projects like curl, the Linux kernel, and Zig are doing differently in response to AI contributionsWhy understanding your open source dependencies and their versions is more urgent than everThe reputational risk of AI-generated vulnerability claims, even when those claims are falseAbout Our GuestShane Warden is Principal Architect at ActiveState and has been involved in open source since the late 1990s. Behind the scenes, he supports security for several well-known free software projects and has been navigating the growing wave of AI-assisted vulnerability submissions firsthand.Register for Live SessionsJoin us every Friday at 10:30am PT for live, interactive discussions with industry experts. Whether you're a seasoned professional or just curious about the field, these sessions offer an engaging dialogue between our guests, hosts, and you, our audience.Register here: https://limacharlie.io/defender-fridaysSubscribe to our YouTube channel and hit the notification bell to never miss a live session or catch up on past episodes on our website!Sponsored by LimaCharlieThis episode is brought to you by LimaCharlie, the Agentic SecOps Workspace (ASW), where AI agents operate security infrastructure using the same controls and authority as human analysts, with every action visible, governed, and auditable.Why LimaCharlie?Eliminate vendor sprawl and tool complexityDeploy and scale effortlessly on native multi-tenant architectureReduce costs with intelligent data routing and free 1-year retentionBuild custom solutions with 100+ security capabilities on-demandAccelerate response with agentic AI that acts directly within predefined workflowsTry the Agentic SecOps Workspace free: https://limacharlie.ioLearn more: https://docs.limacharlie.ioFollow LimaCharlieSign up for free: https://limacharlie.ioLinkedIn: / limacharlieioX: https://x.com/limacharlieioCommunity Discourse: https://community.limacharlie.com/Host: Maxime Lamothe-Brassard - Founder at LimaCharlieGuest: Shane Warden - Principal Architect at ActiveState

Ted Turner had balls and Zig reflects on Turner, the birth off CNN and the fall of journalism in the face of media fragmentation. Ben Shapiro's response to the lunatics. Bill Maher and John Fetterman are the voices of reason. Redistricting, Lane Kiffin, Mike Vrabel and a glimmer of AI hope.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-death-of-journalism--5691723/support.

Find out what people already want, then offer them exactly that. Quit trying to convince customers that they should want what you are selling.Speak to everyone, everywhere, about widely felt needs, deeply held beliefs, and personal values. Quit telling yourself that you need to reach “the right people” with your advertising.A: The media doesn't make the message work. The message makes the media work. I've never seen a business fail because they were were reaching the wrong people. But I've seen hundreds fail because they were saying the wrong things.B: Anyone who has a friend, a relative, a co-worker, or a neighbor is an influencer. Is there anyone that you DON'T want to say good things about you?C: Powerful brands like Ferrari, Rolex, and Harley Davidson are known, loved, and admired by hundreds of millions of people who will never own a Ferrari, a Rolex, or a Harley. Do you think those brands would be better off if they were known only to the people that the brands chose to “target” as potential customers?Customers buy from personalities they know, like, and trust.A: People don't bond with corporations, they bond with personalities.B: Brands that have personalities are exactly as real to us as our favorite characters in novels, television shows, cartoons, and movies. Who doesn't love R2D2, C3PO, and Yoda? You realize those characters are purely imaginary, right? But we feel as though we know them.C: Does your brand have a distinctive personality? If not, why not?I will now summarize each of those 3 Steps in exactly 12 words.People want friends, honesty, encouragement, access, and to know that they matter.Buy mass media. Quit fishing with a hook. Use a net instead.Don't be so boring. Find some courage. Be a distinctively memorable personality.Roy H. WilliamsZig Ziglar would have turned 100 this year.This week, Tom Ziglar shares some little-known stories about his father with roving reporter Rotbart and deputy rover, Maxwell, including the fact that despite Zig's worldwide fame, he once carried a stranger's luggage to the guest's hotel room simply because the out-of-towner took one look at Zig's red sports coat and thought he was a bellman.But todays episode is more than a nostalgic look backward, as Tom Ziglar offers a thoughtful meditation on legacy, leadership, and the enduring power of optimism. Things are looking UP at MondayMorningRadio.com.

Send us Fan MailThis week on Nerdery and Murdery…On the Nerdery, Zig steps into The Twilight Zone, a series that redefined science fiction and psychological horror. A world where nothing is quite what it seems, where morality twists, and where the most unsettling truths often come from within.Then the episode takes a darker turn.For the Murdery, Geoffrey examines the case of Gary Ray Bowles, a serial killer whose crimes stretched across multiple states and left a trail of violence that was both brutal and deeply disturbing. This is a story of patterns, escalation, and the terrifying reality of a predator operating in plain sight.Two very different paths. One unsettling destination.

Had he lived, Zig Ziglar — the legendary motivational speaker who died in 2012 — would have turned 100 later this year.  To honor his father, Tom Ziglar, who now heads the Ziglar organization, is planning a gala event in Franklin, Tennessee, on October 16th. Limited to 1,500 high achievers, the once-in-a-lifetime gathering will feature members of the Ziglar family and well-known inspirational figures, including personal-finance guru Dave Ramsey and UFC champion Michael Chandler. This week, Tom shares little-known stories about his father, including the fact that despite Zig's worldwide fame, he once carried a stranger's luggage to the guest's hotel room simply because the out-of-towner mistook Zig in his red sports coat for a bellman.  More than a nostalgic look backward, this week's episode offers a thoughtful meditation on legacy, leadership, and the enduring power of optimism. [Register for See You At The Top: The Ziglar 100 Transformation Experience. Seats are limited.] Monday Morning Radio is hosted by the father-son duo of Dean and Maxwell Rotbart. Photo: Zig and Tom Ziglar, Ziglar Inc.Posted: May 11, 2026 Monday Morning Run Time: 47:30 Episode: 14.45 Popular Episodes: An Appetite for Success: The Business Lessons Hidden in a Well-Prepared Meal How an Accidental Inventor Built a Life-Saving Company — And What Business Owners Can Learn From Him Veteran Journalist Thomas E. Weber on Weather Literacy as a Form of Strategic Business Intelligence

Send us Fan MailSome of the most disturbing cases aren't defined by mystery…they're defined by warning.This week on Nerdery & Murdery, Zig dives into Advanced Dungeons & Dragons, exploring the game that shaped modern tabletop roleplaying and inspired generations of players long before RPGs went mainstream.Then in the Murdery, we continue our A–Z journey across America's serial killers, arriving at Colorado; not as the site of a final crime, but as a critical point of failure.This episode examines the life and crimes of Joseph Edward Duncan III, a man repeatedly identified as dangerous, repeatedly supervised, and repeatedly allowed to move on. Colorado was one of the last places where intervention could have stopped what came next.This isn't a story about the unknown. It's a story about documented risk…and delayed action.Support the show

Keith explains how to increase real estate cash flow by appealing and reducing property taxes.  Then welcomes high‑energy real estate investor and educator Thach Nguyen.  Thach shares his refugee‑to‑multimillionaire story, breaks down his roadmap to retiring with rentals, and explains how ADUs (Accessory Dwelling Units) are transforming both investor returns and affordable housing—especially in Seattle. Resources: Follow @ThachNguyen on Instagram and all major social platforms. Episode Page: GetRichEducation.com/602 For access to properties or free help with a GRE Investment Coach, start here: GREmarketplace.com GRE Free Investment Coaching: GREinvestmentcoach.com Get mortgage loans for investment property: RidgeLendingGroup.com or call 855-74-RIDGE  or e-mail: info@RidgeLendingGroup.com Invest with Freedom Family Investments.  For predictable 10-12% quarterly returns, visit FreedomFamilyInvestments.com/GRE or text  FAMILY to 66866  Unlock truly passive real estate income—visit flockhomes.com/GRE today to see if your properties qualify for a 721 exchange with Flock Homes. Will you please leave a review for the show? I'd be grateful. Search "how to leave an Apple Podcasts review"  For advertising inquiries, visit: GetRichEducation.com/ad Best Financial Education: GetRichEducation.com Get our wealth-building newsletter free— GREletter.com  Our YouTube Channel: www.youtube.com/c/GetRichEducation Follow us on Instagram: @getricheducation Complete episode transcript:   Keith Weinhold  0:01   Welcome to GRE. I'm your host. Keith Weinhold, talking about how to increase your cash flow by obtaining a successful appeal and reduction in your property taxes. Then real estate personality Thatch Nguyen and I discuss mindset and some creative real estate techniques today on get rich education,   Keith Weinhold  0:23   the same place where I get my own mortgage loans is where you can get yours. Ridge lending group and MLS, 42056, they provided our listeners with more loans than anyone because they specialize in income properties. They help you build a long term plan for growing your real estate empire with leverage. Start your prequel and even chat with President chailey Ridge personally while it's on your mind, start at Ridge lending group.com that's Ridge lending group.com   Speaker 1  0:57   You're listening to the show that has created more financial freedom than nearly any show in the world. This is get rich education.   Keith Weinhold  1:13   Welcome to GRE from Mount Holly New Jersey to Hollywood, California and across 188 nations worldwide. I'm Keith Weinhold. This is get rich education, and I'm still not wearing Dockers, and I am in Hollywood, California today. More on that later. Among all the major investment classes when it's bought right real estate is the second safest investment class to bonds. Bonds are the safest among them all. Real estate has the highest returns, so it's the second safest and has the highest returns. And that's why it's our focus on this show. But if you want to be in real estate for two years or less, well, then it's likely best to invest elsewhere, at least with long term rentals, because you need time to defray your transaction cost. And for real estate pays five ways to start compounding. Coming up shortly, it's pretty popular real estate personality Thatch Nguyen. He will be here, and I did not know Thatch until recently, when we were introduced by our mutual friend Scott Saunders. And Scott, who I had on the show here a few years ago, is one of the nicest guys you'll ever meet in real estate. Well, besides those high return, low risk real estate attributes. Of course, when you own property directly, you also get a big measure of control if you want it. Now, control comes really with that option A lot of times to get involved and make your real estate investing less passive, just an option, because successful real estate can be as simple as buy and hold, but today we're discussing strategies. If you want to get a little hands on, if you so choose, you can attempt a successful appeal of the amount of property tax that you're paying. And of course, every dollar that you lower your property tax is $1 where you increase your income. And this feels like a germane conversation, since tax day in the USA was just last week. Ah, yes, property tax, hmm, it's like a version of the government charges you rent on your own property in perpetuity. That's what it is. And before I get into how to potentially get your property tax lowered, property taxes are under pressure. Some states are still making their serious push to completely eliminate the property tax, namely in Florida, Texas and Indiana. Those are three of the front running states, probably the big three. And I won't get into all of that again, because I devoted an episode segment to that topic a few months back. Others are considering elimination too, Georgia, North Dakota, Pennsylvania, Ohio, Oklahoma, South Dakota, but it's just more talk than anything in those six states. Now, if a state undertook property tax abolition, it would probably only apply to owner occupied property, homeowners or voters, and those property values would soar. But these new comparables, what they could do, in turn, is lift the value of your out of state rental property as well, because you could always sell your investment property to an owner occupant. But in my opinion, no state is going to eliminate the property tax. I mean, sheesh, it's kind of like trying to eliminate gravity. It's just too hard to replace the revenue from elsewhere. Schools, police and fire and infrastructure heavily rely on property tax, so instead, what's realistic is a tax cap, a ceiling on the amount of property tax that you pay, and with an income producing property of course, your tenant essentially pays the property tax for you now, even before buying a property or for one that you already own, the most accurate way you can check the tax amount for your exact address is on the county assessor's website.    Keith Weinhold  5:38   The next best places are listing websites like Zillow and Redfin. This is all public information. The way to find a county assessor's website for your property is with a simple four word search. What you should google is the county name, and then the words assessor property search, those are the only four words that you need. And then what if you discover that you're paying more than you are for nearby, similar properties? Oh, well, there we go. That's a sign that you're over paying. You can usually file an appeal form at the same website. And before we talk about how to do it, realize that only about 5% of property owners ever file an appeal, and in a bit, I'll tell you what your percent chance for success is at lowering your property tax, your chances of it being lowered. So if you believe that you have a case for lower property taxes, first, it helps to know what you're arguing. And this is important, it's something that can trip you up. You're actually not arguing that taxes are too high. You're arguing my property is overvalued compared to the market. That's it. That's your basis of contention. Yeah, if you walk in talking about things like fairness or inflation or government spending, then you've already lost the county assessor's office isn't the place for your best rant on how fiat currency is garbage or something like that. Now you might not even have to physically walk in anywhere today. Sometimes you can get your appeal rewarded informally. Other times you go before what's called a Board of Equalization in most places and in person, hearings have become less common. Video calls have become quite a bit more common since the pandemic, but you want to review your property details with them. You want to be sure to point out if there's incorrect square footage or the wrong lot size, or missing depreciation, or condition issues or upgrades that are overstated and even small errors can swing your value by 10s of 1000s of dollars and then, and it's whether this is with rental property or with your own home build your comparables Like an investor, not a homeowner, because this is really where you win or lose. You need three to five strong comparable sales in the same neighborhood, or really close ones that sold recently, ideally within the last six months, and they should be of a similar size and age and condition. And then make adjustments. Inferior comps support a lower value. And we don't just want to cherry pick garbage comps. We want to keep it credible, and then for your best chance of getting your property tax lowered, find your angle, and really this is your leverage point. Most winning appeals hinge on one clear argument, either a condition gap, meaning that your property is worse than the comps are, or it's an argument like market timing, and this is if values have softened since the assessment date, or the income approach for rentals. Therefore it's the value based on noi, not emotion. You could take that track or other external issues like noise or location drawbacks or obsolescence, so only pick one of those four primary arguments here, condition, gap, market timing, the income approach or external issues and document everything. This is really where you separate yourself. You want to show photos and have them dated and be clear and honest. Nothing dramatic there repair estimates or contractor bids, inspection reports, rent rolls or income statements. So you're not telling a story. You're presenting evidence this way, and be sure to package it cleanly. This matters more than you think. Assessors see sloppy appeals all day. So you're going to stand out by being organized and concise, like a one to two page summary and some exhibits, and keeping it professional meaning, no emotional language, so you're making it clean and easy for them to agree with you, and this is the place to be. Calm and not combative. It isn't a debate club. It's the right form to be respectful, stick to facts, not interrupt and not get defensive, because the person across from you, they actually did not set your rate, they didn't set your tax rate, they're evaluating your evidence, and then it's helpful for you to know the likely outcome. You don't need a gigantic win, even a five to 10% reduction, that can mean 1000s saved over your life of owning the property. You want to remember that some jurisdictions are more flexible than others, and if you're denied informally, like just doing it online, then you can often escalate your property a tax appeal to a board review. And this is a long game, not every swing is going to end up in a base hit. Investors have an advantage. If you own rentals, you've really got a stronger argument, because you can use that income based verification like cap rate and noi, you can show actual rent versus market rent, and you can highlight your expenses, and assessors often default to sales comps. So this is how you can shift the frame here. The blunt truth is that when people lose appeals, it's usually because they show up unprepared, or they argue emotionally, or they just don't understand valuation. And so this is one of those rare moments where being methodical is actually better than being smart. 40 to 60% of property tax appeals succeed nationwide, and with professional level prep, you can make that 70 to 80% for a success rate, and the typical result if you win is a 10 to 15% reduction in assessed value. So that can be worth doing. And you know, just like buying your first out of state rental property seems to be the hardest. Making your first property tax appeal seems to be the hardest as well. And there you go a way to reduce your expenses and increase your cash flow. Yes, I am in LA today, West Hollywood, California. Though I do expect to produce some real estate media here. That's not the typical Hollywood type filmmaking that I'm doing, I just happen to be staying in Hollywood, although I do plan to run up to the Hollywood sign and do some fun stuff out at Venice Beach. Later next week, I will be in Las Vegas, and will probably even bring you the show from the Bellagio with a view of the Bellagio fountain. As for this week, let's meet our guest.   Keith Weinhold  12:49   This week's guest has an amazingly powerful story. Today. He's quite well known in real estate circles for his high energy in person events, but he came to the United States as a Vietnamese refugee, experienced homelessness early in life, and went on to build a real estate portfolio valued at over $100 million I'm not making light of the fact that he's homeless. Once I started talking about this, he kind of, you know, beat his chest a little bit. He's a high energy, playful guy here, but he's completed more than 1000 real estate projects and transactions through his mentorship program, he's helped 1000s of people build long term Real Estate Wealth with his platform, it's called springboard to wealth, and along the way, he's built a strong audience, with 1.4 million followers on Instagram. Hey, welcome to the show Thatch Nguyen.   Thach Nguyen  13:41   I'm honored to be here, my man, I'm honored   Keith Weinhold  13:43   to hear, Oh, it's so good to do it Thatch. And before we're done, we'll discuss some actionable tactics. But first, that is just an amazing story to have started from homelessness. I guess I'm most interested to know what you would identify as kind of that turning point from destitution to success. Talk to us about that.   Thach Nguyen  14:03   You know, coming from Vietnam, we was a refugee. We left out of the last plane. My dad was a translator for the US Army. Back in the days, military pulled out of South Vietnam during the war, they asked my dad, would you want to leave with us? And so we decided to leave. But of course, my dad, the owner, who actually spoke some bit of English. None of us didn't speak no English. We only had $100 one suitcase for eight of us, gosh, and I was five years old. But if my dad didn't leave, he would have been captured, and then he would have been killed. Because you work for the US government, because it's still, you know, is a communist country, right? And so we left, we came over here, we landed in San Diego, lived in the shelter out there, and then we moved up to Washington State, Seattle, and lived in a shelter there for a few months. And then finally, we lived in a sponsorship house, right, with a guy named Charles Zettler. I graduated from high school in. 88 I went off to fix aviation airplane my two older brother, because they in the aviation business. And then I got a job working for Alaska. But I didn't want to leave to Denver to go work out there, so I decided to stay back. And I went to work at, you know, like, odd job, like at a body shop. I was the dairy manager at a grocery store, like, called Ralph. Was called Safeway, and I was parking car in Chinatown. And I think the pivoting point was, I'm sitting there, and one of my friends says, you know, you would do very well in real estate, yeah, because you have a good energy, you have a good mouthpiece, I think you do well, see, but I didn't hear all that. I heard you get 7% commission checks. Oh, Sign me up. You know what? I think, but I didn't realize quickly, selling real estate, you don't make that kind of money unless you do a lot of volume. I got to real estate. I started doing well in real estate as a agent. But the tipping point, I think, for me, was a mentor named Saul. And Saul said to me, Keith, I know you appreciate this. He said, You can be rich selling real estate for the rest of your life. Yeah, you'll never be wealthy unless you own the real estate, right? And that was the light bulb that came off of me that I need to take the money I make from selling real estate to then Park the money in long term rental. But I didn't quit my real estate. I just bought real estate, rented it, let it ride. And I just kept selling real estate for years. And at the moment I made, the more property I bought. The moment I make, the more property I bought. And then from there, I just start to learn new construction. I start to learn fix and flip. I start to learn about the BRRRR strategy. And then today, you know, we're going to talk more about this. But today, the hot thing is adu and accessory dwelling unit, and that's what I do a lot today is a lot of new construction, a lot of ADUs.   Keith Weinhold  16:49   Oh, that's great to hear about your come up. Fetch, yeah, I find it remarkable, too, the amount of people that are in the real estate industry, and they're doing something adjacent to being an investor, which I think is the best place to be. For example, they're a property manager, or they're a mortgage loan officer or the real estate agent, but yet they don't own rental real estate, right? They're so close. How could you not be doing this?   Thach Nguyen  17:13   And I say today, because I understand this. Now, if you don't take the active income you make from whatever you do, say, as a real estate agent, then you always trading your time for money for the rest of your life, and you're always on that treadmill and that grind, but you can't get off, because the moment you get off, Keith, you got no income, and you got no passive income either. So you're stuck on this wheel like a hamster that you got to keep running until you old and die.   Keith Weinhold  17:40   Well, you know, it's unavoidable to talk about you've got the word mindset on big letters on a hooded sweatshirt that you're wearing right now, so, you know, I think you're touching on it somewhat. But yeah, talk to us more about this mindset and how to break through the barriers. Because most people's connotation with income is merely that they have got to trade their time for dollars.   Thach Nguyen  18:01   Of course, you know, mindset is 80% of the result that we want, that we get. Because someone could have a mindset to go, I'm going to be the top real estate agent, and that mindset would drive them to be the top agent for many, many year. But they always trade their time for money so they never get wealthy. I have that mindset because I was selling 100 homes a year in my early 20s. But when Saul said to me, you know one day that when you get into your 40s and your 50s, do you want to keep trading time for money, or do you want to trade your money for time? And see, that's a mindset shift. And of course, who want to be in their 50 Keith with a gun in their head, always trading time for money. And so when I heard that, it shifted the mindset to, you know what, I'm going to make money selling real estate because I need that money, then I'm going to take that money and park it into a rental. So when I get into my 40s and my 50s, I have the option to work or not work, and that was a mindset shift. So owning rental property is a mindset more than a strategy.   Keith Weinhold  19:08   I and I think a lot of us, came up with the mindset that, oh, you get wealthy by obtaining a high salary, and then no later, you learn you don't get wealthy through high salaries, especially if wealth equals freedom, you get wealthy through owning assets. So Thatch after you know your homelessness, and you're new to the United States, and you've come up like you described, and you realize that real estate is the way in doing it with a relative amount of passivity, rather than actively being in it as a realtor, you sort of get this roadmap for retiring with rental properties, even from starting at zero like you did. So tell us more about that roadmap to retire with rental properties.   Thach Nguyen  19:47   You know, when I started, I had this roadmap where you got to learn what you need to learn about real estate investing, what why do you want to own it? What's the benefit? What would it do for you? At the end of the day, and a lot of that is goals and vision and mindset. For me when I got clear Keith on the knowledge, because I start off with knowledge. And of course, I want to own real estate. But here's the thing I always want to say to people, nobody want to own real estate. Just to own real estate, right? They want to own real estate. So what it would actually do for you. And so for me, I think when I was younger, I was counting the doors, but now I got older and wiser, I count the hours I get to have back. So the mindset for me is that when I got clear what I wanted to do was I wanted, you know, the option of working at work, that I also wanted to retire my mom, my dad, right? And then I also wanted to actually help my kids learn how to do this one day, so that they have the same mindset. So those are the reason I in want to invest in real estate. Of course, have an asset, have a net worth, come along with a secondary so once I understand the knowledge of why I'm doing it, I got this clear vision. I got this horizon. Now I'm inspired to actually go out there and take action. Now the action is, what do I want to buy for me? I started with single family. I started with buying ugly houses and rehabbing and keeping it, and then worked my way into multifamily and apartment building, all doing value add today. So those are my action, right? So I'm inspired. I take the action, I make money doing what I'm doing. But then I asked myself, How many property do I need? But it's not even how many property I need. How much passive income do I need to get out of the rat race? I have the option of working at work. For me, when I was like, 21 years old, I said to myself, I have $30,000 a month in passive income, and I'm debt free. I mean, who couldn't live off 360,000 of you debt free, right? Yeah. So I had to go to go after so many doors based on what the rent is, to accumulate it and then to pay them down so I can be out of the rat race as soon as possible. And once I did that, then I started playing the game accumulation again. So today I have a whole set of properties paid off. That's why I have over 100,000 a month in passive income. But I also got a whole bunch of property paid off yet, which I don't care, because this ought to get paid up by itself anyway. But now I'm playing this game where I'm gonna accumulate more property or trade up at the same time pay down other property I want to pay off, so that when I get into my 60, my 70, a lot of it paid off, and I still got other property. I don't know. I don't mind accumulating, because I love to play the game of real estate. So this is the road map that I you know, that my mentor saw. He's a very wealthy Jewish man that taught me. And today I'm just taking that lived it my own life now I'm just sharing it back to other people   Keith Weinhold  22:42   that you said so many interesting things there. I think the most is how you talked about your metric is more outcome based. I think we all think through how many doors we have, and you know, even how much passive income that translates into, but you talked about how many hours you're able to win back way that you can quantify that.   Thach Nguyen  23:05   If I ask someone, I go, Hey, how much does it cost you to live personally every month? And most American will probably say, 10,15, 20,000, Max. And I said to them, what have you had that much in passive income? How would you feel? And 99.9% of it were like, my god, that will be amazing. But the problem we all go to the seminar, we see people on stage. They got 100 doors, 200 door. They got 1000 doors. And nobody needs that much to get out of the rat race, right? So I say the most American is, look how much it costs you to live. Look at the lifestyle you live. You have that in passive income, and if you choose to keep working in active income, it's just a cherry on top of the cake.   Keith Weinhold  23:47   Yeah, there are so many ways to do it. We talk here about being financially free rather than debt free, and sort of letting leverage and inflation in tenants work to our benefit. But you've got this separate way of doing it. You're listening to get rich education. We're talking with real estate, personality, Thatch Nguyen, more when we come back, including some actionable tactics. I'm your host. Keith Weinhold,    Keith Weinhold  24:09   let me throw out a simple idea, sometimes doing nothing with your money is actually a decision. Leaving it parked might feel safe, but over time, purchasing power changes. So the conversation isn't about chasing returns, it's about intentionally placing money somewhere. Freedom, family investments works in real estate people use every day. Housing, senior communities, essential properties, things tied to living and not trends. Their freedom notes offering is built for accredited investors looking for structured income backed by real assets, not speculation. I am an investor with them myself. The Freedom team makes themselves available to walk through their approach, structure and operating philosophy so you can ask questions and determine. Alignment before moving forward, while past performance doesn't guarantee future results, their historical operating philosophy has yielded 100% investor payouts backed by over 20 years of experience. If you want clarity before making any moves, book a clarity call@freedomfamilyinvestments.com or text family to 66 866, text the word family to 66 866.    Keith Weinhold  25:31   Flock homes helps you retire from real estate and landlording, whether it's one problem property or your whole portfolio through a 721, exchange, deferring your capital gains tax and depreciation recapture. It's a strategy long used by the ultra wealthy. Now Mom and Pop landlords can 721 the residential real estate request your initial valuation, see if your properties qualify@flockhomes.com slash GRE, that's F, l, O, C, K, homes.com/gre,   Caeli Ridge  26:09   this is Ridge lending group's president, Shaylee ridge. Listen to get rich education with Keith Weinhold, and remember, don't quit your Daydream. You Keith, welcome   Keith Weinhold  26:27   back to get rich Education. I'm your host, Keith Weinhold we're talking with Thatch win real estate personality, and you know Thatch, on the way up, you've really employed a lot of methods. You're knowledgeable about House hacking and burrs and small multifamily in ADUs. ADUs is something that we haven't talked about here very much. And for those that don't know what that is, we're talking about an accessory dwelling unit, right? Typically, a secondary housing unit on the same lot as a primary residence. You can sort of think of it like a backyard cottage in a lot of cases. So tell us Thatch, what got you into ADUs,   Thach Nguyen  27:03   well, Seattle, about five years ago, was one of the first city and state to adapt this Adu, because the biggest problem we have across America is affordable housing, yeah, and a shortage of housing, let alone a shortage of affordable housing. So Seattle came up with, Hey, we will let you. Got built an accessory dwelling unit in the backyard, maximum 800 square feet, but you have to live in the front house to build the back house. Okay? People got excited. They built it so they can rent it in the back. They live in the front house. But then that didn't really solve as much affordable housing for you to buy. It helped with rental. And then about a year, you and a half later, they came over stage shoe to go, you know what? We're gonna allow up to 1000 square feet of adu. But you don't have to live in the front to build the back. Now, people got excited. Investors go, Oh my God, let me go buy a property. Let me go build something. Rent both of these out, right? And then if they want, they could sell the whole entire piece, you know, with somebody, and that was great, but it still wasn't enough. And then about a year you'd have, later, they came up with stage three. They go, You know what? We want to help create more housing for you to buy. So now what we're going to deal with, we're going to actually give people separate APN tax number for the house in the front and the adu in the back, so you can sell off any one of the and by doing that, they value the house as a single family, and they value the back as a single family, so they can comp it like a house, not as a duplex. And that blew the lid off. I mean, in Seattle, that was a game changer. I mean, like builders started coming in, they're buying property. They they building and they selling these. They're making a killer on it. And then show you how much crazy it is. Okay in Seattle, if you buy the house in the front, you gotta get the land the back freak, because it came with the house. We could build 1000 square foot all in it cost us about $400,000 but with a separate parcel number, they comp it as a regular house. So regular houses right about 1000 square feet, they sell for about $700,000 so you build for four is worth seven, and you can actually design it in four months. Get permit, because they have a special line for adu. And then you can build this. You can actually have it all done in one year. So you instantly create massive equity in one deal. But here's a beautiful part of it. In Seattle's expensive city, it's hard to get the 1% rule. You know the 1% rule with, you know 1% of what you pay for a property, a $200,000 house, you get $2,000 for rent with Seattle, a $700,000 house, you get 4000 but the Adu, it only cost us 400,000 but it's worth 700 but my mortgage is based on 400,000 I can write it for four grand, and I meet the 1% rule Now   Keith Weinhold  29:52   a way to recent rent to value ratio, right?   Thach Nguyen  29:56   So now Adu, they are all. All across America, because two years ago, all the city planners and all the people for other state they came to Seattle for a private, hush, hush meeting to ask Seattle How you guys doing this, and so they can go and copy. So in the last two year, Adu has spread across America like wildfire.   Keith Weinhold  30:19   This is great. Tell us more. And of course, it's going to depend on a lot of factors, but tell us more about that cash on cash return that you're getting after stabilization with an adu.   Thach Nguyen  30:29   Yeah, it's beautiful. So when you have a property that's worth 700 and it only costs you 400 it has so much equity, the bank will finance 100% of the construction cost, so you don't have to come up with no money. Great. So then if you finance 100% which is 400 right, 400,000 the mortgage only three grand, and you ran for four in Seattle with making positive cash flow with zero down payment. So that's infinite return on your money.    Keith Weinhold  30:56   Yes, that's a really beautiful thing to get the infinite return when you don't have any equity left in That's right?   Thach Nguyen  31:03   And the thing is, people can do that across America now, but most city right now on stage two, they don't have the APN. But right now, a lot of city right now are on the verge of going from two to three. Right now, I've been going out there buying home that you could actually Burr, make the house in the front. Work make a cash flow. Have the backyard sitting there, and then you can build it anytime. You can build it now, just for the cash flow. Or you can build it when you get the separate APN. So you can get two separate parso You can sell one, keep one. But bottom line is, if I was anybody out there, I'll be buying property. Now, make it work like you would already be buying, but just make sure you get a backyard so you have access to the back.   Keith Weinhold  31:46   Okay? So in some situations, using the burr strategy on the primary residence with an adu, burrs, buy, renovate, rent, refinance and repeat, beautiful.   Thach Nguyen  31:55   That's what I call the atomic bomb, the burr. Add the adu to the back. Boom. But I'm gonna give your audience something that they can even look forward to. Seattle in November of 2025 this went into stage four. Now in stage four, single family in the front, if the lot's big enough, you can put instead of one, you can put 234, or five property in the back, if the lot's big enough.   Keith Weinhold  32:23   Yeah, this is great. I mean, it solves the problem of affordable housing, and it increases the density in a lot of these metro areas. Yes, right, Thatch, it sounds like Seattle's having a good deal of success with the ADUs. How is that when you extrapolate it out nationally, and are there regulatory bottlenecks out there.   Thach Nguyen  32:40   The only bottleneck right now is most people right now are in state two, where they can't separate it. So if they buy a burr, they can add the house in the back. They just have to be able to comp it where there's a house and another house in the back. So what they do is they look at two different type of comp. They look at, what does it duplex sell for in the area? They could use that as a comp. Or if this is a 2000 square foot home, and you got another 800 square foot, what's a 2800 square foot home is going for? Because they can be added this to the main house, so they can create the ARV. Does that make sense? Yeah. And the only challenge, challenging is that a city that's new, they have to use comp like duplexes and square foot. It to come up with the ARV.   Keith Weinhold  33:23   That's really good. Okay, so Seattle's had these four phases of ADUs, if you will. And then what's next for ADUs?   Thach Nguyen  33:30   I think what's gonna happen after phase four is that all these single family one day will all go to multifamily. It's already in multifamily. You got a single family in the front. You can build three in a back. They're all three single family. But technically it's multi unit, right? It's called multi unit, but it's still on single family zoning, because, you know, the bulk of the real estate where I still have land, or the residential, because most commercial, you and I know, they built out on all the land on the lot, so the biggest portion left is the single family. So this is why I've been doing the adu. And I think in the future, Phase Five could be those single family that whole area might get up zoned to multifamily, more density.   Keith Weinhold  34:11   Yeah, upzoning, that term for allowing more dense housing term really originated because you're building up vertically, although that doesn't have to be the case every time. And yes, I mean, this is really a great way to solve the affordable housing crunch in the United States. I've seen other cities where single family zoning only was allowed now allows for duplexes. That's a common way to upzone as well and fetch you really often talk about creating affordable housing, like we're discussing here, while you're building wealth. Can you speak to us more about that? You kind of get a give back that way?   Thach Nguyen  34:46   Yeah. This is a mindset thing. There's a mindset that says, right? And some people believe it. Some people don't. I love what Zig Ziglar said, Right? Zig. Zig says, If you help enough people get what they want, you eventually get what you want. Yeah. And so. If you go out, then you make enough difference to the world. Take a look at Bill Gates. One day, he probably saying, You know what, I'm going to figure out how to make a computer to actually help your life better, faster, more efficient. And his goal was to do it worldwide. So he solved that problem, and in return, he has massive financial freedom. So for me, real estate isn't just real estate. Real estate what it would do for me as an outcome, real estate also give me an emotional contribution, which is, if I make a difference out there, creating more housing right, to make it more affordable, to make it most of people gonna buy it. What does it do? For me? It will actually fulfill the hierarchy of life, which is contribution. Because once you have money, the only thing that fulfill human being beyond money is life fulfillment.   Keith Weinhold  35:48   That's right. I mean, hey, it's a little brash, but in the business world, really no one cares about you until they know how much you can help them.   Thach Nguyen  35:56   You got it, brother, you got it right. That's why do you think so many wealthy people do thing in nonprofit world, because at some point it was all about them at the beginning. Now it's about basically giving back. So imagine, on your way going to success, you do both, you make a difference and you benefit also. And it's a more fulfilling journey than a journey just push, push, push and grinding and not taking care of you in the process.   Keith Weinhold  36:23   Well, if that's your events, they give you this mentorship platform. And I think you've actually pointed to how mentorship accelerates your own real estate success, even though you're trying to help others first.    Thach Nguyen  36:34   Yeah, you know for me, I always knew that the more you learn, the more you earn. And so what? 1995 I met my first mentor, Saul and then I met my other mentor, Mike ferry. And if I'm there, I met Wayne Dyer, who became one of my great mentor, Tony Robbins, Deepak, Chopra, Abraham Hicks, I mean, all these great people, right, that I got exposed to. And today I still have multiple different mentor from fitness mentor, spiritual mentor, business mentor, you know, financial mentor, and they I have regular meeting with these folks, because I want to constantly, always feel I'm growing mentally, emotionally and financially, physically, and I know that the more I learn, the more I can actually make a difference to other people coming behind me   Keith Weinhold  37:21    even Michael Jordan had his own team of coaches. Yeah, you see, that's why, that's how we all get better with that, you've really helped so many people with your mentorship, your contribution to the industry. Let our audience know how they can learn more about you.   Thach Nguyen  37:36   Yeah, if you gotta go to my Instagram, it's Thatch Nguyen this my name, and you go to YouTube, I drop YouTube every single week. It's my name. Also that's when. And you can find me there. You can find me on Instagram, tik, Tok, Facebook, everywhere. That's where I inspire and empower people all over the world about real estate and mindset.   Keith Weinhold  37:54   If that's before, I ask you if you have any last thoughts as you look him up, it's spelled T, H, A, C, H N, G, u, y, e n, fetch. Let us know if you have any closing thoughts.   Thach Nguyen  38:04   Yeah, this has been on my mind lately a lot. If you want to be successful at anything, you got to get single minded focus. And I remember when I was in Tony Robbins training, we used to do fire walk a lot. And when you are doing fire walk, you have to get single minded focus. And the only thing that you will focus on is perfect health, perfect health, perfect health. As you walk in across five feet, six feet, seven feet, and you have to really stay focused on perfect health, perfect health, perfect health, perfect health. And if you don't, and I've seen what, people lost their concentration and they burn their feet halfway through. But I also see people so powerful where they can walk halfway stop, bend down, pick up a coal and keep walking. Don't burn because they really focus on single minded focus. So I want to say to everybody, make sure you clear on where you want to buy, what you want to buy, and then once you know where you want to buy, what you want to buy, get focused on your main job is to figure out how to find deals every day, because that's your main job. If you can find deal, you solve all of your personal problem.    Keith Weinhold  39:15   I am so with you on the focus of concentration, because diversification is a word that we're fed, and there's something to be said for that. But if you want greatness in anything, you really need to double down and focus. It's sort of like Andrew Carnegie said, put all your eggs in one basket and then watch that basket. Yeah. Well, that's when this has been great. It's been good to have you here on the show.   Thach Nguyen  39:35   I appreciate everybody we talk to y'all soon. Peace out.    Keith Weinhold  39:44   Yeah, good energy from Thatch Nguyen. He's based in Seattle. When you don't live in an investor advantage area, you have to get creative or scrappy, and he's doing it well, using ADUs and a lot of value add if you're merely investing. Investing on the side, well, then you're probably better off with a turnkey type investment, something that's not quite so hands on, but if you're devoted full time to real estate, then you really have some ideas there that you might want to pick up on. He wore a sweatshirt that says mindset on it during our chat. I like that. I mean, real estate investing isn't all about mindset, but that's surely where it begins for the production team here at GRE that's our sound engineer, bedroom Jampa, who has edited every single episode since 2014 QC and show notes, Brenda Almedares, video lead, brendawali strategy, talimagal, video editor, seroza, KC, and producer me, we'll run it back next week for you. I'm your host. Keith Weinhold, don't quit your Daydream.   Speaker 4  40:50   Nothing on this show should be considered specific, personal or professional advice. Please consult an appropriate tax, legal, real estate, financial or business professional for individualized advice. Opinions of guests are their own. Information is not guaranteed. All investment strategies have the potential for profit or loss. The host is operating on behalf of get rich Education LLC, exclusively.   Keith Weinhold  41:18   The preceding program was brought to you by your home for wealth building get richeducation.com  

France pushes digital sovereignty. Adobe rushes an Acrobat Reader patch. Booking.com confirms a targeted breach. SAP fixes a critical SQL injection bug. A sanctions-dodging fraud network resurfaces. ViperTunnel infiltrates U.S. and U.K. firms. GlassWorm spreads across developer tools. Researchers dissect Predator spyware's kernel engine. A lawsuit challenges AI transcription in hospitals. Ted Shorter from Keyfactor unpacks quantum computing at scale. On our Threat Vector segment, David Moulton and ⁠Elad Koren⁠ pull back the curtain on agentic-first security. Preparing for post-quantum perils.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today we are joined by Ted Shorter, CTO and Co-Founder of Keyfactor, discussing the advent of quantum computing at scale, known as "Q-Day". Threat Vector Host David Moulton speaks with returning guest ⁠Elad Koren⁠, Vice President of Product Management for Cortex Cloud at ⁠Palo Alto Networks⁠ on this Threat Vector segment. Together they pull back the curtain on what an agentic-first security experience actually looks like in practice. This isn't a vision deck. The agents are already running. To listen to the full conversation, check it out here. Catch new episodes of Threat Vector every Thursday on your favorite podcast app. Selected Reading France Tees Up Big Public Sector Move Away From US Tech (BankInfo Security) Adobe rolls out emergency fix for Acrobat, Reader zero-day flaw (Bleeping Computer) Booking.com Confirms Data Breach as Hackers Access Customer Details (Hackread) SAP Patches Critical ABAP Vulnerability (SecurityWeek) Triad Nexus Evades Sanctions to Fuel Cybercrime (SecurityWeek) Ransomware-Linked ViperTunnel Malware Hits UK and US Businesses (Hackread) GlassWorm evolves with Zig dropper to infect multiple developer tools (Security Affairs) Predator Spyware's iOS Kernel Exploitation Engine: PAC Bypass, NEON R/W & More (Jamf Threat Labs) Lawsuit: AI Illegally Recorded Doctor-Patient Encounters (BankInfo Security) World Quantum Day (WorldQuantimDay) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices