Podcasts about PostgreSQL

Nik and Michael are joined by Shaun Thomas to discuss estimating work_mem, memory management in general, and writing an extension to help. Here are some links to things they mentioned: Shaun Thomas https://postgres.fm/people/shaun-thomaswork_mem https://www.postgresql.org/docs/current/runtime-config-resource.html#GUC-WORK-MEMpg_stat_database https://www.postgresql.org/docs/current/monitoring-stats.html#MONITORING-PG-STAT-DATABASE-VIEWALTER ROLE SET configuration_parameter https://www.postgresql.org/docs/current/sql-alterrole.html#SQL-ALTERROLE-PARAMS-CONFIGURATION-PARAMETERhash_mem_multiplier https://www.postgresql.org/docs/current/runtime-config-resource.html#GUC-HASH-MEM-MULTIPLIERRecent Postgres releases with 28 CVEs fixed https://www.postgresql.org/about/news/postgresql-186-1711-1615-1519-1424-and-19-beta-3-released-3365/Systemic Risks in the Managed PostgreSQL Industry (part 1 of 6, Mehmet Ince) https://mehmetince.net/part-1-6-systemic-risks-in-the-managed-postgresql-industry-extension-risks-are-real-exploiting-postgis-memory-corruption-bug-at-neondb-supabase-and-many-more/Improving Postgres Connection Scalability: Snapshots (blog post by Andres Freund) https://techcommunity.microsoft.com/blog/adforpostgresql/improving-postgres-connection-scalability-snapshots/1806462~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Nik and Michael are joined by Michael Malis, co-creator of pgrust, to discuss their Postgres rewrite, including reliability problems, compatibility testing, faster analytics, and a new JIT-compiled query engine. Here are some links to things they mentioned: Michael Malis https://postgres.fm/people/michael-malispgrust https://github.com/malisper/pgrust The four horsemen behind Postgres outages (blog post by Michael Malis) https://malisper.me/the-four-horsemen-behind-thousands-of-postgres-outagesRebuilding Postgres for faster analytics: batching, operator fusion, and SIMD (blog post by Michael Malis) https://malisper.me/how-we-made-postgres-hundreds-of-times-faster-the-query-engine/kani https://github.com/model-checking/kaniAntithesis https://antithesis.comfsyncgate mailing list thread https://www.postgresql.org/message-id/flat/CAMsr%2BYHh%2B5Oq4xziwwoEfhoTZgr07vdGG%2Bhu%3D1adXx59aTeaoQ%40mail.gmail.comHow AI Changes the Economics of JIT Compilers (blog post by Michael Malis) https://malisper.me/how-ai-changes-the-economics-of-jit-compilers/Umbra DB https://umbra-db.com~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

What's your gold standard for deciding whether AI-written code is ready for production? In Episode 42 of Talking Postgres, Simon Willison—creator of Datasette, co-creator of Django, and prolific open-source developer—joins Claire to share how AI is changing the way he builds software today. We dig into why his test for shipping AI-generated code is “Could I explain this to somebody else?”, how engineering management skills can be surprisingly useful when managing AI agents, and how the bottleneck is no longer writing code but understanding it. Also: personal credibility, deep research, the Winchester Mystery House, slop proxies, and Simon's observation that “Features are cheap. That doesn't mean you should build them all.”Previously on Talking Postgres:Talking Postgres podcast Ep 30: AI for data engineers with Simon Willison Links mentioned in this episode:Blog: Simon Willison's BlogProject page: Datasette, for finding stories in dataGitHub repo: sqlite-utilsBlog post: Release of alchemy-utils 0.1a0 (featuring Postgres & DuckDB)Blog post: There are no lossless transformations of natural-language text, by Sophie AlpertPodcast episode: The Open Weight Revolution with Simon Willison, on Oxide and FriendsPodcast episode: An AI state of the union, on Lenny's Podcast Wikipedia: Winchester Mystery HouseIdea in Mythical Man-Month: Conceptual integrityBlog post: SQLite compressed text-history prototypes, by Simon WillisonTools: Simon's miscellaneous tools repoIn this episode, we covered:00:00 Intro & music04:00 A week of work before breakfast09:12 Red-green TDD makes agents exercise every line14:42 A million lines mean nothing if you don't understand it18:28 Finding low-hanging fruit among open PRs & issues22:07 Getting work done while walking the dog23:01 Gold standard: “Could I explain this to somebody else?”28:27 Slop proxies add no value at all30:49 Aggressive nitpicking reviews35:01 Everything in software engineering is about trade-offs41:16 Racoon heist game is only fun for 1 minute 15 seconds43:46 New Year's resolution to be more ambitious48:11 You have to learn to throw things away51:57 Features are cheap. That doesn't mean you should build them all1:00:45 Engineering management skills are so useful1:04:44 QA specialists should be having a great time1:07:53 Writing is thinking, don't outsource it1:08:23 Skill atrophy is a choice you make1:10:07 I believe in people who are motivated1:11:55 Systems are not set up to deal with this volume1:18:48 Research agents stopped being absolute garbage1:21:49 The whole point of the “human in the loop”1:26:10 Dream: I want there to be more small businesses

Guy and Eitan discuss news regarding SQL Server 2025, specifically that it's now publicly available on Amazon RDS, but also that Web Edition is now gone. Also, Guy shares an interesting use case with troubleshooting disk IO latency issues. And finally, Guy and Eitan discuss the differences between SQL Server and PostgreSQL, and why would anyone still be paying for SQL Server. Relevant links: Amazon RDS for SQL Server now supports Microsoft SQL Server 2025 SQL Server Web Edition Discontinued. Now What? SQL Server 2025: Web Edition Is Gone SQL Server 2016 end of support is here: Plan your next steps Troubleshoot slow SQL Server performance caused by I/O issues sqlfingers.com: Why Are We Still Paying for SQL Server?

Nik and Michael are joined by Radim Marek to discuss MVCC, including his recent article on how Postgres chose to implement it compared to other systems. Here are some links to things they mentioned: Radim Marek https://postgres.fm/people/radim-marekBoringSQL https://boringsql.comPostgreSQL's MVCC is bad. So is everyone else's (blog post by Radim) https://boringsql.com/posts/mvcc-bad-bad/PostgreSQL MVCC documentation https://www.postgresql.org/docs/current/mvcc-intro.htmlPostgreSQL Storage Internals series by Radim https://boringsql.com/guides/postgresql-storage-internals/PgQue https://github.com/NikolayS/pgqueThe next ten years of Postgres (talk slides by Álvaro Herrera) https://www.postgresql.eu/events/pgconfde2026/sessions/session/7744/slides/866/edb-keynote-pgconfde-2026.pdfEpisode on RegreSQL https://postgres.fm/episodes/regresqlDryRun MCP https://github.com/boringsql/dryrun~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Este episodio va de ser vago. Pero vago en el buen sentido, eh. De esos que prefieren que una herramienta haga el trabajo pesado mientras tú te quedas con lo divertido. Resulta que hay todo un ecosistema de herramientas TUI con el prefijo "lazy" que te evitan tener que memorizar cientos de flags y opciones de comandos como git, docker, rsync o SQL. Y no, no es cutrez: son interfaces de terminal que funcionan a golpe de tecla, sin ratón, sin salir de la terminal, y encima molan.Te cuento cómo nació todo esto, quién es Jesse Duffield (el creador de lazygit y lazydocker, con más de 80K y 52K estrellas en GitHub respectivamente) y por qué esta filosofía de "una tecla, una acción" ha enganchado a tanto linuxero. Y lo mejor: te hago demo de las cuatro herramientas principales para que veas cómo funcionan en vivo y en directo, con sus paneles, sus atajos y sus trucos.Empezamos con lazygit, el rey indiscutible del ecosistema. 80.900 estrellas en GitHub, escrito en Go, y con una comunidad que no para de crecer. Desde stage línea a línea hasta rebase interactivo, pasando por undo/redo vía reflog. Te enseño cómo hacer commits, gestionar ramas, stash y hasta cherry-pick sin tener que acordarte de los flags raros de git.Seguimos con lazysql, el gestor de bases de datos en terminal de Jorge Rojas. Soporta MySQL, PostgreSQL, SQLite, MongoDB, MSSQL y Oracle. Navegación por teclado, autocompletado de queries, exportación a CSV y configuración por proyecto. Ideal para cuando no te apetece abrir DataGrip o DBeaver solo para hacer una consulta rápida.Luego viene lazyrsync, escrito en Rust con ratatui, y con una filosofía muy clara: que no se te olvide el flag ese que evita que borres todo. Perfiles reutilizables, dry-run con previsualización, protección contra --delete accidentales y paths dinámicos con variables. Perfecto para backups sin sustos.Y cerramos con lazydocker, también de Jesse Duffield. Cuatro paneles: contenedores, métricas, imágenes y logs en vivo. Con un vistazo ves qué contenedor consume más CPU, entras en el terminal de uno con una tecla, o ejecutas docker-compose sin acordarte del comando. Y sí, también funciona con Podman.Además te menciono otras herramientas del ecosistema: lazyssh, lazyjj para Jujutsu, lazykube, lazyprune para limpiar node_modules olvidados... Vamos, que hay lazy para todo.Capítulos del episodio:0:00 — Introducción: el problema de memorizar comandos2:05 — La filosofía lazy: scripts, TUIs y el ecosistema lazy4:30 — LazyGit: historia, filosofía "una tecla una acción" y +80K estrellas6:45 — LazyGit: demo de paneles, stage, commits, ramas y stash9:10 — LazySQL: Jorge Rojas, 4K estrellas y soporte multi-base de datos11:30 — LazySQL: demo con autocompletado, consultas y exportación CSV14:00 — LazyRsync: dry-run, perfiles y protección contra errores16:30 — LazyRsync: demo con columnas de estado y confirmación de borrado19:10 — LazyDocker: Jesse Duffield, 52K estrellas y soporte para Podman21:45 — Otras herramientas lazy: lazy-ssh, lazy-jj, lazy-kube, lazy-npm23:15 — Cierre: sé un vago inteligente, valoración y despedidaMás información y enlaces en las notas del episodio

Nik and Michael are joined by Tudor Golubenco, CTO of Xata, to discuss their architecture, progress, and open source tooling. Here are some links to things they mentioned: Tudor Golubenco https://postgres.fm/people/tudor-golubencoXata https://xata.ioXata is now open source https://xata.io/blog/xata-is-now-open-sourceDBLab Engine https://postgres.ai/docs/database-labpgstream https://github.com/xataio/pgstreamXata acquires Privacy Dynamics for advanced anonymization https://xata.io/blog/xata-acquires-privacy-dynamicsTonic AI https://www.tonic.aiA thousand Postgres branches for one dollar https://xata.io/blog/a-thousand-postgres-branches-for-1pgroll https://github.com/xataio/pgrollCloudNativePG https://github.com/cloudnative-pg/cloudnative-pgPGSimCity https://nikolays.github.io/PGSimCityDeltaX https://github.com/xataio/deltax~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

ProposalsAccepted: testing: allow examples with any signatureAccepted: maps: add Identical funcAccepted: sync/atomic: add Max/Min operatorsNo proposal (!?): add algebraic enum typesLightning roundGoLand 2026.2 Is Now AvailableG#Interview with Peter DownsGitHubpgtestdbpgmigratePersonal web siteTwitterCloudXPocketBase ★ Support this podcast on Patreon ★

In this episode, Doug Pagnutti, Developer Advocate at Tiger Data, discusses how time series databases like TimescaleDB are transforming industrial automation, robotics, and AI applications. He shares insights on integrating these databases with various sensors, managing data at scale, and optimizing performance both on the cloud and on the edge. Key Topics: - The role of time series data in robotics and industrial automation - How TimescaleDB extends PostgreSQL for high-performance time series workloads - Differences between open source and managed cloud versions - Strategies for integrating various industrial controllers and messaging pipelines - Techniques for managing intermittent connectivity with edge devices - Advanced tools like continuous aggregates and data compression for big data - Enabling multimodal data queries with hybrid search stacks - Future applications of time series data in AI-driven environments and energy systems - Best practices for storing telemetry, spatial, and metadata efficiently – SPONSORS – This episode is brought to you by Tiger Data Every growing Postgres database eventually hits a wall. Queries slow down, dashboards lag, and teams consider adding a second database. Tiger Data, creators of TimescaleDB, extends Postgres with time-series primitives, columnar storage, and automatic partitioning so your queries stay fast on live data. No pipelines, no migration, no second system. Just Postgres, built for the workload you actually have. Try it free at https://www.tigerdata.com/go/trial?utm_source=content-syndication&utm_medium=referral&utm_campaign=robotics-ads

Nik and Michael are joined by Qian Li and Peter Kraft to talk about DBOS, an open source platform for durable workflows built on Postgres.  Here are some links to things they mentioned: Qian Li https://postgres.fm/people/qian-liPeter Kraft https://postgres.fm/people/peter-kraftDBOS https://www.dbos.devTemporal https://temporal.ioGadget's use of Postgres https://postgres.fm/episodes/gadgets-use-of-postgresPostgres LISTEN/NOTIFY blog post from Recall AI https://www.recall.ai/blog/postgres-listen-notify-does-not-scalePostgres 19 commit that improves LISTEN/NOTIFY, avoiding waking backends that have no need to process the notification messages https://git.postgresql.org/gitweb/?p=postgresql.git;a=commitdiff;h=282b1cde9 PgQue https://github.com/NikolayS/pgquepg_durable https://github.com/microsoft/pg_durableAbsurd https://github.com/earendil-works/absurdLISTEN/NOTIFY performance considerations (docs patch submitted by Nik) https://www.postgresql.org/message-id/flat/CAM527d8oDVb2K%2BtTjpjLkd7g14HLDiwwnvv%3Drcn4BMEs%3DMatkQ%40mail.gmail.com#1b6fa752a96aad22bd8ef794fbaf3ea1~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

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/

Dopo aver parlato di PostgreSQL, continuo il viaggio con TimescaleDB, l'estensione pensata per gestire serie temporali, metriche e telemetria. Vediamo come funziona, perché può essere interessante in un progetto .NET e quali sono le principali differenze rispetto a InfluxDB.https://github.com/timescale/timescaledbhttps://timescaledb.org/https://www.tigerdata.com/#TimescaleDB #PostgreSQL #InfluxDB #TimeSeries #Database #DotNet #CSharp #Npgsql #Dapper #EntityFrameworkCore #IoT #Telemetry #SoftwareDevelopment #DotNetInPillole #podcast

Nik and Michael are joined by Fabrízio Mello to discuss his new extension pg_stat_log.Here are some links to things they mentioned: Fabrízio Mello https://postgres.fm/people/fabrizio-de-royes-melloTimbira https://www.timbira.com.brPlanetScale https://planetscale.compg_stat_log https://github.com/fabriziomello/pg_stat_logCustom Cumulative Statistics (docs) https://www.postgresql.org/docs/current/xfunc-c.html#XFUNC-ADDIN-CUSTOM-CUMULATIVE-STATISTICSCustom Cumulative Statistics (wiki page) https://wiki.postgresql.org/wiki/CustomCumulativeStatslogerrors https://github.com/munakoiso/logerrorspgFouine https://www.postgresql.org/ftp/projects/pgFoundry/pgfouine/pgFouinepgBadger https://github.com/darold/pgbadgerpg_stat_checkpointer https://www.postgresql.org/docs/current/monitoring-stats.html#MONITORING-PG-STAT-CHECKPOINTER-VIEWpg_wait_sampling https://github.com/postgrespro/pg_wait_samplingRFC: pg_stat_logmsg (patch proposal by Joe Conway) https://www.postgresql.org/message-id/flat/89742024-d51a-c66b-90b9-67f837072cd2%40joeconway.com~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

What happens when a database engineer spends 13 years on SQL Server and then starts working on Postgres? In Episode 41 of Talking Postgres, Distinguished Engineer Panagiotis “Panos” Antonopoulos of Microsoft joins Claire to share what surprised him most about making that transition. We explore why Postgres felt familiar from the start—and what shocked Panos about the clean and lean Postgres codebase. We also dig into how public mailing lists and AI can help explain why design decisions were made, why more people are asking “why not Postgres?”, and the shared-storage architecture behind Azure HorizonDB.Previously on Talking Postgres:Talking Postgres Ep 39: From MemSQL to HorizonDB, an engineer's journey with Adam ProutTalking Postgres Ep 38: How I went from Oracle to Postgres (with a big NoSQL detour) with Gwen ShapiraTalking Postgres Ep 40: How I got started running a Postgres user group with Jeremy SchneiderLinks mentioned in this episode:SIGMOD: ACM SIGMOD-PODS Conference 2027  CMUDB talk: HorizonDB: Co-Designing Postgres and Azure for Cloud-Native OLTP, by Adam ProutPostgreSQL mailing list: pgsql-hackersPostgreSQL docs: pg_stat_slru (simple least-recently-used)Docs: Azure HorizonDBPoster: Postgres Happiness Hints

An airhacks.fm conversation with Stanislav Bashkyrtsev about: discussion about testing terminology and the difference between unit tests, component tests, System Tests, and integration tests, defining component tests as in-process invocations without HTTP, using RestAssured with MockMvc-style direct endpoint calls, avoiding mocks in favor of real system tests, why code coverage is a misused management metric, the anti-pattern of using reflection to inflate coverage, distinguishing line and branch coverage from actual verification, using coverage from system tests to detect dead code for pruning, mutation testing with PIT to measure assertion quality, testing Quarkus applications, the default Guice and Guava dependencies in Quarkus RESTEasy, starting a new microservice with a separate system-test module, calling endpoints over HTTP with the MicroProfile REST Client or the Java HTTP client, deploying Quarkus on AWS Lambda as a production-like environment, backward compatibility testing with multiple production versions, turning system tests into stress and load tests, testing connection pools and metrics under load, introducing a test-only private API to verify state changes in serverless systems, contract-driven work in large consulting projects, generating JSON and JSONB directly in PostgreSQL and returning it over JDBC, mapping database rows to Java records instead of DTOs, running GraalVM inside the Oracle Database for stored procedures and table triggers, the pendulum between database-centric and application-centric logic, the convergence of SQL and NoSQL databases, CI/CD pipelines with Jenkins and manual production deployment steps, avoiding Jenkins access to production via CGI shell scripts behind nginx, AWS CodePipeline and CodeBuild with CDK-defined infrastructure, event-driven pipelines triggered by S3 put-object events, multi-account roles with short-lived STS credentials, the size of the AWS SDK and reducing it by excluding unused HTTP clients, health checks and Kubernetes liveness and readiness probes, why health checks make little sense for short-lived Lambdas, a version endpoint for deployment smoke tests Stanislav Bashkyrtsev on twitter: @sbashkirtsev

Nik and Michael are joined by David Steele to talk all things pgBackRest. Here are some links to things they mentioned: David Steele https://postgres.fm/people/david-steelepgBackRest https://pgbackrest.orgpg_basebackup https://www.postgresql.org/docs/current/app-pgbasebackup.htmlBarman https://pgbarman.orgpgmoneta https://github.com/pgmoneta/pgmonetaWAL-G https://github.com/wal-g/wal-gReturn pg_control from pg_backup_stop (patch proposal) https://www.postgresql.org/message-id/flat/86436ff9-eb98-4c8a-825e-3bcae022107b%40pgbackrest.org#e36e2ad2f410d1a9a1152e36aa66bb48Add StorageReadMulti for prefetched multi-file/range reads from object stores (pgBackRest change that needs review) https://github.com/pgbackrest/pgbackrest/pull/2783pg_hardstorage https://github.com/cybertec-postgresql/pg_hardstorageWhy the cycle of open-source sustainability needs to be virtuous (blog post by Gabriele Bartolini) https://www.gabrielebartolini.it/articles/2026/04/why-the-cycle-of-open-source-sustainability-needs-to-be-virtuousARIN https://www.arin.netStefan Fercot https://pgstef.github.io/aboutpgBackRest sponsors at the time of recording: AWS, Supabase, pgEdge, Tiger Data, Percona, Eon, Xata, Dalibo, Data Egret~~~00:00 – Intro & pgBackRest origin story03:07 – Could pgBackRest live in Postgres core? Why it hasn't happened11:37 – Primary vs. standby backups & corruption safety20:36 – Measuring RPO & RTO in practice26:20 – Checksum performance & S3 storage class tips30:02 – Standby replay bottleneck35:12 – Log shipping, streaming replication, and sharding44:56 – Maintenance reality & the sponsorship crisis47:41 – What's next: repo-to-repo backup & RPO-zero streaming56:00 – Incremental backup1:05:01 – Reliability philosophy1:10:05 – Second maintainer, community growth & wrap-up~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Ajay Kulkarni grew up in tech, as his father was a tech entrepreneur selling PC's in the early 80's. He went to college in MIT, and eventually founded a startup that was acquired by GroupMe (while it was being acquired by Skype... while they were being acquired by Microsoft). He's always been attracted to building things, so startups are right up his alley. Outside of tech, he is married with 2 young kids. He is a big exercise guy... he loves to run, swim and track his steps. Additionally, he loves music - to listen, and to play guitar, piano and drums.Ajay and his co-founder met 30 years ago at MIT. They reconnected after years of doing their own thing, starting to dig into the iOT world. In doing this, they built a database because they the best solution to store this data... and in doing so, they unlocked their next venture out of this necessity.This is the creation story of Tiger Data.SponsorsUnblockedTECH DomainsMezmoBraingrid.aiLinkshttps://www.tigerdata.com/https://www.linkedin.com/in/ajaykulkarni/Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Te traigo un tema que me tiene completamente entusiasmado: cómo exprimir todos tus documentos, notas, manuales o archivos locales sin tener que compartirlos con nadie. Te voy a dar una visión general de cómo puedes montar un sistema de recuperación de información para que una inteligencia artificial local se convierta en tu asistente personal definitivo. Todo esto sin salir de tu propia casa, sin APIs de pago y de forma completamente privada.¿Reentrenar o buscar? El gran dilemaPara solucionar esta tremenda limitación, el mundo de la tecnología nos ofrece dos caminos diferentes: hacer un reentrenamiento de un modelo ya existente (lo que conocemos como fine-tuning) o bien montar un sistema RAG (Retrieval-Augmented Generation), que podríamos traducir como generación aumentada por recuperación. En este episodio te desvelo por qué el fine-tuning no es la solución para el común de los mortales: requiere de tarjetas gráficas carísimas, es un proceso lento y estático, y además tus datos privados quedan incrustados dentro del propio modelo, por lo que si se lo entregas a un tercero, estarás regalando tu privacidad. En cambio, un RAG casero es infinitamente más barato, dinámico y respeta tus datos al cien por cien. Imagina que en lugar de obligar al modelo a memorizar toda la biblioteca (que es lo que hace el fine-tuning), le pones a su lado un bibliotecario listísimo que busca la página exacta de los apuntes que necesita antes de responderte. El modelo de lenguaje lee esa página en tiempo real y te contesta basándose únicamente en hechos reales, no en invenciones.La tubería de datos para tu cerebro artificialA lo largo del episodio te explico con todo detalle las piezas que componen esta tubería de datos (o pipeline) que permite hacer magia con tus archivos:La ingestaEl troceado (o chunking)Los embeddings y vectoresLa base de datosLa búsqueda híbridaHerramientas listas para usar y errores que debes evitarSi te da miedo el código, no te preocupes. Te hablo también de alternativas como OpenWeb UI. Y si te va la marcha del desarrollo, te cuento cómo con apenas diez líneas de Python y Streamlit puedes tener una aplicación web propia y completamente funcional.Además, repasamos los tropiezos más habituales que cometemos al empezar en este mundillo, como usar modelos de vectorización que solo entienden inglés para procesar textos en español, no limpiar las cabeceras y pies de página de los PDFs antes de procesarlos, o la importancia crucial de reindexar de forma automática para que tus nuevos documentos estén disponibles al instante.Capítulos del episodio00:00:00 Introducción y de qué va este episodio00:01:54 ¿A qué problema nos enfrentamos con los LLM?00:05:08 Fine-tuning vs. RAG: ¿Cuál es mejor para tus datos?00:08:29 El Pipeline del RAG: De la ingesta a la respuesta00:10:45 ¿Qué es un "embedding" y qué modelos usar con Ollama?00:12:02 El arte de trocear el texto (Chunking)00:13:40 Búsqueda híbrida: Semántica frente a coincidencia exacta00:14:50 Re-ranking: Ordenando los resultados por relevancia00:15:53 El Stack: Ollama, PostgreSQL, pgvector y Podman00:17:25 Alternativas vectoriales: ParadeDB, ChromaDB y Qdrant00:18:36 Manos a la obra con Python y Streamlit00:20:53 OpenWeb UI: La alternativa con RAG integrado y sin código00:21:42 Cómo saber si funciona: El método de las 20 preguntas00:22:51 Errores comunes que debes evitar al montar tu RAG00:23:55 Lo que viene: GraphRAG y RAG agéntico00:24:44 Resumen final y despedidaMás información y enlaces en las notas del episodio

Is it worth continuing to run SQL Server when PostgreSQL licensing is zero? Rebecca Lewis has a well written post on why that looks at some of the pros and cons of paying for SQL Server instead of moving to PostgreSQL. She starts with some of the things PostgreSQL does well, of which I think the Extensibility is really cool. SQL Server has some of this in CLR and the non-SQL language support, but those seem kludgy and complex to me. They aren't really integrated into the SQL Server platform. They're good, but I do wish vendors or the community could add some extensions in a way PostgreSQL does. Of course, I also worry about stability, so maybe this is a wish that isn't really a great idea. Read the rest of SQL Server Still Wins

In this episode of In Depth, Brett sits down with Paul Copplestone, co-founder and CEO of Supabase, the open-source Postgres platform now serving more than seven million developers. Before Supabase, Paul launched a Thumbtack-style marketplace in Southeast Asia and co-founded an office-management startup called Nimbus, experiences that taught him to separate fundraising from building and to find product-market fit before blitzscaling. He breaks down how a single tagline change for Supabase unlocked product-market fit, why he runs a fully distributed async team with near-zero attrition, and how he turned PLG signals into a product-led sales motion comped only on incremental uplift. In today's episode, we discuss: How changing one tagline helped Supabase go to #1 in Hacker News - an early sign of product market fit Why Paul ran Supabase like it had only $100K in the bank despite raising real money How Supabase rode three distinct AI waves, from pgvector to Bolt and Lovable, to Claude Code Why Supabase built a sales team comped only on the incremental uplift over a control group What the Toyota production system's "kaizen" taught Paul about unblocking a scaling team References: Ant Wilson: https://www.linkedin.com/in/ant-wilson-46179937 Bolt: https://bolt.new/ Claude Code: https://www.anthropic.com/claude-code Codex: https://openai.com/codex/ Entrepreneurs First: https://www.joinef.com/ Firebase: https://firebase.google.com/ Lovable: https://lovable.dev/ MongoDB: https://www.mongodb.com/ Next.js: https://nextjs.org/ PostgreSQL: https://www.postgresql.org/ Supabase: https://supabase.com/ Thumbtack: https://www.thumbtack.com/ Y Combinator: https://www.ycombinator.com/ Where to find Paul: LinkedIn: https://www.linkedin.com/in/paulcopplestone Twitter/X: https://x.com/kiwicopple Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 Introduction 01:32 Why Paul's earlier startups were never destined to be huge 07:14 Unlearning the "tall poppy" mindset and going all-in on async 09:54 Reverse-engineering why Supabase was an outstanding idea 12:04 The accidental Hacker News launch and tagline lesson 13:58 Where the early roadmap came from: demand vs. technical taste 17:28 Skill vs. luck, and operating like you have $100K in the bank 21:42 What actually makes a great developer experience 23:10 Solving the "graduation problem" Firebase never could 24:58 The role of open source in Supabase's success 26:10 The three distinct AI tailwinds: From pgvector to Claude Code 35:24 Supabase's egoless, hyper-competitive open-source culture 42:58 A tactical playbook for raising capital 48:37 Product-led sales comped on incremental uplift only 59:27 The production philosophy behind Supabase's operations

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

Atomic Arch: Attackers Hijack Trusted AUR Packages to Deliver Rootkit-Like Malware https://www.sonatype.com/blog/atomic-arch-npm-campaign-adds-malicious-dependency Why Use App-Level Auth When Every Database Has Auth? (Splunk Enterprise CVE-2026-20253 Pre-Auth RCE) https://labs.watchtowr.com/why-use-app-level-auth-when-every-database-has-auth-splunk-enterprise-cve-2026-20253-pre-auth-rce/ A Fake Bug Report Hijacks Your AI Coding Agent and Nothing Catches It. https://tenetsecurity.ai/blog/agentjacking-coding-agents-with-fake-sentry-errors/ My Upcoming Classes https://www.sans.org/profiles/dr-johannes-ullrich

The U.S. government orders Anthropic to shut down foreign access to its Fable 5 and Mythos 5 AI models after the Pentagon labels the company a supply-chain risk. David Shipley examines what may be  behind the decision and what it means for countries and businesses that depend on American AI platforms. The FBI also disrupts Outsider Enterprise, a China-based phishing-as-a-service network linked to more than 9,000 fake websites, one million fraudulent URLs, 3.8 million stolen payment-card records and an estimated $1.9 billion in losses. Also in this episode: A critical Splunk vulnerability could allow an unauthenticated attacker to remotely execute code through a PostgreSQL sidecar service enabled by default in some deployments. A former Iowa school IT worker is sentenced after retaining access for 21 months and using it to delete accounts and disrupt school systems. And FortiWatch returns with a critical FortiSandbox command-injection vulnerability that requires no authentication. Cybersecurity Today is hosted by David Shipley. Chapters 00:00 Cybersecurity Today headlines 00:26 U.S. government shuts down Anthropic AI models 02:59 FBI takes down Outsider Enterprise phishing network 04:47 Critical Splunk vulnerability explained 06:31 Former school IT worker sentenced for cyberattack 08:29 FortiWatch: FortiSandbox command-injection vulnerability 10:08 What's ahead this week

Durante años hemos construido BIM alrededor de una idea aparentemente incuestionable: el modelo es el centro del proyecto. Primero modelamos, después añadimos información y finalmente intentamos gestionar todo desde un único archivo cada vez más pesado y complejo. Pero ¿y si llevamos años haciéndolo al revés? En este episodio hablamos de BIM Data-Driven, bases de datos relacionales, APIs, ISO 19650 y soberanía del dato. Analizamos una propuesta que plantea separar la información de su representación geométrica y convertir el modelo BIM en un subproducto generado bajo demanda. ¿Estamos confundiendo modelo con archivo? ¿Debe la geometría seguir siendo la fuente de verdad del proyecto? ¿Tiene sentido seguir depositando toda la información en herramientas diseñadas para producir geometría? Un episodio sobre datos, gobernanza de la información y una idea que seguramente molestará a más de uno: quizá el verdadero activo nunca fue el modelo. Siempre fueron los datos. Bienvenido al episodio 207 de BIMrras! Contenido del episodio: 00:00 Introducción al episodio y planteamiento del debate 02:05 Presentación del enfoque Data Driven para BIM en plantas solares 04:30 El dato como origen del proyecto y la geometría como representación 18:00 Problemas de escalabilidad de los modelos BIM tradicionales 23:30 Inversión del flujo de trabajo: de los datos a la geometría 29:15 PostgreSQL y PostGIS como base de la gestión de información 35:10 Gobernanza de datos, contenedores de información e ISO 19650 50:10 ¿Puede este enfoque aplicarse a cualquier proyecto BIM? 57:30 Nuevos perfiles profesionales y arquitectura de la información 01:01:30 Soberanía del dato e independencia de las herramientas de autoría 01:04:50 IFC, separación entre datos y geometría e impacto de IFC 5 01:07:00 Conclusiones: ¿ha muerto el BIM modelocéntrico?

Intensely local user groups have been part of Jeremy Schneider's story from the start—from Linux meetups at a Michigan coffee shop to a closet server running an Oracle database nobody knew anything about. In Episode 40 of Talking Postgres, Postgres engineer and Seattle Postgres User Group co-organizer Jeremy Schneider joins Claire to share how community led him to Postgres after 15 years with Oracle—and why "it's like I was born to be here." Plus: the newly-updated Postgres Happiness Hints poster, advice for starting your own user group, and his POSETTE 2026 talk on CloudNativePG.Previously on Talking Postgres:Talking Postgres Ep 38: How I went from Oracle to Postgres (with a big NoSQL detour) with Gwen ShapiraLinks mentioned in this episode:Seattle Postgres User Group: Meetup pageSeattle Postgres User Group: YouTube channelUser Group Map from PGConfEU 2025 talk: 48 Postgres User Groups during PG18 timeframePostgreSQL.org: Listing of local Postgres User GroupsPostgres Meetup For All (a virtual meetup): Meetup pageJeremy Schneider's Blog: Ardent Performance ComputingPoster: Postgres Happiness HintsPGConf.dev 2026: Posters from Poster SessionPGConf.dev 2026 Poster Session: Talking Postgres posterPOSETTE: An Event for Postgres 2026: Jeremy's POSETTE 2026 talk with Leonardo CecchiPOSETTE 2026: Livestream 3 schedule & talksOracle docs: Oracle Database Concepts PDFBook: Oracle Insights: Tales of the Oak Table

Olvídate de hacerle preguntas genéricas a ChatGPT; hoy vamos a ver cómo sacarle partido real y práctico a la tecnología para solucionar problemas cotidianos y quitarnos de encima la fatiga de decisión diaria.Seguro que te suena la película: post-its en la nevera, hojas de cálculo que se quedan desactualizadas y el clásico "¿qué cenamos hoy?" que acaba en improvisación o en una compra desorganizada. Para evitar esto, he diseñado un ecosistema de agentes basados en cuatro cajas de herramientas que llamamos MCP (Model Context Protocol). Estos protocolos permiten que la IA no solo responda preguntas, sino que interactúe de forma directa con mis datos y aplicaciones externas.Te explico de forma muy sencilla las piezas que componen este sistema:El RAG Semántico para las recetas: Tengo una base de datos vectorial con unas 1.700 recetas cargadas en PostgreSQL mediante pgvector. La clave es que no busco platos por coincidencia exacta de palabras. Si le digo que quiero "algo rápido y ligero con verdura", el sistema realiza una búsqueda semántica, entiende lo que busco y me propone las mejores opciones. Todo esto se procesa de forma económica mediante OpenRouter sin necesidad de tener una potente GPU en local.Los Skills y SQLite: Los "Skills" definen los procesos exactos que debe seguir el modelo. Le he marcado unas pautas sencillas: platos únicos mediterráneos para comer y cenas ligeras. Toda esta información se gestiona en una base de datos SQLite muy ligera.Lógica difusa en la lista de la compra: El asistente es capaz de agrupar ingredientes similares. Si dos recetas piden tomates en formatos distintos (por ejemplo, "tomates a granel" y "100g de tomates"), la lógica difusa los unifica bajo un mismo concepto para evitar duplicados en la lista de la compra, organizando además los productos por pasillos o secciones (como frutería o carnicería).Typst para exportar a PDF: Para ver el menú en una tablet o imprimirlo para la nevera, utilizo Typst, una alternativa moderna a LaTeX que me genera unos documentos PDF impecables en cuestión de segundos.Además, te cuento cómo puedes montar todo esto en local de manera gratuita con Ollama, y aprovecho para actualizarte sobre mis andanzas de vuelta al "cacharreo" puro en Linux: desde mis experiencias recientes con el editor Helix y "mkdr" (mi renderizador de Markdown para terminal), hasta "podcli", una pequeña utilidad para exprimir los feeds de podcast desde la consola.Espero que disfrutes de este episodio tanto como yo montando todo este tinglado. ¡A cacharrear!Capítulos del episodio:00:00:00 Agentes de IA que de verdad nos facilitan la vida00:01:42 El ejemplo práctico: Automatizar nuestro menú semanal00:03:51 La fatiga de decisión y por qué la disciplina humana falla00:05:38 Mi caja de herramientas: 4 MCPs (Model Context Protocol)00:06:58 Buscando comida con IA: El RAG semántico de 1700 recetas00:08:45 Búsqueda híbrida y embeddings económicos sin usar GPU local00:10:00 Simplificando las comidas: El papel de los "Skills"00:11:58 Organizando la base de datos de manera sencilla con SQLite00:13:31 Lógica difusa: Evitando duplicados en la lista de la compra00:15:23 Creando PDFs bonitos con Typst (la alternativa moderna a LaTeX)00:17:03 Demostración en directo: Generando el menú de la semana00:19:12 Automatización total: Generación automática de menús con Cron00:20:19 Revisión del menú, las recetas y la alternativa local con Ollama00:23:12 De vuelta al "cacharrero" de Linux: Helix, mkdr y Podcli00:24:51 Próximos episodios: Instalación desde cero a producción de Hermes00:25:38 Despedida y cierre del episodioMás información y enlaces en las notas del episodio

Run enterprise Postgres workloads on Azure HorizonDB with around 3x the throughput of self-managed deployments — zone-resilient by default, no architectural trade-offs. Call AI models directly from SQL, build durable vector pipelines inside the database, and deliver high-accuracy similarity search at massive scale with DiskANN and AI re-ranking, all without leaving PostgreSQL. Debug and optimize queries faster with the Azure HorizonDB VS Code extension. Visualize execution plans, let Copilot generate fixes, and clone production data to test environments in seconds. Charles Feddersen, PostgreSQL Partner Director PM, shares how to put all of it to work on Azure. ► QUICK LINKS:  00:00 - Azure HorizonDB features 00:57 - Open-source PostgreSQL 02:24 - How it works 03:37 - Performance 04:51 - Enterprise-ready security 05:34 - Memory & storage work together 06:29 - AI Model Management + AI Functions 08:24 - AI Pipelines 09:50 - DiskANN + AI Re-ranking 10:50 - VS Code Extension + Data Cloning 12:31 - Wrap up ► Link References Check out our blog at https://aka.ms/azurepostgresblog ► Unfamiliar with Microsoft Mechanics? As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

From developer dependency to AI-powered ownership in 4 weeks Episode Summary: AI entrepreneurs and side hustlers often fail the same way—and it costs them. This episode breaks down the $30,000 mistake that transformed how I build AI side gigs, teach financial freedom to parents, and think about entrepreneur independence. Expect the real playbook behind failing smart so you don't repeat my errors. Parent entrepreneur Tracy Brinkmann shares the raw truth about firing his $120,000 developer and rebuilding his entire backend using Cursor AI in just 4 weeks. This episode reveals the hidden cost of outsourcing your brain, the specific prompting strategies that actually work, and why dependency might be more expensive than you think. Perfect for parents who want to own their technology instead of renting someone else's expertise. https://DarkHorseEntrepreneur.com Key Points 00:00 - Opening Cursor AI saves $90,000 01:40 - The Stupid Decision - Rebuilding entire backend alone in 4 weeks using Cursor AI 02:15 - Vibe Coding Explained - Directing AI through intent rather than instruction, Collins Dictionary Word of the Year 2025 03:00 - Why Cursor AI - Cursor Composer maintains persistent context across entire codebase 04:30 - Day 10 Shift - Realized he was learning architecture for the first time, not just rebuilding 04:55 - The Real Return - Could build features, maintain systems, make decisions without outside help 06:00 - The Hidden Cost - Lost learning by osmosis and institutional knowledge from Marcus 07:00 - Bug Reports Reality Check - Scaling problems that only show up with experience 08:50 - Parent Entrepreneur Connection - Dependency trap affects family time and business freedom 09:45 - Why This Matters - Biggest shift in work since Industrial Revolution 10:15 - New vs. Old Model - Expand zone of genius vs. hire experts and delegate 11:05 - Whiskered Wisdom - Dependency is expensive, ownership is priceless 11:55 - Closing - Goal is understanding everything well enough to make smart decisions   Key Topics Covered: The $30,000 Dependency Trap Why hiring exceptional talent can make you incompetent in your own business The difference between buying expertise and renting ignorance How every day of outsourcing critical functions reduces your own capabilities The Cursor AI Rebuild Strategy "Vibe coding" vs. traditional prompting approaches Why Cursor Composer's persistent context changes everything The constraint-based prompting framework that eliminates AI hallucinations Context-Rich Prompting System Standard prompt: "Build me a user dashboard" Better prompt: Complete context including database schemas, design patterns, previous failures, and specific success criteria Results: 70% usable code on first pass vs. multiple iterations The Real Cost of Expert Dependency Hourly rate: $150 per hour True cost: Infinite dependency and arrested business evolution The moment when you realize you can't make decisions without external approval Ownership vs. Access Paradigm Old model: Hire experts, delegate complexity, focus on zone of genius New model: Use AI to expand your zone of genius to include previously outsourced functions Why the entrepreneurs who thrive will own capabilities, not just access them Key Quotes: "The hourly rate of a developer might be $150. But the cost of dependency is infinite." "Every time you hand off a critical piece of your business to someone else, you're making a bet that their knowledge will always be available to you." "Dependency is expensive, but ownership is priceless." Action Steps: Identify one area where you're completely dependent on outside expertise Spend 30 minutes learning the basics using AI as your teaching assistant Focus on becoming conversational, not expert-level Start owning your business evolution again Tools Mentioned: Cursor AI (Cursor Composer) Claude Sonnet for architectural decisions PostgreSQL for database management Visual Studio Code (Cursor is a fork) Resources: AI Escape Plan Newsletter: Practical AI-powered strategies for parent entrepreneurs https://DarkHorseInsider.com Focus: Building systems you own, understand, and control while protecting family time  

Nik and Michael discuss autovacuum, including what it does, and the basics of why and how to tune it.  Here are some links to things they mentioned: autovacuum https://www.postgresql.org/docs/current/routine-vacuuming.html#AUTOVACUUMautovacuum configuration parameters https://www.postgresql.org/docs/current/runtime-config-vacuum.html#RUNTIME-CONFIG-AUTOVACUUMWhat's Missing in Postgres? (our episode with Bruce Momjian) https://postgres.fm/episodes/what-s-missing-in-postgrespg_squeeze (our episode with Antonín Houska) https://postgres.fm/episodes/pg_squeezeMy queries to monitor autovacuum (post by Laurenz Albe) https://www.cybertec-postgresql.com/en/monitor-autovacuum-my-queries/Autovacuum Tuning Basics (post by Tomas Vondra, originally for 2nd Quadrant blog) https://www.enterprisedb.com/blog/autovacuum-tuning-basicsZero autovacuum_vacuum_cost_delay, Write Storms, and You (post by Jeremy Schneider) https://ardentperf.com/2026/04/12/zero-autovacuum_cost_delay-write-storms-and-you/Our episode on long-running transactions / xmin horizon https://postgres.fm/episodes/long-running-transactions~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Mozilla found 271 unknown Firefox vulnerabilities in days using AI—bugs that millions of automated test runs had missed for years. Steve Gibson argues this isn't a crisis. It's the industry finally paying down decades of security debt, and for the first time, defenders may have the advantage. Cisco meets Mythos Can the aging CVE system survive AI Patch deployment latency in the AI age MSFT's official YellowKey BitLocker bypass mitigation Ubiquiti patches 5 serious vulnerabilities Drupal attacked by a PostgreSQL injection Microsoft terminates SMS as a second factor GitHub hacked - all of its source code exfiltrated Russia is using very old Western software Why to get a no-charge AI chatbot account New Sci-Fi on Netflix What we learn from Mozilla's use of Mythos Show Notes - https://www.grc.com/sn/SN-1080-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: guardsquare.com doppel.com cyberhoot.com/securitynow trustedtech.team/securitynow365 XBOW.com

Mozilla found 271 unknown Firefox vulnerabilities in days using AI—bugs that millions of automated test runs had missed for years. Steve Gibson argues this isn't a crisis. It's the industry finally paying down decades of security debt, and for the first time, defenders may have the advantage. Cisco meets Mythos Can the aging CVE system survive AI Patch deployment latency in the AI age MSFT's official YellowKey BitLocker bypass mitigation Ubiquiti patches 5 serious vulnerabilities Drupal attacked by a PostgreSQL injection Microsoft terminates SMS as a second factor GitHub hacked - all of its source code exfiltrated Russia is using very old Western software Why to get a no-charge AI chatbot account New Sci-Fi on Netflix What we learn from Mozilla's use of Mythos Show Notes - https://www.grc.com/sn/SN-1080-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: guardsquare.com doppel.com cyberhoot.com/securitynow trustedtech.team/securitynow365 XBOW.com

Mozilla found 271 unknown Firefox vulnerabilities in days using AI—bugs that millions of automated test runs had missed for years. Steve Gibson argues this isn't a crisis. It's the industry finally paying down decades of security debt, and for the first time, defenders may have the advantage. Cisco meets Mythos Can the aging CVE system survive AI Patch deployment latency in the AI age MSFT's official YellowKey BitLocker bypass mitigation Ubiquiti patches 5 serious vulnerabilities Drupal attacked by a PostgreSQL injection Microsoft terminates SMS as a second factor GitHub hacked - all of its source code exfiltrated Russia is using very old Western software Why to get a no-charge AI chatbot account New Sci-Fi on Netflix What we learn from Mozilla's use of Mythos Show Notes - https://www.grc.com/sn/SN-1080-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: guardsquare.com doppel.com cyberhoot.com/securitynow trustedtech.team/securitynow365 XBOW.com

Mozilla found 271 unknown Firefox vulnerabilities in days using AI—bugs that millions of automated test runs had missed for years. Steve Gibson argues this isn't a crisis. It's the industry finally paying down decades of security debt, and for the first time, defenders may have the advantage. Cisco meets Mythos Can the aging CVE system survive AI Patch deployment latency in the AI age MSFT's official YellowKey BitLocker bypass mitigation Ubiquiti patches 5 serious vulnerabilities Drupal attacked by a PostgreSQL injection Microsoft terminates SMS as a second factor GitHub hacked - all of its source code exfiltrated Russia is using very old Western software Why to get a no-charge AI chatbot account New Sci-Fi on Netflix What we learn from Mozilla's use of Mythos Show Notes - https://www.grc.com/sn/SN-1080-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: guardsquare.com doppel.com cyberhoot.com/securitynow trustedtech.team/securitynow365 XBOW.com

Mozilla found 271 unknown Firefox vulnerabilities in days using AI—bugs that millions of automated test runs had missed for years. Steve Gibson argues this isn't a crisis. It's the industry finally paying down decades of security debt, and for the first time, defenders may have the advantage. Cisco meets Mythos Can the aging CVE system survive AI Patch deployment latency in the AI age MSFT's official YellowKey BitLocker bypass mitigation Ubiquiti patches 5 serious vulnerabilities Drupal attacked by a PostgreSQL injection Microsoft terminates SMS as a second factor GitHub hacked - all of its source code exfiltrated Russia is using very old Western software Why to get a no-charge AI chatbot account New Sci-Fi on Netflix What we learn from Mozilla's use of Mythos Show Notes - https://www.grc.com/sn/SN-1080-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: guardsquare.com doppel.com cyberhoot.com/securitynow trustedtech.team/securitynow365 XBOW.com

Mozilla found 271 unknown Firefox vulnerabilities in days using AI—bugs that millions of automated test runs had missed for years. Steve Gibson argues this isn't a crisis. It's the industry finally paying down decades of security debt, and for the first time, defenders may have the advantage. Cisco meets Mythos Can the aging CVE system survive AI Patch deployment latency in the AI age MSFT's official YellowKey BitLocker bypass mitigation Ubiquiti patches 5 serious vulnerabilities Drupal attacked by a PostgreSQL injection Microsoft terminates SMS as a second factor GitHub hacked - all of its source code exfiltrated Russia is using very old Western software Why to get a no-charge AI chatbot account New Sci-Fi on Netflix What we learn from Mozilla's use of Mythos Show Notes - https://www.grc.com/sn/SN-1080-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: guardsquare.com doppel.com cyberhoot.com/securitynow trustedtech.team/securitynow365 XBOW.com

CISA has ordered U.S. federal civilian agencies to urgently patch an actively exploited critical Drupal SQL injection vulnerability (CVE-2026-9082) affecting PostgreSQL-backed Drupal deployments, after Imperva reported more than 15,000 attack attempts across 65 countries. Microsoft has confirmed a strange Windows Server 2016 update issue where KB5087537 can break domain controller discovery when server hostnames are exactly 15 characters long, raising more questions about patch reliability as update complexity grows. Google has joined a coalition opposing Canada's proposed lawful access legislation, Bill C-22, warning that secret ministerial orders, possible encryption risks, and mandatory metadata retention could weaken security rather than improve it. Critics point to the Salt Typhoon telecom espionage campaign as evidence that lawful intercept systems themselves can become prime targets. Also in this episode: Check Point says Iran-linked threat group Nimbus Manticore has deployed new malware tools including MiniFast and MiniJunk V2, with researchers noting signs that MiniFast may have been developed with AI-assisted coding techniques. The campaign used SEO poisoning and fake Oracle SQL Developer downloads to lure victims. Timestamps: 00:00 Top Headlines Rundown 00:27 Emergency Drupal Patch Order 02:22 Microsoft Server Update Bug 04:02 Canada Lawful Access Battle 05:18 Google's Security Concerns 06:25 Salt Typhoon Lessons 07:35 Iran-Linked AI Malware 09:26 SEO Poisoning Attack 10:09 Wrap Up and Sign Off

Mozilla found 271 unknown Firefox vulnerabilities in days using AI—bugs that millions of automated test runs had missed for years. Steve Gibson argues this isn't a crisis. It's the industry finally paying down decades of security debt, and for the first time, defenders may have the advantage. Cisco meets Mythos Can the aging CVE system survive AI Patch deployment latency in the AI age MSFT's official YellowKey BitLocker bypass mitigation Ubiquiti patches 5 serious vulnerabilities Drupal attacked by a PostgreSQL injection Microsoft terminates SMS as a second factor GitHub hacked - all of its source code exfiltrated Russia is using very old Western software Why to get a no-charge AI chatbot account New Sci-Fi on Netflix What we learn from Mozilla's use of Mythos Show Notes - https://www.grc.com/sn/SN-1080-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: guardsquare.com doppel.com cyberhoot.com/securitynow trustedtech.team/securitynow365 XBOW.com

Mozilla found 271 unknown Firefox vulnerabilities in days using AI—bugs that millions of automated test runs had missed for years. Steve Gibson argues this isn't a crisis. It's the industry finally paying down decades of security debt, and for the first time, defenders may have the advantage. Cisco meets Mythos Can the aging CVE system survive AI Patch deployment latency in the AI age MSFT's official YellowKey BitLocker bypass mitigation Ubiquiti patches 5 serious vulnerabilities Drupal attacked by a PostgreSQL injection Microsoft terminates SMS as a second factor GitHub hacked - all of its source code exfiltrated Russia is using very old Western software Why to get a no-charge AI chatbot account New Sci-Fi on Netflix What we learn from Mozilla's use of Mythos Show Notes - https://www.grc.com/sn/SN-1080-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: guardsquare.com doppel.com cyberhoot.com/securitynow trustedtech.team/securitynow365 XBOW.com

Send us Fan MailWhat's New in Cloud FinOps: May 2026 Monthly RecapIn this combined monthly recap for May 2026, Frank Contrepois and Stephen Old dive into a vast array of updates across AWS, Google Cloud, and Azure, with a special focus on the evolving landscape of AI FinOps, hybrid cloud challenges, and a barrage of storage news.The Expanding Scope of FinOps: From Data Centre to AIThe discussion opens by exploring the expansion of FinOps beyond the public cloud to encompass on-premise data centres, software, AI, and sustainability. A central theme is the application of the FinOps Open Cost and Usage Specification (FOCUS) to on-premise environments. Stephen shares firsthand experience transposing software data into FOCUS to create a converged platform, highlighting the fundamental data challenges, from ingesting contract data to managing the high velocity of cloud data.The conversation then shifts to the burgeoning role of AI, noting its inclusion alongside SaaS and professional services in the modern FinOps scope. This introduces new forecasting challenges, as traditional 18-month budget cycles clash with the rapid pace of weekly AI model releases.A critical point is also raised regarding sustainability. The hosts discuss Amazon's board rejecting a shareholder proposal for detailed climate disclosures, which poses a significant challenge for companies needing granular data for CSRD and SEC compliance.Major Cloud Updates: April 2026AI & FinOps Visibility:A major theme is the improvement in attributing AI spend. A game-changing update from AWS means Bedrock API calls now automatically record the IAM identity (user or role) of the caller directly into CUR 2.0 and Cost Explorer. This eliminates the complex need to reconcile CloudTrail logs to determine who is driving Bedrock costs.Similarly, Amazon Q is now embedded in the AWS Cost Explorer, allowing users to ask natural language questions about their spending (e.g., "Why did my RDS costs spike last month?"). This conversational analysis approach comes with a free tier of 50 queries per month.On the Google Cloud side, a new billing overview widget for Gemini and Vertex AI spend is now in preview. Google is also introducing a "FinOps Explainability Agent," an autonomous AI agent to investigate AI cost drivers, and "Spend Caps" (Private Preview) for services like AI Studio and Vertex AI, which provide crucial cost control by pausing API traffic when a budget is hit.For those managing GPU workloads, Amazon ECS managed instances now support NVIDIA GPU metrics in CloudWatch Container Insights, enabling real-time visibility into GPU utilisation and health to optimise expensive accelerated computing.Cost & Usage Reporting (CUR) Enhancements:There are hints of a potential enhancement to AWS CUR 2.0, which could see new columns added to directly link API calls with costs, revolutionising cost allocation. AWS has also introduced:Scheduled Email Delivery for Billing Dashboards: Securely send reports to stakeholders without console access.Billing Conductor Pass-Through Plan: Simplifies centralised billing for billing transfer users.Cost Optimization Hub CSV Downloads: Easily export savings recommendations.Find out how to leverage CUR for security: "Identifying security risks using AWS cost and usage report data"Compute & Database Innovations:AWS: Released a wave of 8th Generation Intel Instances (C8i, M8i, R8i and network-optimised versions) powered by custom 6th Gen Xeon processors. EC2 Capacity Manager also now supports tag-based dimensions, allowing for more granular capacity optimisation. Amazon Aurora Serverless now boasts up to 30% better performance and, crucially, scales down to zero, a cost-effective option for unpredictable agentic AI workloads.Google Cloud: At Google Cloud Next, they announced both ends of the performance spectrum. The 8th Generation TPUs (v8t for training, v8i for inference) offer massive scale and performance-per-dollar improvements. In a move to democratise access, Google also made fractional GPUs (1/2, 1/4, or 1/8) on the G4 series generally available, a game-changer for cost-effectively running smaller workloads. The GKE workload recommender is also now integrated into the FinOps Hub.Azure: Now supports NVIDIA's powerful H100 and H200 GPUs on Azure Red Hat OpenShift (ARO) for large-scale AI/HPC workloads. For database users, the GA of Premium SSD v2 for Azure Database for PostgreSQL promises significantly higher IOPS and better price-performance.A Deep Dive into Azure Storage:The episode covers an "overload" of Azure storage updates with significant FinOps implications:Minimum Billable Object Size: From 1st July 2026 for new accounts (and 2027 for all), objects smaller than 128KB in cool, cold, and archive tiers will be billed as if they are 128KB.Smart Tier for Azure Blob & ADLS (GA): To mitigate the above, this feature automatically tiers data based on access patterns but introduces a monitoring fee for objects over 128KB, creating a new optimisation puzzle.Azure NetApp Files (ANF) Ransomware Protection: Now GA and included as part of the service at no extra charge.Finally, the hosts tackle "The Big Silence on Memory Prices," noting that despite DDR memory prices soaring 300-400% from mid-2025 lows, the hyperscalers have remained silent, absorbing the cost and making it difficult for smaller providers to compete.Explore the official announcements:AI Bill of Materials Whitepaper: www.wiz.io/go/ai-security/ai-bill-of-materialsAWS Article on Amazon Q: https://aws.amazon.com/blogs/aws-cloud-financial-management/transforming-finops-with-the-latest-amazon-q-cost-capabilities/ 

En este episodio os cuento cómo Jane, mi asistente digital, me ayudó a montar un servidor VPS para la webapp de la iglesia (SuanzesApp) desde cero.Jane no solo me guió en la configuración del VPS de Contabo (Ubuntu, Nginx, Gunicorn, PostgreSQL), sino que también:•

Nik and Michael are joined by David Ventimiglia to discuss pg_flight_recorder, a new tool he created for monitoring a Postgres database from within. Here are some links to things they mentioned: David Ventimiglia https://postgres.fm/people/david-ventimigliapg_flight_recorder https://github.com/dventimisupabase/pg_flight_recorderSupabase https://supabase.compg_wait_sampling https://github.com/postgrespro/pg_wait_samplingpg_ash https://github.com/NikolayS/pg_ashpg_cron https://github.com/citusdata/pg_cronpg_tle https://github.com/aws/pg_tle~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Nik and Michael discuss Nik's new project PgQue, a descendent of Skype's PgQ, for running queue-like workloads in Postgres. Here are some links to things they mentioned: Our first episode on Queues in Postgres https://postgres.fm/episodes/queues-in-postgresPgQue https://github.com/NikolayS/pgqueHN discussion https://news.ycombinator.com/item?id=47817349PgQ https://github.com/pgq/pgqpgmq https://github.com/pgmq/pgmqRiver https://riverqueue.comKeeping a Postgres queue healthy (blog post by Simeon Griggs / PlanetScale) https://planetscale.com/blog/keeping-a-postgres-queue-healthyPostgres Job Queues & Failure By MVCC (blog post by Brandur) https://brandur.org/postgres-queuesMy queries to monitor autovacuum (blog post by Laurenz Albe) https://www.cybertec-postgresql.com/en/monitor-autovacuum-my-queries/SELECT FOR UPDATE considered harmful (blog post by Laurenz Albe) https://www.cybertec-postgresql.com/en/select-for-update-considered-harmful-postgresql/Christophe Pettus blog post https://thebuild.com/blog/2026/05/03/pgque-two-snapshots-and-a-diffOur episode on pg_ash https://postgres.fm/episodes/pg_ashRediscovering PgQ (Alexander Kukushkin slides) https://speakerdeck.com/cyberdemn/rediscovering-pgqTick frequency tuning https://github.com/NikolayS/PgQue/blob/main/docs/tick-frequency.md~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

What does it take to make Postgres and Azure fit together cleanly, like puzzle pieces? In Episode 39 of Talking Postgres, Adam Prout—distinguished engineer at Microsoft and a founding architect of Azure HorizonDB—joins Claire to trace his engineering journey from MemSQL to Postgres. We dig into shared-storage architecture and how HorizonDB pushes more work into the storage layer; why the team chose Rust; and what “good systems programming” looks like when being paranoid is a feature, not a bug. Along the way: startup vs big company tradeoffs, and how working on databases exposes you to so many interesting parts of computer science.  Previously on Talking Postgres:Talking Postgres Ep29: How I got started leading database teams with Shireesh ThotaLinks mentioned in this episode:Blog post: Announcing Azure HorizonDB, by Charles Feddersen & Affan DarCMUDB talk: HorizonDB: Co-Designing Postgres and Azure for Cloud-Native OLTP, by Adam ProutResearch paper: Socrates: The New SQL Server in the CloudProduct page: Azure HorizonDBVideo of POSETTE 2025 talk: Scaling Postgres to the next level at OpenAIBlog post: Scaling PostgreSQL to power 800 million ChatGPT users, by Bohan ZhangBlog post: Supporting ChatGPT on PostgreSQL in Azure, by Affan Dar, Adam Prout, & Panagiotis Antonopoulos Docs: Azure Database for PostgreSQLGitHub repo: pgrxDiscord: PostgreSQL Hacking serverConference: PGConf.dev 2026Conference Schedule: PGConf.dev 2026 Schedule

Nik and Michael are joined by Dmitry Fomin to discuss his new tool pg_wait_tracer, as well as changes that could be made to core to allow wait event tracing with lower overhead, and on managed services. Here are some links to things they mentioned: Dmitry Fomin https://postgres.fm/people/dmitry-fominpg_wait_tracer https://github.com/DmitryNFomin/pg_wait_tracerpg_wait_sampling https://github.com/postgrespro/pg_wait_samplingpg_10046 https://github.com/DmitryNFomin/pg_10046Jeremy Schneider reply on LinkedIn https://www.linkedin.com/feed/update/urn:li:activity:7414966981847748608RDS for PostgreSQL wait event docs https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/PostgreSQL.Tuning.concepts.summary.htmlCustom wait events for extensions (added in PostgreSQL 17) https://www.postgresql.org/docs/current/xfunc-c.html#XFUNC-ADDIN-WAIT-EVENTSHacking Postgres with Dmitry, Kirk, and Nik (Part 1) https://www.youtube.com/watch?v=3Gtuc2lnnsEHacking Postgres with Dmitry, Kirk, and Nik (Part 2) https://www.youtube.com/watch?v=6kqpjnpl5GcTanel Poder https://tanelpoder.com/about/USDT static tracepoints for wait event tracing (proof of concept by Nik) https://github.com/NikolayS/postgres/pull/18Add wait_event_timing: Oracle-style wait event instrumentation (patch by Dmitry) https://github.com/DmitryNFomin/postgres/pull/1PgQue benchmarks https://github.com/NikolayS/pgque/blob/main/docs/benchmarks.mdThe Art of Computer Systems Performance Analysis Techniques for Experimental Design, Measurement, Simulation and Modeling (by Raj K. Jain) https://www.researchgate.net/publication/259310412_The_Art_of_Computer_Systems_Performance_Analysis_Techniques_For_Experimental_Design_Measurement_Simulation_and_Modeling_NY_WileyPerformance modeling and design of computer systems queueing theory in action (by Prof. Mor Harchol-Balter) https://www.cs.cmu.edu/~harchol/PerformanceModeling/book.htmlOracle Performance Firefighting (by Craig_Shallahamer) https://www.abebooks.co.uk/9780984102303/Oracle-Performance-Firefighting-Craig-Shallahamer-0984102302/plpProcess Mining: Data Science in Action (by Wil van der Aalst) https://link.springer.com/book/10.1007/978-3-662-49851-4~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Nik and Michael discuss a list of things to check when designing new schema in Postgres. Here are some links to things they mentioned:Use BIGINT in Postgres (blog post by Ryan Lambert) https://blog.rustprooflabs.com/2021/06/postgres-bigint-by-defaultPostgres 18 and UUIDv7 (blog post by Gwen Shapira) https://www.thenile.dev/blog/uuidv7How to use UUID (how-to guide by Nik) https://postgres.ai/docs/postgres-howtos/schema-design/data-types/how-to-use-uuidOur episode on constraints https://postgres.fm/episodes/constraintsOur episode on NULLs https://postgres.fm/episodes/nulls-the-good-the-bad-the-ugly-and-the-unknownMultiXact member space exhaustion episode (with Metronome) https://postgres.fm/episodes/multixact-member-space-exhaustionOur Column Tetris episode https://postgres.fm/episodes/column-tetrisSaving Space Basically for Free (blog post by James Coleman from Braintree) https://medium.com/paypal-tech/postgresql-at-scale-saving-space-basically-for-free-d94483d9ed9aOver-indexing episode https://postgres.fm/episodes/over-indexingUnder-indexing episode https://postgres.fm/episodes/under-indexingGadget's use of Postgres https://postgres.fm/episodes/gadgets-use-of-postgresPartitioning episode https://postgres.fm/episodes/partitioningRLS vs performance episode https://postgres.fm/episodes/rls-vs-performance~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

This week on Azure Friday, Scott Hanselman talks with Jonathon Frost about AI-enhanced migration from Oracle to PostgreSQL using the VS Code PostgreSQL extension. See how developers can automate schema conversion, transform application code, and validate results using an intelligent, agent-driven workflow. Chapters 00:00 - Introduction 00:36 - Model Hyperparameter Tuning, Agent Orchestration, and Determinism 02:25 - Architectural Overview of AI-enhanced Schema Migration 04:28 - How it is Built on the VS Code Extension for PostgreSQL 04:56 - Self-correction of AI-enhanced Migration 05:59 - Migration Demo 06:33 - Connect to Oracle Database 07:23 - Connect to PostgreSQL Database 07:50 - Connect to Azure OpenAI Endpoint 08:45 - Run Migration 09:37 - Review Completed Migration Report 11:07 - Visualize Schema of PostgreSQL Database 12:17 - Side-by-side File Diff 13:30 - Where to get the Extension and Learn More Recommended resources Learn Docs VS Code Extension Marketplace Page Azure Product Page Blog Connect Scott Hanselman | Twitter/X: @SHanselman Jonathon Frost | LinkedIn: linkedin.com/in/jjfrost Azure Database for PostgreSQL | LinkedIn: linkedin.com/company/azure-database-for-postgresql Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Topics covered in this episode: Migrating from mypy to ty: Lessons from FastAPI Oxyde ORM Typeshedded CPython docs Raw+DC Database Pattern: A Retrospective Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Migrating from mypy to ty: Lessons from FastAPI Tim Hopper I saw this post by Sebastián Ramírez about all of his projects switching to ty FastAPI, Typer, SQLModel, Asyncer, FastAPI CLI SqlModel is already ty only - mypy removed This signals that ty is ready to use Tim lists some steps to apply ty to your own projects Add ty alongside mypy Set error-on-warning = true Accept the double-ignore comments Pick a smaller project to cut over first Drop mypy when the noise exceeds the signalAdd ty alongside mypy Related anecdote: I had tried out ty with pytest-check in the past with difficulty Tried it again this morning, only a few areas where mypy was happy but ty reported issues At least one ty warning was a potential problem for people running pre-releases of pytest, Not really related: packaging.version.parse is awesome Michael #2: Oxyde ORM Oxyde ORM is a type-safe, Pydantic-centric asynchronous ORM with a high-performance Rust core. Note: Oxyde is a young project under active development. The API may evolve between minor versions. No sync wrappers or thread pools. Oxyde is async from the ground up Includes oxyde-admin Features Django-style API - Familiar Model.objects.filter() syntax Pydantic v2 models - Full validation, type hints, serialization Async-first - Built for modern async Python with asyncio Rust performance - SQL generation and execution in native Rust Multi-database - PostgreSQL, SQLite, MySQL support Transactions - transaction.atomic() context manager with savepoints Migrations - Django-style makemigrations and migrate CLI Brian #3: Typeshedded CPython docs Thanks emmatyping for the suggestion Documentation for Python with typeshed types Source: typeshedding_cpython_docs Michael #4: Raw+DC Database Pattern: A Retrospective A new design pattern I'm seeing gain traction in the software space: Raw+DC: The ORM pattern of 2026 I've had a chance to migrate three of my most important web app. Thrilled to report that yes, the web app is much faster using Raw+DC Plus, this was part of the journey to move from 1.3 GB memory usage to 0.45 GB (more on this next week) Extras Brian: Lean TDD 0.5 update Significant rewrite and focus Michael: pytest-just (for just command file testing), by Michael Booth Something going on with Encode httpx: Anyone know what's up with HTTPX? And forked starlette and uvicorn: Transfer of Uvicorn & Starlette mkdocs: The Slow Collapse of MkDocs django-rest-framework: Move to django commons? Certificates at Talk Python Training Joke: Neue Rich

The Internet has had its fun on another April First. There were a few great gags, a real story hiding behind a joke, and at least one real one that snuck in. Linux usage on Steam has jumped to over 5%, PostgreSQL has a severe regression on Linux 7.0, and there are a handful of really nasty supply chain vulnerabilities. Speaking of dumpster fires, the FCC is threatening to ban basically all the consumer routers on the market, and the major Open Source office suites are all fighting over licenses and forks. For tips, we cover vmstat for looking at memory and other performance metrics, and mosquitto's pub and sub tools for testing out an MQTT server. You can find the show notes at https://bit.ly/41blNtJ and enjoy! Host: Jonathan Bennett Co-Host: Jeff Massie Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: bitwarden.com/twit

AWS Morning Brief for the week of March 30th, with Corey Quinn. Links:Amazon Aurora PostgreSQL now available with the AWS Free TierAmazon EKS announces 99.99% Service Level Agreement and new 8XL scaling tier for Provisioned Control Plane clustersAWS Lambda increases the file descriptor limit to 4,096 for functions running on Lambda Managed InstancesThe AWS Advanced JDBC Wrapper now supports automatic query caching with ValkeyAnnouncing Amazon Aurora PostgreSQL serverless database creation in secondsEnhancing auto scaling resilience by tracking worker utilization metricsAmazon CloudFront flat-rate pricing plans: new features and expanded capabilitiesIAM policy types: How and when to use themPreparing for agentic AI: A financial services approach

Topics covered in this episode: chardet ,AI, and licensing refined-github pgdog: PostgreSQL connection pooler, load balancer and database sharder Agentic Engineering Patterns Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: chardet ,AI, and licensing Thanks Ian Lessing Wow, where to start? A bit of legal precedence research. Chardet dispute shows how AI will kill software licensing, argues Bruce Perens on the Register Also see this GitHub issue. Dan Blanchard, maintainer of a Python character encoding detection library called chardet, released a new version of the library under a new software license. (LGPL → MIT) Dan is allowed to make this change because v7 is a complete “clean room” rewrite using AI BTW, v7 is WAY better: The result is a 48x increase in detection speed for a project that lives in the hot loops of many projects. That will lead to noticeable performance increases for literally millions of users (the package gets ~130M downloads per month). It paves a path towards inclusion in the standard library (assuming they don't institute policies against using AI tools). Thread-safe detect() and detect_all() with no measurable overhead; scales on free-threaded Python 3.13t+ An individual claiming to be Mark Pilgrim, the original creator of the library, opened an issue in the project's GitHub repo arguing that Blanchard had no right to change the software license, citing the LPGL requirement that the license remain unchanged. A 'complete rewrite' is irrelevant, since they had ample exposure to the originally licensed code (i.e. this is not a 'clean room' implementation). Blanchard disagreed, citing how version 7.0.0 and 6.0.0 compare when subjected to JPlag, a library for detecting plagiarism. Blanchard told The Register he had wanted to get chardet added to the Python standard library for more than a decade since it's a core dependency to most Python projects. Brian #2: refined-github Suggested by Matthias Schöttle A browser plugin that improves the GitHub experience A sampling Adds a build/CI status icon next to the repo's name. Adds a link back to the PR that ran the workflow. Enables tab and shift tab for indentation in comment fields. Auto-resizes comment fields to fit their content and no longer show scroll bars. Highlights the most useful comment in issues. Changes the default sort order of issues/PRs to Recently updated. But really, it's a huge list of improvements Michael #3: pgdog: PostgreSQL connection pooler, load balancer and database sharder PgDog is a proxy for scaling PostgreSQL. It supports connection pooling, load balancing queries and sharding entire databases. Written in Rust, PgDog is fast, secure and can manage thousands of connections on commodity hardware. Features PgDog is an application layer load balancer for PostgreSQL Health Checks: PgDog maintains a real-time list of healthy hosts. When a database fails a health check, it's removed from the active rotation and queries are re-routed to other replicas Single Endpoint: PgDog can detect writes (e.g. INSERT, UPDATE, CREATE TABLE, etc.) and send them to the primary, leaving the replicas to serve reads Failover: PgDog monitors Postgres replication state and can automatically redirect writes to a different database if a replica is promoted Sharding: PgDog is able to manage databases with multiple shards Brian #4: Agentic Engineering Patterns Simon Willison So much great stuff here, especially Anti-patterns: things to avoid And 3 sections on testing Red/green TDD First run the test Agentic manual testing Extras Brian: uv python upgrade will upgrade all versions of Python installed with uv to latest patch release suggested by John Hagen Coding After Coders: The End of Computer Programming as We Know It NY Times Article Suggested by Christopher Best quote: “Pushing code that fails pytest is unacceptable and embarrassing.” Michael: Talk Python Training users get a better account dashboard Package Managers Need to Cool Down Will AI Kill Open Source, article + video My Always activate the venv is now a zsh-plugin, sorta. Joke: Ergonomic keyboard Also pretty good and related: Claude Code Mandated Links legal precedence research Chardet dispute shows how AI will kill software licensing, argues Bruce Perens this GitHub issue citing JPlag refined-github Agentic Engineering Patterns Anti-patterns: things to avoid Red/green TDD First run the test Agentic manual testing uv python upgrade Coding After Coders: The End of Computer Programming as We Know It Suggested by Christopher a better account dashboard Package Managers Need to Cool Down Will AI Kill Open Source Always activate the venv now a zsh-plugin Ergonomic keyboard Claude Code Mandated claude-mandated.png blobs.pythonbytes.fm/keyboard-joke.jpeg?cache_id=a6026b